Research ArticleMethod for Determining the Weight of Functional Objectives onManufacturing System

Qingshan Zhang Wei Xu and Jiekun Zhang

School of Management Shenyang University of Technology Shenyang 110870 China

Correspondence should be addressed to Wei Xu 369252882qqcom

Received 9 April 2014 Revised 18 July 2014 Accepted 31 July 2014 Published 28 August 2014

Academic Editor Dehua Xu

Copyright copy 2014 Qingshan Zhang et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

We propose a three-dimensional integrated weight determination to solve manufacturing system functional objectives whereconsumers are weighted by triangular fuzzy numbers to determine the enterprises The weights subjective parts are determinedby the expert scoring method the objective parts are determined by the entropy method with the competitive advantageof determining Based on the integration of three methods and comprehensive weight we provide some suggestions for themanufacturing system This paper provides the numerical example analysis to illustrate the feasibility of this method

1 Introduction

Manufacturing is the basic activity of an enterprisersquos survivaland development [1] Manufacturing system relates to theprocess of converting the input into the output by usingmanufacturing resources and themanufacturing processThemanufacturing process is a series of organizedmanufacturingactivities around a product or service As a process of ldquoinput-conversion-outputrdquo it puts in a certain resource whichwill be value-added through a series and various forms oftransformation providing for the society in some form ofoutput The manufacturing resources refer to the conditionsof supporting manufacturing in the internal enterprise [2] Itis mainly made up of people goods content and technologyas a supporting system of the manufacturing process

Manufacturing system has obvious economic and socialbenefits and this would come true mainly through its func-tion objective The requirements of the enterprise environ-ment and user mainly reflect in high efficiency low costhigh quality short delivery time personalization and greenenvironmental protection There are mutual connectionsrestrictions and even contradictories under these objectives[3] The decision of function objectives is that the originaltraditional manufacturing system pays close attention tothe single coordination and balance between low cost and

high efficiency This already cannot satisfy the needs of theenterprises competition

Establishing a scientific and effective method for manu-facturing system function objective to determine weights isthe vital basis and prerequisite of making strategic decisionsproduct development strategy and manufacturing systemoptimization It is necessary methods of implementing adifferentiation competitive strategy to get customers improvethe market competitiveness and make it into the high-endlink of a value chain This paper tries to establish a kind ofthree dimensional weight decision-making methods whichare used to meet the function objective decision problems inthe design of the manufacturing system

2 Literature Review

Heijltjes and van Witteloostuijn [4] carried out multidi-mensional evaluation for production system structure andcomplexity of the process further deepened productionsystem layout ideas so that it improved efficiency of mak-ing system functional objectives decision Grigoroudis andSiskos [5] presented a multiobjective optimization decisionof product line and hybrid assembly systems about speciesdiversity of contemporary products and production systemscomplexity to counterpoise variety and complexity Levis and

Hindawi Publishing Corporatione Scientific World JournalVolume 2014 Article ID 242368 9 pageshttpdxdoiorg1011552014242368

2 The Scientific World Journal

Papageorgiou [6] developed a cost estimation model theredesign of the product and craft can support investmentdecision analysis with better cost and value index Hon [7]researched the factors of the relationship between servicequality of productive service and the relationship betweenvarious factors they constructed a service quality model oftangible reliability corresponding assurance and empathyfive latent variable based on SERVPREF scale and usedSEM to do analysis of degree of reliability and model fitWacker and Sheu [8] divided the manufacturing system intoplanning subsystem and execution subsystem they also havecollected data from 16 countriesrsquo 768 global manufacturingenterprises and they have done empirical analysis to themanufacturing system functional objectives drawn a con-clusion that running system had a significant role in thecompanyrsquos competitiveness and determined the weights ofseven objectives that may speed the transportation deliveryrate low cost quality production flexibility product flexibil-ity and new product manufacturing capabilities Youssef etal [9] discussed manufacturing system simulation methodsand modeling techniques simulation system structure andthree aspects of simulation algorithm they researched onthe relevant agent model structure of each simulation levelby agent modeling methods and techniques providing thebasis for the group decision-making Dangayach and Desh-mukh [10] proposed a reconfigurable manufacturing systemscalability planning approach which can gradually expandsystem capacity by reconfiguring existing systems and usinggenetic algorithm optimization algorithm to determine themost economical way to reconfigure the existing manufac-turing system objective weight Menguc et al [11] analyzedquantitative description of decision level fusion and dynamicfusion feature by collaborative manufacturing process ofintegration services and service needs and discussed col-laborative service-oriented decision fusion with the positivefeedback from the fusion of tissue morphology innovativefeatures and dynamic decision fusion mechanism Acquaahand Yasai-Ardekani [12] studied ldquoTMS TDS TPS and Scien-tific Total Quality Managementrdquo on the basis of promotingthe TPS and made it the key to decisions Kalogeraki etal [13] used DEA efficiency evaluation method to evaluatethe operational efficiency of the parallel structure produc-tion system digging greater potential of the overall systemperformance improvement than traditional CCR modelBabakus et al [14] introduced weighting methods of the self-reconfigurable KMS based on relations with fuzzy demandsand estimated FunctionObjective Analysis Vichare et al [15]made European companies their research object to proposefour kinds of objective decision-making direction of makingaftermarket service to provide (ASPs) customer supportprovided (CSPs) outsourcing partners (OPs) developmentpartners (DPs) and gave them conditions of use and weightUm et al [16] researched flexible manufacturing systemsmade a quantitative judgment on the ldquoflexiblerdquo in FNS gavethe ldquoflexiblerdquo stochastic dynamic programming quantitativeevaluation model and provided a reference weight to thechoice of flexible manufacturing system determining theexpected investment and technical means Deng et al [17]used CQN model to propose a hybrid genetic algorithm for

optimal allocation of FMS and explained route planning pro-cess based on production and cost optimization Ortega [18]designed a good production system to protect environmentand internal fit (product and market) and raised six areas ofproduction decisions Salunke et al [19] researchedmanufac-turing from the perspective of business performance and gavedecision and optimization of SOP manufacturing enterpriseconfiguration capabilities under an uncertain environmentTo improve the performance of production systems and tohelp managers improve work efficiency and system evalua-tion Agus and Hassan [20] developed tree manufacturingsystem functional objectives improvement strategies Wanget al [21] researched production systems from the output rateand buffering capacity links and used systemdynamicsmodelraising maintenance strategies for unexpected failure andprocessing requirements and the variability of the productioncycle Claver-Cortes et al [22] raised an integrated architec-ture based on agents and services guide and did researchon manufacturing system objectives implement from theperspective of information systems and technology Wangand Koren [23] considered that the costs service levels leadtime and innovation are the key value elements in manufac-turing system functionality objectives centered on the overallperformance of manufacturing systems Claver-Cortes et al[22] have conducted a performance value analysis on GMSgave a weight to the green attribute and raised the factthat GMS is more competitive than non-GMS Esbjerg et al[24] considered the problem of manufacturing companiesto meet individual customer needs in mass customizationproduction systems put the arrangements of productiondown to production assignment of products quantified userneeds and business output and gave a multiattribute productmodel Bolloju et al [25] constructedmultiobjective dynamicunit objective decision based on scatter search against thecharacteristics of market product that short life cycle cus-tomer demand for personalized shorten lead times andso forth Nouri and Hong [26] defined systemrsquos capacity ofinput elements and output service as manufacturing capacityfrom the perspective of management and did analysis ofeach subsystem by System Dynamics Model carefully theychanged the pattern thatmost scholars build an index systemdecomposed function objective decision which has dividedmanufacturing capabilities system in time quality and profitthree subsystems Yang and Chen [27] proposed a fuzzysoft set-based approach to prioritizing technical attributesin quality function deployment In the aspect of identifiedobjective weight there are many research methods whichare mainly focused on (1) carrying on weight distributionto function objective decision based on the expertise (2)carrying on subjective and objective weighting method todetermine by customer demand preferences evolve rule(3) using their own expectations that is business leaderssubjective desires to determine the weight The level of theobjective weights is not to determined based on the casemanufacturing system itself which is in or combined withexternal opportunities and own resources conditions

In this paper the influence of customer demand formanufacturing system function objective is combined withthe strategic requirement of the enterprise manufacturing

The Scientific World Journal 3

Customerreuirement

Competitiveadvantage

ServiceDiversifica

tion

Cost

Time

Highquality

Efficiency

Environment

Ontime

Price

Quality

Green

Quantity

+

++

++

+

+

+

+

+ +

+

+

++

+

+

+

+

+

+

+

+

+

+

+

minus

minus

minus

minus

minus

minus

minus

minus

minus

minus

minus

minusminus

minus

minus

minus

minus

minus

minusminus

Figure 1 The function mechanism between the customer requirements and the system functional objectives

system which solves the competitive advantage of manufac-turing system and the intensity of competition for weightfunction objective from the perspective of competitive strat-egy Based on subjective fuzzy mathematics and objectiveentropy weight method approach to decision-making wasformed which is suitable for a dynamic environment Thereis a link between customer demands and the interactionwith the system functional objectives Some demands andimproving the system functional objectives showed a positivecorrelation and some of the demands are no correlationbetween the objective system and even some of them forthe development of the system have a hindering effectfor designed the system functional objectives Using thedynamic principle of the system we can clearly research thecustomer demand for intuitive system objectives and effectsof competitive advantage as shown in Figure 1

3 Basic Principle

Through put the value function into prioritization to expressthe competitive advantage of enterprise through the variousfunctions gives different weights to realize it is concludedthat the enterprise itself under integrated balance functionobjective focusing on the direction and intensity it is crucialfor its survival and development In response to this situationthe enterprise uses quality function deployment (qualityfunction deployment QFD) to design and manufacturemeet or exceed customer expectations of products andmanufacturing system function goal of the three factors as

input part through the principle of QFD system decision-making weight of each objective by function objective prior-ity sequence The main information contained in this houseof quality is customer demand competitive demand moduleand enterprise demand modules as shown in Figure 2

Weights are determined by two aspects on one handas the elementary weight while on the other hand as thedirectional weight Consider

119882119879= 119882119860+ 119882119878 (1)

where 119882119879 is the weight of manufacturing system functional

orientation119882119860 is the basic weight determined by customerneeds and expectations of enterprises decision and 119882

119878 isthe directional weight determined by the enterprise strategicorientation

Customers need determining the weight

119882119863

= [1198861 1198862 119886

119898] (2)

Enterprises need determining the weigh

119882119862= [1198871 1198872 119887

119898]119879 (3)

The enterprise competitive advantage determines theweight

119882119878= [1198881 1198882 119888

119898] (4)

Calculated results of basic principle are

119882119860=

119882119863times 119882119862

1003817100381710038171003817119882119863 times 119882119862

1003817100381710038171003817

=[1198861times 1198871 1198862times 1198872 119886

119898times 119887119898]

radicsum119898

119894=1119886119894times 119887119894

(5)

4 The Scientific World Journal

Enterprise requirements

Com

petit

ion

satis

fact

ion

Com

petit

ion

stat

usco

mpa

red

with

ote

rs

Com

petit

ive a

dvan

tage

orie

ntat

ion

Com

petit

ive e

xpec

tatio

n

Functional objectivesabsolute weight

Functional objectivespriority sequence

Functional objectivesweight relative value

Custom requirements

Necessary requirments Winning requirements

120572i

1205721

1205722

1205723

1205724

1205725

1205726

120596j

120596j

Dem

and

satis

fact

ion

Kpi K

qi

Dem

and

impo

rtan

ce

1205961 1205962 1205963 1205964 1205965 1205966

1205961 1205962 1205963 1205964 1205965 1205966

1205731 1205732 1205733 1205734 1205735 1205736

Enterprise requirments 120573j

Correlation matrix G

Relationship matrix R

G = ( g11 middot middot middot g1n ⋱

gm1 middot middot middot gmn

)

R = ( middot middot middot ⋱

middot middot middot

)r11 r1n

rm1 rmn

Kmi

120601i

Figure 2 Quality house of module construction

Therefore

119882119879=

1198861times 1198871

radicsum119898

119894=1119886119894times 119887119894

+ 1198881

1198862times 1198872

radicsum119898

119894=1119886119894times 119887119894

+ 1198882

119886119898times 119887119898

radicsum119898

119894=1119886119894times 119887119894

+ 119888119898

(6)

As a consequence

119882119879

119894=

119886119894times 119887119894radicsum119898

119894=1119886119894times 119887119894+ 119888119894

radicsum119898

119894=1(119886119894times 119887119894radicsum119898

119894=1119886119894times 119887119894) + 119888119894

(7)

4 Model Building

41 Decomposition of Basic Weight

411 Customer Weight

(1) Triangular Fuzzy Numbers Membership function graph isas shown in Figure 1 fuzzy numbers 120572 = (119897 119898 119906) are calledtriangular fuzzy number where 119897 119898 119906 is a real number 0 le

119897 le 119898 le 119906119898 is called the main value of triangular fuzzynumber 120572 119897 and 119906 are called the upper and lower bounds of120572 (119898 minus 119897) and (119906 minus119898) are called lower and upper limits of 120572When 119897 = 119898 = 119906 120572 turns into the real number in the ordinarysense When the value of (119906 minus 119897) is larger the triangularfuzzy number 120572 = (119897 119898 119906) is more blurred 119871 in this paperrepresents the clientsrsquominimumexpectations to the objective119898 represents clientsrsquo most expectations and 119906 representsthe customersrsquo highest expectations to the objective as inFigure 3

