Research ArticleAn Approach to Integrating Tactical Decision-Making inIndustrial Maintenance Balance Scorecards Using PrincipalComponents Analysis and Machine Learning

Neacutestor Rodriacuteguez-Padial Marta Mariacuten and Rosario Domingo

Department of Construction and Manufacturing Engineering Universidad Nacional de Educacion a Distancia (UNED)CJuan del Rosal 12 28040 Madrid Spain

Correspondence should be addressed to Rosario Domingo rdomingoindunedes

Received 26 May 2017 Revised 26 July 2017 Accepted 29 August 2017 Published 12 October 2017

Academic Editor Romualdas Bausys

Copyright copy 2017 Nestor Rodrıguez-Padial et alThis is an open access article distributed under theCreativeCommonsAttributionLicense which permits unrestricted use distribution and reproduction in anymedium provided the originalwork is properly cited

The uncertainty of demand has led production systems to become increasingly complex this can affect the availability of themachines and thus their maintenance Therefore it is necessary to adequately manage the information that facilitates decision-making This paper presents a system for making decisions related to the design of customized maintenance plans in a productionplant This paper addresses this tactical goal and aims to provide greater knowledge and better predictions by projecting reliablebehavior in the medium-term integrating this new functionality into classic Balance Scorecards and making it possible to extendtheir current measuring function to a new aptitude predicting evolution based on historical data In the proposed Custom BalanceScorecard design an exploratory data phase is integrated with another analysis and prediction phase using Principal ComponentAnalysis algorithms andMachine Learning that uses Artificial Neural Network algorithmsThis new extension allows better controlover themaintenance function of an industrial plant in themedium-termwith a yearly horizon taken over monthly intervals whichallows themeasurement of the indicators of strategic productive areas and the discovery of hidden behavior patterns in work ordersIn addition this extension enables the prediction of indicator outcomes such as overall equipment efficiency and mean time tofailure

1 Introduction

In business and engineering decision-making approachesand models are developed in response to the uncertaintyof technological and demand conditions In business it ispossible to identify a strategic [1] or operational [2] approachor a more particularly focused approach for suppliers [3] inthe engineering field it is possible to identify cases regardingmanufacturing conditions [4] product design [5] or aspectsrelating to civil engineering [6] In the context of the currentmarket in which delivery times are continually reduced andmore importantly responses to orders are increasingly imme-diate the production response in the industrial environmentis faster and quality and time affect both the complexity andthe flexibility of the system [7] Considering that the capacityof the machines is limited we consider those productiveareas with identified bottlenecks as strategic productive areasof the factory Using this capacity as an invariant value

the system attempts to maintain the maximum availabilityof the machines that comprise a strategic productive areaMoreover if continuous production occurs in this contextfor example in papermaking downtime caused by damageis irrecoverable

Another characteristic of the market context is a widerrange of products resulting in the transformation of man-ufacturing from mass production to flexibility in the lattercase this versatility leads to greater wear and fatigue onmachines because of the high rate of change in the configura-tion potentially resulting in a loss of reliability This findingmeans that it is necessary to considermore extrememeasuresin terms of both the prediction and the anticipation of failureThus predictive maintenance engineering has developed andperfected technologies for condition monitoring and pre-dicting failures before breakage occurs [8ndash10] Although thisapproach is more operational and requires more resourcesand investments than following the scheme [11] it cannot

HindawiComplexityVolume 2017 Article ID 3759514 15 pageshttpsdoiorg10115520173759514

2 Complexity

be established in an entire strategic productive area withoutcritical equipment facilities or machine parts To respondto this problem a methodology has been considered for aproductive area designated as strategic that offers knowledgeextraction and the prediction of availability indicators Thusthe maintenance department can provide a timely responsewith minimal resources to maintain the required reliability

In maintenance field when the decisions-making isrelated to strategies or policies in the long term the consider-ations of fuzzy uncertainty are convenientThus the literaturereview carried out by Mardani et al [12] about the fuzzymultiple criteria decision-making techniques found that inmaintenance environments the fuzzy approach is utilizedin strategic framework in the long term as for examplein the selection of the maintenance strategy [13ndash15] or themaintenance policy [16] This could be extensible to projects[17 18] or in civil engineering [19] environments wherethere is more uncertainty due to the different conditionsof each event However in industrial environments andwith continuous process as papermaking where the samemachines are used in the manufacturing despite productvariety the risks in the predictions are lower

The integration of Principal Component Analysis (PCA)and Machine Learning (ML) techniques can facilitatedecision-making in these environments PCA is a veryefficient method to find attributes that are influential inexplaining the greater variation of a data set characterizedby many explanatory variables in many registers [20] Thisalgorithm is used extensively in the literature particularly forpredictive maintenance as a method of reducing dimensions[21] According to Alpaydin [22] ML is a branch of artificialintelligence whose goal is to programmatically automate acomputerrsquos learning process similar to how humans andanimals naturally learn through experience the algorithms ofML directly employ the data without previously establishingan equation as amodel In addition these algorithms improvetheir efficiency as the quantity of data used as examplesincreases during the learning ML finds natural patternsin the data and helps make better decisions and establishpredictions Because of its versatility ML has been usedin many fields including construction [23] However thisapproach is not habitually combined with the PCA and MLtechniques The grouping of data facilitated by PCA allowsa better interpretation of complex systems such as those inwhich ML is applied this interpretability is considered acharacteristic of achievement through ML methods [24]

This work consists of a segment of a global and modularframework for Maintenance Decision Support Systems [25]whose general objective is to propose a system that assistsan expert in decision-making to design customized mainte-nance programs in a productive plant [26]This systembeginswith the alignment of the companyrsquos strategic objectivesfollowed by the tactical and operational maintenance

This paper addresses that tactical goal and has theobjective of providing better knowledge and predictionsby projecting reliability behavior in a medium-term future(yearly horizon taken over monthly intervals) integratingthis new functionality into the classic Balance Scorecard(BSC) andmaking it possible to extend its current function of

measuring the current situation to a new aptitude predictingevolution based on historical data [27] For this objectivetechniques such as PCA and ML are used

2 Methodology

In the proposed Custom Balance Scorecard design Matlabcopy[28] is used to integrate an exploratory phase of data usingPCA algorithms and another phase of discovery and pre-diction that uses ML we will use Artificial Neural Network(ANN) algorithms The beginning data used to evaluatethe results were obtained from productive area recordscomposed of two main papermaking machines coded in theComputerized Maintenance Management System (CMMS)as M1 and M2 respectively The data have been divided intotwo partsThe first part will be used in the exploratory phasewhich reflects the maintenance work orders received in theproductive area in one year The other part will be used inthe analysis phase in which production values and machineresponses are represented as efficiency variables and failuretimes this part also considers a period of one year Becauseof the continuous improvement process that characterizes thepapermaking industry the maintenance functionrsquos influenceon productive efficiency and sustainability is more sensitivethan in other types of industrial plants [29ndash31] thereforethis study focuses on indicators overall equipment efficiency(OEE) and mean time to failure (MTTF) [32]

The PCA algorithm has been used in the exploratoryphase In the analysis phase using ML techniques ANNis used for its versatility as algorithms for supervised andunsupervised learning and for its suitable behavior againstother ML techniques that are used for prediction [33] Inunsupervised learning two types of algorithms are usedto extract the knowledge of the data structure throughclustering Hierarchical clustering is used as is NeuronalNetwork of Self-Organizing Map (SOM) Both algorithmsidentify groups of individuals by similar behaviors fromindividual data and have been used effectively both to identifythe stages of wear in industrial environments [34] and tocharacterize the energy in electrical supply networks [23]Hierarchical clustering makes it possible to show the naturalgrouping structure of the data as a function of the metric thatis set as a criterion of proximity whereas SOM decomposesthe data into a set number of groups Supervised learningwill useANNregression algorithms for the suitable predictivebehavior of machine maintenance variables [35]

The production plant presents in its management sys-tem a clear division between maintenance and productionoccurring equally for its databases therefore there is nosingle database where we can access all the informationjointly in an integral manner Because of this we have toaccess maintenance and production data separately so wehave two distinct tables identified as dataWO Figure 1(a)corresponding to the maintenance database and dataWOFFigure 1(b) corresponding to production database Bothtables will be defined in more depth later neverthelessto clarify the following two phases dataWO will containthe input data for the PCA and clustering algorithms cor-responding to the unsupervised learning technique while

Complexity 3

(a) (b)

Figure 1 Imported data from CMMS (a) table dataWO (b) dataWOF

dataWOF will serve as input information for the regressionalgorithm according to the supervised learning techniqueThe separation of maintenance and production departmentsat the level of databasemanagement leads to separate analysisand the use of different techniques in terms of the knowledgeextraction process

21 Exploratory Data Phase There is a first preparatorypreliminary data step inwhich the starting data correspond tothe work orders WOs which have received the papermakingmachines M1 and M2 during a calendar year These datahave been extracted from a CMMS database Figure 1(a)shows the treatment of these data once they have beenimported these are defined in the table dataWO The dataobtained present 46 attributes and 1080 instances of WOsafter a prior filtering The work order is a document thatin its original format presents 46 fields that represent the46 original attributes (see Table 1) which can be groupedinto descriptive fields of problem and resolution with freetext of alphanumeric type and other categorical variables ofnumeric type to accommodate the kind of work order suchas order type requester repair shop repair type urgencytype asset condition and implication of failure Numeri-cal categorical variables that hold classes are order statushomogeneous family section and installation type of fixedassets type of work and operative sequences The remainingqualitative variables are of date type that record dates andtimes of request and programming of the intervention andcompletion However it is permissible to perform a PCAon all data for the 46 attributes applying it only on the 7numerical types (4 associated costs of totals orders parts andworkforce 2 repair times and 1 of the number of operators)which are shown in Table 1 as input variables for the PCAdiscarding the rest of the original variables since they are usedto obtain context information as they document the problemand its physical location that is why theywill serve as prefiltervariables for location and situation in which the maintenanceintervention is located

In the exploratory data phase the statistical technique ofPCA has been used to reduce the data dimension and find theprincipal axes that best represent the variation of data Theseaxes are orthogonal to each other and are calculated using alinear base change application by choosing a new coordinate

system for the original set of data in which the largestvariance of the dataset is captured on the first axis (called thefirst component) the second-largest variance is the secondaxis and so on This methodology reduces to a problem ofeigenvalues and eigenvectors on the covariance matrix of thedata obtaining a reduction of the dimensionality of the dataon those axes that make amore substantial contribution to itsvariance in general therefore many principal axes are usedwhose sum represents approximately 80 of the variation ofthe original data [20]

The PCA parts of a data set are tabulated such that eachline represents an observation instance or individual andeach column represents an attribute or variable Consider thata data set consisting of 119899 observations with 119896 attributes isavailable In matrix notation we will express 119860 (119899119896) where119860 is the matrix representing the table with the coefficients(119886119894119895) as the 119894th observation of the jth variable hence thematrix of observations 119860 is formed by 119896 vectors of variables119909119895 sorted by columns and each vector has 119899 componentscorresponding to its 119899 observations as shown in

119860 = (1199091 1199092 sdot sdot sdot 119909119896) = (11990911 sdot sdot sdot 1199091198961 d1199091119899 sdot sdot sdot 119909119896119899) (1)

To reduce the size of the variables one must find anothervector subspace that is aligned with those vector componentsthat involve more variation and one must form a basis forthese components to be represented in an orthogonal thatis a linearly independent system This problem is reducedto finding a vector space whose vectors V represent thevariation of the data that is a system in which (2) is satisfied119860 sdot V = 120582 sdot V (2)

However in this case the variation is not reduced butis used to find the principal components and axes or theirown values and vectors of the data In accordance with thisphilosophy we will attempt to find those components andprincipal axes that explain the maximum variation of thedata Thus instead of matrix 119860 its covariance matrix is

4 Complexity

Table 1 Original attributes of the maintenance work order

STATE WO Numerical categoricalTYPE WO Word categoricalWO NUMBER Alpha numericalREQUESTING DEPT Word categoricalREQUESTING DATE DateREQUESTING HOUR DateWORKSHOP Word categoricalURGENCY Word categoricalTERM DATE DateWORK DESCRIPT 1 Alpha numericalWORK DESCRIPT 2 Alpha numericalHOMOGENEOUS GROUP Numerical categoricalLOCATION Alpha numericalELECTRIC CODE Alpha numericalSECTION Numerical categoricalINSTALLATION Numerical categoricalDEVICE DESCRIPTION Alpha numericalPRINCIPAL WO Alpha numericalENROLLMENT Alpha numericalTYPE REPAIR Word categoricalSEQUENCE NUMBER Numerical categoricalTYPE INMOBILIZED Numerical categoricalOPERATORS NUMBER NumericalESTIMATED REPAIR TIME NumericalASSET CONDITION Word categoricalTYPE WORK Numerical categoricalSCHEDULED DATE DateSCHEDULED HOUR DateWORK DESCRIPT 1 FINAL 1 Alpha numericalWORK DESCRIPT 1 FINAL 2 Alpha numericalIMPLICATION FAULT Word categoricalELEMENT FAULT Alpha numericalCAUSE FAULT Alpha numericalSUBSTITUTION Alpha numericalID SUBSTITUTE ENROLLMENT Alpha numericalID SUBSTITUTED ENROLLMENT Alpha numericalSTART DATE DateSTART HOUR DateFINAL DATE DateFINAL HOUR DateREPAIR TIME NumericalWORKFORCE COST NumericalPARTS COST NumericalORDER COST NumericalTOTAL COST NumericalREPAIR DATE Date

Complexity 5

Machinelearning

Supervisedlearning

Regression

Classication

Unsupervisedlearning Clustering

Figure 2 Machine learning techniques

obtained 119862 which is then normalized with a mean of zeroand a standard deviation of one The result is then used toobtain the values and eigenvectors of 119862 That is the result isused to solve the following equations10038161003816100381610038161003816119862 minus 12058211986810038161003816100381610038161003816 = 0 (3)(119862 minus 120582119894119868) V119894 = 0 (4)

Once the principal components are obtained 120582119894 alongwith the principal axes V119894 they together explain the variationof the data which are ordered as a Pareto diagram selectingexclusively that set of components 119901 that explain at least80 of the variation in the data Thus a reduction in thedimensions of the data of 119896 original variables to 119901 lt 119896variables is obtained In general the matrix of observationsprojected onto the main axes contains 119899 observations ofthe variables that are obtained by (5) where is the matrixformed by columns with the eigenvectors V119894 obtained from(4) (119899119896) = 119860 (119899119896) sdot (119896119896) (5)

This transformation expresses the original data in axesthat coincide with the natural variation One aspect to beconsidered in this analysis is that this transformation islinear therefore it is not suitable for representing nonlinearproblems In cases of nonlinearity it is advisable to use MLclustering algorithms as will be observed later

22 Phase of Analysis through Machine Learning In thisphase the preparatory data step uses as input data inaddition to the previous data the production values andtheir responses as efficiency variables and failure times foran operational year for both papermaking machines (M1 andM2) Data are extracted and grouped from two databasesmaintenance and production The data obtained present 35attributes and 12 instances corresponding to each month foreach machine (identified as M1 and M2) Figure 1(b) showsthe treatment of these data once they have been importedthey are defined in the table dataWOF The manufacturingreport is a document that in its original format presents35 fields that represent the original attributes (see Table 2)which can be grouped in identifying fields of the machinein question of alphanumeric type and numerical cate-gorical variables record the natural month The remaining

attributes are of numeric type and record the values of timecost interventions and production For each papermakingmachine 3 predictor variables associated with productionparameters shown in Table 2 (daily production in tons ofpaper per day average paper weight in grams per squaremeter and average speed in meters per second) are selectedwith the objective of obtaining a target of two simultaneouspredictive responses (OEE in the percentage of machineutilization andMTTF) Answers that evaluate the aptitude ofthe maintenance function applied to the productive area areprovided by bothmachinesThe three predictive variables arethose that from experience characterize production betterAlthough a PCA could be performed as in the exploratoryphase for the dataWO it was not considered in this occasiondue to the few instances 12 which we had for each machineSince PCA is a statistical analysis it has been consideredthat the few instances are not sufficient to carry out such ananalysis considering at this point a selection based on criteriabased on experience However as more productive data andmore instances are obtained a PCA can be performed onall or those productive attributes of Table 2 to reduce thedimension and select those that represent the most influencein the variation of data

ML is divided into two techniques [27] supervised learn-ing which is training a model on known input and outputdata to predict future outputs and unsupervised learningwhich is finding hidden patterns and intrinsic structures inthe input data Figure 2 provides an illustration of ML Foreach technique different algorithms can be used in whichchoosing the ideal is performed by trial and error

Supervised learning uses classification and regressiontechniques to develop predictive models The differencebetween these techniques is that the classification predictsresponses in discrete or categorical variables whereas regres-sion predicts responses in a continuous variable [36] Unsu-pervised learning uses the clustering technique commonlyused in exploratory data analysis to find hidden patterns asclusters in data

From the algorithms of ML (Support Vector MachineDiscriminant Analysis Naive Bayes Nearest Neighbor Deci-sion Trees K-means Hierarchical Gaussian Mixture Hid-den Markov Model and ANN) we will use algorithmsmodeled with ANN for their versatility for both nonsuper-vised techniques (clustering) and supervised techniques(regression) The former groups the input data to recognizepatterns and define the natural groups present in the data

6 Complexity

Table 2 Original attributes of the manufacturing report (production database)

ID MACHINE Alpha numericalMONTH Numerical categoricalNON PLASTERED PRODUCTION NumericalPLASTERED PRODUCTION NumericalVOLUME PLASTERED PRODUCTION NumericalTOTAL PRODUCTION NumericalWORKS DAYS NumericalDAILY PRODUCTION TON NumericalCUTOUT PRODUCTION NumericalDAILY CUTOUT PRODUCTION TON NumericalDECREASE MACHINE NumericalAVAILABLE HOURS NumericalIDLE TIME EXTERNAL CAUSES NumericalMAINTENANCE NumericalSCHEDULED NumericalBREAKS NumericalPRODUCTION REST NumericalTOTAL IDLE NumericalDAILY IDLE TIME NumericalOEE NumericalSTART NUMBERS NumericalCUTOUT TIME CHANGES NumericalAVERAGE PAPER WEIGHTS NumericalAVERAGE REAL WIDTH NumericalAVERAGE SPEED NumericalAVERAGE BUDGETEDWIDTH NumericalWIDTH DECREASE CMS NumericalWIDTH DECREASE TON NumericalCAPE PRODUCTION NumericalCOST NumericalCOSTTON NumericalINTERVENTIONS NUMBER NumericalMEAN TIME BETWEEN FAILURES NumericalMEAN TIME TO REPAIR NumericalMEAN TIME TO FAILURE Numerical

the latterrsquos purpose is to establish correspondence ormappingbetween input values or predictors and the output variables orobjectives to predict Thus the model is trained (or adjusted)by a knowledge base formed by historical examples of knowninputs and outputs

According to Rumelhart et al [37] the process of theback-propagation training of ANN is used for regressiontechniques such as supervised learning The ANN back-propagation training process is divided into two stagesforward and backward propagation A network configurationconsisting of multiperceptron layers as shown in Figure 3and an activation function of the output layer range and [0 1]is used before an input 119909 which is expressed by

119910 (119909) = 119890119909(1 + 119890119909) (6)

In the forward propagation stage we select an input dataset for training (1199091 1199092 119909119868minus1) and apply it to the network to

obtain the outputs 119910119889 For each neuron 119895 of the hidden layerthe value of each nucleus 119899119895 is given by

119899119895 = 119868sum119894=1

(119908119895119894 sdot 119886119894) + 119887 (7)

where each input value of the previous layer 119886119894 is weighted by119908119895119894 and the output of the hidden layer is expressed by119886119895 = 119891 (119899119895) (8)

This is a nucleus activation function and is performediteratively for each output layer until the final output 119910119889 asin

119910119889 = 119891 (119899119910119889) = 119891( 119867sum119895=1

(119908119910119889119896 sdot 119886119896)) (9)

The backward propagation stage consists of measuringthe error committed as the difference between the calculated

Complexity 7

Input layer Hidden layer Output layer

Input data Output data

x1

x2

xi

b

aj

Wji

Wydj

ak

ydn1

n2

n3

n4

n5

n6

n7

n8

n9

n10

n11

n12

n13

nm

ni

Figure 3 ANN regression trained with back-propagation perceptron multilayer

value 119910119889 and the real value 119910119903 We recalculate the weights119908 attempting to minimize the error in the reverse firstobtaining the new weights of the layer of exit 119908119899119910119889119896 based onthe old 119900 equation119908119899119910119889119896 = 119908119900119910119889119896 + 119886119896 sdot [119910119889 sdot (1 minus 119910119889) sdot (119910119903 minus 119910119889)] (10)

and later the new weights of the hidden layer 119908119899119895119894119908119899119895119894 = 119908119900119895119894 + 119908119900119895119894 sdot 119886119894 sdot [119886119895 sdot (1 minus 119886119895) sdot (119908new119896119895 minus 120575119895)] (11)

where 120575119895 is obtained by applying (9) on the followingequation [119910119889 sdot (1 minus 119910119889) sdot (119910119903 minus 119910119889)] = 120575119910119889 (12)

This process is repeated for 119899 observations until a pre-determined acceptable value of the error is achieved usuallyusing the mean square error (MSE) which is defined in (13)To ensure a rapid convergence of the iterative method weusually use mathematical optimization methods in this casewe use Bayesian Regularization [38]

MSE = 1119899 119899sum119889=1

(119910119903 minus 119910119889)2 (13)

3 Results

It is possible to integrate new functionalities into a customcontrol panel of the industrial plant In this case predictiveanalysis was added for the expected availability response of aproductive area considering the main core of the industrial

plant thus it is possible to anticipate the information Thefuture availability in the medium-term (at monthly intervals)of both machines allows the maintenance department tocorrect possible deviations that are out of tolerance beforethey occur improving their response In the first phasewhich is exploratory we use PCA to discover the smallestdimensions that explain the variation of the data Applyingthe PCA to the set of WOs of productive area 2 (composed ofM1 and M2) principal components or axes PCi are foundthese are sufficient to explain the variation of the originaldata contained in the WOs of the productive area As a resultof the PCA 5 components are identified that would explain100 of the variation therefore 2 linearly dependent vectorsare detected among the input variables reducing in two theoriginal dimension on the other hand from 5 principalcomponents 3 would account for 786 of the variation indata This work aims at the number of interventions costsand maintenance times and will represent the results of PCAon the first 3 principal components In Figure 4(a) it isobserved that the first three principal components represent786 variation of the data reason why it is decided torepresent the data using these three components as principalaxes of representation this is visualized in Figure 4(b)with projections of the original data on the three principalcomponents Previously the data were normalized withmean0 and standard deviation 1

Table 3 shows the values obtained in the PCA of themaintenance metrics where the projections are obtained onthe three principal components of the maintenance numeri-cal variables selected from Table 1 (total costs orders partsand workforce cost estimated repair time repair time andnumber of operators) From the seven maintenance variablesstudied it can be observed that the ones that gain more

8 Complexity

Table 3 PCA results Principal components values

Metrics PC1 PC2 PC3 Metrics modulusTOTAL COST 04696 0476 0036 06697ORDER COST 01598 05714 06089 08502PARTS COST 02323 03913 minus05737 07323WORKFORCE COST 05234 minus02668 minus00987 05957REPAIR TIME 05235 minus02668 minus00987 05957ESTIMATED REPAIR TIME 03793 minus02661 02076 05077OPERATORS NUMBER 009 minus02839 04861 05701

1 2 3 4 5Principal components

0

1

2

3

4

5

6

7

Varia

nce e

xpla

ined

0

14

29

43

57

71

86

100

()

(a)

1510 20

ird

prin

cipa

l com

pone

nt

15Second principal component

5 10

First principal component

minus15

minus10

minus5

0

5

10

50 0minus5 minus5

(b)

Figure 4 (a) Principal components variance (b) projected data on principal components

relevance in principal axes are from greater to less order costtotal cost and part cost this has been considered extractingthe Euclidean modulus or norm of each variable in thethree principal axes which is shown in the last column ofTable 3 The rest of the four remaining variables have a moreor less similar amplitude so they are considered of equalimportance

In the second phase ML the clustering technique is usedto discover patterns hidden in the data such as the naturalgrouping For this technique from the data stored in thedataWO table shown in Figure 1(a) only two attributes areused total cost and repair time Both attributes can be keyindicators for the maintenance department and the repairtime can be key also for the production department becauseof downtime Therefore after knowing their influence onthe principal components it is important to deepen theirrelationship

For the clustering technique two algorithms have beenused The first technique hierarchical clustering allows thecreation of a dendrogram which is a tree diagram that mea-sures the number of natural groups or clusters dependingon the distance criterion that is fixed between data In thiscase by setting a distance value on the ordinate axis the treeis trimmed by a horizontal line that cuts the dendrogram inas many intersections as natural groups appear In this caseFigure 5(a) it is observed that for Euclidean mean distancesof 6000 to 7000 the tree presents two natural groups from

3000 to 5500 it presents three groups from 2000 to 3000 itpresents four groups and below 1000 the number of groupsincreases considerably Because of this compression a valueof 900 is used by pruning the tree into 7 natural groups thedifferent groups of color data can be illustrated in Figure 5(b)As can be seen the hierarchical clustering technique allowsan overview of the number of clusters that can be obtainedas a function of the chosen distance value There are severaldistancemetrics you can even define as a custom in this caseEuclidean distance has been used as a metric

The clustering technique is again used performing asecond algorithmof an SOMANN on the subset of total costdata and repair time as the chosen variables reflecting costsand times of the plantrsquos intervention maintenance An SOMor Kohonen consists of a competitive layer that can classifya set of vector data with any number of dimensions into asmany classes as neurons have a layer [39ndash41] Neurons arearranged in a two-dimensional topology of the data set Thetrained networkwith 2 variables and 1080 input data is shownin Figure 5(c) its two-dimensional topologywith data impactis shown in Figure 5(d)

The network is configured by 2 dimensions 2 times 4discovering a pattern of 8 natural groups in the data theseare distributed with a clear linear relationship between themIn addition there are discrepant data that have no linearrelationship and reveal an unconventional repair this isextraordinary and realized in one of the machines and it

Complexity 9

4 11 13 2 8 25 5 16 12 24 1 10 6 29 14 7 15 18 3 19 9 23 26 30 17 20 22 21 27 28

Observations

0

1000

2000

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6000

7000D

istan

ce

(a)

0 2000 4000 6000 8000 10000 12000 140000

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Repa

ir tim

e (h)

Total cost (euro)

(b)

(c)minus1 minus05 0 05 1 15 2 25

minus1

minus05

0

05

1

15

2

25

3

35SOM neighbor weight distances

(d)

Figure 5 (a) Dendrogram (b) data (c) ANN SOM trained (d) SOM topology

was not cataloged like normal repair This finding revealsan error in the introduction of the information in theCMMSThis event was also revealed by the green dot (singlegroup) of the hierarchical clustering figure (see Figure 5(c))Figure 6(a) shows the original data in the two variables(cost time) and Figure 6(b) shows the 7 natural groupsand the linear relationship between them This figure alsoshows that group 8 has no linear relationship as previouslydiscussed The interpretation of these results under themaintenance approach shows that the linear relationshipbetween the groups comes to reflect the following analysison the distribution of the groups observing that the firstfour groups for costs between 0 and 1000 euro are very closeto each other while the centroids of 2000 euro 3000 euro and5000 euro present greater distance With this it is inferred thatthe majority of the interventions cost less than 1000 euro with asmaller number of interventions at intervals of 1000 euro

Finally the regression technique enables prediction ofthe future availability values of both main papermakingmachines (M1 and M2) using the OEE indicators of each

papermakingmachine and itsMTTF such as average runtimebefore failure In addition these values are calculated simulta-neously in the trained ANNmodel A trained neural networkwith input data (predictors) is used that combines the threeproduction variables and the two target output variableswhich are the overall efficiency of each OEE machine andaverage time to MTTF failure measured for 12 months ofthe year for each machine For this technique from the datastored in the dataWOF table shown in Figure 1(b) only threeproduction attributes are used daily production in tons ofpaper per day average paper weight in grams per squaremeter and average speed in meters per second

For the papermaking machines M1 and M2 3 input vari-ables a 10-layer feed-forward network with hidden neuronsand 2 layers of linear output neurons can adjust arbitrarilysuitable multidimensional mapping problems given consis-tent data and sufficient neurons in their hidden layer Thenetwork will be trained with 70 of the data using the back-propagation algorithm of Bayesian Regularization 15 of thedata will be used for validation and the remaining 15 will

10 Complexity

0 2000 4000 6000 8000 10000 12000 140000

50

100

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me r

epai

r (h)

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Total cost (euro)

(a)

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e rep

air (

h)

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

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20

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Total cost (euro)

(b)

Figure 6 (a) Artificial Neural Network SOM trained (b) SOM topology sample hits

be used for the test AnMSE performance function is used asshown in Figures 7(a) and 8(a) for M1 and M2 respectivelyThe result of the trained network is suitably provided accord-ing to the regression adjustment coefficients that are shownin Figures 7(b) and 8(b) for M1 and M2 respectively

For each machine that forms the productive area theresult of the adjustment is observed by comparing the outputvariables OEE andMTTF (in blue) measured with the outputvariables foreseen by the OEEp and MTTFp model (in red)The results of M1 are shown in Figure 9(a) and those of M2are shown in Figure 9(b) In short this network can predictthe maintenance behavior of the production area of the plantin availability terms (efficiency and operating times) feedingthe model with the predictive values of production As moreinstances of inputoutput data are introduced the networkwill be retrained and more reliable since it will be adjustedwith a greater number of real examples that have occurredtherefore the greater quantity will lead to the acquisition ofmore experience and knowledge In maintenance terms it isnecessary to make predictions of availability and operatingtimes according to three characteristic values of productionand the model established in this way allows to predictthe OEE and MTTF observing a nonlinear behavior in thetime as is shown in Figure 9 In this sense the responsesfor M1 are observed where the OEE oscillates between anaverage value of 94 with a low dispersion of plusmn066 thusnot happening with MTTF values of mean 8107 h of highdispersion plusmn2582 h As for M2 the OEE has an average valueof 9552 with a low dispersion of plusmn112 despite MTTFvalues of 11803 h mean with very high dispersion plusmn5935 hconcluding that the regression model can accurately predicthigh and low amplitude oscillation values over time

It is noted that the fit is acceptable for training whichis provided by the global adjustment regression coefficient 119877values as shown in Figure 7(b) for both machines

For machine M1 discrepant values are observed in thevalidation setting for month 10 and for both OEE and MTTFindicators there are two reasons for this reason First there is

overadjustment when the data have not been prepared welland there are datawith erroneous or poorly conditioned inputinformation Second there are a low number of observationsor minimal historical information In this paper it has beenverified that the input data for the ANN did not present poorconditioning therefore the overadjustment problem is dis-carded and the few data (ie the few observations) availableare considered themain cause of discordance of the inputs forvalidationThe problem of feeding the network with few datato train and validate the ANN is due to unavailability of moredata for reasons of good performance in the industrial planta fact that would undoubtedly improve the learning of thenetwork and therefore its efficiency and accuracy Howeverthis fact highlights another very interesting aspect of theANN the network is easily adaptable and configurable givena low number of observations Here this adaptability makesit possible to accurately predict 11 hits of 12 possibilities thusthere is a 9167 probability of success in this case

For machine M2 there is a nearly total adjustment forthe OEE indicator but not for the MTTF indicator for whichit is evident that in month 6 there is a discrepancy in theprediction as in M1 the minimal data (observations) usedfor the validation obtain the same precision as M1

Another relevant aspect of the result is the acceptableprecision in the prediction of availability indicators whichare based exclusively on time by simply using as inputthree productive variables as predictor variables (daily papermass paper surface density and machine speed) In additionto the two output variables OEE and MTTF indicators asobjectives to be predicted are calculated simultaneously afact that reflects an additional value of this type of networksand greater computational efficiency by obtaining in a singlesimulation the prediction ofmore than one objective variable

4 Conclusions

A PCA-ML model has been developed such that it can beintegrated into scorecards with a traditional focus BSC

Complexity 11

0 50 100 150 200 250 300 350 400 450483 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is95888e minus 13 at epoch 320

100

10minus5

10minus10

TrainingTestBest

(a)

60 80 100 120Target

50

60

70

80

90

100

110

120Test R = minus042747

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120

Training R = 1

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120All R = 086783

DataFitY = T

lowastＮＬＡＮ

+14

ＯＮＪＯＮ ≃

087

lowastＮＬＡＮ

+13e+02

ＯＮＪＯＮ ≃

minus037lowastＮＬＡＮ

+minus13eminus08

ＯＮＪＯＮ ≃

1

(b)

Figure 7 (a) ANN regression performance for M1 (b) regression fit

12 Complexity

0 100 200 300 400 500 600 700 800856 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is23797e minus 12 at epoch 753

100

105

10minus5

10minus10

TrainingTestBest

(a)

100 150 200 250Target

100

150

200

250

100

150

200

250

100

150

200

250

Test R = minus051755

DataFitY = T

Training R = 1

DataFitY = T

All R = 095036

DataFitY = T

100 150 200 250Target

100 150 200 250Target

lowastＮＬＡＮ

+minus47eminus07

ＯＮＪＯＮ ≃

1

lowastＮＬＡＮ

+26e+02

ＯＮＪＯＮ ≃

minus15

lowastＮＬＡＮ

+6

ＯＮＪＯＮ ≃

097

(b)