(2) Fuzzy Mean Value and Variance Fuzzy number has twokinds of distribution uniform distribution and proportionaldistribution We can define the respective fuzzy mean 119898(120572)

and variance 120590(120572) as follows

A Uniform distribution is

119898119881 (119886) =

int119878(119886)

119909120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

1205902

119881(119886) =

int119878(119886)

1199092120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

minus 1198982

119881(119886)

(8)

The Scientific World Journal 5

Y

1

0L m u

X

Figure 3 Triangular fuzzy numbers

B Proportional distribution is

119898119875 (119886) =

int119878(119886)

119909120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

1205902

119875(119886) =

int119878(119886)

1199092120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

minus 1198982

119875(119886)

(9)

(3) Value Range In the value evaluation of each programtaking the ambiguity of the indicators and subjectivity intoaccount we use semantic judgment that is divided intoseven specific grading criteria namely ldquovery lowrdquo (VL)ldquolowrdquo (L) the ldquolowerrdquo (ML) ldquogeneralrdquo (M) ldquohighrdquo (MH)ldquohighrdquo (H) and the ldquovery highrdquo (VH) these seven variablesemantic described as triangular fuzzy numbers as illustratedin Table 1

(4) Clearance of Triangular Fuzzy Number We can comparethe size of triangular fuzzy numbers by comprehensivelyutilizing the triangular fuzzy numbers mean variance andfuzzy limit coefficient composed with fuzzy informationcontent

119864 (119886) = 2 times (1205832

119886(1199090) + 1205832

1198861015840 (1199090)) minus 1 (10)

Fuzzy information content is defined as for the arbitraryfuzzy number 120572 on interval [a b]

119864 (119886) =1

119887 minus 119886int

119887

119886

119864 (119886 119909) 119889119909 (11)

known as information of 120572 at the point also called informa-tion of fuzzy number 120572 and for any fuzzy Number 120572 on [a b]has 0 le 119864(120572) le 1 When 120572 is a triangular fuzzy number thetriangular fuzzy number can be calculated from the amountof information according to the definition of triangular fuzzynumber and the amount of information

119864 (119886) = 1 minus2 times (119906 minus 1)

3 times (119887 minus 119886)

120588 (119886) = 119864 (119886)119898 (119886) + (119864 (119886) minus 1) 120590 (119886)

(12)

Table 1 Linguistic terms and related fuzzy numbers for weight ofpreference

Linguistic terms Fuzzy numbers(VL) (0 0 01)(L) (0 01 03)(ML) (01 03 05)(M) (03 05 07)(MH) (05 07 09)(H) (07 09 1)(VH) (09 1 1)

Table 2 Customer needs relative weight scale

Scale (119903119894119895)

1 119894 and 119895 are equally important2 119894 is slightly more important than 1198953 119894 is important than 1198954 119894 is more important than 1198955 119894 is absolutely more important than 119895119903119895119894 = 1119903119894119895

For the triangular fuzzy number 120572 on the interval [a b]which is called the 120572rsquo limit coefficient of triangular fuzzynumber where119898(120572) 120590(120572) 120572 respectively the mean and vari-ance triangular fuzzy numbers In thismethod the limit coef-ficient considers the fuzzy mean fuzzy variance and amountof information influence on fuzzy triangular fuzzy numberssize Among them the combination of fuzzy mean value andvariance are coefficient directly by the triangle fuzzy numberfor a given amount of information the combination of fuzzymean value and variance coefficient directly by the trianglefuzzy number for a given amount of information it has highstability and its calculation process is simple and easy to beprogrammed it also has the characteristics of operability

412 Enterprise Weight The basic principle of analytic hier-archy process (AHP) is to give a qualitative description of a setof objectives for paired comparison by the way of analyzingthe relative importance degree of each pair whereby thequantitative results of each objectiveweight Specificmethodsare as follows

(1) Compare the importance of various customersrsquo needsThis is a process from qualitative to quantitativethrough a criterion to judge comparing the impor-tance of two factors and according to the proportionof 1 to 5 scales to the importance degree of theassignment the ratio scale as illustrated in Table 2

(2) Forming judgment matrix the customer require-ments fill in the first column of the column and thesecond column respectively which lists the impor-tance degree of each other as illustrated in Table 3

(3) Using root method to calculate the important weightof customersrsquo needs 119870

119901

119894 first calculate the absolute

important weight 119870119901

119894 where 119894 = 1 2 119899 For

6 The Scientific World Journal

Table 3 Relative importance for customer needs

Efficiency Cost Quality Time Individuation EnvironmentEfficiencyCostQualityTimeIndividuationEnvironment

Table 4 RI Value table

Verify items 1 2 3 4 5 6 7 8 9RI 0 0 058 090 112 124 132 141 145

119870119901

119894standardization 119870

119901

119894= 119870119901

119894sum119894119870119901

119894 that is the

important weights of customer needs(4) Verify the validity weight If CR lt 01 it shows

that the consistency of a judgment matrix can beaccepted if CR gt 01 it shows that data does notneed to modify the data consistency until satisfied asillustrated in Table 4

42 Strategic Weight (1) The indicators with the quantitativecalculation of the first indicators 119895 relation 119894 option value ofthe proportion of indicators 119901

119894119895 Consider

119901119894119895=

119909119894119895

sum119898

119894=1119909119894119895

(13)

(2) Calculation of the first indicators 119895 entropy 119890119895is as

follows

119890119895= minus119896

119898

sum

119894=1

119901119894119895ln119901119894119895 (14)

Among them 119896 gt 0 ln is natural logarithm 119890119895ge 0 If 119909

119894119895is

all equal to a given 119895 then

119901119894119895=

119909119894119895

sum119898

119894=1119909119894119895

=1

119898 (15)

119864119895gets the maximum at this time that is

119890119895= minus119896

119898

sum

119894=1

1

119898ln 1

119898= 119896 ln119898 (16)

If we suppose 119896 = 1 ln119898 there is 0 le 119890119895le 1

(3) Calculation of the first 119895 indicators of the differencecoefficient 119892

119895is as follows For a given 119895 the smaller the

difference of 119909119894119895is the greater the 119890

119895is when 119909

119894119895are all equal

119890119895= 119890max = 1 at this time there is no effect on indicators 119883

119895

for the program compared the greater the difference betweenthe index values is the smaller the 119890

119895is the bigger role of the

indicators for the program are compared

(4) Improving data is as follows It can be based on expertopinion efficacy coefficient method used for transformation

of data and consistency check taking 119883(ℎ)

119895= max119883

119895 119883(120582)

119895=

min119883119895 the transform using the following formula

119883lowast

119894119895=

119909119894119895minus 119909(120582)

119895

119909(ℎ)

119895minus 119909(120582)

119895

times 119860 + 119861 (17)

If you consider that the indicator weight should be greaterthe date differences in large-scale can be chosen larger andif the data differences are in a small area it can be chosensmaller Also the combination of expert scoring method andthe evaluator can add a certain degree of subjective factorsthus increasing the evaluation-oriented that is in formula

119883lowast

119894119895=

119909119894119895minus 119909120582

119895

119909(ℎ)

119895minus 119909(120582)

119895

times 120572 + (120572 minus 1) (18)

If you want to increase the weights 120572 can be increased whenthe data difference is large and it is large in a similar mannerif we want to reduce the weight of the indicators 120572 can bedecreased when the data difference is small and the weightcalculated with using the entropy weight method is small

(5) The definition of weights is as follows Consider

119886119895=

119892119895

sum119899

119895=1119892119895

(19)

(6) Calculating the value of comprehensive evaluation of119881119894is as follows

119881119894=

119899

sum

119895=1

119886119895119901119894119895 (20)

119881119894option for the first 119894 values of comprehensive evaluation

5 The Numerical Analysis

(1) The Initial Data of Customer Weight See Table 5

(2) Enterprise Weight See Table 6

(3) Strategic Weight See Table 7

(4) Weight of Functional Objectives on Manufacturing SystemIt is proposed that

119882119860= 08 119882

119878= 02 (21)

The Scientific World Journal 7

Table 5

Efficiency Cost Quality Time Individuation Environment(03 05 07) (01 03 05) (05 07 09) (03 05 07) (07 09 1) (09 1 1)Result (013 008 018 013 023 and 025)

Table 6

Efficiency Cost Quality Time Individuation EnvironmentEfficiency 1 12 2 13 13 12Cost 2 1 3 4 1 2Quality 12 13 1 12 14 13Time 3 14 2 1 13 12Individuation 3 1 4 3 1 2Environment 2 12 3 2 12 1Result (009 026 006 014 028 and 018)

Table 7

Efficiency Cost Quality Time Individuation Environment084 079 093 072 096 06408 072 064 083 09 076082 068 086 089 078 078076 08 072 093 09 085Result (013 009 028 018 011 and 021)

Table 8 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment008 012 011 012 032 025

Table 9 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment011 020 012 027 012 018

Table 10 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment017 027 018 015 010 013

The comprehensive result is illustrated in Table 8It is proposed that

119882119860= 05 119882

119878= 05 (22)

The comprehensive result is illustrated in Table 9 Consider

119882119860= 02 119882

119878= 08 (23)

The comprehensive result is illustrated in Table 10By Table 8 it can be concluded that the six key elements

for function objective decision on the manufacturing systemin the sequence are personalization being environmentfriendly prompt delivery low cost good quality and highproduction efficiency From the reality the decision resultsare up to the present market for personalized consumption

and premise of legal requirements associated with environ-mental protection That could be considered to be person-alized and environmental competition orientation in theoverall design the manufacturing system function objectiveoccupies 57 in total and the other four for the intensity ofcompetition occupy the proportion of 43

From Tables 9 and 10 we can find that this decisionmethod is based on the stability and consistency of weightjudgments if there are remarkable differences on the weightwhich need to use additional methods to integrate differ-ences

6 Conclusion

In this paper we established the three dimensional weightdetermination methods comprehensively considering the

8 The Scientific World Journal

customer needs enterprise decision-makers intent andenterprise competitive position It provides scientific andpractical guidance for enterprises in the objective selectionof manufacturing system function From the final calculationof each objective weight we could see that this method hadinteraction of human-machine and universal applicability ofenvironment and it was also easy to operate and computeprogrammingThe deficiency of this paper is that themethodstill needs a large number of actual inspections in order toprove it in line with the objective decision of the practicalneed of the manufacturing system function

In the future main job will be to determine the relation-ship between 119882

119860 and 119882119878 using the scientific method such

as statistics data envelopment analysis (DEA) method andregression method to determine the proportions of them

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by the National Natural ScienceFoundation of China under Grants nos 70172042 70472034and the Soft Science Project no F13-317-5-29 of ShenyangScience and Technology Bureau

References

[1] C-F Tsai and M-Y Chen ldquoVariable selection by associationrules for customer churn prediction ofmultimedia on demandrdquoExpert Systems with Applications vol 37 no 3 pp 2006ndash20152010

[2] A Haensel G Koole and J Erdman ldquoEstimating uncon-strained customer choice set demand a case study on airlinereservation datardquo Journal of Choice Modelling vol 4 no 3 pp75ndash87 2011

[3] H G H TiemessenM Fleischmann G J VanHoutum J A EE VanNunen and E Pratsini ldquoDynamic demand fulfillment inspare parts networks with multiple customer classesrdquo EuropeanJournal of Operational Research vol 228 no 2 pp 367ndash3802013

[4] M Heijltjes and A van Witteloostuijn ldquoConfigurations ofmarket environments competitive strategies manufacturingtechnologies and human resource management policies a two-industry and two-country analysis of fitrdquo Scandinavian Journalof Management vol 19 no 1 pp 31ndash62 2003

[5] E Grigoroudis and Y Siskos ldquoA survey of customer sat-isfaction barometers some results from the transportation-communications sectorrdquo European Journal of OperationalResearch vol 152 no 2 pp 334ndash353 2004

[6] A A Levis and L G Papageorgiou ldquoCustomer demandforecasting via support vector regression analysisrdquo ChemicalEngineering Research and Design vol 83 no 8 A pp 1009ndash10182005

[7] K K B Hon ldquoPerformance and evaluation of manufacturingsystemsrdquo CIRP Annals Manufacturing Technology vol 54 no2 pp 139ndash154 2005

[8] J G Wacker and C Sheu ldquoEffectiveness of manufacturingplanning and controlsystems on manufacturing competitive-ness evidence from global manufacturing datardquo InternationalJournal of Production Research vol 44 no 5 pp 1015ndash10362006

[9] A M A Youssef A Mohib and H A Elmaraghy ldquoAvail-ability assessment of multi-state manufacturing systems usinguniversal generating functionrdquo CIRP Annals ManufacturingTechnology vol 55 no 1 pp 445ndash448 2006

[10] G S Dangayach and S G Deshmukh ldquoAn exploratory study ofmanufacturing strategy practices of machinery manufacturingcompanies in Indiardquo Omega vol 34 no 3 pp 254ndash273 2006