Figure 8 (a) ANN regression performance for M2 (b) regression fit

Complexity 13

0 2 4 6 8 10 12(Month)

925

93

935

94

945

95O

EE (

)

(Month)0 2 4 6 8 10 12

40

50

60

70

80

90

100

110

120

130

MTT

F (h

)

(a)

(Month)

OEE

()

0 2 4 6 8 10 1293

935

94

945

95

955

96

965

97

975

98

(Month)

MTT

F (h

)

0 2 4 6 8 10 1250

100

150

200

250

300

(b)

Figure 9 Output variables measure (blue) versus predicted (red) (a) M1 (b) M2

thus including a tactical definition of longer-term strategicapproaches such as a scorecard based onBSCThis new exten-sion allows better control over themaintenance function of anindustrial plant in themedium-termwith amonthly intervalin such a manner that allows the measurement of certainindicators of those productive areas that were previously con-sidered strategic This model of PCA and an ML algorithmusing ANN can be integrated very easily into any traditionalcontrol panel by converting the developed source code topackages of different programming languages and includingthem in a library to be used as a function in a spreadsheetor a standalone executable application In addition at thecontrol panel this model is provided with ML to discoverstructures and behavior patterns that are relatively hidden inWOs By utilizing a clustering or clustering technique naturalgroups are determined in the cost variables and maintenanceworkforce in addition predictions about the availability ofthe productive area are made through the indicators OEE

andMTTFThus the scorecard model on a paper productionplant has been validated

As possible future works this methodology could beapplied to civil engineering and in this case applying a fuzzyuncertainty due to particular characteristics of this sector

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors want to thank the Department of ConstructionEngineering andManufacturing and the College of IndustrialEngineers of UNED for their support through Projects 2017-IFC08 and 2017-IFC09

14 Complexity

References

[1] DWollmann andM T Steiner ldquoThe strategic decision-makingas a complex adaptive system a conceptual scientific modelrdquoComplexity vol 2017 Article ID 7954289 13 pages 2017

[2] R Calvo R Domingo and M A Sebastian ldquoOperationalflexibility quantification in a make-to-order assembly systemrdquoInternational Journal of Flexible Manufacturing Systems vol 19no 3 pp 247ndash263 2008

[3] M Mahdiloo R F Saen and M Tavana ldquoA novel data envel-opment analysis model for solving supplier selection problemswith undesirable outputs and lack of inputsrdquo InternationalJournal of Logistics Systems and Management vol 11 no 3 pp285ndash305 2012

[4] MMadic J Antucheviciene M Radovanovic and D PetkovicldquoDetermination of manufacturing process conditions by usingMCDM methods Application in laser cuttingrdquo EngineeringEconomics vol 27 no 2 pp 144ndash150 2016

[5] E K Zavadskas R Bausys D Stanujkic andM Magdalinovic-Kalinovic ldquoSelection of lead-zinc flotation circuit design byapplying WASPAS method with single-valued neutrosophicsetrdquo Acta Montanistica Slovaca vol 21 no 2 pp 85ndash92 2016

[6] J Antucheviciene Z Kala M Marzouk and E R VaidogasldquoDecision making methods and applications in civil engineer-ingrdquoMathematical Problems in Engineering vol 2015 Article ID160569 3 pages 2015

[7] R Calvo R Domingo and M A Sebastin ldquoSystemic criterionof sustainability in agile manufacturingrdquo International Journalof Production Research vol 46 no 12 pp 3345ndash3358 2008

[8] F A Olivencia Polo J Ferrero Bermejo J F Gomez Fernandezand A Crespo Marquez ldquoFailure mode prediction and energyforecasting of PV plants to assist dynamic maintenance tasksby ANN based modelsrdquo Renewable Energy vol 81 pp 227ndash2382015

[9] A J C Trappey C V Trappey L Ma and J C M ChangldquoIntelligent engineering asset management system for powertransformer maintenance decision supports under variousoperating conditionsrdquo Computers and Industrial Engineeringvol 84 pp 3ndash11 2015

[10] V Martınez-Martınez F J Gomez-Gil J Gomez-Gil and RRuiz-Gonzalez ldquoAn Artificial Neural Network based expertsystem fitted with Genetic Algorithms for detecting the statusof several rotary components in agro-industrial machines usinga single vibration signalrdquo Expert Systems with Applications vol42 no 17-18 pp 6433ndash6441 2015

[11] J Moubray Reliability-centered Maintenance Industrial PressNew York NY USA 1997

[12] A Mardani A Jusoh and E K Zavadskas ldquoFuzzy multi-ple criteria decision-making techniques and applicationsmdashtwodecades review from 1994 to 2014rdquo Expert Systems with Appli-cations vol 42 no 8 pp 4126ndash4148 2015

[13] M Bashiri H Badri and T H Hejazi ldquoSelecting optimummaintenance strategy by fuzzy interactive linear assignmentmethodrdquo Applied Mathematical Modelling Simulation andComputation for Engineering and Environmental Systems vol35 no 1 pp 152ndash164 2011

[14] MM Fouladgar A Lashgari Z Turskis A Yazdani-Chamziniand E K Zavadskas ldquoMaintenance strategy selection usingAHP and COPRAS under fuzzy environmentrdquo InternationalJournal of Strategic Property Management vol 16 no 1 pp 85ndash104 2012

[15] LWang J Chu and JWu ldquoSelection of optimummaintenancestrategies based on a fuzzy analytic hierarchy processrdquo Interna-tional Journal of Production Economics vol 107 no 1 pp 151ndash163 2007

[16] M Ilangkumaran and S Kumanan ldquoSelection of maintenancepolicy for textile industry using hybrid multi-criteria decisionmaking approachrdquo Journal of Manufacturing Technology Man-agement vol 20 no 7 pp 1009ndash1022 2009

[17] J-H Yu M-Y Jeon and T W Kim ldquoFuzzy-based compositeindicator development methodology for evaluating overallproject performancerdquo Journal of Civil Engineering and Manage-ment vol 21 no 3 pp 343ndash355 2015

[18] A Valipour N Yahaya N Md Noor A Mardani and JAntucheviciene ldquoA new hybrid fuzzy cybernetic analytic net-work process model to identify shared risks in PPP projectsrdquoInternational Journal of Strategic Property Management vol 20no 4 pp 409ndash426 2016

[19] M-Y Cheng D K Wibowo D Prayogo and A F V RoyldquoPredicting productivity loss caused by change orders using theevolutionary fuzzy support vector machine inference modelrdquoJournal of Civil Engineering and Management vol 21 no 7 pp881ndash892 2015

[20] H Abdi and L J Williams ldquoPrincipal component analysisrdquoWiley Interdisciplinary Reviews Computational Statistics vol 2no 4 pp 433ndash459 2010

[21] H Li D Parikh Q He et al ldquoImproving rail network velocityA machine learning approach to predictive maintenancerdquoTransportation Research Part C Emerging Technologies vol 45pp 17ndash26 2014

[22] E Alpaydin Introduction to Machine Learning MIT PressCambridge MA USA 2004

[23] H Naganathan W O Chong and X Chen ldquoBuilding energymodeling (BEM) using clustering algorithms and semi-super-vised machine learning approachesrdquo Automation in Construc-tion vol 72 pp 187ndash194 2016

[24] A Vellido J D Martın-Guerrero and P J G Lisboa ldquoMakingmachine learning models interpretablerdquo in Proceedings of the20th European Symposium on Artificial Neural Networks Com-putational Intelligence and Machine Learning ESANN 2012 pp163ndash172 Bruges Belgium April 2012

[25] N Rodrıguez-Padial M Marın and R Domingo ldquoStrategicFramework to Maintenance Decision Support Systemsrdquo in Pro-ceedings of the Manufacturing Engineering Society InternationalConference MESIC 2015 pp 903ndash910 July 2015

[26] G Waeyenbergh and L Pintelon ldquoA framework for mainte-nance concept developmentrdquo International Journal of Produc-tion Economics vol 77 no 3 pp 299ndash313 2002

[27] S W Palocsay I S Markham and S E Markham ldquoUtilizingand teaching data tools in Excel for exploratory analysisrdquoJournal of Business Research vol 63 no 2 pp 191ndash206 2010

[28] Mathworks IntroducingMachine Learning httpswwwmath-workscom

[29] L M Calvo and R Domingo ldquoInfluence of process operatingparameters on CO2 emissions in continuous industrial plantsrdquoJournal of Cleaner Production vol 96 article no 4308 pp 253ndash262 2015

[30] L M Calvo and R Domingo ldquoA first approach to the useof CO2 emissions as a maintenance indicator in industrialplantsrdquo in Proceedings of the Manufacturing Engineering SocietyInternational Conference MESIC 2013 pp 678ndash686 June 2013

Complexity 3

(a) (b)

Figure 1 Imported data from CMMS (a) table dataWO (b) dataWOF

dataWOF will serve as input information for the regressionalgorithm according to the supervised learning techniqueThe separation of maintenance and production departmentsat the level of databasemanagement leads to separate analysisand the use of different techniques in terms of the knowledgeextraction process

21 Exploratory Data Phase There is a first preparatorypreliminary data step inwhich the starting data correspond tothe work orders WOs which have received the papermakingmachines M1 and M2 during a calendar year These datahave been extracted from a CMMS database Figure 1(a)shows the treatment of these data once they have beenimported these are defined in the table dataWO The dataobtained present 46 attributes and 1080 instances of WOsafter a prior filtering The work order is a document thatin its original format presents 46 fields that represent the46 original attributes (see Table 1) which can be groupedinto descriptive fields of problem and resolution with freetext of alphanumeric type and other categorical variables ofnumeric type to accommodate the kind of work order suchas order type requester repair shop repair type urgencytype asset condition and implication of failure Numeri-cal categorical variables that hold classes are order statushomogeneous family section and installation type of fixedassets type of work and operative sequences The remainingqualitative variables are of date type that record dates andtimes of request and programming of the intervention andcompletion However it is permissible to perform a PCAon all data for the 46 attributes applying it only on the 7numerical types (4 associated costs of totals orders parts andworkforce 2 repair times and 1 of the number of operators)which are shown in Table 1 as input variables for the PCAdiscarding the rest of the original variables since they are usedto obtain context information as they document the problemand its physical location that is why theywill serve as prefiltervariables for location and situation in which the maintenanceintervention is located

In the exploratory data phase the statistical technique ofPCA has been used to reduce the data dimension and find theprincipal axes that best represent the variation of data Theseaxes are orthogonal to each other and are calculated using alinear base change application by choosing a new coordinate

system for the original set of data in which the largestvariance of the dataset is captured on the first axis (called thefirst component) the second-largest variance is the secondaxis and so on This methodology reduces to a problem ofeigenvalues and eigenvectors on the covariance matrix of thedata obtaining a reduction of the dimensionality of the dataon those axes that make amore substantial contribution to itsvariance in general therefore many principal axes are usedwhose sum represents approximately 80 of the variation ofthe original data [20]

The PCA parts of a data set are tabulated such that eachline represents an observation instance or individual andeach column represents an attribute or variable Consider thata data set consisting of 119899 observations with 119896 attributes isavailable In matrix notation we will express 119860 (119899119896) where119860 is the matrix representing the table with the coefficients(119886119894119895) as the 119894th observation of the jth variable hence thematrix of observations 119860 is formed by 119896 vectors of variables119909119895 sorted by columns and each vector has 119899 componentscorresponding to its 119899 observations as shown in

119860 = (1199091 1199092 sdot sdot sdot 119909119896) = (11990911 sdot sdot sdot 1199091198961 d1199091119899 sdot sdot sdot 119909119896119899) (1)

To reduce the size of the variables one must find anothervector subspace that is aligned with those vector componentsthat involve more variation and one must form a basis forthese components to be represented in an orthogonal thatis a linearly independent system This problem is reducedto finding a vector space whose vectors V represent thevariation of the data that is a system in which (2) is satisfied119860 sdot V = 120582 sdot V (2)

However in this case the variation is not reduced butis used to find the principal components and axes or theirown values and vectors of the data In accordance with thisphilosophy we will attempt to find those components andprincipal axes that explain the maximum variation of thedata Thus instead of matrix 119860 its covariance matrix is

4 Complexity

Table 1 Original attributes of the maintenance work order

STATE WO Numerical categoricalTYPE WO Word categoricalWO NUMBER Alpha numericalREQUESTING DEPT Word categoricalREQUESTING DATE DateREQUESTING HOUR DateWORKSHOP Word categoricalURGENCY Word categoricalTERM DATE DateWORK DESCRIPT 1 Alpha numericalWORK DESCRIPT 2 Alpha numericalHOMOGENEOUS GROUP Numerical categoricalLOCATION Alpha numericalELECTRIC CODE Alpha numericalSECTION Numerical categoricalINSTALLATION Numerical categoricalDEVICE DESCRIPTION Alpha numericalPRINCIPAL WO Alpha numericalENROLLMENT Alpha numericalTYPE REPAIR Word categoricalSEQUENCE NUMBER Numerical categoricalTYPE INMOBILIZED Numerical categoricalOPERATORS NUMBER NumericalESTIMATED REPAIR TIME NumericalASSET CONDITION Word categoricalTYPE WORK Numerical categoricalSCHEDULED DATE DateSCHEDULED HOUR DateWORK DESCRIPT 1 FINAL 1 Alpha numericalWORK DESCRIPT 1 FINAL 2 Alpha numericalIMPLICATION FAULT Word categoricalELEMENT FAULT Alpha numericalCAUSE FAULT Alpha numericalSUBSTITUTION Alpha numericalID SUBSTITUTE ENROLLMENT Alpha numericalID SUBSTITUTED ENROLLMENT Alpha numericalSTART DATE DateSTART HOUR DateFINAL DATE DateFINAL HOUR DateREPAIR TIME NumericalWORKFORCE COST NumericalPARTS COST NumericalORDER COST NumericalTOTAL COST NumericalREPAIR DATE Date

Complexity 5

Machinelearning

Supervisedlearning

Regression

Classication

Unsupervisedlearning Clustering

Figure 2 Machine learning techniques

obtained 119862 which is then normalized with a mean of zeroand a standard deviation of one The result is then used toobtain the values and eigenvectors of 119862 That is the result isused to solve the following equations10038161003816100381610038161003816119862 minus 12058211986810038161003816100381610038161003816 = 0 (3)(119862 minus 120582119894119868) V119894 = 0 (4)

Once the principal components are obtained 120582119894 alongwith the principal axes V119894 they together explain the variationof the data which are ordered as a Pareto diagram selectingexclusively that set of components 119901 that explain at least80 of the variation in the data Thus a reduction in thedimensions of the data of 119896 original variables to 119901 lt 119896variables is obtained In general the matrix of observationsprojected onto the main axes contains 119899 observations ofthe variables that are obtained by (5) where is the matrixformed by columns with the eigenvectors V119894 obtained from(4) (119899119896) = 119860 (119899119896) sdot (119896119896) (5)

This transformation expresses the original data in axesthat coincide with the natural variation One aspect to beconsidered in this analysis is that this transformation islinear therefore it is not suitable for representing nonlinearproblems In cases of nonlinearity it is advisable to use MLclustering algorithms as will be observed later

22 Phase of Analysis through Machine Learning In thisphase the preparatory data step uses as input data inaddition to the previous data the production values andtheir responses as efficiency variables and failure times foran operational year for both papermaking machines (M1 andM2) Data are extracted and grouped from two databasesmaintenance and production The data obtained present 35attributes and 12 instances corresponding to each month foreach machine (identified as M1 and M2) Figure 1(b) showsthe treatment of these data once they have been importedthey are defined in the table dataWOF The manufacturingreport is a document that in its original format presents35 fields that represent the original attributes (see Table 2)which can be grouped in identifying fields of the machinein question of alphanumeric type and numerical cate-gorical variables record the natural month The remaining

attributes are of numeric type and record the values of timecost interventions and production For each papermakingmachine 3 predictor variables associated with productionparameters shown in Table 2 (daily production in tons ofpaper per day average paper weight in grams per squaremeter and average speed in meters per second) are selectedwith the objective of obtaining a target of two simultaneouspredictive responses (OEE in the percentage of machineutilization andMTTF) Answers that evaluate the aptitude ofthe maintenance function applied to the productive area areprovided by bothmachinesThe three predictive variables arethose that from experience characterize production betterAlthough a PCA could be performed as in the exploratoryphase for the dataWO it was not considered in this occasiondue to the few instances 12 which we had for each machineSince PCA is a statistical analysis it has been consideredthat the few instances are not sufficient to carry out such ananalysis considering at this point a selection based on criteriabased on experience However as more productive data andmore instances are obtained a PCA can be performed onall or those productive attributes of Table 2 to reduce thedimension and select those that represent the most influencein the variation of data

ML is divided into two techniques [27] supervised learn-ing which is training a model on known input and outputdata to predict future outputs and unsupervised learningwhich is finding hidden patterns and intrinsic structures inthe input data Figure 2 provides an illustration of ML Foreach technique different algorithms can be used in whichchoosing the ideal is performed by trial and error

Supervised learning uses classification and regressiontechniques to develop predictive models The differencebetween these techniques is that the classification predictsresponses in discrete or categorical variables whereas regres-sion predicts responses in a continuous variable [36] Unsu-pervised learning uses the clustering technique commonlyused in exploratory data analysis to find hidden patterns asclusters in data

From the algorithms of ML (Support Vector MachineDiscriminant Analysis Naive Bayes Nearest Neighbor Deci-sion Trees K-means Hierarchical Gaussian Mixture Hid-den Markov Model and ANN) we will use algorithmsmodeled with ANN for their versatility for both nonsuper-vised techniques (clustering) and supervised techniques(regression) The former groups the input data to recognizepatterns and define the natural groups present in the data

6 Complexity

Table 2 Original attributes of the manufacturing report (production database)

ID MACHINE Alpha numericalMONTH Numerical categoricalNON PLASTERED PRODUCTION NumericalPLASTERED PRODUCTION NumericalVOLUME PLASTERED PRODUCTION NumericalTOTAL PRODUCTION NumericalWORKS DAYS NumericalDAILY PRODUCTION TON NumericalCUTOUT PRODUCTION NumericalDAILY CUTOUT PRODUCTION TON NumericalDECREASE MACHINE NumericalAVAILABLE HOURS NumericalIDLE TIME EXTERNAL CAUSES NumericalMAINTENANCE NumericalSCHEDULED NumericalBREAKS NumericalPRODUCTION REST NumericalTOTAL IDLE NumericalDAILY IDLE TIME NumericalOEE NumericalSTART NUMBERS NumericalCUTOUT TIME CHANGES NumericalAVERAGE PAPER WEIGHTS NumericalAVERAGE REAL WIDTH NumericalAVERAGE SPEED NumericalAVERAGE BUDGETEDWIDTH NumericalWIDTH DECREASE CMS NumericalWIDTH DECREASE TON NumericalCAPE PRODUCTION NumericalCOST NumericalCOSTTON NumericalINTERVENTIONS NUMBER NumericalMEAN TIME BETWEEN FAILURES NumericalMEAN TIME TO REPAIR NumericalMEAN TIME TO FAILURE Numerical

the latterrsquos purpose is to establish correspondence ormappingbetween input values or predictors and the output variables orobjectives to predict Thus the model is trained (or adjusted)by a knowledge base formed by historical examples of knowninputs and outputs

According to Rumelhart et al [37] the process of theback-propagation training of ANN is used for regressiontechniques such as supervised learning The ANN back-propagation training process is divided into two stagesforward and backward propagation A network configurationconsisting of multiperceptron layers as shown in Figure 3and an activation function of the output layer range and [0 1]is used before an input 119909 which is expressed by

119910 (119909) = 119890119909(1 + 119890119909) (6)

In the forward propagation stage we select an input dataset for training (1199091 1199092 119909119868minus1) and apply it to the network to

obtain the outputs 119910119889 For each neuron 119895 of the hidden layerthe value of each nucleus 119899119895 is given by

119899119895 = 119868sum119894=1

(119908119895119894 sdot 119886119894) + 119887 (7)

where each input value of the previous layer 119886119894 is weighted by119908119895119894 and the output of the hidden layer is expressed by119886119895 = 119891 (119899119895) (8)

This is a nucleus activation function and is performediteratively for each output layer until the final output 119910119889 asin

119910119889 = 119891 (119899119910119889) = 119891( 119867sum119895=1

(119908119910119889119896 sdot 119886119896)) (9)

The backward propagation stage consists of measuringthe error committed as the difference between the calculated

Complexity 7

Input layer Hidden layer Output layer

Input data Output data

x1

x2

xi

b

aj

Wji

Wydj

ak

ydn1

n2

n3

n4

n5

n6

n7

n8

n9

n10

n11

n12

n13

nm

ni

Figure 3 ANN regression trained with back-propagation perceptron multilayer

value 119910119889 and the real value 119910119903 We recalculate the weights119908 attempting to minimize the error in the reverse firstobtaining the new weights of the layer of exit 119908119899119910119889119896 based onthe old 119900 equation119908119899119910119889119896 = 119908119900119910119889119896 + 119886119896 sdot [119910119889 sdot (1 minus 119910119889) sdot (119910119903 minus 119910119889)] (10)

and later the new weights of the hidden layer 119908119899119895119894119908119899119895119894 = 119908119900119895119894 + 119908119900119895119894 sdot 119886119894 sdot [119886119895 sdot (1 minus 119886119895) sdot (119908new119896119895 minus 120575119895)] (11)

where 120575119895 is obtained by applying (9) on the followingequation [119910119889 sdot (1 minus 119910119889) sdot (119910119903 minus 119910119889)] = 120575119910119889 (12)

This process is repeated for 119899 observations until a pre-determined acceptable value of the error is achieved usuallyusing the mean square error (MSE) which is defined in (13)To ensure a rapid convergence of the iterative method weusually use mathematical optimization methods in this casewe use Bayesian Regularization [38]

MSE = 1119899 119899sum119889=1

(119910119903 minus 119910119889)2 (13)

3 Results

It is possible to integrate new functionalities into a customcontrol panel of the industrial plant In this case predictiveanalysis was added for the expected availability response of aproductive area considering the main core of the industrial

plant thus it is possible to anticipate the information Thefuture availability in the medium-term (at monthly intervals)of both machines allows the maintenance department tocorrect possible deviations that are out of tolerance beforethey occur improving their response In the first phasewhich is exploratory we use PCA to discover the smallestdimensions that explain the variation of the data Applyingthe PCA to the set of WOs of productive area 2 (composed ofM1 and M2) principal components or axes PCi are foundthese are sufficient to explain the variation of the originaldata contained in the WOs of the productive area As a resultof the PCA 5 components are identified that would explain100 of the variation therefore 2 linearly dependent vectorsare detected among the input variables reducing in two theoriginal dimension on the other hand from 5 principalcomponents 3 would account for 786 of the variation indata This work aims at the number of interventions costsand maintenance times and will represent the results of PCAon the first 3 principal components In Figure 4(a) it isobserved that the first three principal components represent786 variation of the data reason why it is decided torepresent the data using these three components as principalaxes of representation this is visualized in Figure 4(b)with projections of the original data on the three principalcomponents Previously the data were normalized withmean0 and standard deviation 1

Table 3 shows the values obtained in the PCA of themaintenance metrics where the projections are obtained onthe three principal components of the maintenance numeri-cal variables selected from Table 1 (total costs orders partsand workforce cost estimated repair time repair time andnumber of operators) From the seven maintenance variablesstudied it can be observed that the ones that gain more

8 Complexity

Table 3 PCA results Principal components values

Metrics PC1 PC2 PC3 Metrics modulusTOTAL COST 04696 0476 0036 06697ORDER COST 01598 05714 06089 08502PARTS COST 02323 03913 minus05737 07323WORKFORCE COST 05234 minus02668 minus00987 05957REPAIR TIME 05235 minus02668 minus00987 05957ESTIMATED REPAIR TIME 03793 minus02661 02076 05077OPERATORS NUMBER 009 minus02839 04861 05701

1 2 3 4 5Principal components

0

1

2

3

4

5

6

7

Varia

nce e

xpla

ined

0

14

29

43

57

71

86

100

()

(a)

1510 20

ird

prin

cipa

l com

pone

nt

15Second principal component

5 10

First principal component

minus15

minus10

minus5

0

5

10

50 0minus5 minus5

(b)

Figure 4 (a) Principal components variance (b) projected data on principal components

relevance in principal axes are from greater to less order costtotal cost and part cost this has been considered extractingthe Euclidean modulus or norm of each variable in thethree principal axes which is shown in the last column ofTable 3 The rest of the four remaining variables have a moreor less similar amplitude so they are considered of equalimportance

In the second phase ML the clustering technique is usedto discover patterns hidden in the data such as the naturalgrouping For this technique from the data stored in thedataWO table shown in Figure 1(a) only two attributes areused total cost and repair time Both attributes can be keyindicators for the maintenance department and the repairtime can be key also for the production department becauseof downtime Therefore after knowing their influence onthe principal components it is important to deepen theirrelationship

For the clustering technique two algorithms have beenused The first technique hierarchical clustering allows thecreation of a dendrogram which is a tree diagram that mea-sures the number of natural groups or clusters dependingon the distance criterion that is fixed between data In thiscase by setting a distance value on the ordinate axis the treeis trimmed by a horizontal line that cuts the dendrogram inas many intersections as natural groups appear In this caseFigure 5(a) it is observed that for Euclidean mean distancesof 6000 to 7000 the tree presents two natural groups from

3000 to 5500 it presents three groups from 2000 to 3000 itpresents four groups and below 1000 the number of groupsincreases considerably Because of this compression a valueof 900 is used by pruning the tree into 7 natural groups thedifferent groups of color data can be illustrated in Figure 5(b)As can be seen the hierarchical clustering technique allowsan overview of the number of clusters that can be obtainedas a function of the chosen distance value There are severaldistancemetrics you can even define as a custom in this caseEuclidean distance has been used as a metric

The clustering technique is again used performing asecond algorithmof an SOMANN on the subset of total costdata and repair time as the chosen variables reflecting costsand times of the plantrsquos intervention maintenance An SOMor Kohonen consists of a competitive layer that can classifya set of vector data with any number of dimensions into asmany classes as neurons have a layer [39ndash41] Neurons arearranged in a two-dimensional topology of the data set Thetrained networkwith 2 variables and 1080 input data is shownin Figure 5(c) its two-dimensional topologywith data impactis shown in Figure 5(d)

The network is configured by 2 dimensions 2 times 4discovering a pattern of 8 natural groups in the data theseare distributed with a clear linear relationship between themIn addition there are discrepant data that have no linearrelationship and reveal an unconventional repair this isextraordinary and realized in one of the machines and it

Complexity 9

4 11 13 2 8 25 5 16 12 24 1 10 6 29 14 7 15 18 3 19 9 23 26 30 17 20 22 21 27 28

Observations

0

1000

2000

3000

4000

5000

6000

7000D

istan

ce

(a)

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300

Repa

ir tim

e (h)

Total cost (euro)

(b)

(c)minus1 minus05 0 05 1 15 2 25

minus1

minus05

0

05

1

15

2

25

3

35SOM neighbor weight distances

(d)

Figure 5 (a) Dendrogram (b) data (c) ANN SOM trained (d) SOM topology

was not cataloged like normal repair This finding revealsan error in the introduction of the information in theCMMSThis event was also revealed by the green dot (singlegroup) of the hierarchical clustering figure (see Figure 5(c))Figure 6(a) shows the original data in the two variables(cost time) and Figure 6(b) shows the 7 natural groupsand the linear relationship between them This figure alsoshows that group 8 has no linear relationship as previouslydiscussed The interpretation of these results under themaintenance approach shows that the linear relationshipbetween the groups comes to reflect the following analysison the distribution of the groups observing that the firstfour groups for costs between 0 and 1000 euro are very closeto each other while the centroids of 2000 euro 3000 euro and5000 euro present greater distance With this it is inferred thatthe majority of the interventions cost less than 1000 euro with asmaller number of interventions at intervals of 1000 euro

Finally the regression technique enables prediction ofthe future availability values of both main papermakingmachines (M1 and M2) using the OEE indicators of each

papermakingmachine and itsMTTF such as average runtimebefore failure In addition these values are calculated simulta-neously in the trained ANNmodel A trained neural networkwith input data (predictors) is used that combines the threeproduction variables and the two target output variableswhich are the overall efficiency of each OEE machine andaverage time to MTTF failure measured for 12 months ofthe year for each machine For this technique from the datastored in the dataWOF table shown in Figure 1(b) only threeproduction attributes are used daily production in tons ofpaper per day average paper weight in grams per squaremeter and average speed in meters per second

For the papermaking machines M1 and M2 3 input vari-ables a 10-layer feed-forward network with hidden neuronsand 2 layers of linear output neurons can adjust arbitrarilysuitable multidimensional mapping problems given consis-tent data and sufficient neurons in their hidden layer Thenetwork will be trained with 70 of the data using the back-propagation algorithm of Bayesian Regularization 15 of thedata will be used for validation and the remaining 15 will

10 Complexity

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300Ti

me r

epai

r (h)

RN

Total cost (euro)

(a)

Tim

e rep

air (

h)

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

10

20

30

40

50

60

70

80

90

100

Total cost (euro)

(b)

Figure 6 (a) Artificial Neural Network SOM trained (b) SOM topology sample hits

be used for the test AnMSE performance function is used asshown in Figures 7(a) and 8(a) for M1 and M2 respectivelyThe result of the trained network is suitably provided accord-ing to the regression adjustment coefficients that are shownin Figures 7(b) and 8(b) for M1 and M2 respectively

For each machine that forms the productive area theresult of the adjustment is observed by comparing the outputvariables OEE andMTTF (in blue) measured with the outputvariables foreseen by the OEEp and MTTFp model (in red)The results of M1 are shown in Figure 9(a) and those of M2are shown in Figure 9(b) In short this network can predictthe maintenance behavior of the production area of the plantin availability terms (efficiency and operating times) feedingthe model with the predictive values of production As moreinstances of inputoutput data are introduced the networkwill be retrained and more reliable since it will be adjustedwith a greater number of real examples that have occurredtherefore the greater quantity will lead to the acquisition ofmore experience and knowledge In maintenance terms it isnecessary to make predictions of availability and operatingtimes according to three characteristic values of productionand the model established in this way allows to predictthe OEE and MTTF observing a nonlinear behavior in thetime as is shown in Figure 9 In this sense the responsesfor M1 are observed where the OEE oscillates between anaverage value of 94 with a low dispersion of plusmn066 thusnot happening with MTTF values of mean 8107 h of highdispersion plusmn2582 h As for M2 the OEE has an average valueof 9552 with a low dispersion of plusmn112 despite MTTFvalues of 11803 h mean with very high dispersion plusmn5935 hconcluding that the regression model can accurately predicthigh and low amplitude oscillation values over time

It is noted that the fit is acceptable for training whichis provided by the global adjustment regression coefficient 119877values as shown in Figure 7(b) for both machines

For machine M1 discrepant values are observed in thevalidation setting for month 10 and for both OEE and MTTFindicators there are two reasons for this reason First there is

overadjustment when the data have not been prepared welland there are datawith erroneous or poorly conditioned inputinformation Second there are a low number of observationsor minimal historical information In this paper it has beenverified that the input data for the ANN did not present poorconditioning therefore the overadjustment problem is dis-carded and the few data (ie the few observations) availableare considered themain cause of discordance of the inputs forvalidationThe problem of feeding the network with few datato train and validate the ANN is due to unavailability of moredata for reasons of good performance in the industrial planta fact that would undoubtedly improve the learning of thenetwork and therefore its efficiency and accuracy Howeverthis fact highlights another very interesting aspect of theANN the network is easily adaptable and configurable givena low number of observations Here this adaptability makesit possible to accurately predict 11 hits of 12 possibilities thusthere is a 9167 probability of success in this case

For machine M2 there is a nearly total adjustment forthe OEE indicator but not for the MTTF indicator for whichit is evident that in month 6 there is a discrepancy in theprediction as in M1 the minimal data (observations) usedfor the validation obtain the same precision as M1

Another relevant aspect of the result is the acceptableprecision in the prediction of availability indicators whichare based exclusively on time by simply using as inputthree productive variables as predictor variables (daily papermass paper surface density and machine speed) In additionto the two output variables OEE and MTTF indicators asobjectives to be predicted are calculated simultaneously afact that reflects an additional value of this type of networksand greater computational efficiency by obtaining in a singlesimulation the prediction ofmore than one objective variable

4 Conclusions

A PCA-ML model has been developed such that it can beintegrated into scorecards with a traditional focus BSC

Complexity 11

0 50 100 150 200 250 300 350 400 450483 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is95888e minus 13 at epoch 320

100

10minus5

10minus10

TrainingTestBest

(a)

60 80 100 120Target

50

60

70

80

90

100

110

120Test R = minus042747

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120

Training R = 1

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120All R = 086783

DataFitY = T

lowastＮＬＡＮ

+14

ＯＮＪＯＮ ≃

087

lowastＮＬＡＮ

+13e+02

ＯＮＪＯＮ ≃

minus037lowastＮＬＡＮ

+minus13eminus08

ＯＮＪＯＮ ≃

1

(b)

Figure 7 (a) ANN regression performance for M1 (b) regression fit

12 Complexity

0 100 200 300 400 500 600 700 800856 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is23797e minus 12 at epoch 753

100

105

10minus5

10minus10

TrainingTestBest

(a)