[11] B Menguc S Auh and E Shih ldquoTransformational leadershipand market orientation implications for the implementation ofcompetitive strategies and business unit performancerdquo Journalof Business Research vol 60 no 4 pp 314ndash321 2007

[12] M Acquaah andM Yasai-Ardekani ldquoDoes the implementationof a combination competitive strategy yield incremental perfor-mance benefits A new perspective from a transition economyin Sub-Saharan Africardquo Journal of Business Research vol 61 no4 pp 346ndash354 2008

[13] V Kalogeraki P MMelliar-Smith L E Moser and Y DrougasldquoResource management using multiple feedback loops in softreal-time distributed object systemsrdquo Journal of Systems andSoftware vol 81 no 7 pp 1144ndash1162 2008

[14] E Babakus U Yavas and N J Ashill ldquoThe role of cus-tomer orientation as a moderator of the job demand-burnout-performance relationship a surface-level trait perspectiverdquoJournal of Retailing vol 85 no 4 pp 480ndash492 2009

[15] P Vichare A Nassehi S Kumar and S T Newman ldquoA unifiedmanufacturing resource model for representing CNC machin-ing systemsrdquo Robotics and Computer-Integrated Manufacturingvol 25 no 6 pp 999ndash1007 2009

[16] I Um H Cheon and H Lee ldquoThe simulation design andanalysis of a flexible manufacturing system with automatedguided vehicle systemrdquo Journal of Manufacturing Systems vol28 no 4 pp 115ndash122 2009

[17] Z Deng Y Lu K K Wei and J Zhang ldquoUnderstandingcustomer satisfaction and loyalty an empirical study of mobileinstant messages in Chinardquo International Journal of InformationManagement vol 30 no 4 pp 289ndash300 2010

[18] M J R Ortega ldquoCompetitive strategies and firm performancetechnological capabilitiesrsquo moderating rolesrdquo Journal of BusinessResearch vol 63 no 12 pp 1273ndash1281 2010

[19] S Salunke J Weerawardena and J R McColl-KennedyldquoTowards a model of dynamic capabilities in innovation-basedcompetitive strategy insights from project-oriented servicefirmsrdquo Industrial Marketing Management vol 40 no 8 pp1251ndash1263 2011

[20] A Agus and Z Hassan ldquoEnhancing production performanceand customer performance through Total QualityManagement(TQM) strategies for competitive advantagerdquo in Proceedings ofthe 7th International Strategic Management Conference vol 24pp 1650ndash1662 July 2011

[21] HWang X ZhuHWang S J Hu Z Lin andGChen ldquoMulti-objective optimization of product variety and manufacturingcomplexity in mixed-model assembly systemsrdquo Journal of Man-ufacturing Systems vol 30 no 1 pp 16ndash27 2011

[22] E Claver-Cortes EM Pertusa-Ortega and J FMolina-AzorınldquoCharacteristics of organizational structure relating to hybridcompetitive strategy implications for performancerdquo Journal ofBusiness Research vol 65 no 7 pp 993ndash1002 2012

The Scientific World Journal 9

[23] W Wang and Y Koren ldquoScalability planning for reconfigurablemanufacturing systemsrdquo Journal of Manufacturing Systems vol31 no 2 pp 83ndash91 2012

[24] L Esbjerg B B Jensen T Bech-Larsen M D de BarcellosY Boztug and K G Grunert ldquoAn integrative conceptualframework for analyzing customer satisfaction with shoppingtrip experiences in grocery retailingrdquo Journal of Retailing andConsumer Services vol 19 no 4 pp 445ndash456 2012

[25] N Bolloju C Schneider and V Sugumaran ldquoA knowledge-based system for improving the consistency between objectmodels and use case narrativesrdquo Expert Systems with Applica-tions vol 39 no 10 pp 9398ndash9410 2012

[26] H Nouri and T S Hong ldquoDevelopment of bacteria foragingoptimization algorithm for cell formation in cellular manu-facturing system considering cell load variationsrdquo Journal ofManufacturing Systems vol 32 no 1 pp 20ndash31 2013

[27] Z Yang and Y Chen ldquoFuzzy soft set-based approach to pri-oritizing technical attributes in quality function deploymentrdquoNeural Computing and Applications vol 23 no 7-8 pp 2493ndash2500 2013

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International Journal of

2 The Scientific World Journal

Papageorgiou [6] developed a cost estimation model theredesign of the product and craft can support investmentdecision analysis with better cost and value index Hon [7]researched the factors of the relationship between servicequality of productive service and the relationship betweenvarious factors they constructed a service quality model oftangible reliability corresponding assurance and empathyfive latent variable based on SERVPREF scale and usedSEM to do analysis of degree of reliability and model fitWacker and Sheu [8] divided the manufacturing system intoplanning subsystem and execution subsystem they also havecollected data from 16 countriesrsquo 768 global manufacturingenterprises and they have done empirical analysis to themanufacturing system functional objectives drawn a con-clusion that running system had a significant role in thecompanyrsquos competitiveness and determined the weights ofseven objectives that may speed the transportation deliveryrate low cost quality production flexibility product flexibil-ity and new product manufacturing capabilities Youssef etal [9] discussed manufacturing system simulation methodsand modeling techniques simulation system structure andthree aspects of simulation algorithm they researched onthe relevant agent model structure of each simulation levelby agent modeling methods and techniques providing thebasis for the group decision-making Dangayach and Desh-mukh [10] proposed a reconfigurable manufacturing systemscalability planning approach which can gradually expandsystem capacity by reconfiguring existing systems and usinggenetic algorithm optimization algorithm to determine themost economical way to reconfigure the existing manufac-turing system objective weight Menguc et al [11] analyzedquantitative description of decision level fusion and dynamicfusion feature by collaborative manufacturing process ofintegration services and service needs and discussed col-laborative service-oriented decision fusion with the positivefeedback from the fusion of tissue morphology innovativefeatures and dynamic decision fusion mechanism Acquaahand Yasai-Ardekani [12] studied ldquoTMS TDS TPS and Scien-tific Total Quality Managementrdquo on the basis of promotingthe TPS and made it the key to decisions Kalogeraki etal [13] used DEA efficiency evaluation method to evaluatethe operational efficiency of the parallel structure produc-tion system digging greater potential of the overall systemperformance improvement than traditional CCR modelBabakus et al [14] introduced weighting methods of the self-reconfigurable KMS based on relations with fuzzy demandsand estimated FunctionObjective Analysis Vichare et al [15]made European companies their research object to proposefour kinds of objective decision-making direction of makingaftermarket service to provide (ASPs) customer supportprovided (CSPs) outsourcing partners (OPs) developmentpartners (DPs) and gave them conditions of use and weightUm et al [16] researched flexible manufacturing systemsmade a quantitative judgment on the ldquoflexiblerdquo in FNS gavethe ldquoflexiblerdquo stochastic dynamic programming quantitativeevaluation model and provided a reference weight to thechoice of flexible manufacturing system determining theexpected investment and technical means Deng et al [17]used CQN model to propose a hybrid genetic algorithm for

optimal allocation of FMS and explained route planning pro-cess based on production and cost optimization Ortega [18]designed a good production system to protect environmentand internal fit (product and market) and raised six areas ofproduction decisions Salunke et al [19] researchedmanufac-turing from the perspective of business performance and gavedecision and optimization of SOP manufacturing enterpriseconfiguration capabilities under an uncertain environmentTo improve the performance of production systems and tohelp managers improve work efficiency and system evalua-tion Agus and Hassan [20] developed tree manufacturingsystem functional objectives improvement strategies Wanget al [21] researched production systems from the output rateand buffering capacity links and used systemdynamicsmodelraising maintenance strategies for unexpected failure andprocessing requirements and the variability of the productioncycle Claver-Cortes et al [22] raised an integrated architec-ture based on agents and services guide and did researchon manufacturing system objectives implement from theperspective of information systems and technology Wangand Koren [23] considered that the costs service levels leadtime and innovation are the key value elements in manufac-turing system functionality objectives centered on the overallperformance of manufacturing systems Claver-Cortes et al[22] have conducted a performance value analysis on GMSgave a weight to the green attribute and raised the factthat GMS is more competitive than non-GMS Esbjerg et al[24] considered the problem of manufacturing companiesto meet individual customer needs in mass customizationproduction systems put the arrangements of productiondown to production assignment of products quantified userneeds and business output and gave a multiattribute productmodel Bolloju et al [25] constructedmultiobjective dynamicunit objective decision based on scatter search against thecharacteristics of market product that short life cycle cus-tomer demand for personalized shorten lead times andso forth Nouri and Hong [26] defined systemrsquos capacity ofinput elements and output service as manufacturing capacityfrom the perspective of management and did analysis ofeach subsystem by System Dynamics Model carefully theychanged the pattern thatmost scholars build an index systemdecomposed function objective decision which has dividedmanufacturing capabilities system in time quality and profitthree subsystems Yang and Chen [27] proposed a fuzzysoft set-based approach to prioritizing technical attributesin quality function deployment In the aspect of identifiedobjective weight there are many research methods whichare mainly focused on (1) carrying on weight distributionto function objective decision based on the expertise (2)carrying on subjective and objective weighting method todetermine by customer demand preferences evolve rule(3) using their own expectations that is business leaderssubjective desires to determine the weight The level of theobjective weights is not to determined based on the casemanufacturing system itself which is in or combined withexternal opportunities and own resources conditions

In this paper the influence of customer demand formanufacturing system function objective is combined withthe strategic requirement of the enterprise manufacturing

The Scientific World Journal 3

Customerreuirement

Competitiveadvantage

ServiceDiversifica

tion

Cost

Time

Highquality

Efficiency

Environment

Ontime

Price

Quality

Green

Quantity

+

++

++

+

+

+

+

+ +

+

+

++

+

+

+

+

+

+

+

+

+

+

+

minus

minus

minus

minus

minus

minus

minus

minus

minus

minus

minus

minusminus

minus

minus

minus

minus

minus

minusminus

Figure 1 The function mechanism between the customer requirements and the system functional objectives

system which solves the competitive advantage of manufac-turing system and the intensity of competition for weightfunction objective from the perspective of competitive strat-egy Based on subjective fuzzy mathematics and objectiveentropy weight method approach to decision-making wasformed which is suitable for a dynamic environment Thereis a link between customer demands and the interactionwith the system functional objectives Some demands andimproving the system functional objectives showed a positivecorrelation and some of the demands are no correlationbetween the objective system and even some of them forthe development of the system have a hindering effectfor designed the system functional objectives Using thedynamic principle of the system we can clearly research thecustomer demand for intuitive system objectives and effectsof competitive advantage as shown in Figure 1

3 Basic Principle

Through put the value function into prioritization to expressthe competitive advantage of enterprise through the variousfunctions gives different weights to realize it is concludedthat the enterprise itself under integrated balance functionobjective focusing on the direction and intensity it is crucialfor its survival and development In response to this situationthe enterprise uses quality function deployment (qualityfunction deployment QFD) to design and manufacturemeet or exceed customer expectations of products andmanufacturing system function goal of the three factors as

input part through the principle of QFD system decision-making weight of each objective by function objective prior-ity sequence The main information contained in this houseof quality is customer demand competitive demand moduleand enterprise demand modules as shown in Figure 2

Weights are determined by two aspects on one handas the elementary weight while on the other hand as thedirectional weight Consider

119882119879= 119882119860+ 119882119878 (1)

where 119882119879 is the weight of manufacturing system functional

orientation119882119860 is the basic weight determined by customerneeds and expectations of enterprises decision and 119882

119878 isthe directional weight determined by the enterprise strategicorientation

Customers need determining the weight

119882119863

= [1198861 1198862 119886

119898] (2)

Enterprises need determining the weigh

119882119862= [1198871 1198872 119887

119898]119879 (3)

The enterprise competitive advantage determines theweight

119882119878= [1198881 1198882 119888

119898] (4)

Calculated results of basic principle are

119882119860=

119882119863times 119882119862

1003817100381710038171003817119882119863 times 119882119862

1003817100381710038171003817

=[1198861times 1198871 1198862times 1198872 119886

119898times 119887119898]

radicsum119898

119894=1119886119894times 119887119894

(5)

4 The Scientific World Journal

Enterprise requirements

Com

petit

ion

satis

fact

ion

Com

petit

ion

stat

usco

mpa

red

with

ote

rs

Com

petit

ive a

dvan

tage

orie

ntat

ion

Com

petit

ive e

xpec

tatio

n

Functional objectivesabsolute weight

Functional objectivespriority sequence

Functional objectivesweight relative value

Custom requirements

Necessary requirments Winning requirements

120572i

1205721

1205722

1205723

1205724

1205725

1205726

120596j

120596j

Dem

and

satis

fact

ion

Kpi K

qi

Dem

and

impo

rtan

ce

1205961 1205962 1205963 1205964 1205965 1205966

1205961 1205962 1205963 1205964 1205965 1205966

1205731 1205732 1205733 1205734 1205735 1205736

Enterprise requirments 120573j

Correlation matrix G

Relationship matrix R

G = ( g11 middot middot middot g1n ⋱

gm1 middot middot middot gmn

)

R = ( middot middot middot ⋱

middot middot middot

)r11 r1n

rm1 rmn

Kmi

120601i

Figure 2 Quality house of module construction

Therefore

119882119879=

1198861times 1198871

radicsum119898

119894=1119886119894times 119887119894

+ 1198881

1198862times 1198872

radicsum119898

119894=1119886119894times 119887119894

+ 1198882

119886119898times 119887119898

radicsum119898

119894=1119886119894times 119887119894

+ 119888119898

(6)

As a consequence

119882119879

119894=

119886119894times 119887119894radicsum119898

119894=1119886119894times 119887119894+ 119888119894

radicsum119898

119894=1(119886119894times 119887119894radicsum119898

119894=1119886119894times 119887119894) + 119888119894

(7)

4 Model Building

41 Decomposition of Basic Weight

411 Customer Weight

(1) Triangular Fuzzy Numbers Membership function graph isas shown in Figure 1 fuzzy numbers 120572 = (119897 119898 119906) are calledtriangular fuzzy number where 119897 119898 119906 is a real number 0 le

119897 le 119898 le 119906119898 is called the main value of triangular fuzzynumber 120572 119897 and 119906 are called the upper and lower bounds of120572 (119898 minus 119897) and (119906 minus119898) are called lower and upper limits of 120572When 119897 = 119898 = 119906 120572 turns into the real number in the ordinarysense When the value of (119906 minus 119897) is larger the triangularfuzzy number 120572 = (119897 119898 119906) is more blurred 119871 in this paperrepresents the clientsrsquominimumexpectations to the objective119898 represents clientsrsquo most expectations and 119906 representsthe customersrsquo highest expectations to the objective as inFigure 3

(2) Fuzzy Mean Value and Variance Fuzzy number has twokinds of distribution uniform distribution and proportionaldistribution We can define the respective fuzzy mean 119898(120572)

and variance 120590(120572) as follows

A Uniform distribution is

119898119881 (119886) =

int119878(119886)

119909120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

1205902

119881(119886) =

int119878(119886)

1199092120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

minus 1198982

119881(119886)

(8)

The Scientific World Journal 5

Y

1

0L m u

X

Figure 3 Triangular fuzzy numbers

B Proportional distribution is

119898119875 (119886) =

int119878(119886)

119909120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

1205902

119875(119886) =

int119878(119886)

1199092120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

minus 1198982

119875(119886)

(9)

(3) Value Range In the value evaluation of each programtaking the ambiguity of the indicators and subjectivity intoaccount we use semantic judgment that is divided intoseven specific grading criteria namely ldquovery lowrdquo (VL)ldquolowrdquo (L) the ldquolowerrdquo (ML) ldquogeneralrdquo (M) ldquohighrdquo (MH)ldquohighrdquo (H) and the ldquovery highrdquo (VH) these seven variablesemantic described as triangular fuzzy numbers as illustratedin Table 1

(4) Clearance of Triangular Fuzzy Number We can comparethe size of triangular fuzzy numbers by comprehensivelyutilizing the triangular fuzzy numbers mean variance andfuzzy limit coefficient composed with fuzzy informationcontent

119864 (119886) = 2 times (1205832

119886(1199090) + 1205832

1198861015840 (1199090)) minus 1 (10)

Fuzzy information content is defined as for the arbitraryfuzzy number 120572 on interval [a b]

119864 (119886) =1

119887 minus 119886int

119887

119886

119864 (119886 119909) 119889119909 (11)

known as information of 120572 at the point also called informa-tion of fuzzy number 120572 and for any fuzzy Number 120572 on [a b]has 0 le 119864(120572) le 1 When 120572 is a triangular fuzzy number thetriangular fuzzy number can be calculated from the amountof information according to the definition of triangular fuzzynumber and the amount of information

119864 (119886) = 1 minus2 times (119906 minus 1)

3 times (119887 minus 119886)

120588 (119886) = 119864 (119886)119898 (119886) + (119864 (119886) minus 1) 120590 (119886)

(12)

Table 1 Linguistic terms and related fuzzy numbers for weight ofpreference

Linguistic terms Fuzzy numbers(VL) (0 0 01)(L) (0 01 03)(ML) (01 03 05)(M) (03 05 07)(MH) (05 07 09)(H) (07 09 1)(VH) (09 1 1)

Table 2 Customer needs relative weight scale

Scale (119903119894119895)

1 119894 and 119895 are equally important2 119894 is slightly more important than 1198953 119894 is important than 1198954 119894 is more important than 1198955 119894 is absolutely more important than 119895119903119895119894 = 1119903119894119895

For the triangular fuzzy number 120572 on the interval [a b]which is called the 120572rsquo limit coefficient of triangular fuzzynumber where119898(120572) 120590(120572) 120572 respectively the mean and vari-ance triangular fuzzy numbers In thismethod the limit coef-ficient considers the fuzzy mean fuzzy variance and amountof information influence on fuzzy triangular fuzzy numberssize Among them the combination of fuzzy mean value andvariance are coefficient directly by the triangle fuzzy numberfor a given amount of information the combination of fuzzymean value and variance coefficient directly by the trianglefuzzy number for a given amount of information it has highstability and its calculation process is simple and easy to beprogrammed it also has the characteristics of operability

412 Enterprise Weight The basic principle of analytic hier-archy process (AHP) is to give a qualitative description of a setof objectives for paired comparison by the way of analyzingthe relative importance degree of each pair whereby thequantitative results of each objectiveweight Specificmethodsare as follows

(1) Compare the importance of various customersrsquo needsThis is a process from qualitative to quantitativethrough a criterion to judge comparing the impor-tance of two factors and according to the proportionof 1 to 5 scales to the importance degree of theassignment the ratio scale as illustrated in Table 2

(2) Forming judgment matrix the customer require-ments fill in the first column of the column and thesecond column respectively which lists the impor-tance degree of each other as illustrated in Table 3

(3) Using root method to calculate the important weightof customersrsquo needs 119870

119901

119894 first calculate the absolute

important weight 119870119901

119894 where 119894 = 1 2 119899 For

6 The Scientific World Journal

Table 3 Relative importance for customer needs

Efficiency Cost Quality Time Individuation EnvironmentEfficiencyCostQualityTimeIndividuationEnvironment

Table 4 RI Value table

Verify items 1 2 3 4 5 6 7 8 9RI 0 0 058 090 112 124 132 141 145

119870119901

119894standardization 119870

119901

119894= 119870119901

119894sum119894119870119901

119894 that is the

important weights of customer needs(4) Verify the validity weight If CR lt 01 it shows

that the consistency of a judgment matrix can beaccepted if CR gt 01 it shows that data does notneed to modify the data consistency until satisfied asillustrated in Table 4

42 Strategic Weight (1) The indicators with the quantitativecalculation of the first indicators 119895 relation 119894 option value ofthe proportion of indicators 119901

119894119895 Consider

119901119894119895=

119909119894119895

sum119898

119894=1119909119894119895

(13)

(2) Calculation of the first indicators 119895 entropy 119890119895is as

follows

119890119895= minus119896

119898

sum

119894=1

119901119894119895ln119901119894119895 (14)

Among them 119896 gt 0 ln is natural logarithm 119890119895ge 0 If 119909

119894119895is

all equal to a given 119895 then

119901119894119895=

119909119894119895

sum119898

119894=1119909119894119895

=1

119898 (15)

119864119895gets the maximum at this time that is

119890119895= minus119896

119898

sum

119894=1

1

119898ln 1

119898= 119896 ln119898 (16)

If we suppose 119896 = 1 ln119898 there is 0 le 119890119895le 1

(3) Calculation of the first 119895 indicators of the differencecoefficient 119892

119895is as follows For a given 119895 the smaller the

difference of 119909119894119895is the greater the 119890

119895is when 119909

119894119895are all equal

119890119895= 119890max = 1 at this time there is no effect on indicators 119883

119895

for the program compared the greater the difference betweenthe index values is the smaller the 119890

119895is the bigger role of the

indicators for the program are compared

(4) Improving data is as follows It can be based on expertopinion efficacy coefficient method used for transformation

of data and consistency check taking 119883(ℎ)

119895= max119883

119895 119883(120582)

119895=

min119883119895 the transform using the following formula

119883lowast

119894119895=

119909119894119895minus 119909(120582)

119895

119909(ℎ)

119895minus 119909(120582)

119895

times 119860 + 119861 (17)

If you consider that the indicator weight should be greaterthe date differences in large-scale can be chosen larger andif the data differences are in a small area it can be chosensmaller Also the combination of expert scoring method andthe evaluator can add a certain degree of subjective factorsthus increasing the evaluation-oriented that is in formula

119883lowast

119894119895=

119909119894119895minus 119909120582

119895

119909(ℎ)

119895minus 119909(120582)

119895

times 120572 + (120572 minus 1) (18)

If you want to increase the weights 120572 can be increased whenthe data difference is large and it is large in a similar mannerif we want to reduce the weight of the indicators 120572 can bedecreased when the data difference is small and the weightcalculated with using the entropy weight method is small

(5) The definition of weights is as follows Consider

119886119895=

119892119895

sum119899

119895=1119892119895

(19)

(6) Calculating the value of comprehensive evaluation of119881119894is as follows

119881119894=

119899

sum

119895=1

119886119895119901119894119895 (20)

119881119894option for the first 119894 values of comprehensive evaluation

5 The Numerical Analysis

(1) The Initial Data of Customer Weight See Table 5

(2) Enterprise Weight See Table 6

(3) Strategic Weight See Table 7

(4) Weight of Functional Objectives on Manufacturing SystemIt is proposed that

119882119860= 08 119882

119878= 02 (21)

The Scientific World Journal 7

Table 5

Efficiency Cost Quality Time Individuation Environment(03 05 07) (01 03 05) (05 07 09) (03 05 07) (07 09 1) (09 1 1)Result (013 008 018 013 023 and 025)

Table 6

Efficiency Cost Quality Time Individuation EnvironmentEfficiency 1 12 2 13 13 12Cost 2 1 3 4 1 2Quality 12 13 1 12 14 13Time 3 14 2 1 13 12Individuation 3 1 4 3 1 2Environment 2 12 3 2 12 1Result (009 026 006 014 028 and 018)

Table 7

Efficiency Cost Quality Time Individuation Environment084 079 093 072 096 06408 072 064 083 09 076082 068 086 089 078 078076 08 072 093 09 085Result (013 009 028 018 011 and 021)

Table 8 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment008 012 011 012 032 025

Table 9 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment011 020 012 027 012 018

Table 10 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment017 027 018 015 010 013

The comprehensive result is illustrated in Table 8It is proposed that

119882119860= 05 119882

119878= 05 (22)

The comprehensive result is illustrated in Table 9 Consider

119882119860= 02 119882

119878= 08 (23)

The comprehensive result is illustrated in Table 10By Table 8 it can be concluded that the six key elements

for function objective decision on the manufacturing systemin the sequence are personalization being environmentfriendly prompt delivery low cost good quality and highproduction efficiency From the reality the decision resultsare up to the present market for personalized consumption

and premise of legal requirements associated with environ-mental protection That could be considered to be person-alized and environmental competition orientation in theoverall design the manufacturing system function objectiveoccupies 57 in total and the other four for the intensity ofcompetition occupy the proportion of 43

From Tables 9 and 10 we can find that this decisionmethod is based on the stability and consistency of weightjudgments if there are remarkable differences on the weightwhich need to use additional methods to integrate differ-ences

6 Conclusion

In this paper we established the three dimensional weightdetermination methods comprehensively considering the

8 The Scientific World Journal

customer needs enterprise decision-makers intent andenterprise competitive position It provides scientific andpractical guidance for enterprises in the objective selectionof manufacturing system function From the final calculationof each objective weight we could see that this method hadinteraction of human-machine and universal applicability ofenvironment and it was also easy to operate and computeprogrammingThe deficiency of this paper is that themethodstill needs a large number of actual inspections in order toprove it in line with the objective decision of the practicalneed of the manufacturing system function

In the future main job will be to determine the relation-ship between 119882

119860 and 119882119878 using the scientific method such

as statistics data envelopment analysis (DEA) method andregression method to determine the proportions of them

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by the National Natural ScienceFoundation of China under Grants nos 70172042 70472034and the Soft Science Project no F13-317-5-29 of ShenyangScience and Technology Bureau

References

[1] C-F Tsai and M-Y Chen ldquoVariable selection by associationrules for customer churn prediction ofmultimedia on demandrdquoExpert Systems with Applications vol 37 no 3 pp 2006ndash20152010

[2] A Haensel G Koole and J Erdman ldquoEstimating uncon-strained customer choice set demand a case study on airlinereservation datardquo Journal of Choice Modelling vol 4 no 3 pp75ndash87 2011

[3] H G H TiemessenM Fleischmann G J VanHoutum J A EE VanNunen and E Pratsini ldquoDynamic demand fulfillment inspare parts networks with multiple customer classesrdquo EuropeanJournal of Operational Research vol 228 no 2 pp 367ndash3802013

[4] M Heijltjes and A van Witteloostuijn ldquoConfigurations ofmarket environments competitive strategies manufacturingtechnologies and human resource management policies a two-industry and two-country analysis of fitrdquo Scandinavian Journalof Management vol 19 no 1 pp 31ndash62 2003