100 150 200 250Target

100

150

200

250

100

150

200

250

100

150

200

250

Test R = minus051755

DataFitY = T

Training R = 1

DataFitY = T

All R = 095036

DataFitY = T

100 150 200 250Target

100 150 200 250Target

lowastＮＬＡＮ

+minus47eminus07

ＯＮＪＯＮ ≃

1

lowastＮＬＡＮ

+26e+02

ＯＮＪＯＮ ≃

minus15

lowastＮＬＡＮ

+6

ＯＮＪＯＮ ≃

097

(b)

Figure 8 (a) ANN regression performance for M2 (b) regression fit

Complexity 13

0 2 4 6 8 10 12(Month)

925

93

935

94

945

95O

EE (

)

(Month)0 2 4 6 8 10 12

40

50

60

70

80

90

100

110

120

130

MTT

F (h

)

(a)

(Month)

OEE

()

0 2 4 6 8 10 1293

935

94

945

95

955

96

965

97

975

98

(Month)

MTT

F (h

)

0 2 4 6 8 10 1250

100

150

200

250

300

(b)

Figure 9 Output variables measure (blue) versus predicted (red) (a) M1 (b) M2

thus including a tactical definition of longer-term strategicapproaches such as a scorecard based onBSCThis new exten-sion allows better control over themaintenance function of anindustrial plant in themedium-termwith amonthly intervalin such a manner that allows the measurement of certainindicators of those productive areas that were previously con-sidered strategic This model of PCA and an ML algorithmusing ANN can be integrated very easily into any traditionalcontrol panel by converting the developed source code topackages of different programming languages and includingthem in a library to be used as a function in a spreadsheetor a standalone executable application In addition at thecontrol panel this model is provided with ML to discoverstructures and behavior patterns that are relatively hidden inWOs By utilizing a clustering or clustering technique naturalgroups are determined in the cost variables and maintenanceworkforce in addition predictions about the availability ofthe productive area are made through the indicators OEE

andMTTFThus the scorecard model on a paper productionplant has been validated

As possible future works this methodology could beapplied to civil engineering and in this case applying a fuzzyuncertainty due to particular characteristics of this sector

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors want to thank the Department of ConstructionEngineering andManufacturing and the College of IndustrialEngineers of UNED for their support through Projects 2017-IFC08 and 2017-IFC09

14 Complexity

References

[1] DWollmann andM T Steiner ldquoThe strategic decision-makingas a complex adaptive system a conceptual scientific modelrdquoComplexity vol 2017 Article ID 7954289 13 pages 2017

[2] R Calvo R Domingo and M A Sebastian ldquoOperationalflexibility quantification in a make-to-order assembly systemrdquoInternational Journal of Flexible Manufacturing Systems vol 19no 3 pp 247ndash263 2008

[3] M Mahdiloo R F Saen and M Tavana ldquoA novel data envel-opment analysis model for solving supplier selection problemswith undesirable outputs and lack of inputsrdquo InternationalJournal of Logistics Systems and Management vol 11 no 3 pp285ndash305 2012

[4] MMadic J Antucheviciene M Radovanovic and D PetkovicldquoDetermination of manufacturing process conditions by usingMCDM methods Application in laser cuttingrdquo EngineeringEconomics vol 27 no 2 pp 144ndash150 2016

[5] E K Zavadskas R Bausys D Stanujkic andM Magdalinovic-Kalinovic ldquoSelection of lead-zinc flotation circuit design byapplying WASPAS method with single-valued neutrosophicsetrdquo Acta Montanistica Slovaca vol 21 no 2 pp 85ndash92 2016

[6] J Antucheviciene Z Kala M Marzouk and E R VaidogasldquoDecision making methods and applications in civil engineer-ingrdquoMathematical Problems in Engineering vol 2015 Article ID160569 3 pages 2015

[7] R Calvo R Domingo and M A Sebastin ldquoSystemic criterionof sustainability in agile manufacturingrdquo International Journalof Production Research vol 46 no 12 pp 3345ndash3358 2008

[8] F A Olivencia Polo J Ferrero Bermejo J F Gomez Fernandezand A Crespo Marquez ldquoFailure mode prediction and energyforecasting of PV plants to assist dynamic maintenance tasksby ANN based modelsrdquo Renewable Energy vol 81 pp 227ndash2382015

[9] A J C Trappey C V Trappey L Ma and J C M ChangldquoIntelligent engineering asset management system for powertransformer maintenance decision supports under variousoperating conditionsrdquo Computers and Industrial Engineeringvol 84 pp 3ndash11 2015

[10] V Martınez-Martınez F J Gomez-Gil J Gomez-Gil and RRuiz-Gonzalez ldquoAn Artificial Neural Network based expertsystem fitted with Genetic Algorithms for detecting the statusof several rotary components in agro-industrial machines usinga single vibration signalrdquo Expert Systems with Applications vol42 no 17-18 pp 6433ndash6441 2015

[11] J Moubray Reliability-centered Maintenance Industrial PressNew York NY USA 1997

[12] A Mardani A Jusoh and E K Zavadskas ldquoFuzzy multi-ple criteria decision-making techniques and applicationsmdashtwodecades review from 1994 to 2014rdquo Expert Systems with Appli-cations vol 42 no 8 pp 4126ndash4148 2015

[13] M Bashiri H Badri and T H Hejazi ldquoSelecting optimummaintenance strategy by fuzzy interactive linear assignmentmethodrdquo Applied Mathematical Modelling Simulation andComputation for Engineering and Environmental Systems vol35 no 1 pp 152ndash164 2011

[14] MM Fouladgar A Lashgari Z Turskis A Yazdani-Chamziniand E K Zavadskas ldquoMaintenance strategy selection usingAHP and COPRAS under fuzzy environmentrdquo InternationalJournal of Strategic Property Management vol 16 no 1 pp 85ndash104 2012

[15] LWang J Chu and JWu ldquoSelection of optimummaintenancestrategies based on a fuzzy analytic hierarchy processrdquo Interna-tional Journal of Production Economics vol 107 no 1 pp 151ndash163 2007

[16] M Ilangkumaran and S Kumanan ldquoSelection of maintenancepolicy for textile industry using hybrid multi-criteria decisionmaking approachrdquo Journal of Manufacturing Technology Man-agement vol 20 no 7 pp 1009ndash1022 2009

[17] J-H Yu M-Y Jeon and T W Kim ldquoFuzzy-based compositeindicator development methodology for evaluating overallproject performancerdquo Journal of Civil Engineering and Manage-ment vol 21 no 3 pp 343ndash355 2015

[18] A Valipour N Yahaya N Md Noor A Mardani and JAntucheviciene ldquoA new hybrid fuzzy cybernetic analytic net-work process model to identify shared risks in PPP projectsrdquoInternational Journal of Strategic Property Management vol 20no 4 pp 409ndash426 2016

[19] M-Y Cheng D K Wibowo D Prayogo and A F V RoyldquoPredicting productivity loss caused by change orders using theevolutionary fuzzy support vector machine inference modelrdquoJournal of Civil Engineering and Management vol 21 no 7 pp881ndash892 2015

[20] H Abdi and L J Williams ldquoPrincipal component analysisrdquoWiley Interdisciplinary Reviews Computational Statistics vol 2no 4 pp 433ndash459 2010

[21] H Li D Parikh Q He et al ldquoImproving rail network velocityA machine learning approach to predictive maintenancerdquoTransportation Research Part C Emerging Technologies vol 45pp 17ndash26 2014

[22] E Alpaydin Introduction to Machine Learning MIT PressCambridge MA USA 2004

[23] H Naganathan W O Chong and X Chen ldquoBuilding energymodeling (BEM) using clustering algorithms and semi-super-vised machine learning approachesrdquo Automation in Construc-tion vol 72 pp 187ndash194 2016

[24] A Vellido J D Martın-Guerrero and P J G Lisboa ldquoMakingmachine learning models interpretablerdquo in Proceedings of the20th European Symposium on Artificial Neural Networks Com-putational Intelligence and Machine Learning ESANN 2012 pp163ndash172 Bruges Belgium April 2012

[25] N Rodrıguez-Padial M Marın and R Domingo ldquoStrategicFramework to Maintenance Decision Support Systemsrdquo in Pro-ceedings of the Manufacturing Engineering Society InternationalConference MESIC 2015 pp 903ndash910 July 2015

[26] G Waeyenbergh and L Pintelon ldquoA framework for mainte-nance concept developmentrdquo International Journal of Produc-tion Economics vol 77 no 3 pp 299ndash313 2002

[27] S W Palocsay I S Markham and S E Markham ldquoUtilizingand teaching data tools in Excel for exploratory analysisrdquoJournal of Business Research vol 63 no 2 pp 191ndash206 2010

[28] Mathworks IntroducingMachine Learning httpswwwmath-workscom

[29] L M Calvo and R Domingo ldquoInfluence of process operatingparameters on CO2 emissions in continuous industrial plantsrdquoJournal of Cleaner Production vol 96 article no 4308 pp 253ndash262 2015

[30] L M Calvo and R Domingo ldquoA first approach to the useof CO2 emissions as a maintenance indicator in industrialplantsrdquo in Proceedings of the Manufacturing Engineering SocietyInternational Conference MESIC 2013 pp 678ndash686 June 2013

4 Complexity

Table 1 Original attributes of the maintenance work order

STATE WO Numerical categoricalTYPE WO Word categoricalWO NUMBER Alpha numericalREQUESTING DEPT Word categoricalREQUESTING DATE DateREQUESTING HOUR DateWORKSHOP Word categoricalURGENCY Word categoricalTERM DATE DateWORK DESCRIPT 1 Alpha numericalWORK DESCRIPT 2 Alpha numericalHOMOGENEOUS GROUP Numerical categoricalLOCATION Alpha numericalELECTRIC CODE Alpha numericalSECTION Numerical categoricalINSTALLATION Numerical categoricalDEVICE DESCRIPTION Alpha numericalPRINCIPAL WO Alpha numericalENROLLMENT Alpha numericalTYPE REPAIR Word categoricalSEQUENCE NUMBER Numerical categoricalTYPE INMOBILIZED Numerical categoricalOPERATORS NUMBER NumericalESTIMATED REPAIR TIME NumericalASSET CONDITION Word categoricalTYPE WORK Numerical categoricalSCHEDULED DATE DateSCHEDULED HOUR DateWORK DESCRIPT 1 FINAL 1 Alpha numericalWORK DESCRIPT 1 FINAL 2 Alpha numericalIMPLICATION FAULT Word categoricalELEMENT FAULT Alpha numericalCAUSE FAULT Alpha numericalSUBSTITUTION Alpha numericalID SUBSTITUTE ENROLLMENT Alpha numericalID SUBSTITUTED ENROLLMENT Alpha numericalSTART DATE DateSTART HOUR DateFINAL DATE DateFINAL HOUR DateREPAIR TIME NumericalWORKFORCE COST NumericalPARTS COST NumericalORDER COST NumericalTOTAL COST NumericalREPAIR DATE Date

Complexity 5

Machinelearning

Supervisedlearning

Regression

Classication

Unsupervisedlearning Clustering

Figure 2 Machine learning techniques

obtained 119862 which is then normalized with a mean of zeroand a standard deviation of one The result is then used toobtain the values and eigenvectors of 119862 That is the result isused to solve the following equations10038161003816100381610038161003816119862 minus 12058211986810038161003816100381610038161003816 = 0 (3)(119862 minus 120582119894119868) V119894 = 0 (4)

Once the principal components are obtained 120582119894 alongwith the principal axes V119894 they together explain the variationof the data which are ordered as a Pareto diagram selectingexclusively that set of components 119901 that explain at least80 of the variation in the data Thus a reduction in thedimensions of the data of 119896 original variables to 119901 lt 119896variables is obtained In general the matrix of observationsprojected onto the main axes contains 119899 observations ofthe variables that are obtained by (5) where is the matrixformed by columns with the eigenvectors V119894 obtained from(4) (119899119896) = 119860 (119899119896) sdot (119896119896) (5)

This transformation expresses the original data in axesthat coincide with the natural variation One aspect to beconsidered in this analysis is that this transformation islinear therefore it is not suitable for representing nonlinearproblems In cases of nonlinearity it is advisable to use MLclustering algorithms as will be observed later

22 Phase of Analysis through Machine Learning In thisphase the preparatory data step uses as input data inaddition to the previous data the production values andtheir responses as efficiency variables and failure times foran operational year for both papermaking machines (M1 andM2) Data are extracted and grouped from two databasesmaintenance and production The data obtained present 35attributes and 12 instances corresponding to each month foreach machine (identified as M1 and M2) Figure 1(b) showsthe treatment of these data once they have been importedthey are defined in the table dataWOF The manufacturingreport is a document that in its original format presents35 fields that represent the original attributes (see Table 2)which can be grouped in identifying fields of the machinein question of alphanumeric type and numerical cate-gorical variables record the natural month The remaining

attributes are of numeric type and record the values of timecost interventions and production For each papermakingmachine 3 predictor variables associated with productionparameters shown in Table 2 (daily production in tons ofpaper per day average paper weight in grams per squaremeter and average speed in meters per second) are selectedwith the objective of obtaining a target of two simultaneouspredictive responses (OEE in the percentage of machineutilization andMTTF) Answers that evaluate the aptitude ofthe maintenance function applied to the productive area areprovided by bothmachinesThe three predictive variables arethose that from experience characterize production betterAlthough a PCA could be performed as in the exploratoryphase for the dataWO it was not considered in this occasiondue to the few instances 12 which we had for each machineSince PCA is a statistical analysis it has been consideredthat the few instances are not sufficient to carry out such ananalysis considering at this point a selection based on criteriabased on experience However as more productive data andmore instances are obtained a PCA can be performed onall or those productive attributes of Table 2 to reduce thedimension and select those that represent the most influencein the variation of data

ML is divided into two techniques [27] supervised learn-ing which is training a model on known input and outputdata to predict future outputs and unsupervised learningwhich is finding hidden patterns and intrinsic structures inthe input data Figure 2 provides an illustration of ML Foreach technique different algorithms can be used in whichchoosing the ideal is performed by trial and error

Supervised learning uses classification and regressiontechniques to develop predictive models The differencebetween these techniques is that the classification predictsresponses in discrete or categorical variables whereas regres-sion predicts responses in a continuous variable [36] Unsu-pervised learning uses the clustering technique commonlyused in exploratory data analysis to find hidden patterns asclusters in data

From the algorithms of ML (Support Vector MachineDiscriminant Analysis Naive Bayes Nearest Neighbor Deci-sion Trees K-means Hierarchical Gaussian Mixture Hid-den Markov Model and ANN) we will use algorithmsmodeled with ANN for their versatility for both nonsuper-vised techniques (clustering) and supervised techniques(regression) The former groups the input data to recognizepatterns and define the natural groups present in the data

6 Complexity

Table 2 Original attributes of the manufacturing report (production database)

ID MACHINE Alpha numericalMONTH Numerical categoricalNON PLASTERED PRODUCTION NumericalPLASTERED PRODUCTION NumericalVOLUME PLASTERED PRODUCTION NumericalTOTAL PRODUCTION NumericalWORKS DAYS NumericalDAILY PRODUCTION TON NumericalCUTOUT PRODUCTION NumericalDAILY CUTOUT PRODUCTION TON NumericalDECREASE MACHINE NumericalAVAILABLE HOURS NumericalIDLE TIME EXTERNAL CAUSES NumericalMAINTENANCE NumericalSCHEDULED NumericalBREAKS NumericalPRODUCTION REST NumericalTOTAL IDLE NumericalDAILY IDLE TIME NumericalOEE NumericalSTART NUMBERS NumericalCUTOUT TIME CHANGES NumericalAVERAGE PAPER WEIGHTS NumericalAVERAGE REAL WIDTH NumericalAVERAGE SPEED NumericalAVERAGE BUDGETEDWIDTH NumericalWIDTH DECREASE CMS NumericalWIDTH DECREASE TON NumericalCAPE PRODUCTION NumericalCOST NumericalCOSTTON NumericalINTERVENTIONS NUMBER NumericalMEAN TIME BETWEEN FAILURES NumericalMEAN TIME TO REPAIR NumericalMEAN TIME TO FAILURE Numerical

the latterrsquos purpose is to establish correspondence ormappingbetween input values or predictors and the output variables orobjectives to predict Thus the model is trained (or adjusted)by a knowledge base formed by historical examples of knowninputs and outputs

According to Rumelhart et al [37] the process of theback-propagation training of ANN is used for regressiontechniques such as supervised learning The ANN back-propagation training process is divided into two stagesforward and backward propagation A network configurationconsisting of multiperceptron layers as shown in Figure 3and an activation function of the output layer range and [0 1]is used before an input 119909 which is expressed by

119910 (119909) = 119890119909(1 + 119890119909) (6)

In the forward propagation stage we select an input dataset for training (1199091 1199092 119909119868minus1) and apply it to the network to

obtain the outputs 119910119889 For each neuron 119895 of the hidden layerthe value of each nucleus 119899119895 is given by

119899119895 = 119868sum119894=1

(119908119895119894 sdot 119886119894) + 119887 (7)

where each input value of the previous layer 119886119894 is weighted by119908119895119894 and the output of the hidden layer is expressed by119886119895 = 119891 (119899119895) (8)

This is a nucleus activation function and is performediteratively for each output layer until the final output 119910119889 asin

119910119889 = 119891 (119899119910119889) = 119891( 119867sum119895=1

(119908119910119889119896 sdot 119886119896)) (9)

The backward propagation stage consists of measuringthe error committed as the difference between the calculated

Complexity 7

Input layer Hidden layer Output layer

Input data Output data

x1

x2

xi

b

aj

Wji

Wydj

ak

ydn1

n2

n3

n4

n5

n6

n7

n8

n9

n10

n11

n12

n13

nm

ni

Figure 3 ANN regression trained with back-propagation perceptron multilayer

value 119910119889 and the real value 119910119903 We recalculate the weights119908 attempting to minimize the error in the reverse firstobtaining the new weights of the layer of exit 119908119899119910119889119896 based onthe old 119900 equation119908119899119910119889119896 = 119908119900119910119889119896 + 119886119896 sdot [119910119889 sdot (1 minus 119910119889) sdot (119910119903 minus 119910119889)] (10)

and later the new weights of the hidden layer 119908119899119895119894119908119899119895119894 = 119908119900119895119894 + 119908119900119895119894 sdot 119886119894 sdot [119886119895 sdot (1 minus 119886119895) sdot (119908new119896119895 minus 120575119895)] (11)

where 120575119895 is obtained by applying (9) on the followingequation [119910119889 sdot (1 minus 119910119889) sdot (119910119903 minus 119910119889)] = 120575119910119889 (12)

This process is repeated for 119899 observations until a pre-determined acceptable value of the error is achieved usuallyusing the mean square error (MSE) which is defined in (13)To ensure a rapid convergence of the iterative method weusually use mathematical optimization methods in this casewe use Bayesian Regularization [38]

MSE = 1119899 119899sum119889=1

(119910119903 minus 119910119889)2 (13)

3 Results

It is possible to integrate new functionalities into a customcontrol panel of the industrial plant In this case predictiveanalysis was added for the expected availability response of aproductive area considering the main core of the industrial

plant thus it is possible to anticipate the information Thefuture availability in the medium-term (at monthly intervals)of both machines allows the maintenance department tocorrect possible deviations that are out of tolerance beforethey occur improving their response In the first phasewhich is exploratory we use PCA to discover the smallestdimensions that explain the variation of the data Applyingthe PCA to the set of WOs of productive area 2 (composed ofM1 and M2) principal components or axes PCi are foundthese are sufficient to explain the variation of the originaldata contained in the WOs of the productive area As a resultof the PCA 5 components are identified that would explain100 of the variation therefore 2 linearly dependent vectorsare detected among the input variables reducing in two theoriginal dimension on the other hand from 5 principalcomponents 3 would account for 786 of the variation indata This work aims at the number of interventions costsand maintenance times and will represent the results of PCAon the first 3 principal components In Figure 4(a) it isobserved that the first three principal components represent786 variation of the data reason why it is decided torepresent the data using these three components as principalaxes of representation this is visualized in Figure 4(b)with projections of the original data on the three principalcomponents Previously the data were normalized withmean0 and standard deviation 1

Table 3 shows the values obtained in the PCA of themaintenance metrics where the projections are obtained onthe three principal components of the maintenance numeri-cal variables selected from Table 1 (total costs orders partsand workforce cost estimated repair time repair time andnumber of operators) From the seven maintenance variablesstudied it can be observed that the ones that gain more

8 Complexity

Table 3 PCA results Principal components values

Metrics PC1 PC2 PC3 Metrics modulusTOTAL COST 04696 0476 0036 06697ORDER COST 01598 05714 06089 08502PARTS COST 02323 03913 minus05737 07323WORKFORCE COST 05234 minus02668 minus00987 05957REPAIR TIME 05235 minus02668 minus00987 05957ESTIMATED REPAIR TIME 03793 minus02661 02076 05077OPERATORS NUMBER 009 minus02839 04861 05701

1 2 3 4 5Principal components

0

1

2

3

4

5

6

7

Varia

nce e

xpla

ined

0

14

29

43

57

71

86

100

()

(a)

1510 20

ird

prin

cipa

l com

pone

nt

15Second principal component

5 10

First principal component

minus15

minus10

minus5

0

5

10

50 0minus5 minus5

(b)

Figure 4 (a) Principal components variance (b) projected data on principal components

relevance in principal axes are from greater to less order costtotal cost and part cost this has been considered extractingthe Euclidean modulus or norm of each variable in thethree principal axes which is shown in the last column ofTable 3 The rest of the four remaining variables have a moreor less similar amplitude so they are considered of equalimportance

In the second phase ML the clustering technique is usedto discover patterns hidden in the data such as the naturalgrouping For this technique from the data stored in thedataWO table shown in Figure 1(a) only two attributes areused total cost and repair time Both attributes can be keyindicators for the maintenance department and the repairtime can be key also for the production department becauseof downtime Therefore after knowing their influence onthe principal components it is important to deepen theirrelationship

For the clustering technique two algorithms have beenused The first technique hierarchical clustering allows thecreation of a dendrogram which is a tree diagram that mea-sures the number of natural groups or clusters dependingon the distance criterion that is fixed between data In thiscase by setting a distance value on the ordinate axis the treeis trimmed by a horizontal line that cuts the dendrogram inas many intersections as natural groups appear In this caseFigure 5(a) it is observed that for Euclidean mean distancesof 6000 to 7000 the tree presents two natural groups from

3000 to 5500 it presents three groups from 2000 to 3000 itpresents four groups and below 1000 the number of groupsincreases considerably Because of this compression a valueof 900 is used by pruning the tree into 7 natural groups thedifferent groups of color data can be illustrated in Figure 5(b)As can be seen the hierarchical clustering technique allowsan overview of the number of clusters that can be obtainedas a function of the chosen distance value There are severaldistancemetrics you can even define as a custom in this caseEuclidean distance has been used as a metric

The clustering technique is again used performing asecond algorithmof an SOMANN on the subset of total costdata and repair time as the chosen variables reflecting costsand times of the plantrsquos intervention maintenance An SOMor Kohonen consists of a competitive layer that can classifya set of vector data with any number of dimensions into asmany classes as neurons have a layer [39ndash41] Neurons arearranged in a two-dimensional topology of the data set Thetrained networkwith 2 variables and 1080 input data is shownin Figure 5(c) its two-dimensional topologywith data impactis shown in Figure 5(d)

The network is configured by 2 dimensions 2 times 4discovering a pattern of 8 natural groups in the data theseare distributed with a clear linear relationship between themIn addition there are discrepant data that have no linearrelationship and reveal an unconventional repair this isextraordinary and realized in one of the machines and it

Complexity 9

4 11 13 2 8 25 5 16 12 24 1 10 6 29 14 7 15 18 3 19 9 23 26 30 17 20 22 21 27 28

Observations

0

1000

2000

3000

4000

5000

6000

7000D

istan

ce

(a)

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300

Repa

ir tim

e (h)

Total cost (euro)

(b)

(c)minus1 minus05 0 05 1 15 2 25

minus1

minus05

0

05

1

15

2

25

3

35SOM neighbor weight distances

(d)

Figure 5 (a) Dendrogram (b) data (c) ANN SOM trained (d) SOM topology

was not cataloged like normal repair This finding revealsan error in the introduction of the information in theCMMSThis event was also revealed by the green dot (singlegroup) of the hierarchical clustering figure (see Figure 5(c))Figure 6(a) shows the original data in the two variables(cost time) and Figure 6(b) shows the 7 natural groupsand the linear relationship between them This figure alsoshows that group 8 has no linear relationship as previouslydiscussed The interpretation of these results under themaintenance approach shows that the linear relationshipbetween the groups comes to reflect the following analysison the distribution of the groups observing that the firstfour groups for costs between 0 and 1000 euro are very closeto each other while the centroids of 2000 euro 3000 euro and5000 euro present greater distance With this it is inferred thatthe majority of the interventions cost less than 1000 euro with asmaller number of interventions at intervals of 1000 euro

Finally the regression technique enables prediction ofthe future availability values of both main papermakingmachines (M1 and M2) using the OEE indicators of each

papermakingmachine and itsMTTF such as average runtimebefore failure In addition these values are calculated simulta-neously in the trained ANNmodel A trained neural networkwith input data (predictors) is used that combines the threeproduction variables and the two target output variableswhich are the overall efficiency of each OEE machine andaverage time to MTTF failure measured for 12 months ofthe year for each machine For this technique from the datastored in the dataWOF table shown in Figure 1(b) only threeproduction attributes are used daily production in tons ofpaper per day average paper weight in grams per squaremeter and average speed in meters per second

For the papermaking machines M1 and M2 3 input vari-ables a 10-layer feed-forward network with hidden neuronsand 2 layers of linear output neurons can adjust arbitrarilysuitable multidimensional mapping problems given consis-tent data and sufficient neurons in their hidden layer Thenetwork will be trained with 70 of the data using the back-propagation algorithm of Bayesian Regularization 15 of thedata will be used for validation and the remaining 15 will

10 Complexity

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300Ti

me r

epai

r (h)

RN

Total cost (euro)

(a)

Tim

e rep

air (

h)

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

10

20

30

40

50

60

70

80

90

100

Total cost (euro)

(b)

Figure 6 (a) Artificial Neural Network SOM trained (b) SOM topology sample hits

be used for the test AnMSE performance function is used asshown in Figures 7(a) and 8(a) for M1 and M2 respectivelyThe result of the trained network is suitably provided accord-ing to the regression adjustment coefficients that are shownin Figures 7(b) and 8(b) for M1 and M2 respectively

For each machine that forms the productive area theresult of the adjustment is observed by comparing the outputvariables OEE andMTTF (in blue) measured with the outputvariables foreseen by the OEEp and MTTFp model (in red)The results of M1 are shown in Figure 9(a) and those of M2are shown in Figure 9(b) In short this network can predictthe maintenance behavior of the production area of the plantin availability terms (efficiency and operating times) feedingthe model with the predictive values of production As moreinstances of inputoutput data are introduced the networkwill be retrained and more reliable since it will be adjustedwith a greater number of real examples that have occurredtherefore the greater quantity will lead to the acquisition ofmore experience and knowledge In maintenance terms it isnecessary to make predictions of availability and operatingtimes according to three characteristic values of productionand the model established in this way allows to predictthe OEE and MTTF observing a nonlinear behavior in thetime as is shown in Figure 9 In this sense the responsesfor M1 are observed where the OEE oscillates between anaverage value of 94 with a low dispersion of plusmn066 thusnot happening with MTTF values of mean 8107 h of highdispersion plusmn2582 h As for M2 the OEE has an average valueof 9552 with a low dispersion of plusmn112 despite MTTFvalues of 11803 h mean with very high dispersion plusmn5935 hconcluding that the regression model can accurately predicthigh and low amplitude oscillation values over time

It is noted that the fit is acceptable for training whichis provided by the global adjustment regression coefficient 119877values as shown in Figure 7(b) for both machines

For machine M1 discrepant values are observed in thevalidation setting for month 10 and for both OEE and MTTFindicators there are two reasons for this reason First there is

overadjustment when the data have not been prepared welland there are datawith erroneous or poorly conditioned inputinformation Second there are a low number of observationsor minimal historical information In this paper it has beenverified that the input data for the ANN did not present poorconditioning therefore the overadjustment problem is dis-carded and the few data (ie the few observations) availableare considered themain cause of discordance of the inputs forvalidationThe problem of feeding the network with few datato train and validate the ANN is due to unavailability of moredata for reasons of good performance in the industrial planta fact that would undoubtedly improve the learning of thenetwork and therefore its efficiency and accuracy Howeverthis fact highlights another very interesting aspect of theANN the network is easily adaptable and configurable givena low number of observations Here this adaptability makesit possible to accurately predict 11 hits of 12 possibilities thusthere is a 9167 probability of success in this case

For machine M2 there is a nearly total adjustment forthe OEE indicator but not for the MTTF indicator for whichit is evident that in month 6 there is a discrepancy in theprediction as in M1 the minimal data (observations) usedfor the validation obtain the same precision as M1

Another relevant aspect of the result is the acceptableprecision in the prediction of availability indicators whichare based exclusively on time by simply using as inputthree productive variables as predictor variables (daily papermass paper surface density and machine speed) In additionto the two output variables OEE and MTTF indicators asobjectives to be predicted are calculated simultaneously afact that reflects an additional value of this type of networksand greater computational efficiency by obtaining in a singlesimulation the prediction ofmore than one objective variable

4 Conclusions

A PCA-ML model has been developed such that it can beintegrated into scorecards with a traditional focus BSC

Complexity 11

0 50 100 150 200 250 300 350 400 450483 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is95888e minus 13 at epoch 320

100

10minus5

10minus10

TrainingTestBest

(a)

60 80 100 120Target

50

60

70

80

90

100

110

120Test R = minus042747

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120

Training R = 1

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120All R = 086783

DataFitY = T

lowastＮＬＡＮ

+14

ＯＮＪＯＮ ≃

087

lowastＮＬＡＮ

+13e+02

ＯＮＪＯＮ ≃

minus037lowastＮＬＡＮ

+minus13eminus08

ＯＮＪＯＮ ≃

1

(b)

Figure 7 (a) ANN regression performance for M1 (b) regression fit

12 Complexity

0 100 200 300 400 500 600 700 800856 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is23797e minus 12 at epoch 753

100

105

10minus5

10minus10

TrainingTestBest

(a)

100 150 200 250Target

100

150

200

250

100

150

200

250

100

150

200

250

Test R = minus051755

DataFitY = T

Training R = 1

DataFitY = T

All R = 095036

DataFitY = T

100 150 200 250Target

100 150 200 250Target

lowastＮＬＡＮ

+minus47eminus07

ＯＮＪＯＮ ≃

1

lowastＮＬＡＮ

+26e+02

ＯＮＪＯＮ ≃

minus15

lowastＮＬＡＮ

+6

ＯＮＪＯＮ ≃

097

(b)

Figure 8 (a) ANN regression performance for M2 (b) regression fit

Complexity 13

0 2 4 6 8 10 12(Month)

925

93

935

94

945

95O

EE (

)

(Month)0 2 4 6 8 10 12

40

50

60

70

80

90

100

110

120

130

MTT

F (h

)

(a)

(Month)

OEE

()

0 2 4 6 8 10 1293

935

94

945

95

955

96

965

97

975

98

(Month)

MTT

F (h

)

0 2 4 6 8 10 1250

100

150

200

250

300

(b)

Figure 9 Output variables measure (blue) versus predicted (red) (a) M1 (b) M2

thus including a tactical definition of longer-term strategicapproaches such as a scorecard based onBSCThis new exten-sion allows better control over themaintenance function of anindustrial plant in themedium-termwith amonthly intervalin such a manner that allows the measurement of certainindicators of those productive areas that were previously con-sidered strategic This model of PCA and an ML algorithmusing ANN can be integrated very easily into any traditionalcontrol panel by converting the developed source code topackages of different programming languages and includingthem in a library to be used as a function in a spreadsheetor a standalone executable application In addition at thecontrol panel this model is provided with ML to discoverstructures and behavior patterns that are relatively hidden inWOs By utilizing a clustering or clustering technique naturalgroups are determined in the cost variables and maintenanceworkforce in addition predictions about the availability ofthe productive area are made through the indicators OEE

andMTTFThus the scorecard model on a paper productionplant has been validated

As possible future works this methodology could beapplied to civil engineering and in this case applying a fuzzyuncertainty due to particular characteristics of this sector

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors want to thank the Department of ConstructionEngineering andManufacturing and the College of IndustrialEngineers of UNED for their support through Projects 2017-IFC08 and 2017-IFC09

14 Complexity

References

[1] DWollmann andM T Steiner ldquoThe strategic decision-makingas a complex adaptive system a conceptual scientific modelrdquoComplexity vol 2017 Article ID 7954289 13 pages 2017

[2] R Calvo R Domingo and M A Sebastian ldquoOperationalflexibility quantification in a make-to-order assembly systemrdquoInternational Journal of Flexible Manufacturing Systems vol 19no 3 pp 247ndash263 2008

[3] M Mahdiloo R F Saen and M Tavana ldquoA novel data envel-opment analysis model for solving supplier selection problemswith undesirable outputs and lack of inputsrdquo InternationalJournal of Logistics Systems and Management vol 11 no 3 pp285ndash305 2012

[4] MMadic J Antucheviciene M Radovanovic and D PetkovicldquoDetermination of manufacturing process conditions by usingMCDM methods Application in laser cuttingrdquo EngineeringEconomics vol 27 no 2 pp 144ndash150 2016