[5] E Grigoroudis and Y Siskos ldquoA survey of customer sat-isfaction barometers some results from the transportation-communications sectorrdquo European Journal of OperationalResearch vol 152 no 2 pp 334ndash353 2004

[6] A A Levis and L G Papageorgiou ldquoCustomer demandforecasting via support vector regression analysisrdquo ChemicalEngineering Research and Design vol 83 no 8 A pp 1009ndash10182005

[7] K K B Hon ldquoPerformance and evaluation of manufacturingsystemsrdquo CIRP Annals Manufacturing Technology vol 54 no2 pp 139ndash154 2005

[8] J G Wacker and C Sheu ldquoEffectiveness of manufacturingplanning and controlsystems on manufacturing competitive-ness evidence from global manufacturing datardquo InternationalJournal of Production Research vol 44 no 5 pp 1015ndash10362006

[9] A M A Youssef A Mohib and H A Elmaraghy ldquoAvail-ability assessment of multi-state manufacturing systems usinguniversal generating functionrdquo CIRP Annals ManufacturingTechnology vol 55 no 1 pp 445ndash448 2006

[10] G S Dangayach and S G Deshmukh ldquoAn exploratory study ofmanufacturing strategy practices of machinery manufacturingcompanies in Indiardquo Omega vol 34 no 3 pp 254ndash273 2006

[11] B Menguc S Auh and E Shih ldquoTransformational leadershipand market orientation implications for the implementation ofcompetitive strategies and business unit performancerdquo Journalof Business Research vol 60 no 4 pp 314ndash321 2007

[12] M Acquaah andM Yasai-Ardekani ldquoDoes the implementationof a combination competitive strategy yield incremental perfor-mance benefits A new perspective from a transition economyin Sub-Saharan Africardquo Journal of Business Research vol 61 no4 pp 346ndash354 2008

[13] V Kalogeraki P MMelliar-Smith L E Moser and Y DrougasldquoResource management using multiple feedback loops in softreal-time distributed object systemsrdquo Journal of Systems andSoftware vol 81 no 7 pp 1144ndash1162 2008

[14] E Babakus U Yavas and N J Ashill ldquoThe role of cus-tomer orientation as a moderator of the job demand-burnout-performance relationship a surface-level trait perspectiverdquoJournal of Retailing vol 85 no 4 pp 480ndash492 2009

[15] P Vichare A Nassehi S Kumar and S T Newman ldquoA unifiedmanufacturing resource model for representing CNC machin-ing systemsrdquo Robotics and Computer-Integrated Manufacturingvol 25 no 6 pp 999ndash1007 2009

[16] I Um H Cheon and H Lee ldquoThe simulation design andanalysis of a flexible manufacturing system with automatedguided vehicle systemrdquo Journal of Manufacturing Systems vol28 no 4 pp 115ndash122 2009

[17] Z Deng Y Lu K K Wei and J Zhang ldquoUnderstandingcustomer satisfaction and loyalty an empirical study of mobileinstant messages in Chinardquo International Journal of InformationManagement vol 30 no 4 pp 289ndash300 2010

[18] M J R Ortega ldquoCompetitive strategies and firm performancetechnological capabilitiesrsquo moderating rolesrdquo Journal of BusinessResearch vol 63 no 12 pp 1273ndash1281 2010

[19] S Salunke J Weerawardena and J R McColl-KennedyldquoTowards a model of dynamic capabilities in innovation-basedcompetitive strategy insights from project-oriented servicefirmsrdquo Industrial Marketing Management vol 40 no 8 pp1251ndash1263 2011

[20] A Agus and Z Hassan ldquoEnhancing production performanceand customer performance through Total QualityManagement(TQM) strategies for competitive advantagerdquo in Proceedings ofthe 7th International Strategic Management Conference vol 24pp 1650ndash1662 July 2011

[21] HWang X ZhuHWang S J Hu Z Lin andGChen ldquoMulti-objective optimization of product variety and manufacturingcomplexity in mixed-model assembly systemsrdquo Journal of Man-ufacturing Systems vol 30 no 1 pp 16ndash27 2011

[22] E Claver-Cortes EM Pertusa-Ortega and J FMolina-AzorınldquoCharacteristics of organizational structure relating to hybridcompetitive strategy implications for performancerdquo Journal ofBusiness Research vol 65 no 7 pp 993ndash1002 2012

The Scientific World Journal 9

[23] W Wang and Y Koren ldquoScalability planning for reconfigurablemanufacturing systemsrdquo Journal of Manufacturing Systems vol31 no 2 pp 83ndash91 2012

[24] L Esbjerg B B Jensen T Bech-Larsen M D de BarcellosY Boztug and K G Grunert ldquoAn integrative conceptualframework for analyzing customer satisfaction with shoppingtrip experiences in grocery retailingrdquo Journal of Retailing andConsumer Services vol 19 no 4 pp 445ndash456 2012

[25] N Bolloju C Schneider and V Sugumaran ldquoA knowledge-based system for improving the consistency between objectmodels and use case narrativesrdquo Expert Systems with Applica-tions vol 39 no 10 pp 9398ndash9410 2012

[26] H Nouri and T S Hong ldquoDevelopment of bacteria foragingoptimization algorithm for cell formation in cellular manu-facturing system considering cell load variationsrdquo Journal ofManufacturing Systems vol 32 no 1 pp 20ndash31 2013

[27] Z Yang and Y Chen ldquoFuzzy soft set-based approach to pri-oritizing technical attributes in quality function deploymentrdquoNeural Computing and Applications vol 23 no 7-8 pp 2493ndash2500 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

The Scientific World Journal 3

Customerreuirement

Competitiveadvantage

ServiceDiversifica

tion

Cost

Time

Highquality

Efficiency

Environment

Ontime

Price

Quality

Green

Quantity

+

++

++

+

+

+

+

+ +

+

+

++

+

+

+

+

+

+

+

+

+

+

+

minus

minus

minus

minus

minus

minus

minus

minus

minus

minus

minus

minusminus

minus

minus

minus

minus

minus

minusminus

Figure 1 The function mechanism between the customer requirements and the system functional objectives

system which solves the competitive advantage of manufac-turing system and the intensity of competition for weightfunction objective from the perspective of competitive strat-egy Based on subjective fuzzy mathematics and objectiveentropy weight method approach to decision-making wasformed which is suitable for a dynamic environment Thereis a link between customer demands and the interactionwith the system functional objectives Some demands andimproving the system functional objectives showed a positivecorrelation and some of the demands are no correlationbetween the objective system and even some of them forthe development of the system have a hindering effectfor designed the system functional objectives Using thedynamic principle of the system we can clearly research thecustomer demand for intuitive system objectives and effectsof competitive advantage as shown in Figure 1

3 Basic Principle

Through put the value function into prioritization to expressthe competitive advantage of enterprise through the variousfunctions gives different weights to realize it is concludedthat the enterprise itself under integrated balance functionobjective focusing on the direction and intensity it is crucialfor its survival and development In response to this situationthe enterprise uses quality function deployment (qualityfunction deployment QFD) to design and manufacturemeet or exceed customer expectations of products andmanufacturing system function goal of the three factors as

input part through the principle of QFD system decision-making weight of each objective by function objective prior-ity sequence The main information contained in this houseof quality is customer demand competitive demand moduleand enterprise demand modules as shown in Figure 2

Weights are determined by two aspects on one handas the elementary weight while on the other hand as thedirectional weight Consider

119882119879= 119882119860+ 119882119878 (1)

where 119882119879 is the weight of manufacturing system functional

orientation119882119860 is the basic weight determined by customerneeds and expectations of enterprises decision and 119882

119878 isthe directional weight determined by the enterprise strategicorientation

Customers need determining the weight

119882119863

= [1198861 1198862 119886

119898] (2)

Enterprises need determining the weigh

119882119862= [1198871 1198872 119887

119898]119879 (3)

The enterprise competitive advantage determines theweight

119882119878= [1198881 1198882 119888

119898] (4)

Calculated results of basic principle are

119882119860=

119882119863times 119882119862

1003817100381710038171003817119882119863 times 119882119862

1003817100381710038171003817

=[1198861times 1198871 1198862times 1198872 119886

119898times 119887119898]

radicsum119898

119894=1119886119894times 119887119894

(5)

4 The Scientific World Journal

Enterprise requirements

Com

petit

ion

satis

fact

ion

Com

petit

ion

stat

usco

mpa

red

with

ote

rs

Com

petit

ive a

dvan

tage

orie

ntat

ion

Com

petit

ive e

xpec

tatio

n

Functional objectivesabsolute weight

Functional objectivespriority sequence

Functional objectivesweight relative value

Custom requirements

Necessary requirments Winning requirements

120572i

1205721

1205722

1205723

1205724

1205725

1205726

120596j

120596j

Dem

and

satis

fact

ion

Kpi K

qi

Dem

and

impo

rtan

ce

1205961 1205962 1205963 1205964 1205965 1205966

1205961 1205962 1205963 1205964 1205965 1205966

1205731 1205732 1205733 1205734 1205735 1205736

Enterprise requirments 120573j

Correlation matrix G

Relationship matrix R

G = ( g11 middot middot middot g1n ⋱

gm1 middot middot middot gmn

)

R = ( middot middot middot ⋱

middot middot middot

)r11 r1n

rm1 rmn

Kmi

120601i

Figure 2 Quality house of module construction

Therefore

119882119879=

1198861times 1198871

radicsum119898

119894=1119886119894times 119887119894

+ 1198881

1198862times 1198872

radicsum119898

119894=1119886119894times 119887119894

+ 1198882

119886119898times 119887119898

radicsum119898

119894=1119886119894times 119887119894

+ 119888119898

(6)

As a consequence

119882119879

119894=

119886119894times 119887119894radicsum119898

119894=1119886119894times 119887119894+ 119888119894

radicsum119898

119894=1(119886119894times 119887119894radicsum119898

119894=1119886119894times 119887119894) + 119888119894

(7)

4 Model Building

41 Decomposition of Basic Weight

411 Customer Weight

(1) Triangular Fuzzy Numbers Membership function graph isas shown in Figure 1 fuzzy numbers 120572 = (119897 119898 119906) are calledtriangular fuzzy number where 119897 119898 119906 is a real number 0 le

119897 le 119898 le 119906119898 is called the main value of triangular fuzzynumber 120572 119897 and 119906 are called the upper and lower bounds of120572 (119898 minus 119897) and (119906 minus119898) are called lower and upper limits of 120572When 119897 = 119898 = 119906 120572 turns into the real number in the ordinarysense When the value of (119906 minus 119897) is larger the triangularfuzzy number 120572 = (119897 119898 119906) is more blurred 119871 in this paperrepresents the clientsrsquominimumexpectations to the objective119898 represents clientsrsquo most expectations and 119906 representsthe customersrsquo highest expectations to the objective as inFigure 3

(2) Fuzzy Mean Value and Variance Fuzzy number has twokinds of distribution uniform distribution and proportionaldistribution We can define the respective fuzzy mean 119898(120572)

and variance 120590(120572) as follows

A Uniform distribution is

119898119881 (119886) =

int119878(119886)

119909120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

1205902

119881(119886) =

int119878(119886)

1199092120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

minus 1198982

119881(119886)

(8)

The Scientific World Journal 5

Y

1

0L m u

X

Figure 3 Triangular fuzzy numbers

B Proportional distribution is

119898119875 (119886) =

int119878(119886)

119909120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

1205902

119875(119886) =

int119878(119886)

1199092120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

minus 1198982

119875(119886)

(9)

(3) Value Range In the value evaluation of each programtaking the ambiguity of the indicators and subjectivity intoaccount we use semantic judgment that is divided intoseven specific grading criteria namely ldquovery lowrdquo (VL)ldquolowrdquo (L) the ldquolowerrdquo (ML) ldquogeneralrdquo (M) ldquohighrdquo (MH)ldquohighrdquo (H) and the ldquovery highrdquo (VH) these seven variablesemantic described as triangular fuzzy numbers as illustratedin Table 1

(4) Clearance of Triangular Fuzzy Number We can comparethe size of triangular fuzzy numbers by comprehensivelyutilizing the triangular fuzzy numbers mean variance andfuzzy limit coefficient composed with fuzzy informationcontent

119864 (119886) = 2 times (1205832

119886(1199090) + 1205832

1198861015840 (1199090)) minus 1 (10)

Fuzzy information content is defined as for the arbitraryfuzzy number 120572 on interval [a b]

119864 (119886) =1

119887 minus 119886int

119887

119886

119864 (119886 119909) 119889119909 (11)

known as information of 120572 at the point also called informa-tion of fuzzy number 120572 and for any fuzzy Number 120572 on [a b]has 0 le 119864(120572) le 1 When 120572 is a triangular fuzzy number thetriangular fuzzy number can be calculated from the amountof information according to the definition of triangular fuzzynumber and the amount of information

119864 (119886) = 1 minus2 times (119906 minus 1)

3 times (119887 minus 119886)

120588 (119886) = 119864 (119886)119898 (119886) + (119864 (119886) minus 1) 120590 (119886)

(12)

Table 1 Linguistic terms and related fuzzy numbers for weight ofpreference

Linguistic terms Fuzzy numbers(VL) (0 0 01)(L) (0 01 03)(ML) (01 03 05)(M) (03 05 07)(MH) (05 07 09)(H) (07 09 1)(VH) (09 1 1)