[5] E K Zavadskas R Bausys D Stanujkic andM Magdalinovic-Kalinovic ldquoSelection of lead-zinc flotation circuit design byapplying WASPAS method with single-valued neutrosophicsetrdquo Acta Montanistica Slovaca vol 21 no 2 pp 85ndash92 2016

[6] J Antucheviciene Z Kala M Marzouk and E R VaidogasldquoDecision making methods and applications in civil engineer-ingrdquoMathematical Problems in Engineering vol 2015 Article ID160569 3 pages 2015

[7] R Calvo R Domingo and M A Sebastin ldquoSystemic criterionof sustainability in agile manufacturingrdquo International Journalof Production Research vol 46 no 12 pp 3345ndash3358 2008

[8] F A Olivencia Polo J Ferrero Bermejo J F Gomez Fernandezand A Crespo Marquez ldquoFailure mode prediction and energyforecasting of PV plants to assist dynamic maintenance tasksby ANN based modelsrdquo Renewable Energy vol 81 pp 227ndash2382015

[9] A J C Trappey C V Trappey L Ma and J C M ChangldquoIntelligent engineering asset management system for powertransformer maintenance decision supports under variousoperating conditionsrdquo Computers and Industrial Engineeringvol 84 pp 3ndash11 2015

[10] V Martınez-Martınez F J Gomez-Gil J Gomez-Gil and RRuiz-Gonzalez ldquoAn Artificial Neural Network based expertsystem fitted with Genetic Algorithms for detecting the statusof several rotary components in agro-industrial machines usinga single vibration signalrdquo Expert Systems with Applications vol42 no 17-18 pp 6433ndash6441 2015

[11] J Moubray Reliability-centered Maintenance Industrial PressNew York NY USA 1997

[12] A Mardani A Jusoh and E K Zavadskas ldquoFuzzy multi-ple criteria decision-making techniques and applicationsmdashtwodecades review from 1994 to 2014rdquo Expert Systems with Appli-cations vol 42 no 8 pp 4126ndash4148 2015

[13] M Bashiri H Badri and T H Hejazi ldquoSelecting optimummaintenance strategy by fuzzy interactive linear assignmentmethodrdquo Applied Mathematical Modelling Simulation andComputation for Engineering and Environmental Systems vol35 no 1 pp 152ndash164 2011

[14] MM Fouladgar A Lashgari Z Turskis A Yazdani-Chamziniand E K Zavadskas ldquoMaintenance strategy selection usingAHP and COPRAS under fuzzy environmentrdquo InternationalJournal of Strategic Property Management vol 16 no 1 pp 85ndash104 2012

[15] LWang J Chu and JWu ldquoSelection of optimummaintenancestrategies based on a fuzzy analytic hierarchy processrdquo Interna-tional Journal of Production Economics vol 107 no 1 pp 151ndash163 2007

[16] M Ilangkumaran and S Kumanan ldquoSelection of maintenancepolicy for textile industry using hybrid multi-criteria decisionmaking approachrdquo Journal of Manufacturing Technology Man-agement vol 20 no 7 pp 1009ndash1022 2009

[17] J-H Yu M-Y Jeon and T W Kim ldquoFuzzy-based compositeindicator development methodology for evaluating overallproject performancerdquo Journal of Civil Engineering and Manage-ment vol 21 no 3 pp 343ndash355 2015

[18] A Valipour N Yahaya N Md Noor A Mardani and JAntucheviciene ldquoA new hybrid fuzzy cybernetic analytic net-work process model to identify shared risks in PPP projectsrdquoInternational Journal of Strategic Property Management vol 20no 4 pp 409ndash426 2016

[19] M-Y Cheng D K Wibowo D Prayogo and A F V RoyldquoPredicting productivity loss caused by change orders using theevolutionary fuzzy support vector machine inference modelrdquoJournal of Civil Engineering and Management vol 21 no 7 pp881ndash892 2015

[20] H Abdi and L J Williams ldquoPrincipal component analysisrdquoWiley Interdisciplinary Reviews Computational Statistics vol 2no 4 pp 433ndash459 2010

[21] H Li D Parikh Q He et al ldquoImproving rail network velocityA machine learning approach to predictive maintenancerdquoTransportation Research Part C Emerging Technologies vol 45pp 17ndash26 2014

[22] E Alpaydin Introduction to Machine Learning MIT PressCambridge MA USA 2004

[23] H Naganathan W O Chong and X Chen ldquoBuilding energymodeling (BEM) using clustering algorithms and semi-super-vised machine learning approachesrdquo Automation in Construc-tion vol 72 pp 187ndash194 2016

[24] A Vellido J D Martın-Guerrero and P J G Lisboa ldquoMakingmachine learning models interpretablerdquo in Proceedings of the20th European Symposium on Artificial Neural Networks Com-putational Intelligence and Machine Learning ESANN 2012 pp163ndash172 Bruges Belgium April 2012

[25] N Rodrıguez-Padial M Marın and R Domingo ldquoStrategicFramework to Maintenance Decision Support Systemsrdquo in Pro-ceedings of the Manufacturing Engineering Society InternationalConference MESIC 2015 pp 903ndash910 July 2015

[26] G Waeyenbergh and L Pintelon ldquoA framework for mainte-nance concept developmentrdquo International Journal of Produc-tion Economics vol 77 no 3 pp 299ndash313 2002

[27] S W Palocsay I S Markham and S E Markham ldquoUtilizingand teaching data tools in Excel for exploratory analysisrdquoJournal of Business Research vol 63 no 2 pp 191ndash206 2010

[28] Mathworks IntroducingMachine Learning httpswwwmath-workscom

[29] L M Calvo and R Domingo ldquoInfluence of process operatingparameters on CO2 emissions in continuous industrial plantsrdquoJournal of Cleaner Production vol 96 article no 4308 pp 253ndash262 2015

[30] L M Calvo and R Domingo ldquoA first approach to the useof CO2 emissions as a maintenance indicator in industrialplantsrdquo in Proceedings of the Manufacturing Engineering SocietyInternational Conference MESIC 2013 pp 678ndash686 June 2013

Complexity 5

Machinelearning

Supervisedlearning

Regression

Classication

Unsupervisedlearning Clustering

Figure 2 Machine learning techniques

obtained 119862 which is then normalized with a mean of zeroand a standard deviation of one The result is then used toobtain the values and eigenvectors of 119862 That is the result isused to solve the following equations10038161003816100381610038161003816119862 minus 12058211986810038161003816100381610038161003816 = 0 (3)(119862 minus 120582119894119868) V119894 = 0 (4)

Once the principal components are obtained 120582119894 alongwith the principal axes V119894 they together explain the variationof the data which are ordered as a Pareto diagram selectingexclusively that set of components 119901 that explain at least80 of the variation in the data Thus a reduction in thedimensions of the data of 119896 original variables to 119901 lt 119896variables is obtained In general the matrix of observationsprojected onto the main axes contains 119899 observations ofthe variables that are obtained by (5) where is the matrixformed by columns with the eigenvectors V119894 obtained from(4) (119899119896) = 119860 (119899119896) sdot (119896119896) (5)

This transformation expresses the original data in axesthat coincide with the natural variation One aspect to beconsidered in this analysis is that this transformation islinear therefore it is not suitable for representing nonlinearproblems In cases of nonlinearity it is advisable to use MLclustering algorithms as will be observed later

22 Phase of Analysis through Machine Learning In thisphase the preparatory data step uses as input data inaddition to the previous data the production values andtheir responses as efficiency variables and failure times foran operational year for both papermaking machines (M1 andM2) Data are extracted and grouped from two databasesmaintenance and production The data obtained present 35attributes and 12 instances corresponding to each month foreach machine (identified as M1 and M2) Figure 1(b) showsthe treatment of these data once they have been importedthey are defined in the table dataWOF The manufacturingreport is a document that in its original format presents35 fields that represent the original attributes (see Table 2)which can be grouped in identifying fields of the machinein question of alphanumeric type and numerical cate-gorical variables record the natural month The remaining

attributes are of numeric type and record the values of timecost interventions and production For each papermakingmachine 3 predictor variables associated with productionparameters shown in Table 2 (daily production in tons ofpaper per day average paper weight in grams per squaremeter and average speed in meters per second) are selectedwith the objective of obtaining a target of two simultaneouspredictive responses (OEE in the percentage of machineutilization andMTTF) Answers that evaluate the aptitude ofthe maintenance function applied to the productive area areprovided by bothmachinesThe three predictive variables arethose that from experience characterize production betterAlthough a PCA could be performed as in the exploratoryphase for the dataWO it was not considered in this occasiondue to the few instances 12 which we had for each machineSince PCA is a statistical analysis it has been consideredthat the few instances are not sufficient to carry out such ananalysis considering at this point a selection based on criteriabased on experience However as more productive data andmore instances are obtained a PCA can be performed onall or those productive attributes of Table 2 to reduce thedimension and select those that represent the most influencein the variation of data

ML is divided into two techniques [27] supervised learn-ing which is training a model on known input and outputdata to predict future outputs and unsupervised learningwhich is finding hidden patterns and intrinsic structures inthe input data Figure 2 provides an illustration of ML Foreach technique different algorithms can be used in whichchoosing the ideal is performed by trial and error

Supervised learning uses classification and regressiontechniques to develop predictive models The differencebetween these techniques is that the classification predictsresponses in discrete or categorical variables whereas regres-sion predicts responses in a continuous variable [36] Unsu-pervised learning uses the clustering technique commonlyused in exploratory data analysis to find hidden patterns asclusters in data

From the algorithms of ML (Support Vector MachineDiscriminant Analysis Naive Bayes Nearest Neighbor Deci-sion Trees K-means Hierarchical Gaussian Mixture Hid-den Markov Model and ANN) we will use algorithmsmodeled with ANN for their versatility for both nonsuper-vised techniques (clustering) and supervised techniques(regression) The former groups the input data to recognizepatterns and define the natural groups present in the data

6 Complexity

Table 2 Original attributes of the manufacturing report (production database)

ID MACHINE Alpha numericalMONTH Numerical categoricalNON PLASTERED PRODUCTION NumericalPLASTERED PRODUCTION NumericalVOLUME PLASTERED PRODUCTION NumericalTOTAL PRODUCTION NumericalWORKS DAYS NumericalDAILY PRODUCTION TON NumericalCUTOUT PRODUCTION NumericalDAILY CUTOUT PRODUCTION TON NumericalDECREASE MACHINE NumericalAVAILABLE HOURS NumericalIDLE TIME EXTERNAL CAUSES NumericalMAINTENANCE NumericalSCHEDULED NumericalBREAKS NumericalPRODUCTION REST NumericalTOTAL IDLE NumericalDAILY IDLE TIME NumericalOEE NumericalSTART NUMBERS NumericalCUTOUT TIME CHANGES NumericalAVERAGE PAPER WEIGHTS NumericalAVERAGE REAL WIDTH NumericalAVERAGE SPEED NumericalAVERAGE BUDGETEDWIDTH NumericalWIDTH DECREASE CMS NumericalWIDTH DECREASE TON NumericalCAPE PRODUCTION NumericalCOST NumericalCOSTTON NumericalINTERVENTIONS NUMBER NumericalMEAN TIME BETWEEN FAILURES NumericalMEAN TIME TO REPAIR NumericalMEAN TIME TO FAILURE Numerical

the latterrsquos purpose is to establish correspondence ormappingbetween input values or predictors and the output variables orobjectives to predict Thus the model is trained (or adjusted)by a knowledge base formed by historical examples of knowninputs and outputs

According to Rumelhart et al [37] the process of theback-propagation training of ANN is used for regressiontechniques such as supervised learning The ANN back-propagation training process is divided into two stagesforward and backward propagation A network configurationconsisting of multiperceptron layers as shown in Figure 3and an activation function of the output layer range and [0 1]is used before an input 119909 which is expressed by

119910 (119909) = 119890119909(1 + 119890119909) (6)

In the forward propagation stage we select an input dataset for training (1199091 1199092 119909119868minus1) and apply it to the network to

obtain the outputs 119910119889 For each neuron 119895 of the hidden layerthe value of each nucleus 119899119895 is given by

119899119895 = 119868sum119894=1

(119908119895119894 sdot 119886119894) + 119887 (7)

where each input value of the previous layer 119886119894 is weighted by119908119895119894 and the output of the hidden layer is expressed by119886119895 = 119891 (119899119895) (8)

This is a nucleus activation function and is performediteratively for each output layer until the final output 119910119889 asin

119910119889 = 119891 (119899119910119889) = 119891( 119867sum119895=1

(119908119910119889119896 sdot 119886119896)) (9)

The backward propagation stage consists of measuringthe error committed as the difference between the calculated

Complexity 7

Input layer Hidden layer Output layer

Input data Output data

x1

x2

xi

b

aj

Wji

Wydj

ak

ydn1

n2

n3

n4

n5

n6

n7

n8

n9

n10

n11

n12

n13

nm

ni

Figure 3 ANN regression trained with back-propagation perceptron multilayer

value 119910119889 and the real value 119910119903 We recalculate the weights119908 attempting to minimize the error in the reverse firstobtaining the new weights of the layer of exit 119908119899119910119889119896 based onthe old 119900 equation119908119899119910119889119896 = 119908119900119910119889119896 + 119886119896 sdot [119910119889 sdot (1 minus 119910119889) sdot (119910119903 minus 119910119889)] (10)

and later the new weights of the hidden layer 119908119899119895119894119908119899119895119894 = 119908119900119895119894 + 119908119900119895119894 sdot 119886119894 sdot [119886119895 sdot (1 minus 119886119895) sdot (119908new119896119895 minus 120575119895)] (11)

where 120575119895 is obtained by applying (9) on the followingequation [119910119889 sdot (1 minus 119910119889) sdot (119910119903 minus 119910119889)] = 120575119910119889 (12)

This process is repeated for 119899 observations until a pre-determined acceptable value of the error is achieved usuallyusing the mean square error (MSE) which is defined in (13)To ensure a rapid convergence of the iterative method weusually use mathematical optimization methods in this casewe use Bayesian Regularization [38]

MSE = 1119899 119899sum119889=1

(119910119903 minus 119910119889)2 (13)

3 Results

It is possible to integrate new functionalities into a customcontrol panel of the industrial plant In this case predictiveanalysis was added for the expected availability response of aproductive area considering the main core of the industrial

plant thus it is possible to anticipate the information Thefuture availability in the medium-term (at monthly intervals)of both machines allows the maintenance department tocorrect possible deviations that are out of tolerance beforethey occur improving their response In the first phasewhich is exploratory we use PCA to discover the smallestdimensions that explain the variation of the data Applyingthe PCA to the set of WOs of productive area 2 (composed ofM1 and M2) principal components or axes PCi are foundthese are sufficient to explain the variation of the originaldata contained in the WOs of the productive area As a resultof the PCA 5 components are identified that would explain100 of the variation therefore 2 linearly dependent vectorsare detected among the input variables reducing in two theoriginal dimension on the other hand from 5 principalcomponents 3 would account for 786 of the variation indata This work aims at the number of interventions costsand maintenance times and will represent the results of PCAon the first 3 principal components In Figure 4(a) it isobserved that the first three principal components represent786 variation of the data reason why it is decided torepresent the data using these three components as principalaxes of representation this is visualized in Figure 4(b)with projections of the original data on the three principalcomponents Previously the data were normalized withmean0 and standard deviation 1

Table 3 shows the values obtained in the PCA of themaintenance metrics where the projections are obtained onthe three principal components of the maintenance numeri-cal variables selected from Table 1 (total costs orders partsand workforce cost estimated repair time repair time andnumber of operators) From the seven maintenance variablesstudied it can be observed that the ones that gain more

8 Complexity

Table 3 PCA results Principal components values

Metrics PC1 PC2 PC3 Metrics modulusTOTAL COST 04696 0476 0036 06697ORDER COST 01598 05714 06089 08502PARTS COST 02323 03913 minus05737 07323WORKFORCE COST 05234 minus02668 minus00987 05957REPAIR TIME 05235 minus02668 minus00987 05957ESTIMATED REPAIR TIME 03793 minus02661 02076 05077OPERATORS NUMBER 009 minus02839 04861 05701

1 2 3 4 5Principal components

0

1

2

3

4

5

6

7

Varia

nce e

xpla

ined

0

14

29

43

57

71

86

100

()

(a)

1510 20

ird

prin

cipa

l com

pone

nt

15Second principal component

5 10

First principal component

minus15

minus10

minus5

0

5

10

50 0minus5 minus5

(b)

Figure 4 (a) Principal components variance (b) projected data on principal components

relevance in principal axes are from greater to less order costtotal cost and part cost this has been considered extractingthe Euclidean modulus or norm of each variable in thethree principal axes which is shown in the last column ofTable 3 The rest of the four remaining variables have a moreor less similar amplitude so they are considered of equalimportance

In the second phase ML the clustering technique is usedto discover patterns hidden in the data such as the naturalgrouping For this technique from the data stored in thedataWO table shown in Figure 1(a) only two attributes areused total cost and repair time Both attributes can be keyindicators for the maintenance department and the repairtime can be key also for the production department becauseof downtime Therefore after knowing their influence onthe principal components it is important to deepen theirrelationship

For the clustering technique two algorithms have beenused The first technique hierarchical clustering allows thecreation of a dendrogram which is a tree diagram that mea-sures the number of natural groups or clusters dependingon the distance criterion that is fixed between data In thiscase by setting a distance value on the ordinate axis the treeis trimmed by a horizontal line that cuts the dendrogram inas many intersections as natural groups appear In this caseFigure 5(a) it is observed that for Euclidean mean distancesof 6000 to 7000 the tree presents two natural groups from

3000 to 5500 it presents three groups from 2000 to 3000 itpresents four groups and below 1000 the number of groupsincreases considerably Because of this compression a valueof 900 is used by pruning the tree into 7 natural groups thedifferent groups of color data can be illustrated in Figure 5(b)As can be seen the hierarchical clustering technique allowsan overview of the number of clusters that can be obtainedas a function of the chosen distance value There are severaldistancemetrics you can even define as a custom in this caseEuclidean distance has been used as a metric

The clustering technique is again used performing asecond algorithmof an SOMANN on the subset of total costdata and repair time as the chosen variables reflecting costsand times of the plantrsquos intervention maintenance An SOMor Kohonen consists of a competitive layer that can classifya set of vector data with any number of dimensions into asmany classes as neurons have a layer [39ndash41] Neurons arearranged in a two-dimensional topology of the data set Thetrained networkwith 2 variables and 1080 input data is shownin Figure 5(c) its two-dimensional topologywith data impactis shown in Figure 5(d)

The network is configured by 2 dimensions 2 times 4discovering a pattern of 8 natural groups in the data theseare distributed with a clear linear relationship between themIn addition there are discrepant data that have no linearrelationship and reveal an unconventional repair this isextraordinary and realized in one of the machines and it

Complexity 9

4 11 13 2 8 25 5 16 12 24 1 10 6 29 14 7 15 18 3 19 9 23 26 30 17 20 22 21 27 28

Observations

0

1000

2000

3000

4000

5000

6000

7000D

istan

ce

(a)

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300

Repa

ir tim

e (h)

Total cost (euro)

(b)

(c)minus1 minus05 0 05 1 15 2 25

minus1

minus05

0

05

1

15

2

25

3

35SOM neighbor weight distances

(d)

Figure 5 (a) Dendrogram (b) data (c) ANN SOM trained (d) SOM topology

was not cataloged like normal repair This finding revealsan error in the introduction of the information in theCMMSThis event was also revealed by the green dot (singlegroup) of the hierarchical clustering figure (see Figure 5(c))Figure 6(a) shows the original data in the two variables(cost time) and Figure 6(b) shows the 7 natural groupsand the linear relationship between them This figure alsoshows that group 8 has no linear relationship as previouslydiscussed The interpretation of these results under themaintenance approach shows that the linear relationshipbetween the groups comes to reflect the following analysison the distribution of the groups observing that the firstfour groups for costs between 0 and 1000 euro are very closeto each other while the centroids of 2000 euro 3000 euro and5000 euro present greater distance With this it is inferred thatthe majority of the interventions cost less than 1000 euro with asmaller number of interventions at intervals of 1000 euro

Finally the regression technique enables prediction ofthe future availability values of both main papermakingmachines (M1 and M2) using the OEE indicators of each

papermakingmachine and itsMTTF such as average runtimebefore failure In addition these values are calculated simulta-neously in the trained ANNmodel A trained neural networkwith input data (predictors) is used that combines the threeproduction variables and the two target output variableswhich are the overall efficiency of each OEE machine andaverage time to MTTF failure measured for 12 months ofthe year for each machine For this technique from the datastored in the dataWOF table shown in Figure 1(b) only threeproduction attributes are used daily production in tons ofpaper per day average paper weight in grams per squaremeter and average speed in meters per second

For the papermaking machines M1 and M2 3 input vari-ables a 10-layer feed-forward network with hidden neuronsand 2 layers of linear output neurons can adjust arbitrarilysuitable multidimensional mapping problems given consis-tent data and sufficient neurons in their hidden layer Thenetwork will be trained with 70 of the data using the back-propagation algorithm of Bayesian Regularization 15 of thedata will be used for validation and the remaining 15 will

10 Complexity

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300Ti

me r

epai

r (h)

RN

Total cost (euro)

(a)

Tim

e rep

air (

h)

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

10

20

30

40

50

60

70

80

90

100

Total cost (euro)

(b)

Figure 6 (a) Artificial Neural Network SOM trained (b) SOM topology sample hits

be used for the test AnMSE performance function is used asshown in Figures 7(a) and 8(a) for M1 and M2 respectivelyThe result of the trained network is suitably provided accord-ing to the regression adjustment coefficients that are shownin Figures 7(b) and 8(b) for M1 and M2 respectively

For each machine that forms the productive area theresult of the adjustment is observed by comparing the outputvariables OEE andMTTF (in blue) measured with the outputvariables foreseen by the OEEp and MTTFp model (in red)The results of M1 are shown in Figure 9(a) and those of M2are shown in Figure 9(b) In short this network can predictthe maintenance behavior of the production area of the plantin availability terms (efficiency and operating times) feedingthe model with the predictive values of production As moreinstances of inputoutput data are introduced the networkwill be retrained and more reliable since it will be adjustedwith a greater number of real examples that have occurredtherefore the greater quantity will lead to the acquisition ofmore experience and knowledge In maintenance terms it isnecessary to make predictions of availability and operatingtimes according to three characteristic values of productionand the model established in this way allows to predictthe OEE and MTTF observing a nonlinear behavior in thetime as is shown in Figure 9 In this sense the responsesfor M1 are observed where the OEE oscillates between anaverage value of 94 with a low dispersion of plusmn066 thusnot happening with MTTF values of mean 8107 h of highdispersion plusmn2582 h As for M2 the OEE has an average valueof 9552 with a low dispersion of plusmn112 despite MTTFvalues of 11803 h mean with very high dispersion plusmn5935 hconcluding that the regression model can accurately predicthigh and low amplitude oscillation values over time

It is noted that the fit is acceptable for training whichis provided by the global adjustment regression coefficient 119877values as shown in Figure 7(b) for both machines

For machine M1 discrepant values are observed in thevalidation setting for month 10 and for both OEE and MTTFindicators there are two reasons for this reason First there is

overadjustment when the data have not been prepared welland there are datawith erroneous or poorly conditioned inputinformation Second there are a low number of observationsor minimal historical information In this paper it has beenverified that the input data for the ANN did not present poorconditioning therefore the overadjustment problem is dis-carded and the few data (ie the few observations) availableare considered themain cause of discordance of the inputs forvalidationThe problem of feeding the network with few datato train and validate the ANN is due to unavailability of moredata for reasons of good performance in the industrial planta fact that would undoubtedly improve the learning of thenetwork and therefore its efficiency and accuracy Howeverthis fact highlights another very interesting aspect of theANN the network is easily adaptable and configurable givena low number of observations Here this adaptability makesit possible to accurately predict 11 hits of 12 possibilities thusthere is a 9167 probability of success in this case

For machine M2 there is a nearly total adjustment forthe OEE indicator but not for the MTTF indicator for whichit is evident that in month 6 there is a discrepancy in theprediction as in M1 the minimal data (observations) usedfor the validation obtain the same precision as M1

Another relevant aspect of the result is the acceptableprecision in the prediction of availability indicators whichare based exclusively on time by simply using as inputthree productive variables as predictor variables (daily papermass paper surface density and machine speed) In additionto the two output variables OEE and MTTF indicators asobjectives to be predicted are calculated simultaneously afact that reflects an additional value of this type of networksand greater computational efficiency by obtaining in a singlesimulation the prediction ofmore than one objective variable

4 Conclusions

A PCA-ML model has been developed such that it can beintegrated into scorecards with a traditional focus BSC

Complexity 11

0 50 100 150 200 250 300 350 400 450483 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is95888e minus 13 at epoch 320

100

10minus5

10minus10

TrainingTestBest

(a)

60 80 100 120Target

50

60

70

80

90

100

110

120Test R = minus042747

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120

Training R = 1

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120All R = 086783

DataFitY = T

lowastＮＬＡＮ

+14

ＯＮＪＯＮ ≃

087

lowastＮＬＡＮ

+13e+02

ＯＮＪＯＮ ≃

minus037lowastＮＬＡＮ

+minus13eminus08

ＯＮＪＯＮ ≃

1

(b)

Figure 7 (a) ANN regression performance for M1 (b) regression fit

12 Complexity

0 100 200 300 400 500 600 700 800856 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is23797e minus 12 at epoch 753

100

105

10minus5

10minus10

TrainingTestBest

(a)

100 150 200 250Target

100

150

200

250

100

150

200

250

100

150

200

250

Test R = minus051755

DataFitY = T

Training R = 1

DataFitY = T

All R = 095036

DataFitY = T

100 150 200 250Target

100 150 200 250Target

lowastＮＬＡＮ

+minus47eminus07

ＯＮＪＯＮ ≃

1

lowastＮＬＡＮ

+26e+02

ＯＮＪＯＮ ≃

minus15

lowastＮＬＡＮ

+6

ＯＮＪＯＮ ≃

097

(b)

Figure 8 (a) ANN regression performance for M2 (b) regression fit

Complexity 13

0 2 4 6 8 10 12(Month)

925

93

935

94

945

95O

EE (

)

(Month)0 2 4 6 8 10 12

40

50

60

70

80

90

100

110

120

130

MTT

F (h

)

(a)

(Month)

OEE

()

0 2 4 6 8 10 1293

935

94

945

95

955

96

965

97

975

98

(Month)

MTT

F (h

)

0 2 4 6 8 10 1250

100

150

200

250

300

(b)

Figure 9 Output variables measure (blue) versus predicted (red) (a) M1 (b) M2

thus including a tactical definition of longer-term strategicapproaches such as a scorecard based onBSCThis new exten-sion allows better control over themaintenance function of anindustrial plant in themedium-termwith amonthly intervalin such a manner that allows the measurement of certainindicators of those productive areas that were previously con-sidered strategic This model of PCA and an ML algorithmusing ANN can be integrated very easily into any traditionalcontrol panel by converting the developed source code topackages of different programming languages and includingthem in a library to be used as a function in a spreadsheetor a standalone executable application In addition at thecontrol panel this model is provided with ML to discoverstructures and behavior patterns that are relatively hidden inWOs By utilizing a clustering or clustering technique naturalgroups are determined in the cost variables and maintenanceworkforce in addition predictions about the availability ofthe productive area are made through the indicators OEE

andMTTFThus the scorecard model on a paper productionplant has been validated

As possible future works this methodology could beapplied to civil engineering and in this case applying a fuzzyuncertainty due to particular characteristics of this sector

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors want to thank the Department of ConstructionEngineering andManufacturing and the College of IndustrialEngineers of UNED for their support through Projects 2017-IFC08 and 2017-IFC09

14 Complexity

References

[1] DWollmann andM T Steiner ldquoThe strategic decision-makingas a complex adaptive system a conceptual scientific modelrdquoComplexity vol 2017 Article ID 7954289 13 pages 2017

[2] R Calvo R Domingo and M A Sebastian ldquoOperationalflexibility quantification in a make-to-order assembly systemrdquoInternational Journal of Flexible Manufacturing Systems vol 19no 3 pp 247ndash263 2008

[3] M Mahdiloo R F Saen and M Tavana ldquoA novel data envel-opment analysis model for solving supplier selection problemswith undesirable outputs and lack of inputsrdquo InternationalJournal of Logistics Systems and Management vol 11 no 3 pp285ndash305 2012

[4] MMadic J Antucheviciene M Radovanovic and D PetkovicldquoDetermination of manufacturing process conditions by usingMCDM methods Application in laser cuttingrdquo EngineeringEconomics vol 27 no 2 pp 144ndash150 2016

[5] E K Zavadskas R Bausys D Stanujkic andM Magdalinovic-Kalinovic ldquoSelection of lead-zinc flotation circuit design byapplying WASPAS method with single-valued neutrosophicsetrdquo Acta Montanistica Slovaca vol 21 no 2 pp 85ndash92 2016

[6] J Antucheviciene Z Kala M Marzouk and E R VaidogasldquoDecision making methods and applications in civil engineer-ingrdquoMathematical Problems in Engineering vol 2015 Article ID160569 3 pages 2015

[7] R Calvo R Domingo and M A Sebastin ldquoSystemic criterionof sustainability in agile manufacturingrdquo International Journalof Production Research vol 46 no 12 pp 3345ndash3358 2008

[8] F A Olivencia Polo J Ferrero Bermejo J F Gomez Fernandezand A Crespo Marquez ldquoFailure mode prediction and energyforecasting of PV plants to assist dynamic maintenance tasksby ANN based modelsrdquo Renewable Energy vol 81 pp 227ndash2382015

[9] A J C Trappey C V Trappey L Ma and J C M ChangldquoIntelligent engineering asset management system for powertransformer maintenance decision supports under variousoperating conditionsrdquo Computers and Industrial Engineeringvol 84 pp 3ndash11 2015

[10] V Martınez-Martınez F J Gomez-Gil J Gomez-Gil and RRuiz-Gonzalez ldquoAn Artificial Neural Network based expertsystem fitted with Genetic Algorithms for detecting the statusof several rotary components in agro-industrial machines usinga single vibration signalrdquo Expert Systems with Applications vol42 no 17-18 pp 6433ndash6441 2015

[11] J Moubray Reliability-centered Maintenance Industrial PressNew York NY USA 1997

[12] A Mardani A Jusoh and E K Zavadskas ldquoFuzzy multi-ple criteria decision-making techniques and applicationsmdashtwodecades review from 1994 to 2014rdquo Expert Systems with Appli-cations vol 42 no 8 pp 4126ndash4148 2015

[13] M Bashiri H Badri and T H Hejazi ldquoSelecting optimummaintenance strategy by fuzzy interactive linear assignmentmethodrdquo Applied Mathematical Modelling Simulation andComputation for Engineering and Environmental Systems vol35 no 1 pp 152ndash164 2011

[14] MM Fouladgar A Lashgari Z Turskis A Yazdani-Chamziniand E K Zavadskas ldquoMaintenance strategy selection usingAHP and COPRAS under fuzzy environmentrdquo InternationalJournal of Strategic Property Management vol 16 no 1 pp 85ndash104 2012

[15] LWang J Chu and JWu ldquoSelection of optimummaintenancestrategies based on a fuzzy analytic hierarchy processrdquo Interna-tional Journal of Production Economics vol 107 no 1 pp 151ndash163 2007

[16] M Ilangkumaran and S Kumanan ldquoSelection of maintenancepolicy for textile industry using hybrid multi-criteria decisionmaking approachrdquo Journal of Manufacturing Technology Man-agement vol 20 no 7 pp 1009ndash1022 2009

[17] J-H Yu M-Y Jeon and T W Kim ldquoFuzzy-based compositeindicator development methodology for evaluating overallproject performancerdquo Journal of Civil Engineering and Manage-ment vol 21 no 3 pp 343ndash355 2015

[18] A Valipour N Yahaya N Md Noor A Mardani and JAntucheviciene ldquoA new hybrid fuzzy cybernetic analytic net-work process model to identify shared risks in PPP projectsrdquoInternational Journal of Strategic Property Management vol 20no 4 pp 409ndash426 2016

[19] M-Y Cheng D K Wibowo D Prayogo and A F V RoyldquoPredicting productivity loss caused by change orders using theevolutionary fuzzy support vector machine inference modelrdquoJournal of Civil Engineering and Management vol 21 no 7 pp881ndash892 2015

[20] H Abdi and L J Williams ldquoPrincipal component analysisrdquoWiley Interdisciplinary Reviews Computational Statistics vol 2no 4 pp 433ndash459 2010

[21] H Li D Parikh Q He et al ldquoImproving rail network velocityA machine learning approach to predictive maintenancerdquoTransportation Research Part C Emerging Technologies vol 45pp 17ndash26 2014

[22] E Alpaydin Introduction to Machine Learning MIT PressCambridge MA USA 2004

[23] H Naganathan W O Chong and X Chen ldquoBuilding energymodeling (BEM) using clustering algorithms and semi-super-vised machine learning approachesrdquo Automation in Construc-tion vol 72 pp 187ndash194 2016

[24] A Vellido J D Martın-Guerrero and P J G Lisboa ldquoMakingmachine learning models interpretablerdquo in Proceedings of the20th European Symposium on Artificial Neural Networks Com-putational Intelligence and Machine Learning ESANN 2012 pp163ndash172 Bruges Belgium April 2012

[25] N Rodrıguez-Padial M Marın and R Domingo ldquoStrategicFramework to Maintenance Decision Support Systemsrdquo in Pro-ceedings of the Manufacturing Engineering Society InternationalConference MESIC 2015 pp 903ndash910 July 2015

[26] G Waeyenbergh and L Pintelon ldquoA framework for mainte-nance concept developmentrdquo International Journal of Produc-tion Economics vol 77 no 3 pp 299ndash313 2002

[27] S W Palocsay I S Markham and S E Markham ldquoUtilizingand teaching data tools in Excel for exploratory analysisrdquoJournal of Business Research vol 63 no 2 pp 191ndash206 2010

[28] Mathworks IntroducingMachine Learning httpswwwmath-workscom

[29] L M Calvo and R Domingo ldquoInfluence of process operatingparameters on CO2 emissions in continuous industrial plantsrdquoJournal of Cleaner Production vol 96 article no 4308 pp 253ndash262 2015

[30] L M Calvo and R Domingo ldquoA first approach to the useof CO2 emissions as a maintenance indicator in industrialplantsrdquo in Proceedings of the Manufacturing Engineering SocietyInternational Conference MESIC 2013 pp 678ndash686 June 2013

6 Complexity

Table 2 Original attributes of the manufacturing report (production database)

ID MACHINE Alpha numericalMONTH Numerical categoricalNON PLASTERED PRODUCTION NumericalPLASTERED PRODUCTION NumericalVOLUME PLASTERED PRODUCTION NumericalTOTAL PRODUCTION NumericalWORKS DAYS NumericalDAILY PRODUCTION TON NumericalCUTOUT PRODUCTION NumericalDAILY CUTOUT PRODUCTION TON NumericalDECREASE MACHINE NumericalAVAILABLE HOURS NumericalIDLE TIME EXTERNAL CAUSES NumericalMAINTENANCE NumericalSCHEDULED NumericalBREAKS NumericalPRODUCTION REST NumericalTOTAL IDLE NumericalDAILY IDLE TIME NumericalOEE NumericalSTART NUMBERS NumericalCUTOUT TIME CHANGES NumericalAVERAGE PAPER WEIGHTS NumericalAVERAGE REAL WIDTH NumericalAVERAGE SPEED NumericalAVERAGE BUDGETEDWIDTH NumericalWIDTH DECREASE CMS NumericalWIDTH DECREASE TON NumericalCAPE PRODUCTION NumericalCOST NumericalCOSTTON NumericalINTERVENTIONS NUMBER NumericalMEAN TIME BETWEEN FAILURES NumericalMEAN TIME TO REPAIR NumericalMEAN TIME TO FAILURE Numerical

the latterrsquos purpose is to establish correspondence ormappingbetween input values or predictors and the output variables orobjectives to predict Thus the model is trained (or adjusted)by a knowledge base formed by historical examples of knowninputs and outputs

According to Rumelhart et al [37] the process of theback-propagation training of ANN is used for regressiontechniques such as supervised learning The ANN back-propagation training process is divided into two stagesforward and backward propagation A network configurationconsisting of multiperceptron layers as shown in Figure 3and an activation function of the output layer range and [0 1]is used before an input 119909 which is expressed by

119910 (119909) = 119890119909(1 + 119890119909) (6)

In the forward propagation stage we select an input dataset for training (1199091 1199092 119909119868minus1) and apply it to the network to

obtain the outputs 119910119889 For each neuron 119895 of the hidden layerthe value of each nucleus 119899119895 is given by

119899119895 = 119868sum119894=1

(119908119895119894 sdot 119886119894) + 119887 (7)

where each input value of the previous layer 119886119894 is weighted by119908119895119894 and the output of the hidden layer is expressed by119886119895 = 119891 (119899119895) (8)

This is a nucleus activation function and is performediteratively for each output layer until the final output 119910119889 asin

119910119889 = 119891 (119899119910119889) = 119891( 119867sum119895=1

(119908119910119889119896 sdot 119886119896)) (9)

The backward propagation stage consists of measuringthe error committed as the difference between the calculated

Complexity 7

Input layer Hidden layer Output layer

Input data Output data

x1

x2

xi

b

aj

Wji

Wydj

ak

ydn1

n2

n3

n4

n5

n6

n7

n8

n9

n10

n11

n12

n13

nm

ni

Figure 3 ANN regression trained with back-propagation perceptron multilayer

value 119910119889 and the real value 119910119903 We recalculate the weights119908 attempting to minimize the error in the reverse firstobtaining the new weights of the layer of exit 119908119899119910119889119896 based onthe old 119900 equation119908119899119910119889119896 = 119908119900119910119889119896 + 119886119896 sdot [119910119889 sdot (1 minus 119910119889) sdot (119910119903 minus 119910119889)] (10)

and later the new weights of the hidden layer 119908119899119895119894119908119899119895119894 = 119908119900119895119894 + 119908119900119895119894 sdot 119886119894 sdot [119886119895 sdot (1 minus 119886119895) sdot (119908new119896119895 minus 120575119895)] (11)

where 120575119895 is obtained by applying (9) on the followingequation [119910119889 sdot (1 minus 119910119889) sdot (119910119903 minus 119910119889)] = 120575119910119889 (12)

This process is repeated for 119899 observations until a pre-determined acceptable value of the error is achieved usuallyusing the mean square error (MSE) which is defined in (13)To ensure a rapid convergence of the iterative method weusually use mathematical optimization methods in this casewe use Bayesian Regularization [38]

MSE = 1119899 119899sum119889=1

(119910119903 minus 119910119889)2 (13)

3 Results

It is possible to integrate new functionalities into a customcontrol panel of the industrial plant In this case predictiveanalysis was added for the expected availability response of aproductive area considering the main core of the industrial

plant thus it is possible to anticipate the information Thefuture availability in the medium-term (at monthly intervals)of both machines allows the maintenance department tocorrect possible deviations that are out of tolerance beforethey occur improving their response In the first phasewhich is exploratory we use PCA to discover the smallestdimensions that explain the variation of the data Applyingthe PCA to the set of WOs of productive area 2 (composed ofM1 and M2) principal components or axes PCi are foundthese are sufficient to explain the variation of the originaldata contained in the WOs of the productive area As a resultof the PCA 5 components are identified that would explain100 of the variation therefore 2 linearly dependent vectorsare detected among the input variables reducing in two theoriginal dimension on the other hand from 5 principalcomponents 3 would account for 786 of the variation indata This work aims at the number of interventions costsand maintenance times and will represent the results of PCAon the first 3 principal components In Figure 4(a) it isobserved that the first three principal components represent786 variation of the data reason why it is decided torepresent the data using these three components as principalaxes of representation this is visualized in Figure 4(b)with projections of the original data on the three principalcomponents Previously the data were normalized withmean0 and standard deviation 1

Table 3 shows the values obtained in the PCA of themaintenance metrics where the projections are obtained onthe three principal components of the maintenance numeri-cal variables selected from Table 1 (total costs orders partsand workforce cost estimated repair time repair time andnumber of operators) From the seven maintenance variablesstudied it can be observed that the ones that gain more

8 Complexity

Table 3 PCA results Principal components values

Metrics PC1 PC2 PC3 Metrics modulusTOTAL COST 04696 0476 0036 06697ORDER COST 01598 05714 06089 08502PARTS COST 02323 03913 minus05737 07323WORKFORCE COST 05234 minus02668 minus00987 05957REPAIR TIME 05235 minus02668 minus00987 05957ESTIMATED REPAIR TIME 03793 minus02661 02076 05077OPERATORS NUMBER 009 minus02839 04861 05701

1 2 3 4 5Principal components

0

1

2

3

4

5

6

7

Varia

nce e

xpla

ined

0

14

29

43

57

71

86

100

()

(a)

1510 20

ird

prin

cipa

l com

pone

nt

15Second principal component

5 10

First principal component

minus15

minus10

minus5

0

5

10

50 0minus5 minus5

(b)

Figure 4 (a) Principal components variance (b) projected data on principal components

relevance in principal axes are from greater to less order costtotal cost and part cost this has been considered extractingthe Euclidean modulus or norm of each variable in thethree principal axes which is shown in the last column ofTable 3 The rest of the four remaining variables have a moreor less similar amplitude so they are considered of equalimportance

In the second phase ML the clustering technique is usedto discover patterns hidden in the data such as the naturalgrouping For this technique from the data stored in thedataWO table shown in Figure 1(a) only two attributes areused total cost and repair time Both attributes can be keyindicators for the maintenance department and the repairtime can be key also for the production department becauseof downtime Therefore after knowing their influence onthe principal components it is important to deepen theirrelationship

For the clustering technique two algorithms have beenused The first technique hierarchical clustering allows thecreation of a dendrogram which is a tree diagram that mea-sures the number of natural groups or clusters dependingon the distance criterion that is fixed between data In thiscase by setting a distance value on the ordinate axis the treeis trimmed by a horizontal line that cuts the dendrogram inas many intersections as natural groups appear In this caseFigure 5(a) it is observed that for Euclidean mean distancesof 6000 to 7000 the tree presents two natural groups from

3000 to 5500 it presents three groups from 2000 to 3000 itpresents four groups and below 1000 the number of groupsincreases considerably Because of this compression a valueof 900 is used by pruning the tree into 7 natural groups thedifferent groups of color data can be illustrated in Figure 5(b)As can be seen the hierarchical clustering technique allowsan overview of the number of clusters that can be obtainedas a function of the chosen distance value There are severaldistancemetrics you can even define as a custom in this caseEuclidean distance has been used as a metric

The clustering technique is again used performing asecond algorithmof an SOMANN on the subset of total costdata and repair time as the chosen variables reflecting costsand times of the plantrsquos intervention maintenance An SOMor Kohonen consists of a competitive layer that can classifya set of vector data with any number of dimensions into asmany classes as neurons have a layer [39ndash41] Neurons arearranged in a two-dimensional topology of the data set Thetrained networkwith 2 variables and 1080 input data is shownin Figure 5(c) its two-dimensional topologywith data impactis shown in Figure 5(d)

The network is configured by 2 dimensions 2 times 4discovering a pattern of 8 natural groups in the data theseare distributed with a clear linear relationship between themIn addition there are discrepant data that have no linearrelationship and reveal an unconventional repair this isextraordinary and realized in one of the machines and it

Complexity 9

4 11 13 2 8 25 5 16 12 24 1 10 6 29 14 7 15 18 3 19 9 23 26 30 17 20 22 21 27 28

Observations

0

1000

2000

3000

4000

5000

6000

7000D

istan

ce

(a)

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300

Repa

ir tim

e (h)

Total cost (euro)

(b)

(c)minus1 minus05 0 05 1 15 2 25

minus1

minus05

0

05

1

15

2

25

3

35SOM neighbor weight distances

(d)

Figure 5 (a) Dendrogram (b) data (c) ANN SOM trained (d) SOM topology

was not cataloged like normal repair This finding revealsan error in the introduction of the information in theCMMSThis event was also revealed by the green dot (singlegroup) of the hierarchical clustering figure (see Figure 5(c))Figure 6(a) shows the original data in the two variables(cost time) and Figure 6(b) shows the 7 natural groupsand the linear relationship between them This figure alsoshows that group 8 has no linear relationship as previouslydiscussed The interpretation of these results under themaintenance approach shows that the linear relationshipbetween the groups comes to reflect the following analysison the distribution of the groups observing that the firstfour groups for costs between 0 and 1000 euro are very closeto each other while the centroids of 2000 euro 3000 euro and5000 euro present greater distance With this it is inferred thatthe majority of the interventions cost less than 1000 euro with asmaller number of interventions at intervals of 1000 euro

Finally the regression technique enables prediction ofthe future availability values of both main papermakingmachines (M1 and M2) using the OEE indicators of each

papermakingmachine and itsMTTF such as average runtimebefore failure In addition these values are calculated simulta-neously in the trained ANNmodel A trained neural networkwith input data (predictors) is used that combines the threeproduction variables and the two target output variableswhich are the overall efficiency of each OEE machine andaverage time to MTTF failure measured for 12 months ofthe year for each machine For this technique from the datastored in the dataWOF table shown in Figure 1(b) only threeproduction attributes are used daily production in tons ofpaper per day average paper weight in grams per squaremeter and average speed in meters per second

For the papermaking machines M1 and M2 3 input vari-ables a 10-layer feed-forward network with hidden neuronsand 2 layers of linear output neurons can adjust arbitrarilysuitable multidimensional mapping problems given consis-tent data and sufficient neurons in their hidden layer Thenetwork will be trained with 70 of the data using the back-propagation algorithm of Bayesian Regularization 15 of thedata will be used for validation and the remaining 15 will

10 Complexity

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300Ti

me r

epai

r (h)

RN

Total cost (euro)

(a)

Tim

e rep

air (

h)

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

10

20

30

40

50

60

70

80

90

100

Total cost (euro)

(b)

Figure 6 (a) Artificial Neural Network SOM trained (b) SOM topology sample hits

be used for the test AnMSE performance function is used asshown in Figures 7(a) and 8(a) for M1 and M2 respectivelyThe result of the trained network is suitably provided accord-ing to the regression adjustment coefficients that are shownin Figures 7(b) and 8(b) for M1 and M2 respectively

For each machine that forms the productive area theresult of the adjustment is observed by comparing the outputvariables OEE andMTTF (in blue) measured with the outputvariables foreseen by the OEEp and MTTFp model (in red)The results of M1 are shown in Figure 9(a) and those of M2are shown in Figure 9(b) In short this network can predictthe maintenance behavior of the production area of the plantin availability terms (efficiency and operating times) feedingthe model with the predictive values of production As moreinstances of inputoutput data are introduced the networkwill be retrained and more reliable since it will be adjustedwith a greater number of real examples that have occurredtherefore the greater quantity will lead to the acquisition ofmore experience and knowledge In maintenance terms it isnecessary to make predictions of availability and operatingtimes according to three characteristic values of productionand the model established in this way allows to predictthe OEE and MTTF observing a nonlinear behavior in thetime as is shown in Figure 9 In this sense the responsesfor M1 are observed where the OEE oscillates between anaverage value of 94 with a low dispersion of plusmn066 thusnot happening with MTTF values of mean 8107 h of highdispersion plusmn2582 h As for M2 the OEE has an average valueof 9552 with a low dispersion of plusmn112 despite MTTFvalues of 11803 h mean with very high dispersion plusmn5935 hconcluding that the regression model can accurately predicthigh and low amplitude oscillation values over time

It is noted that the fit is acceptable for training whichis provided by the global adjustment regression coefficient 119877values as shown in Figure 7(b) for both machines

For machine M1 discrepant values are observed in thevalidation setting for month 10 and for both OEE and MTTFindicators there are two reasons for this reason First there is

overadjustment when the data have not been prepared welland there are datawith erroneous or poorly conditioned inputinformation Second there are a low number of observationsor minimal historical information In this paper it has beenverified that the input data for the ANN did not present poorconditioning therefore the overadjustment problem is dis-carded and the few data (ie the few observations) availableare considered themain cause of discordance of the inputs forvalidationThe problem of feeding the network with few datato train and validate the ANN is due to unavailability of moredata for reasons of good performance in the industrial planta fact that would undoubtedly improve the learning of thenetwork and therefore its efficiency and accuracy Howeverthis fact highlights another very interesting aspect of theANN the network is easily adaptable and configurable givena low number of observations Here this adaptability makesit possible to accurately predict 11 hits of 12 possibilities thusthere is a 9167 probability of success in this case

For machine M2 there is a nearly total adjustment forthe OEE indicator but not for the MTTF indicator for whichit is evident that in month 6 there is a discrepancy in theprediction as in M1 the minimal data (observations) usedfor the validation obtain the same precision as M1

Another relevant aspect of the result is the acceptableprecision in the prediction of availability indicators whichare based exclusively on time by simply using as inputthree productive variables as predictor variables (daily papermass paper surface density and machine speed) In additionto the two output variables OEE and MTTF indicators asobjectives to be predicted are calculated simultaneously afact that reflects an additional value of this type of networksand greater computational efficiency by obtaining in a singlesimulation the prediction ofmore than one objective variable

4 Conclusions

A PCA-ML model has been developed such that it can beintegrated into scorecards with a traditional focus BSC

Complexity 11

0 50 100 150 200 250 300 350 400 450483 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is95888e minus 13 at epoch 320

100

10minus5

10minus10

TrainingTestBest

(a)

60 80 100 120Target

50

60

70

80

90

100

110

120Test R = minus042747

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120

Training R = 1

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120All R = 086783

DataFitY = T

lowastＮＬＡＮ

+14

ＯＮＪＯＮ ≃

087

lowastＮＬＡＮ

+13e+02

ＯＮＪＯＮ ≃

minus037lowastＮＬＡＮ

+minus13eminus08

ＯＮＪＯＮ ≃

1

(b)

Figure 7 (a) ANN regression performance for M1 (b) regression fit

12 Complexity

0 100 200 300 400 500 600 700 800856 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is23797e minus 12 at epoch 753

100

105

10minus5

10minus10

TrainingTestBest

(a)

100 150 200 250Target

100

150

200

250

100

150

200

250

100

150

200

250

Test R = minus051755

DataFitY = T

Training R = 1

DataFitY = T

All R = 095036

DataFitY = T

100 150 200 250Target

100 150 200 250Target

lowastＮＬＡＮ

+minus47eminus07

ＯＮＪＯＮ ≃

1

lowastＮＬＡＮ

+26e+02

ＯＮＪＯＮ ≃

minus15

lowastＮＬＡＮ

+6

ＯＮＪＯＮ ≃

097

(b)

Figure 8 (a) ANN regression performance for M2 (b) regression fit

Complexity 13

0 2 4 6 8 10 12(Month)

925

93

935

94

945

95O

EE (

)

(Month)0 2 4 6 8 10 12

40

50

60

70

80

90

100

110

120

130

MTT

F (h

)

(a)

(Month)

OEE

()

0 2 4 6 8 10 1293

935

94

945

95

955

96

965

97

975

98

(Month)

MTT

F (h

)

0 2 4 6 8 10 1250

100

150

200

250

300

(b)

Figure 9 Output variables measure (blue) versus predicted (red) (a) M1 (b) M2

thus including a tactical definition of longer-term strategicapproaches such as a scorecard based onBSCThis new exten-sion allows better control over themaintenance function of anindustrial plant in themedium-termwith amonthly intervalin such a manner that allows the measurement of certainindicators of those productive areas that were previously con-sidered strategic This model of PCA and an ML algorithmusing ANN can be integrated very easily into any traditionalcontrol panel by converting the developed source code topackages of different programming languages and includingthem in a library to be used as a function in a spreadsheetor a standalone executable application In addition at thecontrol panel this model is provided with ML to discoverstructures and behavior patterns that are relatively hidden inWOs By utilizing a clustering or clustering technique naturalgroups are determined in the cost variables and maintenanceworkforce in addition predictions about the availability ofthe productive area are made through the indicators OEE

andMTTFThus the scorecard model on a paper productionplant has been validated

As possible future works this methodology could beapplied to civil engineering and in this case applying a fuzzyuncertainty due to particular characteristics of this sector

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors want to thank the Department of ConstructionEngineering andManufacturing and the College of IndustrialEngineers of UNED for their support through Projects 2017-IFC08 and 2017-IFC09

14 Complexity

References

[1] DWollmann andM T Steiner ldquoThe strategic decision-makingas a complex adaptive system a conceptual scientific modelrdquoComplexity vol 2017 Article ID 7954289 13 pages 2017

[2] R Calvo R Domingo and M A Sebastian ldquoOperationalflexibility quantification in a make-to-order assembly systemrdquoInternational Journal of Flexible Manufacturing Systems vol 19no 3 pp 247ndash263 2008

[3] M Mahdiloo R F Saen and M Tavana ldquoA novel data envel-opment analysis model for solving supplier selection problemswith undesirable outputs and lack of inputsrdquo InternationalJournal of Logistics Systems and Management vol 11 no 3 pp285ndash305 2012

[4] MMadic J Antucheviciene M Radovanovic and D PetkovicldquoDetermination of manufacturing process conditions by usingMCDM methods Application in laser cuttingrdquo EngineeringEconomics vol 27 no 2 pp 144ndash150 2016

[5] E K Zavadskas R Bausys D Stanujkic andM Magdalinovic-Kalinovic ldquoSelection of lead-zinc flotation circuit design byapplying WASPAS method with single-valued neutrosophicsetrdquo Acta Montanistica Slovaca vol 21 no 2 pp 85ndash92 2016

[6] J Antucheviciene Z Kala M Marzouk and E R VaidogasldquoDecision making methods and applications in civil engineer-ingrdquoMathematical Problems in Engineering vol 2015 Article ID160569 3 pages 2015

[7] R Calvo R Domingo and M A Sebastin ldquoSystemic criterionof sustainability in agile manufacturingrdquo International Journalof Production Research vol 46 no 12 pp 3345ndash3358 2008

[8] F A Olivencia Polo J Ferrero Bermejo J F Gomez Fernandezand A Crespo Marquez ldquoFailure mode prediction and energyforecasting of PV plants to assist dynamic maintenance tasksby ANN based modelsrdquo Renewable Energy vol 81 pp 227ndash2382015

[9] A J C Trappey C V Trappey L Ma and J C M ChangldquoIntelligent engineering asset management system for powertransformer maintenance decision supports under variousoperating conditionsrdquo Computers and Industrial Engineeringvol 84 pp 3ndash11 2015

[10] V Martınez-Martınez F J Gomez-Gil J Gomez-Gil and RRuiz-Gonzalez ldquoAn Artificial Neural Network based expertsystem fitted with Genetic Algorithms for detecting the statusof several rotary components in agro-industrial machines usinga single vibration signalrdquo Expert Systems with Applications vol42 no 17-18 pp 6433ndash6441 2015

[11] J Moubray Reliability-centered Maintenance Industrial PressNew York NY USA 1997

[12] A Mardani A Jusoh and E K Zavadskas ldquoFuzzy multi-ple criteria decision-making techniques and applicationsmdashtwodecades review from 1994 to 2014rdquo Expert Systems with Appli-cations vol 42 no 8 pp 4126ndash4148 2015

[13] M Bashiri H Badri and T H Hejazi ldquoSelecting optimummaintenance strategy by fuzzy interactive linear assignmentmethodrdquo Applied Mathematical Modelling Simulation andComputation for Engineering and Environmental Systems vol35 no 1 pp 152ndash164 2011

[14] MM Fouladgar A Lashgari Z Turskis A Yazdani-Chamziniand E K Zavadskas ldquoMaintenance strategy selection usingAHP and COPRAS under fuzzy environmentrdquo InternationalJournal of Strategic Property Management vol 16 no 1 pp 85ndash104 2012

[15] LWang J Chu and JWu ldquoSelection of optimummaintenancestrategies based on a fuzzy analytic hierarchy processrdquo Interna-tional Journal of Production Economics vol 107 no 1 pp 151ndash163 2007

[16] M Ilangkumaran and S Kumanan ldquoSelection of maintenancepolicy for textile industry using hybrid multi-criteria decisionmaking approachrdquo Journal of Manufacturing Technology Man-agement vol 20 no 7 pp 1009ndash1022 2009

[17] J-H Yu M-Y Jeon and T W Kim ldquoFuzzy-based compositeindicator development methodology for evaluating overallproject performancerdquo Journal of Civil Engineering and Manage-ment vol 21 no 3 pp 343ndash355 2015

[18] A Valipour N Yahaya N Md Noor A Mardani and JAntucheviciene ldquoA new hybrid fuzzy cybernetic analytic net-work process model to identify shared risks in PPP projectsrdquoInternational Journal of Strategic Property Management vol 20no 4 pp 409ndash426 2016

[19] M-Y Cheng D K Wibowo D Prayogo and A F V RoyldquoPredicting productivity loss caused by change orders using theevolutionary fuzzy support vector machine inference modelrdquoJournal of Civil Engineering and Management vol 21 no 7 pp881ndash892 2015

[20] H Abdi and L J Williams ldquoPrincipal component analysisrdquoWiley Interdisciplinary Reviews Computational Statistics vol 2no 4 pp 433ndash459 2010

[21] H Li D Parikh Q He et al ldquoImproving rail network velocityA machine learning approach to predictive maintenancerdquoTransportation Research Part C Emerging Technologies vol 45pp 17ndash26 2014

[22] E Alpaydin Introduction to Machine Learning MIT PressCambridge MA USA 2004

[23] H Naganathan W O Chong and X Chen ldquoBuilding energymodeling (BEM) using clustering algorithms and semi-super-vised machine learning approachesrdquo Automation in Construc-tion vol 72 pp 187ndash194 2016

[24] A Vellido J D Martın-Guerrero and P J G Lisboa ldquoMakingmachine learning models interpretablerdquo in Proceedings of the20th European Symposium on Artificial Neural Networks Com-putational Intelligence and Machine Learning ESANN 2012 pp163ndash172 Bruges Belgium April 2012

[25] N Rodrıguez-Padial M Marın and R Domingo ldquoStrategicFramework to Maintenance Decision Support Systemsrdquo in Pro-ceedings of the Manufacturing Engineering Society InternationalConference MESIC 2015 pp 903ndash910 July 2015

[26] G Waeyenbergh and L Pintelon ldquoA framework for mainte-nance concept developmentrdquo International Journal of Produc-tion Economics vol 77 no 3 pp 299ndash313 2002

[27] S W Palocsay I S Markham and S E Markham ldquoUtilizingand teaching data tools in Excel for exploratory analysisrdquoJournal of Business Research vol 63 no 2 pp 191ndash206 2010

[28] Mathworks IntroducingMachine Learning httpswwwmath-workscom

[29] L M Calvo and R Domingo ldquoInfluence of process operatingparameters on CO2 emissions in continuous industrial plantsrdquoJournal of Cleaner Production vol 96 article no 4308 pp 253ndash262 2015

[30] L M Calvo and R Domingo ldquoA first approach to the useof CO2 emissions as a maintenance indicator in industrialplantsrdquo in Proceedings of the Manufacturing Engineering SocietyInternational Conference MESIC 2013 pp 678ndash686 June 2013

Complexity 7

Input layer Hidden layer Output layer

Input data Output data

x1

x2

xi

b

aj

Wji

Wydj

ak

ydn1

n2

n3

n4

n5

n6

n7

n8

n9

n10

n11

n12

n13

nm

ni

Figure 3 ANN regression trained with back-propagation perceptron multilayer

value 119910119889 and the real value 119910119903 We recalculate the weights119908 attempting to minimize the error in the reverse firstobtaining the new weights of the layer of exit 119908119899119910119889119896 based onthe old 119900 equation119908119899119910119889119896 = 119908119900119910119889119896 + 119886119896 sdot [119910119889 sdot (1 minus 119910119889) sdot (119910119903 minus 119910119889)] (10)

and later the new weights of the hidden layer 119908119899119895119894119908119899119895119894 = 119908119900119895119894 + 119908119900119895119894 sdot 119886119894 sdot [119886119895 sdot (1 minus 119886119895) sdot (119908new119896119895 minus 120575119895)] (11)

where 120575119895 is obtained by applying (9) on the followingequation [119910119889 sdot (1 minus 119910119889) sdot (119910119903 minus 119910119889)] = 120575119910119889 (12)

This process is repeated for 119899 observations until a pre-determined acceptable value of the error is achieved usuallyusing the mean square error (MSE) which is defined in (13)To ensure a rapid convergence of the iterative method weusually use mathematical optimization methods in this casewe use Bayesian Regularization [38]

MSE = 1119899 119899sum119889=1

(119910119903 minus 119910119889)2 (13)

3 Results

It is possible to integrate new functionalities into a customcontrol panel of the industrial plant In this case predictiveanalysis was added for the expected availability response of aproductive area considering the main core of the industrial

plant thus it is possible to anticipate the information Thefuture availability in the medium-term (at monthly intervals)of both machines allows the maintenance department tocorrect possible deviations that are out of tolerance beforethey occur improving their response In the first phasewhich is exploratory we use PCA to discover the smallestdimensions that explain the variation of the data Applyingthe PCA to the set of WOs of productive area 2 (composed ofM1 and M2) principal components or axes PCi are foundthese are sufficient to explain the variation of the originaldata contained in the WOs of the productive area As a resultof the PCA 5 components are identified that would explain100 of the variation therefore 2 linearly dependent vectorsare detected among the input variables reducing in two theoriginal dimension on the other hand from 5 principalcomponents 3 would account for 786 of the variation indata This work aims at the number of interventions costsand maintenance times and will represent the results of PCAon the first 3 principal components In Figure 4(a) it isobserved that the first three principal components represent786 variation of the data reason why it is decided torepresent the data using these three components as principalaxes of representation this is visualized in Figure 4(b)with projections of the original data on the three principalcomponents Previously the data were normalized withmean0 and standard deviation 1

Table 3 shows the values obtained in the PCA of themaintenance metrics where the projections are obtained onthe three principal components of the maintenance numeri-cal variables selected from Table 1 (total costs orders partsand workforce cost estimated repair time repair time andnumber of operators) From the seven maintenance variablesstudied it can be observed that the ones that gain more

8 Complexity

Table 3 PCA results Principal components values

Metrics PC1 PC2 PC3 Metrics modulusTOTAL COST 04696 0476 0036 06697ORDER COST 01598 05714 06089 08502PARTS COST 02323 03913 minus05737 07323WORKFORCE COST 05234 minus02668 minus00987 05957REPAIR TIME 05235 minus02668 minus00987 05957ESTIMATED REPAIR TIME 03793 minus02661 02076 05077OPERATORS NUMBER 009 minus02839 04861 05701

1 2 3 4 5Principal components

0

1

2

3

4

5

6

7

Varia

nce e

xpla

ined

0

14

29

43

57

71

86

100

()

(a)

1510 20

ird

prin

cipa

l com

pone

nt

15Second principal component

5 10

First principal component

minus15

minus10

minus5

0

5

10

50 0minus5 minus5

(b)

Figure 4 (a) Principal components variance (b) projected data on principal components

relevance in principal axes are from greater to less order costtotal cost and part cost this has been considered extractingthe Euclidean modulus or norm of each variable in thethree principal axes which is shown in the last column ofTable 3 The rest of the four remaining variables have a moreor less similar amplitude so they are considered of equalimportance

In the second phase ML the clustering technique is usedto discover patterns hidden in the data such as the naturalgrouping For this technique from the data stored in thedataWO table shown in Figure 1(a) only two attributes areused total cost and repair time Both attributes can be keyindicators for the maintenance department and the repairtime can be key also for the production department becauseof downtime Therefore after knowing their influence onthe principal components it is important to deepen theirrelationship

For the clustering technique two algorithms have beenused The first technique hierarchical clustering allows thecreation of a dendrogram which is a tree diagram that mea-sures the number of natural groups or clusters dependingon the distance criterion that is fixed between data In thiscase by setting a distance value on the ordinate axis the treeis trimmed by a horizontal line that cuts the dendrogram inas many intersections as natural groups appear In this caseFigure 5(a) it is observed that for Euclidean mean distancesof 6000 to 7000 the tree presents two natural groups from

3000 to 5500 it presents three groups from 2000 to 3000 itpresents four groups and below 1000 the number of groupsincreases considerably Because of this compression a valueof 900 is used by pruning the tree into 7 natural groups thedifferent groups of color data can be illustrated in Figure 5(b)As can be seen the hierarchical clustering technique allowsan overview of the number of clusters that can be obtainedas a function of the chosen distance value There are severaldistancemetrics you can even define as a custom in this caseEuclidean distance has been used as a metric

The clustering technique is again used performing asecond algorithmof an SOMANN on the subset of total costdata and repair time as the chosen variables reflecting costsand times of the plantrsquos intervention maintenance An SOMor Kohonen consists of a competitive layer that can classifya set of vector data with any number of dimensions into asmany classes as neurons have a layer [39ndash41] Neurons arearranged in a two-dimensional topology of the data set Thetrained networkwith 2 variables and 1080 input data is shownin Figure 5(c) its two-dimensional topologywith data impactis shown in Figure 5(d)

The network is configured by 2 dimensions 2 times 4discovering a pattern of 8 natural groups in the data theseare distributed with a clear linear relationship between themIn addition there are discrepant data that have no linearrelationship and reveal an unconventional repair this isextraordinary and realized in one of the machines and it

Complexity 9

4 11 13 2 8 25 5 16 12 24 1 10 6 29 14 7 15 18 3 19 9 23 26 30 17 20 22 21 27 28

Observations

0

1000

2000

3000

4000

5000

6000

7000D

istan

ce

(a)

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300

Repa

ir tim

e (h)

Total cost (euro)

(b)

(c)minus1 minus05 0 05 1 15 2 25

minus1

minus05

0

05

1

15

2

25

3

35SOM neighbor weight distances

(d)

Figure 5 (a) Dendrogram (b) data (c) ANN SOM trained (d) SOM topology

was not cataloged like normal repair This finding revealsan error in the introduction of the information in theCMMSThis event was also revealed by the green dot (singlegroup) of the hierarchical clustering figure (see Figure 5(c))Figure 6(a) shows the original data in the two variables(cost time) and Figure 6(b) shows the 7 natural groupsand the linear relationship between them This figure alsoshows that group 8 has no linear relationship as previouslydiscussed The interpretation of these results under themaintenance approach shows that the linear relationshipbetween the groups comes to reflect the following analysison the distribution of the groups observing that the firstfour groups for costs between 0 and 1000 euro are very closeto each other while the centroids of 2000 euro 3000 euro and5000 euro present greater distance With this it is inferred thatthe majority of the interventions cost less than 1000 euro with asmaller number of interventions at intervals of 1000 euro