Table 2 Customer needs relative weight scale

Scale (119903119894119895)

1 119894 and 119895 are equally important2 119894 is slightly more important than 1198953 119894 is important than 1198954 119894 is more important than 1198955 119894 is absolutely more important than 119895119903119895119894 = 1119903119894119895

For the triangular fuzzy number 120572 on the interval [a b]which is called the 120572rsquo limit coefficient of triangular fuzzynumber where119898(120572) 120590(120572) 120572 respectively the mean and vari-ance triangular fuzzy numbers In thismethod the limit coef-ficient considers the fuzzy mean fuzzy variance and amountof information influence on fuzzy triangular fuzzy numberssize Among them the combination of fuzzy mean value andvariance are coefficient directly by the triangle fuzzy numberfor a given amount of information the combination of fuzzymean value and variance coefficient directly by the trianglefuzzy number for a given amount of information it has highstability and its calculation process is simple and easy to beprogrammed it also has the characteristics of operability

412 Enterprise Weight The basic principle of analytic hier-archy process (AHP) is to give a qualitative description of a setof objectives for paired comparison by the way of analyzingthe relative importance degree of each pair whereby thequantitative results of each objectiveweight Specificmethodsare as follows

(1) Compare the importance of various customersrsquo needsThis is a process from qualitative to quantitativethrough a criterion to judge comparing the impor-tance of two factors and according to the proportionof 1 to 5 scales to the importance degree of theassignment the ratio scale as illustrated in Table 2

(2) Forming judgment matrix the customer require-ments fill in the first column of the column and thesecond column respectively which lists the impor-tance degree of each other as illustrated in Table 3

(3) Using root method to calculate the important weightof customersrsquo needs 119870

119901

119894 first calculate the absolute

important weight 119870119901

119894 where 119894 = 1 2 119899 For

6 The Scientific World Journal

Table 3 Relative importance for customer needs

Efficiency Cost Quality Time Individuation EnvironmentEfficiencyCostQualityTimeIndividuationEnvironment

Table 4 RI Value table

Verify items 1 2 3 4 5 6 7 8 9RI 0 0 058 090 112 124 132 141 145

119870119901

119894standardization 119870

119901

119894= 119870119901

119894sum119894119870119901

119894 that is the

important weights of customer needs(4) Verify the validity weight If CR lt 01 it shows

that the consistency of a judgment matrix can beaccepted if CR gt 01 it shows that data does notneed to modify the data consistency until satisfied asillustrated in Table 4

42 Strategic Weight (1) The indicators with the quantitativecalculation of the first indicators 119895 relation 119894 option value ofthe proportion of indicators 119901

119894119895 Consider

119901119894119895=

119909119894119895

sum119898

119894=1119909119894119895

(13)

(2) Calculation of the first indicators 119895 entropy 119890119895is as

follows

119890119895= minus119896

119898

sum

119894=1

119901119894119895ln119901119894119895 (14)

Among them 119896 gt 0 ln is natural logarithm 119890119895ge 0 If 119909

119894119895is

all equal to a given 119895 then

119901119894119895=

119909119894119895

sum119898

119894=1119909119894119895

=1

119898 (15)

119864119895gets the maximum at this time that is

119890119895= minus119896

119898

sum

119894=1

1

119898ln 1

119898= 119896 ln119898 (16)

If we suppose 119896 = 1 ln119898 there is 0 le 119890119895le 1

(3) Calculation of the first 119895 indicators of the differencecoefficient 119892

119895is as follows For a given 119895 the smaller the

difference of 119909119894119895is the greater the 119890

119895is when 119909

119894119895are all equal

119890119895= 119890max = 1 at this time there is no effect on indicators 119883

119895

for the program compared the greater the difference betweenthe index values is the smaller the 119890

119895is the bigger role of the

indicators for the program are compared

(4) Improving data is as follows It can be based on expertopinion efficacy coefficient method used for transformation

of data and consistency check taking 119883(ℎ)

119895= max119883

119895 119883(120582)

119895=

min119883119895 the transform using the following formula

119883lowast

119894119895=

119909119894119895minus 119909(120582)

119895

119909(ℎ)

119895minus 119909(120582)

119895

times 119860 + 119861 (17)

If you consider that the indicator weight should be greaterthe date differences in large-scale can be chosen larger andif the data differences are in a small area it can be chosensmaller Also the combination of expert scoring method andthe evaluator can add a certain degree of subjective factorsthus increasing the evaluation-oriented that is in formula

119883lowast

119894119895=

119909119894119895minus 119909120582

119895

119909(ℎ)

119895minus 119909(120582)

119895

times 120572 + (120572 minus 1) (18)

If you want to increase the weights 120572 can be increased whenthe data difference is large and it is large in a similar mannerif we want to reduce the weight of the indicators 120572 can bedecreased when the data difference is small and the weightcalculated with using the entropy weight method is small

(5) The definition of weights is as follows Consider

119886119895=

119892119895

sum119899

119895=1119892119895

(19)

(6) Calculating the value of comprehensive evaluation of119881119894is as follows

119881119894=

119899

sum

119895=1

119886119895119901119894119895 (20)

119881119894option for the first 119894 values of comprehensive evaluation

5 The Numerical Analysis

(1) The Initial Data of Customer Weight See Table 5

(2) Enterprise Weight See Table 6

(3) Strategic Weight See Table 7

(4) Weight of Functional Objectives on Manufacturing SystemIt is proposed that

119882119860= 08 119882

119878= 02 (21)

The Scientific World Journal 7

Table 5

Efficiency Cost Quality Time Individuation Environment(03 05 07) (01 03 05) (05 07 09) (03 05 07) (07 09 1) (09 1 1)Result (013 008 018 013 023 and 025)

Table 6

Efficiency Cost Quality Time Individuation EnvironmentEfficiency 1 12 2 13 13 12Cost 2 1 3 4 1 2Quality 12 13 1 12 14 13Time 3 14 2 1 13 12Individuation 3 1 4 3 1 2Environment 2 12 3 2 12 1Result (009 026 006 014 028 and 018)

Table 7

Efficiency Cost Quality Time Individuation Environment084 079 093 072 096 06408 072 064 083 09 076082 068 086 089 078 078076 08 072 093 09 085Result (013 009 028 018 011 and 021)

Table 8 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment008 012 011 012 032 025

Table 9 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment011 020 012 027 012 018

Table 10 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment017 027 018 015 010 013

The comprehensive result is illustrated in Table 8It is proposed that

119882119860= 05 119882

119878= 05 (22)

The comprehensive result is illustrated in Table 9 Consider

119882119860= 02 119882

119878= 08 (23)

The comprehensive result is illustrated in Table 10By Table 8 it can be concluded that the six key elements

for function objective decision on the manufacturing systemin the sequence are personalization being environmentfriendly prompt delivery low cost good quality and highproduction efficiency From the reality the decision resultsare up to the present market for personalized consumption

and premise of legal requirements associated with environ-mental protection That could be considered to be person-alized and environmental competition orientation in theoverall design the manufacturing system function objectiveoccupies 57 in total and the other four for the intensity ofcompetition occupy the proportion of 43

From Tables 9 and 10 we can find that this decisionmethod is based on the stability and consistency of weightjudgments if there are remarkable differences on the weightwhich need to use additional methods to integrate differ-ences

6 Conclusion

In this paper we established the three dimensional weightdetermination methods comprehensively considering the

8 The Scientific World Journal

customer needs enterprise decision-makers intent andenterprise competitive position It provides scientific andpractical guidance for enterprises in the objective selectionof manufacturing system function From the final calculationof each objective weight we could see that this method hadinteraction of human-machine and universal applicability ofenvironment and it was also easy to operate and computeprogrammingThe deficiency of this paper is that themethodstill needs a large number of actual inspections in order toprove it in line with the objective decision of the practicalneed of the manufacturing system function

In the future main job will be to determine the relation-ship between 119882

119860 and 119882119878 using the scientific method such

as statistics data envelopment analysis (DEA) method andregression method to determine the proportions of them

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by the National Natural ScienceFoundation of China under Grants nos 70172042 70472034and the Soft Science Project no F13-317-5-29 of ShenyangScience and Technology Bureau

References

[1] C-F Tsai and M-Y Chen ldquoVariable selection by associationrules for customer churn prediction ofmultimedia on demandrdquoExpert Systems with Applications vol 37 no 3 pp 2006ndash20152010

[2] A Haensel G Koole and J Erdman ldquoEstimating uncon-strained customer choice set demand a case study on airlinereservation datardquo Journal of Choice Modelling vol 4 no 3 pp75ndash87 2011

[3] H G H TiemessenM Fleischmann G J VanHoutum J A EE VanNunen and E Pratsini ldquoDynamic demand fulfillment inspare parts networks with multiple customer classesrdquo EuropeanJournal of Operational Research vol 228 no 2 pp 367ndash3802013

[4] M Heijltjes and A van Witteloostuijn ldquoConfigurations ofmarket environments competitive strategies manufacturingtechnologies and human resource management policies a two-industry and two-country analysis of fitrdquo Scandinavian Journalof Management vol 19 no 1 pp 31ndash62 2003

[5] E Grigoroudis and Y Siskos ldquoA survey of customer sat-isfaction barometers some results from the transportation-communications sectorrdquo European Journal of OperationalResearch vol 152 no 2 pp 334ndash353 2004

[6] A A Levis and L G Papageorgiou ldquoCustomer demandforecasting via support vector regression analysisrdquo ChemicalEngineering Research and Design vol 83 no 8 A pp 1009ndash10182005

[7] K K B Hon ldquoPerformance and evaluation of manufacturingsystemsrdquo CIRP Annals Manufacturing Technology vol 54 no2 pp 139ndash154 2005

[8] J G Wacker and C Sheu ldquoEffectiveness of manufacturingplanning and controlsystems on manufacturing competitive-ness evidence from global manufacturing datardquo InternationalJournal of Production Research vol 44 no 5 pp 1015ndash10362006

[9] A M A Youssef A Mohib and H A Elmaraghy ldquoAvail-ability assessment of multi-state manufacturing systems usinguniversal generating functionrdquo CIRP Annals ManufacturingTechnology vol 55 no 1 pp 445ndash448 2006

[10] G S Dangayach and S G Deshmukh ldquoAn exploratory study ofmanufacturing strategy practices of machinery manufacturingcompanies in Indiardquo Omega vol 34 no 3 pp 254ndash273 2006

[11] B Menguc S Auh and E Shih ldquoTransformational leadershipand market orientation implications for the implementation ofcompetitive strategies and business unit performancerdquo Journalof Business Research vol 60 no 4 pp 314ndash321 2007

[12] M Acquaah andM Yasai-Ardekani ldquoDoes the implementationof a combination competitive strategy yield incremental perfor-mance benefits A new perspective from a transition economyin Sub-Saharan Africardquo Journal of Business Research vol 61 no4 pp 346ndash354 2008

[13] V Kalogeraki P MMelliar-Smith L E Moser and Y DrougasldquoResource management using multiple feedback loops in softreal-time distributed object systemsrdquo Journal of Systems andSoftware vol 81 no 7 pp 1144ndash1162 2008

[14] E Babakus U Yavas and N J Ashill ldquoThe role of cus-tomer orientation as a moderator of the job demand-burnout-performance relationship a surface-level trait perspectiverdquoJournal of Retailing vol 85 no 4 pp 480ndash492 2009

[15] P Vichare A Nassehi S Kumar and S T Newman ldquoA unifiedmanufacturing resource model for representing CNC machin-ing systemsrdquo Robotics and Computer-Integrated Manufacturingvol 25 no 6 pp 999ndash1007 2009

[16] I Um H Cheon and H Lee ldquoThe simulation design andanalysis of a flexible manufacturing system with automatedguided vehicle systemrdquo Journal of Manufacturing Systems vol28 no 4 pp 115ndash122 2009

[17] Z Deng Y Lu K K Wei and J Zhang ldquoUnderstandingcustomer satisfaction and loyalty an empirical study of mobileinstant messages in Chinardquo International Journal of InformationManagement vol 30 no 4 pp 289ndash300 2010

[18] M J R Ortega ldquoCompetitive strategies and firm performancetechnological capabilitiesrsquo moderating rolesrdquo Journal of BusinessResearch vol 63 no 12 pp 1273ndash1281 2010

[19] S Salunke J Weerawardena and J R McColl-KennedyldquoTowards a model of dynamic capabilities in innovation-basedcompetitive strategy insights from project-oriented servicefirmsrdquo Industrial Marketing Management vol 40 no 8 pp1251ndash1263 2011

[20] A Agus and Z Hassan ldquoEnhancing production performanceand customer performance through Total QualityManagement(TQM) strategies for competitive advantagerdquo in Proceedings ofthe 7th International Strategic Management Conference vol 24pp 1650ndash1662 July 2011

[21] HWang X ZhuHWang S J Hu Z Lin andGChen ldquoMulti-objective optimization of product variety and manufacturingcomplexity in mixed-model assembly systemsrdquo Journal of Man-ufacturing Systems vol 30 no 1 pp 16ndash27 2011