Finally the regression technique enables prediction ofthe future availability values of both main papermakingmachines (M1 and M2) using the OEE indicators of each

papermakingmachine and itsMTTF such as average runtimebefore failure In addition these values are calculated simulta-neously in the trained ANNmodel A trained neural networkwith input data (predictors) is used that combines the threeproduction variables and the two target output variableswhich are the overall efficiency of each OEE machine andaverage time to MTTF failure measured for 12 months ofthe year for each machine For this technique from the datastored in the dataWOF table shown in Figure 1(b) only threeproduction attributes are used daily production in tons ofpaper per day average paper weight in grams per squaremeter and average speed in meters per second

For the papermaking machines M1 and M2 3 input vari-ables a 10-layer feed-forward network with hidden neuronsand 2 layers of linear output neurons can adjust arbitrarilysuitable multidimensional mapping problems given consis-tent data and sufficient neurons in their hidden layer Thenetwork will be trained with 70 of the data using the back-propagation algorithm of Bayesian Regularization 15 of thedata will be used for validation and the remaining 15 will

10 Complexity

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300Ti

me r

epai

r (h)

RN

Total cost (euro)

(a)

Tim

e rep

air (

h)

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

10

20

30

40

50

60

70

80

90

100

Total cost (euro)

(b)

Figure 6 (a) Artificial Neural Network SOM trained (b) SOM topology sample hits

be used for the test AnMSE performance function is used asshown in Figures 7(a) and 8(a) for M1 and M2 respectivelyThe result of the trained network is suitably provided accord-ing to the regression adjustment coefficients that are shownin Figures 7(b) and 8(b) for M1 and M2 respectively

For each machine that forms the productive area theresult of the adjustment is observed by comparing the outputvariables OEE andMTTF (in blue) measured with the outputvariables foreseen by the OEEp and MTTFp model (in red)The results of M1 are shown in Figure 9(a) and those of M2are shown in Figure 9(b) In short this network can predictthe maintenance behavior of the production area of the plantin availability terms (efficiency and operating times) feedingthe model with the predictive values of production As moreinstances of inputoutput data are introduced the networkwill be retrained and more reliable since it will be adjustedwith a greater number of real examples that have occurredtherefore the greater quantity will lead to the acquisition ofmore experience and knowledge In maintenance terms it isnecessary to make predictions of availability and operatingtimes according to three characteristic values of productionand the model established in this way allows to predictthe OEE and MTTF observing a nonlinear behavior in thetime as is shown in Figure 9 In this sense the responsesfor M1 are observed where the OEE oscillates between anaverage value of 94 with a low dispersion of plusmn066 thusnot happening with MTTF values of mean 8107 h of highdispersion plusmn2582 h As for M2 the OEE has an average valueof 9552 with a low dispersion of plusmn112 despite MTTFvalues of 11803 h mean with very high dispersion plusmn5935 hconcluding that the regression model can accurately predicthigh and low amplitude oscillation values over time

It is noted that the fit is acceptable for training whichis provided by the global adjustment regression coefficient 119877values as shown in Figure 7(b) for both machines

For machine M1 discrepant values are observed in thevalidation setting for month 10 and for both OEE and MTTFindicators there are two reasons for this reason First there is

overadjustment when the data have not been prepared welland there are datawith erroneous or poorly conditioned inputinformation Second there are a low number of observationsor minimal historical information In this paper it has beenverified that the input data for the ANN did not present poorconditioning therefore the overadjustment problem is dis-carded and the few data (ie the few observations) availableare considered themain cause of discordance of the inputs forvalidationThe problem of feeding the network with few datato train and validate the ANN is due to unavailability of moredata for reasons of good performance in the industrial planta fact that would undoubtedly improve the learning of thenetwork and therefore its efficiency and accuracy Howeverthis fact highlights another very interesting aspect of theANN the network is easily adaptable and configurable givena low number of observations Here this adaptability makesit possible to accurately predict 11 hits of 12 possibilities thusthere is a 9167 probability of success in this case

For machine M2 there is a nearly total adjustment forthe OEE indicator but not for the MTTF indicator for whichit is evident that in month 6 there is a discrepancy in theprediction as in M1 the minimal data (observations) usedfor the validation obtain the same precision as M1

Another relevant aspect of the result is the acceptableprecision in the prediction of availability indicators whichare based exclusively on time by simply using as inputthree productive variables as predictor variables (daily papermass paper surface density and machine speed) In additionto the two output variables OEE and MTTF indicators asobjectives to be predicted are calculated simultaneously afact that reflects an additional value of this type of networksand greater computational efficiency by obtaining in a singlesimulation the prediction ofmore than one objective variable

4 Conclusions

A PCA-ML model has been developed such that it can beintegrated into scorecards with a traditional focus BSC

Complexity 11

0 50 100 150 200 250 300 350 400 450483 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is95888e minus 13 at epoch 320

100

10minus5

10minus10

TrainingTestBest

(a)

60 80 100 120Target

50

60

70

80

90

100

110

120Test R = minus042747

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120

Training R = 1

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120All R = 086783

DataFitY = T

lowastＮＬＡＮ

+14

ＯＮＪＯＮ ≃

087

lowastＮＬＡＮ

+13e+02

ＯＮＪＯＮ ≃

minus037lowastＮＬＡＮ

+minus13eminus08

ＯＮＪＯＮ ≃

1

(b)

Figure 7 (a) ANN regression performance for M1 (b) regression fit

12 Complexity

0 100 200 300 400 500 600 700 800856 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is23797e minus 12 at epoch 753

100

105

10minus5

10minus10

TrainingTestBest

(a)

100 150 200 250Target

100

150

200

250

100

150

200

250

100

150

200

250

Test R = minus051755

DataFitY = T

Training R = 1

DataFitY = T

All R = 095036

DataFitY = T

100 150 200 250Target

100 150 200 250Target

lowastＮＬＡＮ

+minus47eminus07

ＯＮＪＯＮ ≃

1

lowastＮＬＡＮ

+26e+02

ＯＮＪＯＮ ≃

minus15

lowastＮＬＡＮ

+6

ＯＮＪＯＮ ≃

097

(b)

Figure 8 (a) ANN regression performance for M2 (b) regression fit

Complexity 13

0 2 4 6 8 10 12(Month)

925

93

935

94

945

95O

EE (

)

(Month)0 2 4 6 8 10 12

40

50

60

70

80

90

100

110

120

130

MTT

F (h

)

(a)

(Month)

OEE

()

0 2 4 6 8 10 1293

935

94

945

95

955

96

965

97

975

98

(Month)

MTT

F (h

)

0 2 4 6 8 10 1250

100

150

200

250

300

(b)

Figure 9 Output variables measure (blue) versus predicted (red) (a) M1 (b) M2

thus including a tactical definition of longer-term strategicapproaches such as a scorecard based onBSCThis new exten-sion allows better control over themaintenance function of anindustrial plant in themedium-termwith amonthly intervalin such a manner that allows the measurement of certainindicators of those productive areas that were previously con-sidered strategic This model of PCA and an ML algorithmusing ANN can be integrated very easily into any traditionalcontrol panel by converting the developed source code topackages of different programming languages and includingthem in a library to be used as a function in a spreadsheetor a standalone executable application In addition at thecontrol panel this model is provided with ML to discoverstructures and behavior patterns that are relatively hidden inWOs By utilizing a clustering or clustering technique naturalgroups are determined in the cost variables and maintenanceworkforce in addition predictions about the availability ofthe productive area are made through the indicators OEE

andMTTFThus the scorecard model on a paper productionplant has been validated

As possible future works this methodology could beapplied to civil engineering and in this case applying a fuzzyuncertainty due to particular characteristics of this sector

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors want to thank the Department of ConstructionEngineering andManufacturing and the College of IndustrialEngineers of UNED for their support through Projects 2017-IFC08 and 2017-IFC09

14 Complexity

References

[1] DWollmann andM T Steiner ldquoThe strategic decision-makingas a complex adaptive system a conceptual scientific modelrdquoComplexity vol 2017 Article ID 7954289 13 pages 2017

[2] R Calvo R Domingo and M A Sebastian ldquoOperationalflexibility quantification in a make-to-order assembly systemrdquoInternational Journal of Flexible Manufacturing Systems vol 19no 3 pp 247ndash263 2008

[3] M Mahdiloo R F Saen and M Tavana ldquoA novel data envel-opment analysis model for solving supplier selection problemswith undesirable outputs and lack of inputsrdquo InternationalJournal of Logistics Systems and Management vol 11 no 3 pp285ndash305 2012

[4] MMadic J Antucheviciene M Radovanovic and D PetkovicldquoDetermination of manufacturing process conditions by usingMCDM methods Application in laser cuttingrdquo EngineeringEconomics vol 27 no 2 pp 144ndash150 2016

[5] E K Zavadskas R Bausys D Stanujkic andM Magdalinovic-Kalinovic ldquoSelection of lead-zinc flotation circuit design byapplying WASPAS method with single-valued neutrosophicsetrdquo Acta Montanistica Slovaca vol 21 no 2 pp 85ndash92 2016

[6] J Antucheviciene Z Kala M Marzouk and E R VaidogasldquoDecision making methods and applications in civil engineer-ingrdquoMathematical Problems in Engineering vol 2015 Article ID160569 3 pages 2015

[7] R Calvo R Domingo and M A Sebastin ldquoSystemic criterionof sustainability in agile manufacturingrdquo International Journalof Production Research vol 46 no 12 pp 3345ndash3358 2008

[8] F A Olivencia Polo J Ferrero Bermejo J F Gomez Fernandezand A Crespo Marquez ldquoFailure mode prediction and energyforecasting of PV plants to assist dynamic maintenance tasksby ANN based modelsrdquo Renewable Energy vol 81 pp 227ndash2382015

[9] A J C Trappey C V Trappey L Ma and J C M ChangldquoIntelligent engineering asset management system for powertransformer maintenance decision supports under variousoperating conditionsrdquo Computers and Industrial Engineeringvol 84 pp 3ndash11 2015

[10] V Martınez-Martınez F J Gomez-Gil J Gomez-Gil and RRuiz-Gonzalez ldquoAn Artificial Neural Network based expertsystem fitted with Genetic Algorithms for detecting the statusof several rotary components in agro-industrial machines usinga single vibration signalrdquo Expert Systems with Applications vol42 no 17-18 pp 6433ndash6441 2015

[11] J Moubray Reliability-centered Maintenance Industrial PressNew York NY USA 1997

[12] A Mardani A Jusoh and E K Zavadskas ldquoFuzzy multi-ple criteria decision-making techniques and applicationsmdashtwodecades review from 1994 to 2014rdquo Expert Systems with Appli-cations vol 42 no 8 pp 4126ndash4148 2015

[13] M Bashiri H Badri and T H Hejazi ldquoSelecting optimummaintenance strategy by fuzzy interactive linear assignmentmethodrdquo Applied Mathematical Modelling Simulation andComputation for Engineering and Environmental Systems vol35 no 1 pp 152ndash164 2011

[14] MM Fouladgar A Lashgari Z Turskis A Yazdani-Chamziniand E K Zavadskas ldquoMaintenance strategy selection usingAHP and COPRAS under fuzzy environmentrdquo InternationalJournal of Strategic Property Management vol 16 no 1 pp 85ndash104 2012

[15] LWang J Chu and JWu ldquoSelection of optimummaintenancestrategies based on a fuzzy analytic hierarchy processrdquo Interna-tional Journal of Production Economics vol 107 no 1 pp 151ndash163 2007

[16] M Ilangkumaran and S Kumanan ldquoSelection of maintenancepolicy for textile industry using hybrid multi-criteria decisionmaking approachrdquo Journal of Manufacturing Technology Man-agement vol 20 no 7 pp 1009ndash1022 2009

[17] J-H Yu M-Y Jeon and T W Kim ldquoFuzzy-based compositeindicator development methodology for evaluating overallproject performancerdquo Journal of Civil Engineering and Manage-ment vol 21 no 3 pp 343ndash355 2015

[18] A Valipour N Yahaya N Md Noor A Mardani and JAntucheviciene ldquoA new hybrid fuzzy cybernetic analytic net-work process model to identify shared risks in PPP projectsrdquoInternational Journal of Strategic Property Management vol 20no 4 pp 409ndash426 2016

[19] M-Y Cheng D K Wibowo D Prayogo and A F V RoyldquoPredicting productivity loss caused by change orders using theevolutionary fuzzy support vector machine inference modelrdquoJournal of Civil Engineering and Management vol 21 no 7 pp881ndash892 2015

[20] H Abdi and L J Williams ldquoPrincipal component analysisrdquoWiley Interdisciplinary Reviews Computational Statistics vol 2no 4 pp 433ndash459 2010

[21] H Li D Parikh Q He et al ldquoImproving rail network velocityA machine learning approach to predictive maintenancerdquoTransportation Research Part C Emerging Technologies vol 45pp 17ndash26 2014

[22] E Alpaydin Introduction to Machine Learning MIT PressCambridge MA USA 2004

[23] H Naganathan W O Chong and X Chen ldquoBuilding energymodeling (BEM) using clustering algorithms and semi-super-vised machine learning approachesrdquo Automation in Construc-tion vol 72 pp 187ndash194 2016

[24] A Vellido J D Martın-Guerrero and P J G Lisboa ldquoMakingmachine learning models interpretablerdquo in Proceedings of the20th European Symposium on Artificial Neural Networks Com-putational Intelligence and Machine Learning ESANN 2012 pp163ndash172 Bruges Belgium April 2012

[25] N Rodrıguez-Padial M Marın and R Domingo ldquoStrategicFramework to Maintenance Decision Support Systemsrdquo in Pro-ceedings of the Manufacturing Engineering Society InternationalConference MESIC 2015 pp 903ndash910 July 2015

[26] G Waeyenbergh and L Pintelon ldquoA framework for mainte-nance concept developmentrdquo International Journal of Produc-tion Economics vol 77 no 3 pp 299ndash313 2002

[27] S W Palocsay I S Markham and S E Markham ldquoUtilizingand teaching data tools in Excel for exploratory analysisrdquoJournal of Business Research vol 63 no 2 pp 191ndash206 2010

[28] Mathworks IntroducingMachine Learning httpswwwmath-workscom

[29] L M Calvo and R Domingo ldquoInfluence of process operatingparameters on CO2 emissions in continuous industrial plantsrdquoJournal of Cleaner Production vol 96 article no 4308 pp 253ndash262 2015

[30] L M Calvo and R Domingo ldquoA first approach to the useof CO2 emissions as a maintenance indicator in industrialplantsrdquo in Proceedings of the Manufacturing Engineering SocietyInternational Conference MESIC 2013 pp 678ndash686 June 2013

8 Complexity

Table 3 PCA results Principal components values

Metrics PC1 PC2 PC3 Metrics modulusTOTAL COST 04696 0476 0036 06697ORDER COST 01598 05714 06089 08502PARTS COST 02323 03913 minus05737 07323WORKFORCE COST 05234 minus02668 minus00987 05957REPAIR TIME 05235 minus02668 minus00987 05957ESTIMATED REPAIR TIME 03793 minus02661 02076 05077OPERATORS NUMBER 009 minus02839 04861 05701

1 2 3 4 5Principal components

0

1

2

3

4

5

6

7

Varia

nce e

xpla

ined

0

14

29

43

57

71

86

100

()

(a)

1510 20

ird

prin

cipa

l com

pone

nt

15Second principal component

5 10

First principal component

minus15

minus10

minus5

0

5

10

50 0minus5 minus5

(b)

Figure 4 (a) Principal components variance (b) projected data on principal components

relevance in principal axes are from greater to less order costtotal cost and part cost this has been considered extractingthe Euclidean modulus or norm of each variable in thethree principal axes which is shown in the last column ofTable 3 The rest of the four remaining variables have a moreor less similar amplitude so they are considered of equalimportance

In the second phase ML the clustering technique is usedto discover patterns hidden in the data such as the naturalgrouping For this technique from the data stored in thedataWO table shown in Figure 1(a) only two attributes areused total cost and repair time Both attributes can be keyindicators for the maintenance department and the repairtime can be key also for the production department becauseof downtime Therefore after knowing their influence onthe principal components it is important to deepen theirrelationship

For the clustering technique two algorithms have beenused The first technique hierarchical clustering allows thecreation of a dendrogram which is a tree diagram that mea-sures the number of natural groups or clusters dependingon the distance criterion that is fixed between data In thiscase by setting a distance value on the ordinate axis the treeis trimmed by a horizontal line that cuts the dendrogram inas many intersections as natural groups appear In this caseFigure 5(a) it is observed that for Euclidean mean distancesof 6000 to 7000 the tree presents two natural groups from

3000 to 5500 it presents three groups from 2000 to 3000 itpresents four groups and below 1000 the number of groupsincreases considerably Because of this compression a valueof 900 is used by pruning the tree into 7 natural groups thedifferent groups of color data can be illustrated in Figure 5(b)As can be seen the hierarchical clustering technique allowsan overview of the number of clusters that can be obtainedas a function of the chosen distance value There are severaldistancemetrics you can even define as a custom in this caseEuclidean distance has been used as a metric

The clustering technique is again used performing asecond algorithmof an SOMANN on the subset of total costdata and repair time as the chosen variables reflecting costsand times of the plantrsquos intervention maintenance An SOMor Kohonen consists of a competitive layer that can classifya set of vector data with any number of dimensions into asmany classes as neurons have a layer [39ndash41] Neurons arearranged in a two-dimensional topology of the data set Thetrained networkwith 2 variables and 1080 input data is shownin Figure 5(c) its two-dimensional topologywith data impactis shown in Figure 5(d)

The network is configured by 2 dimensions 2 times 4discovering a pattern of 8 natural groups in the data theseare distributed with a clear linear relationship between themIn addition there are discrepant data that have no linearrelationship and reveal an unconventional repair this isextraordinary and realized in one of the machines and it

Complexity 9

4 11 13 2 8 25 5 16 12 24 1 10 6 29 14 7 15 18 3 19 9 23 26 30 17 20 22 21 27 28

Observations

0

1000

2000

3000

4000

5000

6000

7000D

istan

ce

(a)

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300

Repa

ir tim

e (h)

Total cost (euro)

(b)

(c)minus1 minus05 0 05 1 15 2 25

minus1

minus05

0

05

1

15

2

25

3

35SOM neighbor weight distances

(d)

Figure 5 (a) Dendrogram (b) data (c) ANN SOM trained (d) SOM topology

was not cataloged like normal repair This finding revealsan error in the introduction of the information in theCMMSThis event was also revealed by the green dot (singlegroup) of the hierarchical clustering figure (see Figure 5(c))Figure 6(a) shows the original data in the two variables(cost time) and Figure 6(b) shows the 7 natural groupsand the linear relationship between them This figure alsoshows that group 8 has no linear relationship as previouslydiscussed The interpretation of these results under themaintenance approach shows that the linear relationshipbetween the groups comes to reflect the following analysison the distribution of the groups observing that the firstfour groups for costs between 0 and 1000 euro are very closeto each other while the centroids of 2000 euro 3000 euro and5000 euro present greater distance With this it is inferred thatthe majority of the interventions cost less than 1000 euro with asmaller number of interventions at intervals of 1000 euro

Finally the regression technique enables prediction ofthe future availability values of both main papermakingmachines (M1 and M2) using the OEE indicators of each

papermakingmachine and itsMTTF such as average runtimebefore failure In addition these values are calculated simulta-neously in the trained ANNmodel A trained neural networkwith input data (predictors) is used that combines the threeproduction variables and the two target output variableswhich are the overall efficiency of each OEE machine andaverage time to MTTF failure measured for 12 months ofthe year for each machine For this technique from the datastored in the dataWOF table shown in Figure 1(b) only threeproduction attributes are used daily production in tons ofpaper per day average paper weight in grams per squaremeter and average speed in meters per second

For the papermaking machines M1 and M2 3 input vari-ables a 10-layer feed-forward network with hidden neuronsand 2 layers of linear output neurons can adjust arbitrarilysuitable multidimensional mapping problems given consis-tent data and sufficient neurons in their hidden layer Thenetwork will be trained with 70 of the data using the back-propagation algorithm of Bayesian Regularization 15 of thedata will be used for validation and the remaining 15 will

10 Complexity

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300Ti

me r

epai

r (h)

RN

Total cost (euro)

(a)

Tim

e rep

air (

h)

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

10

20

30

40

50

60

70

80

90

100

Total cost (euro)

(b)

Figure 6 (a) Artificial Neural Network SOM trained (b) SOM topology sample hits

be used for the test AnMSE performance function is used asshown in Figures 7(a) and 8(a) for M1 and M2 respectivelyThe result of the trained network is suitably provided accord-ing to the regression adjustment coefficients that are shownin Figures 7(b) and 8(b) for M1 and M2 respectively

For each machine that forms the productive area theresult of the adjustment is observed by comparing the outputvariables OEE andMTTF (in blue) measured with the outputvariables foreseen by the OEEp and MTTFp model (in red)The results of M1 are shown in Figure 9(a) and those of M2are shown in Figure 9(b) In short this network can predictthe maintenance behavior of the production area of the plantin availability terms (efficiency and operating times) feedingthe model with the predictive values of production As moreinstances of inputoutput data are introduced the networkwill be retrained and more reliable since it will be adjustedwith a greater number of real examples that have occurredtherefore the greater quantity will lead to the acquisition ofmore experience and knowledge In maintenance terms it isnecessary to make predictions of availability and operatingtimes according to three characteristic values of productionand the model established in this way allows to predictthe OEE and MTTF observing a nonlinear behavior in thetime as is shown in Figure 9 In this sense the responsesfor M1 are observed where the OEE oscillates between anaverage value of 94 with a low dispersion of plusmn066 thusnot happening with MTTF values of mean 8107 h of highdispersion plusmn2582 h As for M2 the OEE has an average valueof 9552 with a low dispersion of plusmn112 despite MTTFvalues of 11803 h mean with very high dispersion plusmn5935 hconcluding that the regression model can accurately predicthigh and low amplitude oscillation values over time

It is noted that the fit is acceptable for training whichis provided by the global adjustment regression coefficient 119877values as shown in Figure 7(b) for both machines

For machine M1 discrepant values are observed in thevalidation setting for month 10 and for both OEE and MTTFindicators there are two reasons for this reason First there is

overadjustment when the data have not been prepared welland there are datawith erroneous or poorly conditioned inputinformation Second there are a low number of observationsor minimal historical information In this paper it has beenverified that the input data for the ANN did not present poorconditioning therefore the overadjustment problem is dis-carded and the few data (ie the few observations) availableare considered themain cause of discordance of the inputs forvalidationThe problem of feeding the network with few datato train and validate the ANN is due to unavailability of moredata for reasons of good performance in the industrial planta fact that would undoubtedly improve the learning of thenetwork and therefore its efficiency and accuracy Howeverthis fact highlights another very interesting aspect of theANN the network is easily adaptable and configurable givena low number of observations Here this adaptability makesit possible to accurately predict 11 hits of 12 possibilities thusthere is a 9167 probability of success in this case

For machine M2 there is a nearly total adjustment forthe OEE indicator but not for the MTTF indicator for whichit is evident that in month 6 there is a discrepancy in theprediction as in M1 the minimal data (observations) usedfor the validation obtain the same precision as M1

Another relevant aspect of the result is the acceptableprecision in the prediction of availability indicators whichare based exclusively on time by simply using as inputthree productive variables as predictor variables (daily papermass paper surface density and machine speed) In additionto the two output variables OEE and MTTF indicators asobjectives to be predicted are calculated simultaneously afact that reflects an additional value of this type of networksand greater computational efficiency by obtaining in a singlesimulation the prediction ofmore than one objective variable

4 Conclusions

A PCA-ML model has been developed such that it can beintegrated into scorecards with a traditional focus BSC

Complexity 11

0 50 100 150 200 250 300 350 400 450483 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is95888e minus 13 at epoch 320

100

10minus5

10minus10

TrainingTestBest

(a)

60 80 100 120Target

50

60

70

80

90

100

110

120Test R = minus042747

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120

Training R = 1

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120All R = 086783

DataFitY = T

lowastＮＬＡＮ

+14

ＯＮＪＯＮ ≃

087

lowastＮＬＡＮ

+13e+02

ＯＮＪＯＮ ≃

minus037lowastＮＬＡＮ

+minus13eminus08

ＯＮＪＯＮ ≃

1

(b)

Figure 7 (a) ANN regression performance for M1 (b) regression fit

12 Complexity

0 100 200 300 400 500 600 700 800856 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is23797e minus 12 at epoch 753

100

105

10minus5

10minus10

TrainingTestBest

(a)

100 150 200 250Target

100

150

200

250

100

150

200

250

100

150

200

250

Test R = minus051755

DataFitY = T

Training R = 1

DataFitY = T

All R = 095036

DataFitY = T

100 150 200 250Target

100 150 200 250Target

lowastＮＬＡＮ

+minus47eminus07

ＯＮＪＯＮ ≃

1

lowastＮＬＡＮ

+26e+02

ＯＮＪＯＮ ≃

minus15

lowastＮＬＡＮ

+6

ＯＮＪＯＮ ≃

097

(b)

Figure 8 (a) ANN regression performance for M2 (b) regression fit

Complexity 13

0 2 4 6 8 10 12(Month)

925

93

935

94

945

95O

EE (

)

(Month)0 2 4 6 8 10 12

40

50

60

70

80

90

100

110

120

130

MTT

F (h

)

(a)

(Month)

OEE

()

0 2 4 6 8 10 1293

935

94

945

95

955

96

965

97

975

98

(Month)

MTT

F (h

)

0 2 4 6 8 10 1250

100

150

200

250

300

(b)

Figure 9 Output variables measure (blue) versus predicted (red) (a) M1 (b) M2

thus including a tactical definition of longer-term strategicapproaches such as a scorecard based onBSCThis new exten-sion allows better control over themaintenance function of anindustrial plant in themedium-termwith amonthly intervalin such a manner that allows the measurement of certainindicators of those productive areas that were previously con-sidered strategic This model of PCA and an ML algorithmusing ANN can be integrated very easily into any traditionalcontrol panel by converting the developed source code topackages of different programming languages and includingthem in a library to be used as a function in a spreadsheetor a standalone executable application In addition at thecontrol panel this model is provided with ML to discoverstructures and behavior patterns that are relatively hidden inWOs By utilizing a clustering or clustering technique naturalgroups are determined in the cost variables and maintenanceworkforce in addition predictions about the availability ofthe productive area are made through the indicators OEE

andMTTFThus the scorecard model on a paper productionplant has been validated

As possible future works this methodology could beapplied to civil engineering and in this case applying a fuzzyuncertainty due to particular characteristics of this sector

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors want to thank the Department of ConstructionEngineering andManufacturing and the College of IndustrialEngineers of UNED for their support through Projects 2017-IFC08 and 2017-IFC09

14 Complexity

References

[1] DWollmann andM T Steiner ldquoThe strategic decision-makingas a complex adaptive system a conceptual scientific modelrdquoComplexity vol 2017 Article ID 7954289 13 pages 2017

[2] R Calvo R Domingo and M A Sebastian ldquoOperationalflexibility quantification in a make-to-order assembly systemrdquoInternational Journal of Flexible Manufacturing Systems vol 19no 3 pp 247ndash263 2008

[3] M Mahdiloo R F Saen and M Tavana ldquoA novel data envel-opment analysis model for solving supplier selection problemswith undesirable outputs and lack of inputsrdquo InternationalJournal of Logistics Systems and Management vol 11 no 3 pp285ndash305 2012

[4] MMadic J Antucheviciene M Radovanovic and D PetkovicldquoDetermination of manufacturing process conditions by usingMCDM methods Application in laser cuttingrdquo EngineeringEconomics vol 27 no 2 pp 144ndash150 2016

[5] E K Zavadskas R Bausys D Stanujkic andM Magdalinovic-Kalinovic ldquoSelection of lead-zinc flotation circuit design byapplying WASPAS method with single-valued neutrosophicsetrdquo Acta Montanistica Slovaca vol 21 no 2 pp 85ndash92 2016

[6] J Antucheviciene Z Kala M Marzouk and E R VaidogasldquoDecision making methods and applications in civil engineer-ingrdquoMathematical Problems in Engineering vol 2015 Article ID160569 3 pages 2015

[7] R Calvo R Domingo and M A Sebastin ldquoSystemic criterionof sustainability in agile manufacturingrdquo International Journalof Production Research vol 46 no 12 pp 3345ndash3358 2008

[8] F A Olivencia Polo J Ferrero Bermejo J F Gomez Fernandezand A Crespo Marquez ldquoFailure mode prediction and energyforecasting of PV plants to assist dynamic maintenance tasksby ANN based modelsrdquo Renewable Energy vol 81 pp 227ndash2382015

[9] A J C Trappey C V Trappey L Ma and J C M ChangldquoIntelligent engineering asset management system for powertransformer maintenance decision supports under variousoperating conditionsrdquo Computers and Industrial Engineeringvol 84 pp 3ndash11 2015

[10] V Martınez-Martınez F J Gomez-Gil J Gomez-Gil and RRuiz-Gonzalez ldquoAn Artificial Neural Network based expertsystem fitted with Genetic Algorithms for detecting the statusof several rotary components in agro-industrial machines usinga single vibration signalrdquo Expert Systems with Applications vol42 no 17-18 pp 6433ndash6441 2015

[11] J Moubray Reliability-centered Maintenance Industrial PressNew York NY USA 1997

[12] A Mardani A Jusoh and E K Zavadskas ldquoFuzzy multi-ple criteria decision-making techniques and applicationsmdashtwodecades review from 1994 to 2014rdquo Expert Systems with Appli-cations vol 42 no 8 pp 4126ndash4148 2015

[13] M Bashiri H Badri and T H Hejazi ldquoSelecting optimummaintenance strategy by fuzzy interactive linear assignmentmethodrdquo Applied Mathematical Modelling Simulation andComputation for Engineering and Environmental Systems vol35 no 1 pp 152ndash164 2011

[14] MM Fouladgar A Lashgari Z Turskis A Yazdani-Chamziniand E K Zavadskas ldquoMaintenance strategy selection usingAHP and COPRAS under fuzzy environmentrdquo InternationalJournal of Strategic Property Management vol 16 no 1 pp 85ndash104 2012

[15] LWang J Chu and JWu ldquoSelection of optimummaintenancestrategies based on a fuzzy analytic hierarchy processrdquo Interna-tional Journal of Production Economics vol 107 no 1 pp 151ndash163 2007

[16] M Ilangkumaran and S Kumanan ldquoSelection of maintenancepolicy for textile industry using hybrid multi-criteria decisionmaking approachrdquo Journal of Manufacturing Technology Man-agement vol 20 no 7 pp 1009ndash1022 2009

[17] J-H Yu M-Y Jeon and T W Kim ldquoFuzzy-based compositeindicator development methodology for evaluating overallproject performancerdquo Journal of Civil Engineering and Manage-ment vol 21 no 3 pp 343ndash355 2015

[18] A Valipour N Yahaya N Md Noor A Mardani and JAntucheviciene ldquoA new hybrid fuzzy cybernetic analytic net-work process model to identify shared risks in PPP projectsrdquoInternational Journal of Strategic Property Management vol 20no 4 pp 409ndash426 2016

[19] M-Y Cheng D K Wibowo D Prayogo and A F V RoyldquoPredicting productivity loss caused by change orders using theevolutionary fuzzy support vector machine inference modelrdquoJournal of Civil Engineering and Management vol 21 no 7 pp881ndash892 2015

[20] H Abdi and L J Williams ldquoPrincipal component analysisrdquoWiley Interdisciplinary Reviews Computational Statistics vol 2no 4 pp 433ndash459 2010

[21] H Li D Parikh Q He et al ldquoImproving rail network velocityA machine learning approach to predictive maintenancerdquoTransportation Research Part C Emerging Technologies vol 45pp 17ndash26 2014

[22] E Alpaydin Introduction to Machine Learning MIT PressCambridge MA USA 2004

[23] H Naganathan W O Chong and X Chen ldquoBuilding energymodeling (BEM) using clustering algorithms and semi-super-vised machine learning approachesrdquo Automation in Construc-tion vol 72 pp 187ndash194 2016

[24] A Vellido J D Martın-Guerrero and P J G Lisboa ldquoMakingmachine learning models interpretablerdquo in Proceedings of the20th European Symposium on Artificial Neural Networks Com-putational Intelligence and Machine Learning ESANN 2012 pp163ndash172 Bruges Belgium April 2012

[25] N Rodrıguez-Padial M Marın and R Domingo ldquoStrategicFramework to Maintenance Decision Support Systemsrdquo in Pro-ceedings of the Manufacturing Engineering Society InternationalConference MESIC 2015 pp 903ndash910 July 2015

[26] G Waeyenbergh and L Pintelon ldquoA framework for mainte-nance concept developmentrdquo International Journal of Produc-tion Economics vol 77 no 3 pp 299ndash313 2002

[27] S W Palocsay I S Markham and S E Markham ldquoUtilizingand teaching data tools in Excel for exploratory analysisrdquoJournal of Business Research vol 63 no 2 pp 191ndash206 2010

[28] Mathworks IntroducingMachine Learning httpswwwmath-workscom

[29] L M Calvo and R Domingo ldquoInfluence of process operatingparameters on CO2 emissions in continuous industrial plantsrdquoJournal of Cleaner Production vol 96 article no 4308 pp 253ndash262 2015

[30] L M Calvo and R Domingo ldquoA first approach to the useof CO2 emissions as a maintenance indicator in industrialplantsrdquo in Proceedings of the Manufacturing Engineering SocietyInternational Conference MESIC 2013 pp 678ndash686 June 2013

Complexity 9

4 11 13 2 8 25 5 16 12 24 1 10 6 29 14 7 15 18 3 19 9 23 26 30 17 20 22 21 27 28

Observations

0

1000

2000

3000

4000

5000

6000

7000D

istan

ce

(a)

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300

Repa

ir tim

e (h)

Total cost (euro)

(b)

(c)minus1 minus05 0 05 1 15 2 25

minus1

minus05

0

05

1

15

2

25

3

35SOM neighbor weight distances

(d)

Figure 5 (a) Dendrogram (b) data (c) ANN SOM trained (d) SOM topology

was not cataloged like normal repair This finding revealsan error in the introduction of the information in theCMMSThis event was also revealed by the green dot (singlegroup) of the hierarchical clustering figure (see Figure 5(c))Figure 6(a) shows the original data in the two variables(cost time) and Figure 6(b) shows the 7 natural groupsand the linear relationship between them This figure alsoshows that group 8 has no linear relationship as previouslydiscussed The interpretation of these results under themaintenance approach shows that the linear relationshipbetween the groups comes to reflect the following analysison the distribution of the groups observing that the firstfour groups for costs between 0 and 1000 euro are very closeto each other while the centroids of 2000 euro 3000 euro and5000 euro present greater distance With this it is inferred thatthe majority of the interventions cost less than 1000 euro with asmaller number of interventions at intervals of 1000 euro

Finally the regression technique enables prediction ofthe future availability values of both main papermakingmachines (M1 and M2) using the OEE indicators of each

papermakingmachine and itsMTTF such as average runtimebefore failure In addition these values are calculated simulta-neously in the trained ANNmodel A trained neural networkwith input data (predictors) is used that combines the threeproduction variables and the two target output variableswhich are the overall efficiency of each OEE machine andaverage time to MTTF failure measured for 12 months ofthe year for each machine For this technique from the datastored in the dataWOF table shown in Figure 1(b) only threeproduction attributes are used daily production in tons ofpaper per day average paper weight in grams per squaremeter and average speed in meters per second

For the papermaking machines M1 and M2 3 input vari-ables a 10-layer feed-forward network with hidden neuronsand 2 layers of linear output neurons can adjust arbitrarilysuitable multidimensional mapping problems given consis-tent data and sufficient neurons in their hidden layer Thenetwork will be trained with 70 of the data using the back-propagation algorithm of Bayesian Regularization 15 of thedata will be used for validation and the remaining 15 will

10 Complexity

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300Ti

me r

epai

r (h)

RN

Total cost (euro)

(a)

Tim

e rep

air (

h)

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

10

20

30

40

50

60

70

80

90

100

Total cost (euro)

(b)

Figure 6 (a) Artificial Neural Network SOM trained (b) SOM topology sample hits

be used for the test AnMSE performance function is used asshown in Figures 7(a) and 8(a) for M1 and M2 respectivelyThe result of the trained network is suitably provided accord-ing to the regression adjustment coefficients that are shownin Figures 7(b) and 8(b) for M1 and M2 respectively

For each machine that forms the productive area theresult of the adjustment is observed by comparing the outputvariables OEE andMTTF (in blue) measured with the outputvariables foreseen by the OEEp and MTTFp model (in red)The results of M1 are shown in Figure 9(a) and those of M2are shown in Figure 9(b) In short this network can predictthe maintenance behavior of the production area of the plantin availability terms (efficiency and operating times) feedingthe model with the predictive values of production As moreinstances of inputoutput data are introduced the networkwill be retrained and more reliable since it will be adjustedwith a greater number of real examples that have occurredtherefore the greater quantity will lead to the acquisition ofmore experience and knowledge In maintenance terms it isnecessary to make predictions of availability and operatingtimes according to three characteristic values of productionand the model established in this way allows to predictthe OEE and MTTF observing a nonlinear behavior in thetime as is shown in Figure 9 In this sense the responsesfor M1 are observed where the OEE oscillates between anaverage value of 94 with a low dispersion of plusmn066 thusnot happening with MTTF values of mean 8107 h of highdispersion plusmn2582 h As for M2 the OEE has an average valueof 9552 with a low dispersion of plusmn112 despite MTTFvalues of 11803 h mean with very high dispersion plusmn5935 hconcluding that the regression model can accurately predicthigh and low amplitude oscillation values over time

It is noted that the fit is acceptable for training whichis provided by the global adjustment regression coefficient 119877values as shown in Figure 7(b) for both machines

For machine M1 discrepant values are observed in thevalidation setting for month 10 and for both OEE and MTTFindicators there are two reasons for this reason First there is

overadjustment when the data have not been prepared welland there are datawith erroneous or poorly conditioned inputinformation Second there are a low number of observationsor minimal historical information In this paper it has beenverified that the input data for the ANN did not present poorconditioning therefore the overadjustment problem is dis-carded and the few data (ie the few observations) availableare considered themain cause of discordance of the inputs forvalidationThe problem of feeding the network with few datato train and validate the ANN is due to unavailability of moredata for reasons of good performance in the industrial planta fact that would undoubtedly improve the learning of thenetwork and therefore its efficiency and accuracy Howeverthis fact highlights another very interesting aspect of theANN the network is easily adaptable and configurable givena low number of observations Here this adaptability makesit possible to accurately predict 11 hits of 12 possibilities thusthere is a 9167 probability of success in this case

For machine M2 there is a nearly total adjustment forthe OEE indicator but not for the MTTF indicator for whichit is evident that in month 6 there is a discrepancy in theprediction as in M1 the minimal data (observations) usedfor the validation obtain the same precision as M1

Another relevant aspect of the result is the acceptableprecision in the prediction of availability indicators whichare based exclusively on time by simply using as inputthree productive variables as predictor variables (daily papermass paper surface density and machine speed) In additionto the two output variables OEE and MTTF indicators asobjectives to be predicted are calculated simultaneously afact that reflects an additional value of this type of networksand greater computational efficiency by obtaining in a singlesimulation the prediction ofmore than one objective variable

4 Conclusions

A PCA-ML model has been developed such that it can beintegrated into scorecards with a traditional focus BSC

Complexity 11

0 50 100 150 200 250 300 350 400 450483 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is95888e minus 13 at epoch 320

100

10minus5

10minus10

TrainingTestBest

(a)

60 80 100 120Target

50

60

70

80

90

100

110

120Test R = minus042747

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120

Training R = 1

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120All R = 086783

DataFitY = T

lowastＮＬＡＮ

+14

ＯＮＪＯＮ ≃

087

lowastＮＬＡＮ

+13e+02

ＯＮＪＯＮ ≃

minus037lowastＮＬＡＮ

+minus13eminus08

ＯＮＪＯＮ ≃

1

(b)

Figure 7 (a) ANN regression performance for M1 (b) regression fit

12 Complexity

0 100 200 300 400 500 600 700 800856 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is23797e minus 12 at epoch 753

100

105

10minus5

10minus10

TrainingTestBest

(a)

100 150 200 250Target

100

150

200

250

100

150

200

250

100

150

200

250

Test R = minus051755

DataFitY = T

Training R = 1

DataFitY = T

All R = 095036

DataFitY = T

100 150 200 250Target

100 150 200 250Target

lowastＮＬＡＮ

+minus47eminus07

ＯＮＪＯＮ ≃

1

lowastＮＬＡＮ

+26e+02

ＯＮＪＯＮ ≃

minus15

lowastＮＬＡＮ

+6

ＯＮＪＯＮ ≃

097

(b)

Figure 8 (a) ANN regression performance for M2 (b) regression fit

Complexity 13

0 2 4 6 8 10 12(Month)

925

93

935

94

945

95O

EE (

)

(Month)0 2 4 6 8 10 12

40

50

60

70

80

90

100

110

120

130

MTT

F (h

)

(a)

(Month)

OEE

()

0 2 4 6 8 10 1293

935

94

945

95

955

96

965

97

975

98

(Month)

MTT

F (h

)

0 2 4 6 8 10 1250

100

150

200

250

300

(b)

Figure 9 Output variables measure (blue) versus predicted (red) (a) M1 (b) M2

thus including a tactical definition of longer-term strategicapproaches such as a scorecard based onBSCThis new exten-sion allows better control over themaintenance function of anindustrial plant in themedium-termwith amonthly intervalin such a manner that allows the measurement of certainindicators of those productive areas that were previously con-sidered strategic This model of PCA and an ML algorithmusing ANN can be integrated very easily into any traditionalcontrol panel by converting the developed source code topackages of different programming languages and includingthem in a library to be used as a function in a spreadsheetor a standalone executable application In addition at thecontrol panel this model is provided with ML to discoverstructures and behavior patterns that are relatively hidden inWOs By utilizing a clustering or clustering technique naturalgroups are determined in the cost variables and maintenanceworkforce in addition predictions about the availability ofthe productive area are made through the indicators OEE

andMTTFThus the scorecard model on a paper productionplant has been validated

As possible future works this methodology could beapplied to civil engineering and in this case applying a fuzzyuncertainty due to particular characteristics of this sector

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors want to thank the Department of ConstructionEngineering andManufacturing and the College of IndustrialEngineers of UNED for their support through Projects 2017-IFC08 and 2017-IFC09

14 Complexity

References

[1] DWollmann andM T Steiner ldquoThe strategic decision-makingas a complex adaptive system a conceptual scientific modelrdquoComplexity vol 2017 Article ID 7954289 13 pages 2017

[2] R Calvo R Domingo and M A Sebastian ldquoOperationalflexibility quantification in a make-to-order assembly systemrdquoInternational Journal of Flexible Manufacturing Systems vol 19no 3 pp 247ndash263 2008

[3] M Mahdiloo R F Saen and M Tavana ldquoA novel data envel-opment analysis model for solving supplier selection problemswith undesirable outputs and lack of inputsrdquo InternationalJournal of Logistics Systems and Management vol 11 no 3 pp285ndash305 2012

[4] MMadic J Antucheviciene M Radovanovic and D PetkovicldquoDetermination of manufacturing process conditions by usingMCDM methods Application in laser cuttingrdquo EngineeringEconomics vol 27 no 2 pp 144ndash150 2016

[5] E K Zavadskas R Bausys D Stanujkic andM Magdalinovic-Kalinovic ldquoSelection of lead-zinc flotation circuit design byapplying WASPAS method with single-valued neutrosophicsetrdquo Acta Montanistica Slovaca vol 21 no 2 pp 85ndash92 2016

[6] J Antucheviciene Z Kala M Marzouk and E R VaidogasldquoDecision making methods and applications in civil engineer-ingrdquoMathematical Problems in Engineering vol 2015 Article ID160569 3 pages 2015

[7] R Calvo R Domingo and M A Sebastin ldquoSystemic criterionof sustainability in agile manufacturingrdquo International Journalof Production Research vol 46 no 12 pp 3345ndash3358 2008

[8] F A Olivencia Polo J Ferrero Bermejo J F Gomez Fernandezand A Crespo Marquez ldquoFailure mode prediction and energyforecasting of PV plants to assist dynamic maintenance tasksby ANN based modelsrdquo Renewable Energy vol 81 pp 227ndash2382015

[9] A J C Trappey C V Trappey L Ma and J C M ChangldquoIntelligent engineering asset management system for powertransformer maintenance decision supports under variousoperating conditionsrdquo Computers and Industrial Engineeringvol 84 pp 3ndash11 2015

[10] V Martınez-Martınez F J Gomez-Gil J Gomez-Gil and RRuiz-Gonzalez ldquoAn Artificial Neural Network based expertsystem fitted with Genetic Algorithms for detecting the statusof several rotary components in agro-industrial machines usinga single vibration signalrdquo Expert Systems with Applications vol42 no 17-18 pp 6433ndash6441 2015

[11] J Moubray Reliability-centered Maintenance Industrial PressNew York NY USA 1997

[12] A Mardani A Jusoh and E K Zavadskas ldquoFuzzy multi-ple criteria decision-making techniques and applicationsmdashtwodecades review from 1994 to 2014rdquo Expert Systems with Appli-cations vol 42 no 8 pp 4126ndash4148 2015

[13] M Bashiri H Badri and T H Hejazi ldquoSelecting optimummaintenance strategy by fuzzy interactive linear assignmentmethodrdquo Applied Mathematical Modelling Simulation andComputation for Engineering and Environmental Systems vol35 no 1 pp 152ndash164 2011

[14] MM Fouladgar A Lashgari Z Turskis A Yazdani-Chamziniand E K Zavadskas ldquoMaintenance strategy selection usingAHP and COPRAS under fuzzy environmentrdquo InternationalJournal of Strategic Property Management vol 16 no 1 pp 85ndash104 2012

[15] LWang J Chu and JWu ldquoSelection of optimummaintenancestrategies based on a fuzzy analytic hierarchy processrdquo Interna-tional Journal of Production Economics vol 107 no 1 pp 151ndash163 2007

[16] M Ilangkumaran and S Kumanan ldquoSelection of maintenancepolicy for textile industry using hybrid multi-criteria decisionmaking approachrdquo Journal of Manufacturing Technology Man-agement vol 20 no 7 pp 1009ndash1022 2009

[17] J-H Yu M-Y Jeon and T W Kim ldquoFuzzy-based compositeindicator development methodology for evaluating overallproject performancerdquo Journal of Civil Engineering and Manage-ment vol 21 no 3 pp 343ndash355 2015

[18] A Valipour N Yahaya N Md Noor A Mardani and JAntucheviciene ldquoA new hybrid fuzzy cybernetic analytic net-work process model to identify shared risks in PPP projectsrdquoInternational Journal of Strategic Property Management vol 20no 4 pp 409ndash426 2016

[19] M-Y Cheng D K Wibowo D Prayogo and A F V RoyldquoPredicting productivity loss caused by change orders using theevolutionary fuzzy support vector machine inference modelrdquoJournal of Civil Engineering and Management vol 21 no 7 pp881ndash892 2015

[20] H Abdi and L J Williams ldquoPrincipal component analysisrdquoWiley Interdisciplinary Reviews Computational Statistics vol 2no 4 pp 433ndash459 2010

[21] H Li D Parikh Q He et al ldquoImproving rail network velocityA machine learning approach to predictive maintenancerdquoTransportation Research Part C Emerging Technologies vol 45pp 17ndash26 2014

[22] E Alpaydin Introduction to Machine Learning MIT PressCambridge MA USA 2004

[23] H Naganathan W O Chong and X Chen ldquoBuilding energymodeling (BEM) using clustering algorithms and semi-super-vised machine learning approachesrdquo Automation in Construc-tion vol 72 pp 187ndash194 2016

[24] A Vellido J D Martın-Guerrero and P J G Lisboa ldquoMakingmachine learning models interpretablerdquo in Proceedings of the20th European Symposium on Artificial Neural Networks Com-putational Intelligence and Machine Learning ESANN 2012 pp163ndash172 Bruges Belgium April 2012

[25] N Rodrıguez-Padial M Marın and R Domingo ldquoStrategicFramework to Maintenance Decision Support Systemsrdquo in Pro-ceedings of the Manufacturing Engineering Society InternationalConference MESIC 2015 pp 903ndash910 July 2015

[26] G Waeyenbergh and L Pintelon ldquoA framework for mainte-nance concept developmentrdquo International Journal of Produc-tion Economics vol 77 no 3 pp 299ndash313 2002

[27] S W Palocsay I S Markham and S E Markham ldquoUtilizingand teaching data tools in Excel for exploratory analysisrdquoJournal of Business Research vol 63 no 2 pp 191ndash206 2010

[28] Mathworks IntroducingMachine Learning httpswwwmath-workscom

[29] L M Calvo and R Domingo ldquoInfluence of process operatingparameters on CO2 emissions in continuous industrial plantsrdquoJournal of Cleaner Production vol 96 article no 4308 pp 253ndash262 2015

[30] L M Calvo and R Domingo ldquoA first approach to the useof CO2 emissions as a maintenance indicator in industrialplantsrdquo in Proceedings of the Manufacturing Engineering SocietyInternational Conference MESIC 2013 pp 678ndash686 June 2013

10 Complexity

0 2000 4000 6000 8000 10000 12000 140000

50

100

150

200

250

300Ti

me r

epai

r (h)

RN

Total cost (euro)

(a)

Tim

e rep

air (

h)

0 1000 2000 3000 4000 5000 6000 7000 8000 90000

10

20

30

40

50

60

70

80

90

100

Total cost (euro)

(b)

Figure 6 (a) Artificial Neural Network SOM trained (b) SOM topology sample hits

be used for the test AnMSE performance function is used asshown in Figures 7(a) and 8(a) for M1 and M2 respectivelyThe result of the trained network is suitably provided accord-ing to the regression adjustment coefficients that are shownin Figures 7(b) and 8(b) for M1 and M2 respectively

For each machine that forms the productive area theresult of the adjustment is observed by comparing the outputvariables OEE andMTTF (in blue) measured with the outputvariables foreseen by the OEEp and MTTFp model (in red)The results of M1 are shown in Figure 9(a) and those of M2are shown in Figure 9(b) In short this network can predictthe maintenance behavior of the production area of the plantin availability terms (efficiency and operating times) feedingthe model with the predictive values of production As moreinstances of inputoutput data are introduced the networkwill be retrained and more reliable since it will be adjustedwith a greater number of real examples that have occurredtherefore the greater quantity will lead to the acquisition ofmore experience and knowledge In maintenance terms it isnecessary to make predictions of availability and operatingtimes according to three characteristic values of productionand the model established in this way allows to predictthe OEE and MTTF observing a nonlinear behavior in thetime as is shown in Figure 9 In this sense the responsesfor M1 are observed where the OEE oscillates between anaverage value of 94 with a low dispersion of plusmn066 thusnot happening with MTTF values of mean 8107 h of highdispersion plusmn2582 h As for M2 the OEE has an average valueof 9552 with a low dispersion of plusmn112 despite MTTFvalues of 11803 h mean with very high dispersion plusmn5935 hconcluding that the regression model can accurately predicthigh and low amplitude oscillation values over time

It is noted that the fit is acceptable for training whichis provided by the global adjustment regression coefficient 119877values as shown in Figure 7(b) for both machines

For machine M1 discrepant values are observed in thevalidation setting for month 10 and for both OEE and MTTFindicators there are two reasons for this reason First there is

overadjustment when the data have not been prepared welland there are datawith erroneous or poorly conditioned inputinformation Second there are a low number of observationsor minimal historical information In this paper it has beenverified that the input data for the ANN did not present poorconditioning therefore the overadjustment problem is dis-carded and the few data (ie the few observations) availableare considered themain cause of discordance of the inputs forvalidationThe problem of feeding the network with few datato train and validate the ANN is due to unavailability of moredata for reasons of good performance in the industrial planta fact that would undoubtedly improve the learning of thenetwork and therefore its efficiency and accuracy Howeverthis fact highlights another very interesting aspect of theANN the network is easily adaptable and configurable givena low number of observations Here this adaptability makesit possible to accurately predict 11 hits of 12 possibilities thusthere is a 9167 probability of success in this case

For machine M2 there is a nearly total adjustment forthe OEE indicator but not for the MTTF indicator for whichit is evident that in month 6 there is a discrepancy in theprediction as in M1 the minimal data (observations) usedfor the validation obtain the same precision as M1

Another relevant aspect of the result is the acceptableprecision in the prediction of availability indicators whichare based exclusively on time by simply using as inputthree productive variables as predictor variables (daily papermass paper surface density and machine speed) In additionto the two output variables OEE and MTTF indicators asobjectives to be predicted are calculated simultaneously afact that reflects an additional value of this type of networksand greater computational efficiency by obtaining in a singlesimulation the prediction ofmore than one objective variable

4 Conclusions

A PCA-ML model has been developed such that it can beintegrated into scorecards with a traditional focus BSC

Complexity 11

0 50 100 150 200 250 300 350 400 450483 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is95888e minus 13 at epoch 320

100

10minus5

10minus10

TrainingTestBest

(a)

60 80 100 120Target

50

60

70

80

90

100

110

120Test R = minus042747

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120

Training R = 1

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120All R = 086783

DataFitY = T

lowastＮＬＡＮ

+14

ＯＮＪＯＮ ≃

087

lowastＮＬＡＮ

+13e+02

ＯＮＪＯＮ ≃

minus037lowastＮＬＡＮ

+minus13eminus08

ＯＮＪＯＮ ≃

1

(b)

Figure 7 (a) ANN regression performance for M1 (b) regression fit

12 Complexity

0 100 200 300 400 500 600 700 800856 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is23797e minus 12 at epoch 753

100

105

10minus5

10minus10

TrainingTestBest

(a)

100 150 200 250Target

100

150

200

250

100

150

200

250

100

150

200

250

Test R = minus051755

DataFitY = T

Training R = 1

DataFitY = T

All R = 095036

DataFitY = T

100 150 200 250Target

100 150 200 250Target

lowastＮＬＡＮ

+minus47eminus07

ＯＮＪＯＮ ≃

1

lowastＮＬＡＮ

+26e+02

ＯＮＪＯＮ ≃

minus15

lowastＮＬＡＮ

+6

ＯＮＪＯＮ ≃

097

(b)

Figure 8 (a) ANN regression performance for M2 (b) regression fit

Complexity 13

0 2 4 6 8 10 12(Month)

925

93

935

94

945

95O

EE (

)

(Month)0 2 4 6 8 10 12

40

50

60

70

80

90

100

110

120

130

MTT

F (h

)

(a)

(Month)

OEE

()

0 2 4 6 8 10 1293

935

94

945

95

955

96

965

97

975

98

(Month)

MTT

F (h

)

0 2 4 6 8 10 1250

100

150

200

250

300

(b)

Figure 9 Output variables measure (blue) versus predicted (red) (a) M1 (b) M2

thus including a tactical definition of longer-term strategicapproaches such as a scorecard based onBSCThis new exten-sion allows better control over themaintenance function of anindustrial plant in themedium-termwith amonthly intervalin such a manner that allows the measurement of certainindicators of those productive areas that were previously con-sidered strategic This model of PCA and an ML algorithmusing ANN can be integrated very easily into any traditionalcontrol panel by converting the developed source code topackages of different programming languages and includingthem in a library to be used as a function in a spreadsheetor a standalone executable application In addition at thecontrol panel this model is provided with ML to discoverstructures and behavior patterns that are relatively hidden inWOs By utilizing a clustering or clustering technique naturalgroups are determined in the cost variables and maintenanceworkforce in addition predictions about the availability ofthe productive area are made through the indicators OEE

andMTTFThus the scorecard model on a paper productionplant has been validated

As possible future works this methodology could beapplied to civil engineering and in this case applying a fuzzyuncertainty due to particular characteristics of this sector

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors want to thank the Department of ConstructionEngineering andManufacturing and the College of IndustrialEngineers of UNED for their support through Projects 2017-IFC08 and 2017-IFC09

14 Complexity

References

[1] DWollmann andM T Steiner ldquoThe strategic decision-makingas a complex adaptive system a conceptual scientific modelrdquoComplexity vol 2017 Article ID 7954289 13 pages 2017

[2] R Calvo R Domingo and M A Sebastian ldquoOperationalflexibility quantification in a make-to-order assembly systemrdquoInternational Journal of Flexible Manufacturing Systems vol 19no 3 pp 247ndash263 2008

[3] M Mahdiloo R F Saen and M Tavana ldquoA novel data envel-opment analysis model for solving supplier selection problemswith undesirable outputs and lack of inputsrdquo InternationalJournal of Logistics Systems and Management vol 11 no 3 pp285ndash305 2012

[4] MMadic J Antucheviciene M Radovanovic and D PetkovicldquoDetermination of manufacturing process conditions by usingMCDM methods Application in laser cuttingrdquo EngineeringEconomics vol 27 no 2 pp 144ndash150 2016

[5] E K Zavadskas R Bausys D Stanujkic andM Magdalinovic-Kalinovic ldquoSelection of lead-zinc flotation circuit design byapplying WASPAS method with single-valued neutrosophicsetrdquo Acta Montanistica Slovaca vol 21 no 2 pp 85ndash92 2016

[6] J Antucheviciene Z Kala M Marzouk and E R VaidogasldquoDecision making methods and applications in civil engineer-ingrdquoMathematical Problems in Engineering vol 2015 Article ID160569 3 pages 2015

[7] R Calvo R Domingo and M A Sebastin ldquoSystemic criterionof sustainability in agile manufacturingrdquo International Journalof Production Research vol 46 no 12 pp 3345ndash3358 2008

[8] F A Olivencia Polo J Ferrero Bermejo J F Gomez Fernandezand A Crespo Marquez ldquoFailure mode prediction and energyforecasting of PV plants to assist dynamic maintenance tasksby ANN based modelsrdquo Renewable Energy vol 81 pp 227ndash2382015

[9] A J C Trappey C V Trappey L Ma and J C M ChangldquoIntelligent engineering asset management system for powertransformer maintenance decision supports under variousoperating conditionsrdquo Computers and Industrial Engineeringvol 84 pp 3ndash11 2015

[10] V Martınez-Martınez F J Gomez-Gil J Gomez-Gil and RRuiz-Gonzalez ldquoAn Artificial Neural Network based expertsystem fitted with Genetic Algorithms for detecting the statusof several rotary components in agro-industrial machines usinga single vibration signalrdquo Expert Systems with Applications vol42 no 17-18 pp 6433ndash6441 2015

[11] J Moubray Reliability-centered Maintenance Industrial PressNew York NY USA 1997

[12] A Mardani A Jusoh and E K Zavadskas ldquoFuzzy multi-ple criteria decision-making techniques and applicationsmdashtwodecades review from 1994 to 2014rdquo Expert Systems with Appli-cations vol 42 no 8 pp 4126ndash4148 2015

[13] M Bashiri H Badri and T H Hejazi ldquoSelecting optimummaintenance strategy by fuzzy interactive linear assignmentmethodrdquo Applied Mathematical Modelling Simulation andComputation for Engineering and Environmental Systems vol35 no 1 pp 152ndash164 2011

[14] MM Fouladgar A Lashgari Z Turskis A Yazdani-Chamziniand E K Zavadskas ldquoMaintenance strategy selection usingAHP and COPRAS under fuzzy environmentrdquo InternationalJournal of Strategic Property Management vol 16 no 1 pp 85ndash104 2012

[15] LWang J Chu and JWu ldquoSelection of optimummaintenancestrategies based on a fuzzy analytic hierarchy processrdquo Interna-tional Journal of Production Economics vol 107 no 1 pp 151ndash163 2007

[16] M Ilangkumaran and S Kumanan ldquoSelection of maintenancepolicy for textile industry using hybrid multi-criteria decisionmaking approachrdquo Journal of Manufacturing Technology Man-agement vol 20 no 7 pp 1009ndash1022 2009

[17] J-H Yu M-Y Jeon and T W Kim ldquoFuzzy-based compositeindicator development methodology for evaluating overallproject performancerdquo Journal of Civil Engineering and Manage-ment vol 21 no 3 pp 343ndash355 2015

[18] A Valipour N Yahaya N Md Noor A Mardani and JAntucheviciene ldquoA new hybrid fuzzy cybernetic analytic net-work process model to identify shared risks in PPP projectsrdquoInternational Journal of Strategic Property Management vol 20no 4 pp 409ndash426 2016

[19] M-Y Cheng D K Wibowo D Prayogo and A F V RoyldquoPredicting productivity loss caused by change orders using theevolutionary fuzzy support vector machine inference modelrdquoJournal of Civil Engineering and Management vol 21 no 7 pp881ndash892 2015

[20] H Abdi and L J Williams ldquoPrincipal component analysisrdquoWiley Interdisciplinary Reviews Computational Statistics vol 2no 4 pp 433ndash459 2010

[21] H Li D Parikh Q He et al ldquoImproving rail network velocityA machine learning approach to predictive maintenancerdquoTransportation Research Part C Emerging Technologies vol 45pp 17ndash26 2014

[22] E Alpaydin Introduction to Machine Learning MIT PressCambridge MA USA 2004

[23] H Naganathan W O Chong and X Chen ldquoBuilding energymodeling (BEM) using clustering algorithms and semi-super-vised machine learning approachesrdquo Automation in Construc-tion vol 72 pp 187ndash194 2016

[24] A Vellido J D Martın-Guerrero and P J G Lisboa ldquoMakingmachine learning models interpretablerdquo in Proceedings of the20th European Symposium on Artificial Neural Networks Com-putational Intelligence and Machine Learning ESANN 2012 pp163ndash172 Bruges Belgium April 2012

[25] N Rodrıguez-Padial M Marın and R Domingo ldquoStrategicFramework to Maintenance Decision Support Systemsrdquo in Pro-ceedings of the Manufacturing Engineering Society InternationalConference MESIC 2015 pp 903ndash910 July 2015

[26] G Waeyenbergh and L Pintelon ldquoA framework for mainte-nance concept developmentrdquo International Journal of Produc-tion Economics vol 77 no 3 pp 299ndash313 2002

[27] S W Palocsay I S Markham and S E Markham ldquoUtilizingand teaching data tools in Excel for exploratory analysisrdquoJournal of Business Research vol 63 no 2 pp 191ndash206 2010

[28] Mathworks IntroducingMachine Learning httpswwwmath-workscom

[29] L M Calvo and R Domingo ldquoInfluence of process operatingparameters on CO2 emissions in continuous industrial plantsrdquoJournal of Cleaner Production vol 96 article no 4308 pp 253ndash262 2015

[30] L M Calvo and R Domingo ldquoA first approach to the useof CO2 emissions as a maintenance indicator in industrialplantsrdquo in Proceedings of the Manufacturing Engineering SocietyInternational Conference MESIC 2013 pp 678ndash686 June 2013

Complexity 11

0 50 100 150 200 250 300 350 400 450483 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is95888e minus 13 at epoch 320

100

10minus5

10minus10

TrainingTestBest

(a)

60 80 100 120Target

50

60

70

80

90

100

110

120Test R = minus042747

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120

Training R = 1

DataFitY = T

60 80 100 120Target

50

60

70

80

90

100

110

120All R = 086783

DataFitY = T

lowastＮＬＡＮ

+14

ＯＮＪＯＮ ≃

087

lowastＮＬＡＮ

+13e+02

ＯＮＪＯＮ ≃

minus037lowastＮＬＡＮ

+minus13eminus08

ＯＮＪＯＮ ≃

1

(b)

Figure 7 (a) ANN regression performance for M1 (b) regression fit

12 Complexity

0 100 200 300 400 500 600 700 800856 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is23797e minus 12 at epoch 753

100

105

10minus5

10minus10

TrainingTestBest

(a)

100 150 200 250Target

100

150

200

250

100

150

200

250

100

150

200

250

Test R = minus051755

DataFitY = T

Training R = 1

DataFitY = T

All R = 095036

DataFitY = T

100 150 200 250Target

100 150 200 250Target

lowastＮＬＡＮ

+minus47eminus07

ＯＮＪＯＮ ≃

1

lowastＮＬＡＮ

+26e+02

ＯＮＪＯＮ ≃

minus15

lowastＮＬＡＮ

+6

ＯＮＪＯＮ ≃

097

(b)

Figure 8 (a) ANN regression performance for M2 (b) regression fit

Complexity 13

0 2 4 6 8 10 12(Month)

925

93

935

94

945

95O

EE (

)

(Month)0 2 4 6 8 10 12

40

50

60

70

80

90

100

110

120

130

MTT

F (h

)

(a)

(Month)

OEE

()

0 2 4 6 8 10 1293

935

94

945

95

955

96

965

97

975

98

(Month)

MTT

F (h

)

0 2 4 6 8 10 1250

100

150

200

250

300

(b)

Figure 9 Output variables measure (blue) versus predicted (red) (a) M1 (b) M2

thus including a tactical definition of longer-term strategicapproaches such as a scorecard based onBSCThis new exten-sion allows better control over themaintenance function of anindustrial plant in themedium-termwith amonthly intervalin such a manner that allows the measurement of certainindicators of those productive areas that were previously con-sidered strategic This model of PCA and an ML algorithmusing ANN can be integrated very easily into any traditionalcontrol panel by converting the developed source code topackages of different programming languages and includingthem in a library to be used as a function in a spreadsheetor a standalone executable application In addition at thecontrol panel this model is provided with ML to discoverstructures and behavior patterns that are relatively hidden inWOs By utilizing a clustering or clustering technique naturalgroups are determined in the cost variables and maintenanceworkforce in addition predictions about the availability ofthe productive area are made through the indicators OEE

andMTTFThus the scorecard model on a paper productionplant has been validated

As possible future works this methodology could beapplied to civil engineering and in this case applying a fuzzyuncertainty due to particular characteristics of this sector

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors want to thank the Department of ConstructionEngineering andManufacturing and the College of IndustrialEngineers of UNED for their support through Projects 2017-IFC08 and 2017-IFC09

14 Complexity

References

[1] DWollmann andM T Steiner ldquoThe strategic decision-makingas a complex adaptive system a conceptual scientific modelrdquoComplexity vol 2017 Article ID 7954289 13 pages 2017

[2] R Calvo R Domingo and M A Sebastian ldquoOperationalflexibility quantification in a make-to-order assembly systemrdquoInternational Journal of Flexible Manufacturing Systems vol 19no 3 pp 247ndash263 2008

[3] M Mahdiloo R F Saen and M Tavana ldquoA novel data envel-opment analysis model for solving supplier selection problemswith undesirable outputs and lack of inputsrdquo InternationalJournal of Logistics Systems and Management vol 11 no 3 pp285ndash305 2012