[22] E Claver-Cortes EM Pertusa-Ortega and J FMolina-AzorınldquoCharacteristics of organizational structure relating to hybridcompetitive strategy implications for performancerdquo Journal ofBusiness Research vol 65 no 7 pp 993ndash1002 2012

The Scientific World Journal 9

[23] W Wang and Y Koren ldquoScalability planning for reconfigurablemanufacturing systemsrdquo Journal of Manufacturing Systems vol31 no 2 pp 83ndash91 2012

[24] L Esbjerg B B Jensen T Bech-Larsen M D de BarcellosY Boztug and K G Grunert ldquoAn integrative conceptualframework for analyzing customer satisfaction with shoppingtrip experiences in grocery retailingrdquo Journal of Retailing andConsumer Services vol 19 no 4 pp 445ndash456 2012

[25] N Bolloju C Schneider and V Sugumaran ldquoA knowledge-based system for improving the consistency between objectmodels and use case narrativesrdquo Expert Systems with Applica-tions vol 39 no 10 pp 9398ndash9410 2012

[26] H Nouri and T S Hong ldquoDevelopment of bacteria foragingoptimization algorithm for cell formation in cellular manu-facturing system considering cell load variationsrdquo Journal ofManufacturing Systems vol 32 no 1 pp 20ndash31 2013

[27] Z Yang and Y Chen ldquoFuzzy soft set-based approach to pri-oritizing technical attributes in quality function deploymentrdquoNeural Computing and Applications vol 23 no 7-8 pp 2493ndash2500 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

4 The Scientific World Journal

Enterprise requirements

Com

petit

ion

satis

fact

ion

Com

petit

ion

stat

usco

mpa

red

with

ote

rs

Com

petit

ive a

dvan

tage

orie

ntat

ion

Com

petit

ive e

xpec

tatio

n

Functional objectivesabsolute weight

Functional objectivespriority sequence

Functional objectivesweight relative value

Custom requirements

Necessary requirments Winning requirements

120572i

1205721

1205722

1205723

1205724

1205725

1205726

120596j

120596j

Dem

and

satis

fact

ion

Kpi K

qi

Dem

and

impo

rtan

ce

1205961 1205962 1205963 1205964 1205965 1205966

1205961 1205962 1205963 1205964 1205965 1205966

1205731 1205732 1205733 1205734 1205735 1205736

Enterprise requirments 120573j

Correlation matrix G

Relationship matrix R

G = ( g11 middot middot middot g1n ⋱

gm1 middot middot middot gmn

)

R = ( middot middot middot ⋱

middot middot middot

)r11 r1n

rm1 rmn

Kmi

120601i

Figure 2 Quality house of module construction

Therefore

119882119879=

1198861times 1198871

radicsum119898

119894=1119886119894times 119887119894

+ 1198881

1198862times 1198872

radicsum119898

119894=1119886119894times 119887119894

+ 1198882

119886119898times 119887119898

radicsum119898

119894=1119886119894times 119887119894

+ 119888119898

(6)

As a consequence

119882119879

119894=

119886119894times 119887119894radicsum119898

119894=1119886119894times 119887119894+ 119888119894

radicsum119898

119894=1(119886119894times 119887119894radicsum119898

119894=1119886119894times 119887119894) + 119888119894

(7)

4 Model Building

41 Decomposition of Basic Weight

411 Customer Weight

(1) Triangular Fuzzy Numbers Membership function graph isas shown in Figure 1 fuzzy numbers 120572 = (119897 119898 119906) are calledtriangular fuzzy number where 119897 119898 119906 is a real number 0 le

119897 le 119898 le 119906119898 is called the main value of triangular fuzzynumber 120572 119897 and 119906 are called the upper and lower bounds of120572 (119898 minus 119897) and (119906 minus119898) are called lower and upper limits of 120572When 119897 = 119898 = 119906 120572 turns into the real number in the ordinarysense When the value of (119906 minus 119897) is larger the triangularfuzzy number 120572 = (119897 119898 119906) is more blurred 119871 in this paperrepresents the clientsrsquominimumexpectations to the objective119898 represents clientsrsquo most expectations and 119906 representsthe customersrsquo highest expectations to the objective as inFigure 3

(2) Fuzzy Mean Value and Variance Fuzzy number has twokinds of distribution uniform distribution and proportionaldistribution We can define the respective fuzzy mean 119898(120572)

and variance 120590(120572) as follows

A Uniform distribution is

119898119881 (119886) =

int119878(119886)

119909120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

1205902

119881(119886) =

int119878(119886)

1199092120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

minus 1198982

119881(119886)

(8)

The Scientific World Journal 5

Y

1

0L m u

X

Figure 3 Triangular fuzzy numbers

B Proportional distribution is

119898119875 (119886) =

int119878(119886)

119909120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

1205902

119875(119886) =

int119878(119886)

1199092120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

minus 1198982

119875(119886)

(9)

(3) Value Range In the value evaluation of each programtaking the ambiguity of the indicators and subjectivity intoaccount we use semantic judgment that is divided intoseven specific grading criteria namely ldquovery lowrdquo (VL)ldquolowrdquo (L) the ldquolowerrdquo (ML) ldquogeneralrdquo (M) ldquohighrdquo (MH)ldquohighrdquo (H) and the ldquovery highrdquo (VH) these seven variablesemantic described as triangular fuzzy numbers as illustratedin Table 1

(4) Clearance of Triangular Fuzzy Number We can comparethe size of triangular fuzzy numbers by comprehensivelyutilizing the triangular fuzzy numbers mean variance andfuzzy limit coefficient composed with fuzzy informationcontent

119864 (119886) = 2 times (1205832

119886(1199090) + 1205832

1198861015840 (1199090)) minus 1 (10)

Fuzzy information content is defined as for the arbitraryfuzzy number 120572 on interval [a b]

119864 (119886) =1

119887 minus 119886int

119887

119886

119864 (119886 119909) 119889119909 (11)

known as information of 120572 at the point also called informa-tion of fuzzy number 120572 and for any fuzzy Number 120572 on [a b]has 0 le 119864(120572) le 1 When 120572 is a triangular fuzzy number thetriangular fuzzy number can be calculated from the amountof information according to the definition of triangular fuzzynumber and the amount of information

119864 (119886) = 1 minus2 times (119906 minus 1)

3 times (119887 minus 119886)

120588 (119886) = 119864 (119886)119898 (119886) + (119864 (119886) minus 1) 120590 (119886)

(12)

Table 1 Linguistic terms and related fuzzy numbers for weight ofpreference

Linguistic terms Fuzzy numbers(VL) (0 0 01)(L) (0 01 03)(ML) (01 03 05)(M) (03 05 07)(MH) (05 07 09)(H) (07 09 1)(VH) (09 1 1)

Table 2 Customer needs relative weight scale

Scale (119903119894119895)

1 119894 and 119895 are equally important2 119894 is slightly more important than 1198953 119894 is important than 1198954 119894 is more important than 1198955 119894 is absolutely more important than 119895119903119895119894 = 1119903119894119895

For the triangular fuzzy number 120572 on the interval [a b]which is called the 120572rsquo limit coefficient of triangular fuzzynumber where119898(120572) 120590(120572) 120572 respectively the mean and vari-ance triangular fuzzy numbers In thismethod the limit coef-ficient considers the fuzzy mean fuzzy variance and amountof information influence on fuzzy triangular fuzzy numberssize Among them the combination of fuzzy mean value andvariance are coefficient directly by the triangle fuzzy numberfor a given amount of information the combination of fuzzymean value and variance coefficient directly by the trianglefuzzy number for a given amount of information it has highstability and its calculation process is simple and easy to beprogrammed it also has the characteristics of operability

412 Enterprise Weight The basic principle of analytic hier-archy process (AHP) is to give a qualitative description of a setof objectives for paired comparison by the way of analyzingthe relative importance degree of each pair whereby thequantitative results of each objectiveweight Specificmethodsare as follows

(1) Compare the importance of various customersrsquo needsThis is a process from qualitative to quantitativethrough a criterion to judge comparing the impor-tance of two factors and according to the proportionof 1 to 5 scales to the importance degree of theassignment the ratio scale as illustrated in Table 2

(2) Forming judgment matrix the customer require-ments fill in the first column of the column and thesecond column respectively which lists the impor-tance degree of each other as illustrated in Table 3

(3) Using root method to calculate the important weightof customersrsquo needs 119870

119901

119894 first calculate the absolute

important weight 119870119901

119894 where 119894 = 1 2 119899 For

6 The Scientific World Journal

Table 3 Relative importance for customer needs

Efficiency Cost Quality Time Individuation EnvironmentEfficiencyCostQualityTimeIndividuationEnvironment

Table 4 RI Value table

Verify items 1 2 3 4 5 6 7 8 9RI 0 0 058 090 112 124 132 141 145

119870119901

119894standardization 119870

119901

119894= 119870119901

119894sum119894119870119901

119894 that is the

important weights of customer needs(4) Verify the validity weight If CR lt 01 it shows

that the consistency of a judgment matrix can beaccepted if CR gt 01 it shows that data does notneed to modify the data consistency until satisfied asillustrated in Table 4

42 Strategic Weight (1) The indicators with the quantitativecalculation of the first indicators 119895 relation 119894 option value ofthe proportion of indicators 119901

119894119895 Consider

119901119894119895=

119909119894119895

sum119898

119894=1119909119894119895

(13)

(2) Calculation of the first indicators 119895 entropy 119890119895is as

follows

119890119895= minus119896

119898

sum

119894=1

119901119894119895ln119901119894119895 (14)

Among them 119896 gt 0 ln is natural logarithm 119890119895ge 0 If 119909

119894119895is

all equal to a given 119895 then

119901119894119895=

119909119894119895

sum119898

119894=1119909119894119895

=1

119898 (15)

119864119895gets the maximum at this time that is

119890119895= minus119896

119898

sum

119894=1

1

119898ln 1

119898= 119896 ln119898 (16)

If we suppose 119896 = 1 ln119898 there is 0 le 119890119895le 1

(3) Calculation of the first 119895 indicators of the differencecoefficient 119892

119895is as follows For a given 119895 the smaller the

difference of 119909119894119895is the greater the 119890

119895is when 119909

119894119895are all equal

119890119895= 119890max = 1 at this time there is no effect on indicators 119883

119895

for the program compared the greater the difference betweenthe index values is the smaller the 119890

119895is the bigger role of the

indicators for the program are compared

(4) Improving data is as follows It can be based on expertopinion efficacy coefficient method used for transformation

of data and consistency check taking 119883(ℎ)

119895= max119883

119895 119883(120582)

119895=

min119883119895 the transform using the following formula

119883lowast

119894119895=

119909119894119895minus 119909(120582)

119895

119909(ℎ)

119895minus 119909(120582)

119895

times 119860 + 119861 (17)

If you consider that the indicator weight should be greaterthe date differences in large-scale can be chosen larger andif the data differences are in a small area it can be chosensmaller Also the combination of expert scoring method andthe evaluator can add a certain degree of subjective factorsthus increasing the evaluation-oriented that is in formula

119883lowast

119894119895=

119909119894119895minus 119909120582

119895

119909(ℎ)

119895minus 119909(120582)

119895

times 120572 + (120572 minus 1) (18)

If you want to increase the weights 120572 can be increased whenthe data difference is large and it is large in a similar mannerif we want to reduce the weight of the indicators 120572 can bedecreased when the data difference is small and the weightcalculated with using the entropy weight method is small

(5) The definition of weights is as follows Consider

119886119895=

119892119895

sum119899

119895=1119892119895

(19)

(6) Calculating the value of comprehensive evaluation of119881119894is as follows

119881119894=

119899

sum

119895=1

119886119895119901119894119895 (20)

119881119894option for the first 119894 values of comprehensive evaluation

5 The Numerical Analysis

(1) The Initial Data of Customer Weight See Table 5

(2) Enterprise Weight See Table 6

(3) Strategic Weight See Table 7

(4) Weight of Functional Objectives on Manufacturing SystemIt is proposed that

119882119860= 08 119882

119878= 02 (21)

The Scientific World Journal 7

Table 5

Efficiency Cost Quality Time Individuation Environment(03 05 07) (01 03 05) (05 07 09) (03 05 07) (07 09 1) (09 1 1)Result (013 008 018 013 023 and 025)

Table 6

Efficiency Cost Quality Time Individuation EnvironmentEfficiency 1 12 2 13 13 12Cost 2 1 3 4 1 2Quality 12 13 1 12 14 13Time 3 14 2 1 13 12Individuation 3 1 4 3 1 2Environment 2 12 3 2 12 1Result (009 026 006 014 028 and 018)

Table 7

Efficiency Cost Quality Time Individuation Environment084 079 093 072 096 06408 072 064 083 09 076082 068 086 089 078 078076 08 072 093 09 085Result (013 009 028 018 011 and 021)

Table 8 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment008 012 011 012 032 025

Table 9 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment011 020 012 027 012 018

Table 10 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment017 027 018 015 010 013

The comprehensive result is illustrated in Table 8It is proposed that

119882119860= 05 119882

119878= 05 (22)

The comprehensive result is illustrated in Table 9 Consider

119882119860= 02 119882

119878= 08 (23)