[4] MMadic J Antucheviciene M Radovanovic and D PetkovicldquoDetermination of manufacturing process conditions by usingMCDM methods Application in laser cuttingrdquo EngineeringEconomics vol 27 no 2 pp 144ndash150 2016

[5] E K Zavadskas R Bausys D Stanujkic andM Magdalinovic-Kalinovic ldquoSelection of lead-zinc flotation circuit design byapplying WASPAS method with single-valued neutrosophicsetrdquo Acta Montanistica Slovaca vol 21 no 2 pp 85ndash92 2016

[6] J Antucheviciene Z Kala M Marzouk and E R VaidogasldquoDecision making methods and applications in civil engineer-ingrdquoMathematical Problems in Engineering vol 2015 Article ID160569 3 pages 2015

[7] R Calvo R Domingo and M A Sebastin ldquoSystemic criterionof sustainability in agile manufacturingrdquo International Journalof Production Research vol 46 no 12 pp 3345ndash3358 2008

[8] F A Olivencia Polo J Ferrero Bermejo J F Gomez Fernandezand A Crespo Marquez ldquoFailure mode prediction and energyforecasting of PV plants to assist dynamic maintenance tasksby ANN based modelsrdquo Renewable Energy vol 81 pp 227ndash2382015

[9] A J C Trappey C V Trappey L Ma and J C M ChangldquoIntelligent engineering asset management system for powertransformer maintenance decision supports under variousoperating conditionsrdquo Computers and Industrial Engineeringvol 84 pp 3ndash11 2015

[10] V Martınez-Martınez F J Gomez-Gil J Gomez-Gil and RRuiz-Gonzalez ldquoAn Artificial Neural Network based expertsystem fitted with Genetic Algorithms for detecting the statusof several rotary components in agro-industrial machines usinga single vibration signalrdquo Expert Systems with Applications vol42 no 17-18 pp 6433ndash6441 2015

[11] J Moubray Reliability-centered Maintenance Industrial PressNew York NY USA 1997

[12] A Mardani A Jusoh and E K Zavadskas ldquoFuzzy multi-ple criteria decision-making techniques and applicationsmdashtwodecades review from 1994 to 2014rdquo Expert Systems with Appli-cations vol 42 no 8 pp 4126ndash4148 2015

[13] M Bashiri H Badri and T H Hejazi ldquoSelecting optimummaintenance strategy by fuzzy interactive linear assignmentmethodrdquo Applied Mathematical Modelling Simulation andComputation for Engineering and Environmental Systems vol35 no 1 pp 152ndash164 2011

[14] MM Fouladgar A Lashgari Z Turskis A Yazdani-Chamziniand E K Zavadskas ldquoMaintenance strategy selection usingAHP and COPRAS under fuzzy environmentrdquo InternationalJournal of Strategic Property Management vol 16 no 1 pp 85ndash104 2012

[15] LWang J Chu and JWu ldquoSelection of optimummaintenancestrategies based on a fuzzy analytic hierarchy processrdquo Interna-tional Journal of Production Economics vol 107 no 1 pp 151ndash163 2007

[16] M Ilangkumaran and S Kumanan ldquoSelection of maintenancepolicy for textile industry using hybrid multi-criteria decisionmaking approachrdquo Journal of Manufacturing Technology Man-agement vol 20 no 7 pp 1009ndash1022 2009

[17] J-H Yu M-Y Jeon and T W Kim ldquoFuzzy-based compositeindicator development methodology for evaluating overallproject performancerdquo Journal of Civil Engineering and Manage-ment vol 21 no 3 pp 343ndash355 2015

[18] A Valipour N Yahaya N Md Noor A Mardani and JAntucheviciene ldquoA new hybrid fuzzy cybernetic analytic net-work process model to identify shared risks in PPP projectsrdquoInternational Journal of Strategic Property Management vol 20no 4 pp 409ndash426 2016

[19] M-Y Cheng D K Wibowo D Prayogo and A F V RoyldquoPredicting productivity loss caused by change orders using theevolutionary fuzzy support vector machine inference modelrdquoJournal of Civil Engineering and Management vol 21 no 7 pp881ndash892 2015

[20] H Abdi and L J Williams ldquoPrincipal component analysisrdquoWiley Interdisciplinary Reviews Computational Statistics vol 2no 4 pp 433ndash459 2010

[21] H Li D Parikh Q He et al ldquoImproving rail network velocityA machine learning approach to predictive maintenancerdquoTransportation Research Part C Emerging Technologies vol 45pp 17ndash26 2014

[22] E Alpaydin Introduction to Machine Learning MIT PressCambridge MA USA 2004

[23] H Naganathan W O Chong and X Chen ldquoBuilding energymodeling (BEM) using clustering algorithms and semi-super-vised machine learning approachesrdquo Automation in Construc-tion vol 72 pp 187ndash194 2016

[24] A Vellido J D Martın-Guerrero and P J G Lisboa ldquoMakingmachine learning models interpretablerdquo in Proceedings of the20th European Symposium on Artificial Neural Networks Com-putational Intelligence and Machine Learning ESANN 2012 pp163ndash172 Bruges Belgium April 2012

[25] N Rodrıguez-Padial M Marın and R Domingo ldquoStrategicFramework to Maintenance Decision Support Systemsrdquo in Pro-ceedings of the Manufacturing Engineering Society InternationalConference MESIC 2015 pp 903ndash910 July 2015

[26] G Waeyenbergh and L Pintelon ldquoA framework for mainte-nance concept developmentrdquo International Journal of Produc-tion Economics vol 77 no 3 pp 299ndash313 2002

[27] S W Palocsay I S Markham and S E Markham ldquoUtilizingand teaching data tools in Excel for exploratory analysisrdquoJournal of Business Research vol 63 no 2 pp 191ndash206 2010

[28] Mathworks IntroducingMachine Learning httpswwwmath-workscom

[29] L M Calvo and R Domingo ldquoInfluence of process operatingparameters on CO2 emissions in continuous industrial plantsrdquoJournal of Cleaner Production vol 96 article no 4308 pp 253ndash262 2015

[30] L M Calvo and R Domingo ldquoA first approach to the useof CO2 emissions as a maintenance indicator in industrialplantsrdquo in Proceedings of the Manufacturing Engineering SocietyInternational Conference MESIC 2013 pp 678ndash686 June 2013

12 Complexity

0 100 200 300 400 500 600 700 800856 epochs

Mea

n sq

uare

d er

ror (

mse

)

Best training performance is23797e minus 12 at epoch 753

100

105

10minus5

10minus10

TrainingTestBest

(a)

100 150 200 250Target

100

150

200

250

100

150

200

250

100

150

200

250

Test R = minus051755

DataFitY = T

Training R = 1

DataFitY = T

All R = 095036

DataFitY = T

100 150 200 250Target

100 150 200 250Target

lowastＮＬＡＮ

+minus47eminus07

ＯＮＪＯＮ ≃

1

lowastＮＬＡＮ

+26e+02

ＯＮＪＯＮ ≃

minus15

lowastＮＬＡＮ

+6

ＯＮＪＯＮ ≃

097

(b)

Figure 8 (a) ANN regression performance for M2 (b) regression fit

Complexity 13

0 2 4 6 8 10 12(Month)

925

93

935

94

945

95O

EE (

)

(Month)0 2 4 6 8 10 12

40

50

60

70

80

90

100

110

120

130

MTT

F (h

)

(a)

(Month)

OEE

()

0 2 4 6 8 10 1293

935

94

945

95

955

96

965

97

975

98

(Month)

MTT

F (h

)

0 2 4 6 8 10 1250

100

150

200

250

300

(b)

Figure 9 Output variables measure (blue) versus predicted (red) (a) M1 (b) M2

thus including a tactical definition of longer-term strategicapproaches such as a scorecard based onBSCThis new exten-sion allows better control over themaintenance function of anindustrial plant in themedium-termwith amonthly intervalin such a manner that allows the measurement of certainindicators of those productive areas that were previously con-sidered strategic This model of PCA and an ML algorithmusing ANN can be integrated very easily into any traditionalcontrol panel by converting the developed source code topackages of different programming languages and includingthem in a library to be used as a function in a spreadsheetor a standalone executable application In addition at thecontrol panel this model is provided with ML to discoverstructures and behavior patterns that are relatively hidden inWOs By utilizing a clustering or clustering technique naturalgroups are determined in the cost variables and maintenanceworkforce in addition predictions about the availability ofthe productive area are made through the indicators OEE

andMTTFThus the scorecard model on a paper productionplant has been validated

As possible future works this methodology could beapplied to civil engineering and in this case applying a fuzzyuncertainty due to particular characteristics of this sector

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors want to thank the Department of ConstructionEngineering andManufacturing and the College of IndustrialEngineers of UNED for their support through Projects 2017-IFC08 and 2017-IFC09

14 Complexity

References

[1] DWollmann andM T Steiner ldquoThe strategic decision-makingas a complex adaptive system a conceptual scientific modelrdquoComplexity vol 2017 Article ID 7954289 13 pages 2017

[2] R Calvo R Domingo and M A Sebastian ldquoOperationalflexibility quantification in a make-to-order assembly systemrdquoInternational Journal of Flexible Manufacturing Systems vol 19no 3 pp 247ndash263 2008

[3] M Mahdiloo R F Saen and M Tavana ldquoA novel data envel-opment analysis model for solving supplier selection problemswith undesirable outputs and lack of inputsrdquo InternationalJournal of Logistics Systems and Management vol 11 no 3 pp285ndash305 2012

[4] MMadic J Antucheviciene M Radovanovic and D PetkovicldquoDetermination of manufacturing process conditions by usingMCDM methods Application in laser cuttingrdquo EngineeringEconomics vol 27 no 2 pp 144ndash150 2016

[5] E K Zavadskas R Bausys D Stanujkic andM Magdalinovic-Kalinovic ldquoSelection of lead-zinc flotation circuit design byapplying WASPAS method with single-valued neutrosophicsetrdquo Acta Montanistica Slovaca vol 21 no 2 pp 85ndash92 2016

[6] J Antucheviciene Z Kala M Marzouk and E R VaidogasldquoDecision making methods and applications in civil engineer-ingrdquoMathematical Problems in Engineering vol 2015 Article ID160569 3 pages 2015

[7] R Calvo R Domingo and M A Sebastin ldquoSystemic criterionof sustainability in agile manufacturingrdquo International Journalof Production Research vol 46 no 12 pp 3345ndash3358 2008

[8] F A Olivencia Polo J Ferrero Bermejo J F Gomez Fernandezand A Crespo Marquez ldquoFailure mode prediction and energyforecasting of PV plants to assist dynamic maintenance tasksby ANN based modelsrdquo Renewable Energy vol 81 pp 227ndash2382015

[9] A J C Trappey C V Trappey L Ma and J C M ChangldquoIntelligent engineering asset management system for powertransformer maintenance decision supports under variousoperating conditionsrdquo Computers and Industrial Engineeringvol 84 pp 3ndash11 2015

[10] V Martınez-Martınez F J Gomez-Gil J Gomez-Gil and RRuiz-Gonzalez ldquoAn Artificial Neural Network based expertsystem fitted with Genetic Algorithms for detecting the statusof several rotary components in agro-industrial machines usinga single vibration signalrdquo Expert Systems with Applications vol42 no 17-18 pp 6433ndash6441 2015

[11] J Moubray Reliability-centered Maintenance Industrial PressNew York NY USA 1997

[12] A Mardani A Jusoh and E K Zavadskas ldquoFuzzy multi-ple criteria decision-making techniques and applicationsmdashtwodecades review from 1994 to 2014rdquo Expert Systems with Appli-cations vol 42 no 8 pp 4126ndash4148 2015

[13] M Bashiri H Badri and T H Hejazi ldquoSelecting optimummaintenance strategy by fuzzy interactive linear assignmentmethodrdquo Applied Mathematical Modelling Simulation andComputation for Engineering and Environmental Systems vol35 no 1 pp 152ndash164 2011

[14] MM Fouladgar A Lashgari Z Turskis A Yazdani-Chamziniand E K Zavadskas ldquoMaintenance strategy selection usingAHP and COPRAS under fuzzy environmentrdquo InternationalJournal of Strategic Property Management vol 16 no 1 pp 85ndash104 2012

[15] LWang J Chu and JWu ldquoSelection of optimummaintenancestrategies based on a fuzzy analytic hierarchy processrdquo Interna-tional Journal of Production Economics vol 107 no 1 pp 151ndash163 2007

[16] M Ilangkumaran and S Kumanan ldquoSelection of maintenancepolicy for textile industry using hybrid multi-criteria decisionmaking approachrdquo Journal of Manufacturing Technology Man-agement vol 20 no 7 pp 1009ndash1022 2009

[17] J-H Yu M-Y Jeon and T W Kim ldquoFuzzy-based compositeindicator development methodology for evaluating overallproject performancerdquo Journal of Civil Engineering and Manage-ment vol 21 no 3 pp 343ndash355 2015

[18] A Valipour N Yahaya N Md Noor A Mardani and JAntucheviciene ldquoA new hybrid fuzzy cybernetic analytic net-work process model to identify shared risks in PPP projectsrdquoInternational Journal of Strategic Property Management vol 20no 4 pp 409ndash426 2016

[19] M-Y Cheng D K Wibowo D Prayogo and A F V RoyldquoPredicting productivity loss caused by change orders using theevolutionary fuzzy support vector machine inference modelrdquoJournal of Civil Engineering and Management vol 21 no 7 pp881ndash892 2015

[20] H Abdi and L J Williams ldquoPrincipal component analysisrdquoWiley Interdisciplinary Reviews Computational Statistics vol 2no 4 pp 433ndash459 2010

[21] H Li D Parikh Q He et al ldquoImproving rail network velocityA machine learning approach to predictive maintenancerdquoTransportation Research Part C Emerging Technologies vol 45pp 17ndash26 2014

[22] E Alpaydin Introduction to Machine Learning MIT PressCambridge MA USA 2004

[23] H Naganathan W O Chong and X Chen ldquoBuilding energymodeling (BEM) using clustering algorithms and semi-super-vised machine learning approachesrdquo Automation in Construc-tion vol 72 pp 187ndash194 2016

[24] A Vellido J D Martın-Guerrero and P J G Lisboa ldquoMakingmachine learning models interpretablerdquo in Proceedings of the20th European Symposium on Artificial Neural Networks Com-putational Intelligence and Machine Learning ESANN 2012 pp163ndash172 Bruges Belgium April 2012

[25] N Rodrıguez-Padial M Marın and R Domingo ldquoStrategicFramework to Maintenance Decision Support Systemsrdquo in Pro-ceedings of the Manufacturing Engineering Society InternationalConference MESIC 2015 pp 903ndash910 July 2015

[26] G Waeyenbergh and L Pintelon ldquoA framework for mainte-nance concept developmentrdquo International Journal of Produc-tion Economics vol 77 no 3 pp 299ndash313 2002

[27] S W Palocsay I S Markham and S E Markham ldquoUtilizingand teaching data tools in Excel for exploratory analysisrdquoJournal of Business Research vol 63 no 2 pp 191ndash206 2010

[28] Mathworks IntroducingMachine Learning httpswwwmath-workscom

[29] L M Calvo and R Domingo ldquoInfluence of process operatingparameters on CO2 emissions in continuous industrial plantsrdquoJournal of Cleaner Production vol 96 article no 4308 pp 253ndash262 2015

[30] L M Calvo and R Domingo ldquoA first approach to the useof CO2 emissions as a maintenance indicator in industrialplantsrdquo in Proceedings of the Manufacturing Engineering SocietyInternational Conference MESIC 2013 pp 678ndash686 June 2013

Complexity 13

0 2 4 6 8 10 12(Month)

925

93

935

94

945

95O

EE (

)

(Month)0 2 4 6 8 10 12

40

50

60

70

80

90

100

110

120

130

MTT

F (h

)

(a)

(Month)

OEE

()

0 2 4 6 8 10 1293

935

94

945

95

955

96

965

97

975

98

(Month)

MTT

F (h

)

0 2 4 6 8 10 1250

100

150

200

250

300

(b)

Figure 9 Output variables measure (blue) versus predicted (red) (a) M1 (b) M2

thus including a tactical definition of longer-term strategicapproaches such as a scorecard based onBSCThis new exten-sion allows better control over themaintenance function of anindustrial plant in themedium-termwith amonthly intervalin such a manner that allows the measurement of certainindicators of those productive areas that were previously con-sidered strategic This model of PCA and an ML algorithmusing ANN can be integrated very easily into any traditionalcontrol panel by converting the developed source code topackages of different programming languages and includingthem in a library to be used as a function in a spreadsheetor a standalone executable application In addition at thecontrol panel this model is provided with ML to discoverstructures and behavior patterns that are relatively hidden inWOs By utilizing a clustering or clustering technique naturalgroups are determined in the cost variables and maintenanceworkforce in addition predictions about the availability ofthe productive area are made through the indicators OEE

andMTTFThus the scorecard model on a paper productionplant has been validated

As possible future works this methodology could beapplied to civil engineering and in this case applying a fuzzyuncertainty due to particular characteristics of this sector

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

The authors want to thank the Department of ConstructionEngineering andManufacturing and the College of IndustrialEngineers of UNED for their support through Projects 2017-IFC08 and 2017-IFC09

14 Complexity

References

[1] DWollmann andM T Steiner ldquoThe strategic decision-makingas a complex adaptive system a conceptual scientific modelrdquoComplexity vol 2017 Article ID 7954289 13 pages 2017

[2] R Calvo R Domingo and M A Sebastian ldquoOperationalflexibility quantification in a make-to-order assembly systemrdquoInternational Journal of Flexible Manufacturing Systems vol 19no 3 pp 247ndash263 2008

[3] M Mahdiloo R F Saen and M Tavana ldquoA novel data envel-opment analysis model for solving supplier selection problemswith undesirable outputs and lack of inputsrdquo InternationalJournal of Logistics Systems and Management vol 11 no 3 pp285ndash305 2012

[4] MMadic J Antucheviciene M Radovanovic and D PetkovicldquoDetermination of manufacturing process conditions by usingMCDM methods Application in laser cuttingrdquo EngineeringEconomics vol 27 no 2 pp 144ndash150 2016

[5] E K Zavadskas R Bausys D Stanujkic andM Magdalinovic-Kalinovic ldquoSelection of lead-zinc flotation circuit design byapplying WASPAS method with single-valued neutrosophicsetrdquo Acta Montanistica Slovaca vol 21 no 2 pp 85ndash92 2016

[6] J Antucheviciene Z Kala M Marzouk and E R VaidogasldquoDecision making methods and applications in civil engineer-ingrdquoMathematical Problems in Engineering vol 2015 Article ID160569 3 pages 2015

[7] R Calvo R Domingo and M A Sebastin ldquoSystemic criterionof sustainability in agile manufacturingrdquo International Journalof Production Research vol 46 no 12 pp 3345ndash3358 2008

[8] F A Olivencia Polo J Ferrero Bermejo J F Gomez Fernandezand A Crespo Marquez ldquoFailure mode prediction and energyforecasting of PV plants to assist dynamic maintenance tasksby ANN based modelsrdquo Renewable Energy vol 81 pp 227ndash2382015

[9] A J C Trappey C V Trappey L Ma and J C M ChangldquoIntelligent engineering asset management system for powertransformer maintenance decision supports under variousoperating conditionsrdquo Computers and Industrial Engineeringvol 84 pp 3ndash11 2015

[10] V Martınez-Martınez F J Gomez-Gil J Gomez-Gil and RRuiz-Gonzalez ldquoAn Artificial Neural Network based expertsystem fitted with Genetic Algorithms for detecting the statusof several rotary components in agro-industrial machines usinga single vibration signalrdquo Expert Systems with Applications vol42 no 17-18 pp 6433ndash6441 2015

[11] J Moubray Reliability-centered Maintenance Industrial PressNew York NY USA 1997

[12] A Mardani A Jusoh and E K Zavadskas ldquoFuzzy multi-ple criteria decision-making techniques and applicationsmdashtwodecades review from 1994 to 2014rdquo Expert Systems with Appli-cations vol 42 no 8 pp 4126ndash4148 2015

[13] M Bashiri H Badri and T H Hejazi ldquoSelecting optimummaintenance strategy by fuzzy interactive linear assignmentmethodrdquo Applied Mathematical Modelling Simulation andComputation for Engineering and Environmental Systems vol35 no 1 pp 152ndash164 2011

[14] MM Fouladgar A Lashgari Z Turskis A Yazdani-Chamziniand E K Zavadskas ldquoMaintenance strategy selection usingAHP and COPRAS under fuzzy environmentrdquo InternationalJournal of Strategic Property Management vol 16 no 1 pp 85ndash104 2012

[15] LWang J Chu and JWu ldquoSelection of optimummaintenancestrategies based on a fuzzy analytic hierarchy processrdquo Interna-tional Journal of Production Economics vol 107 no 1 pp 151ndash163 2007

[16] M Ilangkumaran and S Kumanan ldquoSelection of maintenancepolicy for textile industry using hybrid multi-criteria decisionmaking approachrdquo Journal of Manufacturing Technology Man-agement vol 20 no 7 pp 1009ndash1022 2009

[17] J-H Yu M-Y Jeon and T W Kim ldquoFuzzy-based compositeindicator development methodology for evaluating overallproject performancerdquo Journal of Civil Engineering and Manage-ment vol 21 no 3 pp 343ndash355 2015

[18] A Valipour N Yahaya N Md Noor A Mardani and JAntucheviciene ldquoA new hybrid fuzzy cybernetic analytic net-work process model to identify shared risks in PPP projectsrdquoInternational Journal of Strategic Property Management vol 20no 4 pp 409ndash426 2016

[19] M-Y Cheng D K Wibowo D Prayogo and A F V RoyldquoPredicting productivity loss caused by change orders using theevolutionary fuzzy support vector machine inference modelrdquoJournal of Civil Engineering and Management vol 21 no 7 pp881ndash892 2015

[20] H Abdi and L J Williams ldquoPrincipal component analysisrdquoWiley Interdisciplinary Reviews Computational Statistics vol 2no 4 pp 433ndash459 2010

[21] H Li D Parikh Q He et al ldquoImproving rail network velocityA machine learning approach to predictive maintenancerdquoTransportation Research Part C Emerging Technologies vol 45pp 17ndash26 2014

[22] E Alpaydin Introduction to Machine Learning MIT PressCambridge MA USA 2004

[23] H Naganathan W O Chong and X Chen ldquoBuilding energymodeling (BEM) using clustering algorithms and semi-super-vised machine learning approachesrdquo Automation in Construc-tion vol 72 pp 187ndash194 2016

[24] A Vellido J D Martın-Guerrero and P J G Lisboa ldquoMakingmachine learning models interpretablerdquo in Proceedings of the20th European Symposium on Artificial Neural Networks Com-putational Intelligence and Machine Learning ESANN 2012 pp163ndash172 Bruges Belgium April 2012

[25] N Rodrıguez-Padial M Marın and R Domingo ldquoStrategicFramework to Maintenance Decision Support Systemsrdquo in Pro-ceedings of the Manufacturing Engineering Society InternationalConference MESIC 2015 pp 903ndash910 July 2015

[26] G Waeyenbergh and L Pintelon ldquoA framework for mainte-nance concept developmentrdquo International Journal of Produc-tion Economics vol 77 no 3 pp 299ndash313 2002

[27] S W Palocsay I S Markham and S E Markham ldquoUtilizingand teaching data tools in Excel for exploratory analysisrdquoJournal of Business Research vol 63 no 2 pp 191ndash206 2010

[28] Mathworks IntroducingMachine Learning httpswwwmath-workscom

[29] L M Calvo and R Domingo ldquoInfluence of process operatingparameters on CO2 emissions in continuous industrial plantsrdquoJournal of Cleaner Production vol 96 article no 4308 pp 253ndash262 2015

[30] L M Calvo and R Domingo ldquoA first approach to the useof CO2 emissions as a maintenance indicator in industrialplantsrdquo in Proceedings of the Manufacturing Engineering SocietyInternational Conference MESIC 2013 pp 678ndash686 June 2013

14 Complexity

References

[1] DWollmann andM T Steiner ldquoThe strategic decision-makingas a complex adaptive system a conceptual scientific modelrdquoComplexity vol 2017 Article ID 7954289 13 pages 2017

[2] R Calvo R Domingo and M A Sebastian ldquoOperationalflexibility quantification in a make-to-order assembly systemrdquoInternational Journal of Flexible Manufacturing Systems vol 19no 3 pp 247ndash263 2008

[3] M Mahdiloo R F Saen and M Tavana ldquoA novel data envel-opment analysis model for solving supplier selection problemswith undesirable outputs and lack of inputsrdquo InternationalJournal of Logistics Systems and Management vol 11 no 3 pp285ndash305 2012

[4] MMadic J Antucheviciene M Radovanovic and D PetkovicldquoDetermination of manufacturing process conditions by usingMCDM methods Application in laser cuttingrdquo EngineeringEconomics vol 27 no 2 pp 144ndash150 2016

[5] E K Zavadskas R Bausys D Stanujkic andM Magdalinovic-Kalinovic ldquoSelection of lead-zinc flotation circuit design byapplying WASPAS method with single-valued neutrosophicsetrdquo Acta Montanistica Slovaca vol 21 no 2 pp 85ndash92 2016

[6] J Antucheviciene Z Kala M Marzouk and E R VaidogasldquoDecision making methods and applications in civil engineer-ingrdquoMathematical Problems in Engineering vol 2015 Article ID160569 3 pages 2015

[7] R Calvo R Domingo and M A Sebastin ldquoSystemic criterionof sustainability in agile manufacturingrdquo International Journalof Production Research vol 46 no 12 pp 3345ndash3358 2008

[8] F A Olivencia Polo J Ferrero Bermejo J F Gomez Fernandezand A Crespo Marquez ldquoFailure mode prediction and energyforecasting of PV plants to assist dynamic maintenance tasksby ANN based modelsrdquo Renewable Energy vol 81 pp 227ndash2382015

[9] A J C Trappey C V Trappey L Ma and J C M ChangldquoIntelligent engineering asset management system for powertransformer maintenance decision supports under variousoperating conditionsrdquo Computers and Industrial Engineeringvol 84 pp 3ndash11 2015

[10] V Martınez-Martınez F J Gomez-Gil J Gomez-Gil and RRuiz-Gonzalez ldquoAn Artificial Neural Network based expertsystem fitted with Genetic Algorithms for detecting the statusof several rotary components in agro-industrial machines usinga single vibration signalrdquo Expert Systems with Applications vol42 no 17-18 pp 6433ndash6441 2015

[11] J Moubray Reliability-centered Maintenance Industrial PressNew York NY USA 1997

[12] A Mardani A Jusoh and E K Zavadskas ldquoFuzzy multi-ple criteria decision-making techniques and applicationsmdashtwodecades review from 1994 to 2014rdquo Expert Systems with Appli-cations vol 42 no 8 pp 4126ndash4148 2015

[13] M Bashiri H Badri and T H Hejazi ldquoSelecting optimummaintenance strategy by fuzzy interactive linear assignmentmethodrdquo Applied Mathematical Modelling Simulation andComputation for Engineering and Environmental Systems vol35 no 1 pp 152ndash164 2011

[14] MM Fouladgar A Lashgari Z Turskis A Yazdani-Chamziniand E K Zavadskas ldquoMaintenance strategy selection usingAHP and COPRAS under fuzzy environmentrdquo InternationalJournal of Strategic Property Management vol 16 no 1 pp 85ndash104 2012

[15] LWang J Chu and JWu ldquoSelection of optimummaintenancestrategies based on a fuzzy analytic hierarchy processrdquo Interna-tional Journal of Production Economics vol 107 no 1 pp 151ndash163 2007

[16] M Ilangkumaran and S Kumanan ldquoSelection of maintenancepolicy for textile industry using hybrid multi-criteria decisionmaking approachrdquo Journal of Manufacturing Technology Man-agement vol 20 no 7 pp 1009ndash1022 2009

[17] J-H Yu M-Y Jeon and T W Kim ldquoFuzzy-based compositeindicator development methodology for evaluating overallproject performancerdquo Journal of Civil Engineering and Manage-ment vol 21 no 3 pp 343ndash355 2015

[18] A Valipour N Yahaya N Md Noor A Mardani and JAntucheviciene ldquoA new hybrid fuzzy cybernetic analytic net-work process model to identify shared risks in PPP projectsrdquoInternational Journal of Strategic Property Management vol 20no 4 pp 409ndash426 2016

[19] M-Y Cheng D K Wibowo D Prayogo and A F V RoyldquoPredicting productivity loss caused by change orders using theevolutionary fuzzy support vector machine inference modelrdquoJournal of Civil Engineering and Management vol 21 no 7 pp881ndash892 2015

[20] H Abdi and L J Williams ldquoPrincipal component analysisrdquoWiley Interdisciplinary Reviews Computational Statistics vol 2no 4 pp 433ndash459 2010

[21] H Li D Parikh Q He et al ldquoImproving rail network velocityA machine learning approach to predictive maintenancerdquoTransportation Research Part C Emerging Technologies vol 45pp 17ndash26 2014

[22] E Alpaydin Introduction to Machine Learning MIT PressCambridge MA USA 2004

[23] H Naganathan W O Chong and X Chen ldquoBuilding energymodeling (BEM) using clustering algorithms and semi-super-vised machine learning approachesrdquo Automation in Construc-tion vol 72 pp 187ndash194 2016

[24] A Vellido J D Martın-Guerrero and P J G Lisboa ldquoMakingmachine learning models interpretablerdquo in Proceedings of the20th European Symposium on Artificial Neural Networks Com-putational Intelligence and Machine Learning ESANN 2012 pp163ndash172 Bruges Belgium April 2012

[25] N Rodrıguez-Padial M Marın and R Domingo ldquoStrategicFramework to Maintenance Decision Support Systemsrdquo in Pro-ceedings of the Manufacturing Engineering Society InternationalConference MESIC 2015 pp 903ndash910 July 2015

[26] G Waeyenbergh and L Pintelon ldquoA framework for mainte-nance concept developmentrdquo International Journal of Produc-tion Economics vol 77 no 3 pp 299ndash313 2002

[27] S W Palocsay I S Markham and S E Markham ldquoUtilizingand teaching data tools in Excel for exploratory analysisrdquoJournal of Business Research vol 63 no 2 pp 191ndash206 2010

[28] Mathworks IntroducingMachine Learning httpswwwmath-workscom

[29] L M Calvo and R Domingo ldquoInfluence of process operatingparameters on CO2 emissions in continuous industrial plantsrdquoJournal of Cleaner Production vol 96 article no 4308 pp 253ndash262 2015

[30] L M Calvo and R Domingo ldquoA first approach to the useof CO2 emissions as a maintenance indicator in industrialplantsrdquo in Proceedings of the Manufacturing Engineering SocietyInternational Conference MESIC 2013 pp 678ndash686 June 2013

Complexity 15

[31] L M Calvo and R Domingo ldquoCO2 emissions reductionand energy efficiency improvements in paper making dryingprocess control by sensorsrdquo Sustainability (Switzerland) vol 9no 4 article 514 2017

[32] R Domingo and S Aguado ldquoOverall environmental equipmenteffectiveness as a metric of a lean and green manufacturingsystemrdquo Sustainability (Switzerland) vol 7 no 7 pp 9031ndash90472015

[33] H Jiang Y Zou S Zhang J Tang and Y Wang ldquoShort-Term Speed Prediction Using Remote Microwave Sensor DataMachine Learning versus Statistical Modelrdquo MathematicalProblems in Engineering vol 2016 Article ID 9236156 p 132016

[34] D A Tobon-Mejia K Medjaher and N Zerhouni ldquoCNCmachine toolswear diagnostic andprognostic by using dynamicBayesian networksrdquo Mechanical Systems and Signal Processingvol 28 pp 167ndash182 2012

[35] A Rohani M H Abbaspour-Fard and S Abdolahpour ldquoPre-diction of tractor repair and maintenance costs using ArtificialNeural Networkrdquo Expert Systems with Applications vol 38 no7 pp 8999ndash9007 2011

[36] M Nilashi M Z Esfahani T Ramayah O Ibrahim M DEsfahani and M Z Roudbaraki ldquoA multicriteria collaborativefiltering recommender system using clustering and regressiontechniquesrdquo Journal of Soft Computing and Decision SupportSystem vol 3 pp 24ndash30 2016

[37] D E Rumelhart G E Hinton and R J Williams ldquoLearningrepresentations by back-propagating errorsrdquo Nature vol 323no 6088 pp 533ndash536 1986

[38] M Kayri ldquoPredictive abilities of Bayesian regularization andLevenberg-Marquardt algorithms in artificial neural networksa comparative empirical study on social datardquoMathematical ampComputational Applications vol 21 no 20 11 pages 2016

[39] I Bose andRKMahapatra ldquoBusiness datamining -Amachinelearning perspectiverdquo Information andManagement vol 39 no3 pp 211ndash225 2001

[40] G Koksal I Batmaz and M C Testik ldquoA review of datamining applications for quality improvement in manufacturingindustryrdquo Expert Systems with Applications vol 38 no 10 pp13448ndash13467 2011

[41] J Hernandez Orallo M J Ramırez Quintana C Ferri RamırezJ Hernandez Orallo M J Ramırez Quintana and C FerriRamırez Introduccion a la minerıa de datos Pearson PrenticeHall Madrid Spain 2004

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 201

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of