The comprehensive result is illustrated in Table 10By Table 8 it can be concluded that the six key elements

for function objective decision on the manufacturing systemin the sequence are personalization being environmentfriendly prompt delivery low cost good quality and highproduction efficiency From the reality the decision resultsare up to the present market for personalized consumption

and premise of legal requirements associated with environ-mental protection That could be considered to be person-alized and environmental competition orientation in theoverall design the manufacturing system function objectiveoccupies 57 in total and the other four for the intensity ofcompetition occupy the proportion of 43

From Tables 9 and 10 we can find that this decisionmethod is based on the stability and consistency of weightjudgments if there are remarkable differences on the weightwhich need to use additional methods to integrate differ-ences

6 Conclusion

In this paper we established the three dimensional weightdetermination methods comprehensively considering the

8 The Scientific World Journal

customer needs enterprise decision-makers intent andenterprise competitive position It provides scientific andpractical guidance for enterprises in the objective selectionof manufacturing system function From the final calculationof each objective weight we could see that this method hadinteraction of human-machine and universal applicability ofenvironment and it was also easy to operate and computeprogrammingThe deficiency of this paper is that themethodstill needs a large number of actual inspections in order toprove it in line with the objective decision of the practicalneed of the manufacturing system function

In the future main job will be to determine the relation-ship between 119882

119860 and 119882119878 using the scientific method such

as statistics data envelopment analysis (DEA) method andregression method to determine the proportions of them

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by the National Natural ScienceFoundation of China under Grants nos 70172042 70472034and the Soft Science Project no F13-317-5-29 of ShenyangScience and Technology Bureau

References

[1] C-F Tsai and M-Y Chen ldquoVariable selection by associationrules for customer churn prediction ofmultimedia on demandrdquoExpert Systems with Applications vol 37 no 3 pp 2006ndash20152010

[2] A Haensel G Koole and J Erdman ldquoEstimating uncon-strained customer choice set demand a case study on airlinereservation datardquo Journal of Choice Modelling vol 4 no 3 pp75ndash87 2011

[3] H G H TiemessenM Fleischmann G J VanHoutum J A EE VanNunen and E Pratsini ldquoDynamic demand fulfillment inspare parts networks with multiple customer classesrdquo EuropeanJournal of Operational Research vol 228 no 2 pp 367ndash3802013

[4] M Heijltjes and A van Witteloostuijn ldquoConfigurations ofmarket environments competitive strategies manufacturingtechnologies and human resource management policies a two-industry and two-country analysis of fitrdquo Scandinavian Journalof Management vol 19 no 1 pp 31ndash62 2003

[5] E Grigoroudis and Y Siskos ldquoA survey of customer sat-isfaction barometers some results from the transportation-communications sectorrdquo European Journal of OperationalResearch vol 152 no 2 pp 334ndash353 2004

[6] A A Levis and L G Papageorgiou ldquoCustomer demandforecasting via support vector regression analysisrdquo ChemicalEngineering Research and Design vol 83 no 8 A pp 1009ndash10182005

[7] K K B Hon ldquoPerformance and evaluation of manufacturingsystemsrdquo CIRP Annals Manufacturing Technology vol 54 no2 pp 139ndash154 2005

[8] J G Wacker and C Sheu ldquoEffectiveness of manufacturingplanning and controlsystems on manufacturing competitive-ness evidence from global manufacturing datardquo InternationalJournal of Production Research vol 44 no 5 pp 1015ndash10362006

[9] A M A Youssef A Mohib and H A Elmaraghy ldquoAvail-ability assessment of multi-state manufacturing systems usinguniversal generating functionrdquo CIRP Annals ManufacturingTechnology vol 55 no 1 pp 445ndash448 2006

[10] G S Dangayach and S G Deshmukh ldquoAn exploratory study ofmanufacturing strategy practices of machinery manufacturingcompanies in Indiardquo Omega vol 34 no 3 pp 254ndash273 2006

[11] B Menguc S Auh and E Shih ldquoTransformational leadershipand market orientation implications for the implementation ofcompetitive strategies and business unit performancerdquo Journalof Business Research vol 60 no 4 pp 314ndash321 2007

[12] M Acquaah andM Yasai-Ardekani ldquoDoes the implementationof a combination competitive strategy yield incremental perfor-mance benefits A new perspective from a transition economyin Sub-Saharan Africardquo Journal of Business Research vol 61 no4 pp 346ndash354 2008

[13] V Kalogeraki P MMelliar-Smith L E Moser and Y DrougasldquoResource management using multiple feedback loops in softreal-time distributed object systemsrdquo Journal of Systems andSoftware vol 81 no 7 pp 1144ndash1162 2008

[14] E Babakus U Yavas and N J Ashill ldquoThe role of cus-tomer orientation as a moderator of the job demand-burnout-performance relationship a surface-level trait perspectiverdquoJournal of Retailing vol 85 no 4 pp 480ndash492 2009

[15] P Vichare A Nassehi S Kumar and S T Newman ldquoA unifiedmanufacturing resource model for representing CNC machin-ing systemsrdquo Robotics and Computer-Integrated Manufacturingvol 25 no 6 pp 999ndash1007 2009

[16] I Um H Cheon and H Lee ldquoThe simulation design andanalysis of a flexible manufacturing system with automatedguided vehicle systemrdquo Journal of Manufacturing Systems vol28 no 4 pp 115ndash122 2009

[17] Z Deng Y Lu K K Wei and J Zhang ldquoUnderstandingcustomer satisfaction and loyalty an empirical study of mobileinstant messages in Chinardquo International Journal of InformationManagement vol 30 no 4 pp 289ndash300 2010

[18] M J R Ortega ldquoCompetitive strategies and firm performancetechnological capabilitiesrsquo moderating rolesrdquo Journal of BusinessResearch vol 63 no 12 pp 1273ndash1281 2010

[19] S Salunke J Weerawardena and J R McColl-KennedyldquoTowards a model of dynamic capabilities in innovation-basedcompetitive strategy insights from project-oriented servicefirmsrdquo Industrial Marketing Management vol 40 no 8 pp1251ndash1263 2011

[20] A Agus and Z Hassan ldquoEnhancing production performanceand customer performance through Total QualityManagement(TQM) strategies for competitive advantagerdquo in Proceedings ofthe 7th International Strategic Management Conference vol 24pp 1650ndash1662 July 2011

[21] HWang X ZhuHWang S J Hu Z Lin andGChen ldquoMulti-objective optimization of product variety and manufacturingcomplexity in mixed-model assembly systemsrdquo Journal of Man-ufacturing Systems vol 30 no 1 pp 16ndash27 2011

[22] E Claver-Cortes EM Pertusa-Ortega and J FMolina-AzorınldquoCharacteristics of organizational structure relating to hybridcompetitive strategy implications for performancerdquo Journal ofBusiness Research vol 65 no 7 pp 993ndash1002 2012

The Scientific World Journal 9

[23] W Wang and Y Koren ldquoScalability planning for reconfigurablemanufacturing systemsrdquo Journal of Manufacturing Systems vol31 no 2 pp 83ndash91 2012

[24] L Esbjerg B B Jensen T Bech-Larsen M D de BarcellosY Boztug and K G Grunert ldquoAn integrative conceptualframework for analyzing customer satisfaction with shoppingtrip experiences in grocery retailingrdquo Journal of Retailing andConsumer Services vol 19 no 4 pp 445ndash456 2012

[25] N Bolloju C Schneider and V Sugumaran ldquoA knowledge-based system for improving the consistency between objectmodels and use case narrativesrdquo Expert Systems with Applica-tions vol 39 no 10 pp 9398ndash9410 2012

[26] H Nouri and T S Hong ldquoDevelopment of bacteria foragingoptimization algorithm for cell formation in cellular manu-facturing system considering cell load variationsrdquo Journal ofManufacturing Systems vol 32 no 1 pp 20ndash31 2013

[27] Z Yang and Y Chen ldquoFuzzy soft set-based approach to pri-oritizing technical attributes in quality function deploymentrdquoNeural Computing and Applications vol 23 no 7-8 pp 2493ndash2500 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

The Scientific World Journal 5

Y

1

0L m u

X

Figure 3 Triangular fuzzy numbers

B Proportional distribution is

119898119875 (119886) =

int119878(119886)

119909120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

1205902

119875(119886) =

int119878(119886)

1199092120583119886 (119909) 119889119909

int119878(119886)

120583119886 (119909) 119889119909

minus 1198982

119875(119886)

(9)

(3) Value Range In the value evaluation of each programtaking the ambiguity of the indicators and subjectivity intoaccount we use semantic judgment that is divided intoseven specific grading criteria namely ldquovery lowrdquo (VL)ldquolowrdquo (L) the ldquolowerrdquo (ML) ldquogeneralrdquo (M) ldquohighrdquo (MH)ldquohighrdquo (H) and the ldquovery highrdquo (VH) these seven variablesemantic described as triangular fuzzy numbers as illustratedin Table 1

(4) Clearance of Triangular Fuzzy Number We can comparethe size of triangular fuzzy numbers by comprehensivelyutilizing the triangular fuzzy numbers mean variance andfuzzy limit coefficient composed with fuzzy informationcontent

119864 (119886) = 2 times (1205832

119886(1199090) + 1205832

1198861015840 (1199090)) minus 1 (10)

Fuzzy information content is defined as for the arbitraryfuzzy number 120572 on interval [a b]

119864 (119886) =1

119887 minus 119886int

119887

119886

119864 (119886 119909) 119889119909 (11)

known as information of 120572 at the point also called informa-tion of fuzzy number 120572 and for any fuzzy Number 120572 on [a b]has 0 le 119864(120572) le 1 When 120572 is a triangular fuzzy number thetriangular fuzzy number can be calculated from the amountof information according to the definition of triangular fuzzynumber and the amount of information

119864 (119886) = 1 minus2 times (119906 minus 1)

3 times (119887 minus 119886)

120588 (119886) = 119864 (119886)119898 (119886) + (119864 (119886) minus 1) 120590 (119886)

(12)

Table 1 Linguistic terms and related fuzzy numbers for weight ofpreference

Linguistic terms Fuzzy numbers(VL) (0 0 01)(L) (0 01 03)(ML) (01 03 05)(M) (03 05 07)(MH) (05 07 09)(H) (07 09 1)(VH) (09 1 1)

Table 2 Customer needs relative weight scale

Scale (119903119894119895)

1 119894 and 119895 are equally important2 119894 is slightly more important than 1198953 119894 is important than 1198954 119894 is more important than 1198955 119894 is absolutely more important than 119895119903119895119894 = 1119903119894119895

For the triangular fuzzy number 120572 on the interval [a b]which is called the 120572rsquo limit coefficient of triangular fuzzynumber where119898(120572) 120590(120572) 120572 respectively the mean and vari-ance triangular fuzzy numbers In thismethod the limit coef-ficient considers the fuzzy mean fuzzy variance and amountof information influence on fuzzy triangular fuzzy numberssize Among them the combination of fuzzy mean value andvariance are coefficient directly by the triangle fuzzy numberfor a given amount of information the combination of fuzzymean value and variance coefficient directly by the trianglefuzzy number for a given amount of information it has highstability and its calculation process is simple and easy to beprogrammed it also has the characteristics of operability

412 Enterprise Weight The basic principle of analytic hier-archy process (AHP) is to give a qualitative description of a setof objectives for paired comparison by the way of analyzingthe relative importance degree of each pair whereby thequantitative results of each objectiveweight Specificmethodsare as follows

(1) Compare the importance of various customersrsquo needsThis is a process from qualitative to quantitativethrough a criterion to judge comparing the impor-tance of two factors and according to the proportionof 1 to 5 scales to the importance degree of theassignment the ratio scale as illustrated in Table 2

(2) Forming judgment matrix the customer require-ments fill in the first column of the column and thesecond column respectively which lists the impor-tance degree of each other as illustrated in Table 3

(3) Using root method to calculate the important weightof customersrsquo needs 119870

119901

119894 first calculate the absolute

important weight 119870119901

119894 where 119894 = 1 2 119899 For

6 The Scientific World Journal

Table 3 Relative importance for customer needs

Efficiency Cost Quality Time Individuation EnvironmentEfficiencyCostQualityTimeIndividuationEnvironment

Table 4 RI Value table

Verify items 1 2 3 4 5 6 7 8 9RI 0 0 058 090 112 124 132 141 145

119870119901

119894standardization 119870

119901

119894= 119870119901

119894sum119894119870119901

119894 that is the

important weights of customer needs(4) Verify the validity weight If CR lt 01 it shows

that the consistency of a judgment matrix can beaccepted if CR gt 01 it shows that data does notneed to modify the data consistency until satisfied asillustrated in Table 4

42 Strategic Weight (1) The indicators with the quantitativecalculation of the first indicators 119895 relation 119894 option value ofthe proportion of indicators 119901

119894119895 Consider

119901119894119895=

119909119894119895

sum119898

119894=1119909119894119895

(13)

(2) Calculation of the first indicators 119895 entropy 119890119895is as

follows

119890119895= minus119896

119898

sum

119894=1

119901119894119895ln119901119894119895 (14)

Among them 119896 gt 0 ln is natural logarithm 119890119895ge 0 If 119909

119894119895is

all equal to a given 119895 then

119901119894119895=

119909119894119895

sum119898

119894=1119909119894119895

=1

119898 (15)

119864119895gets the maximum at this time that is

119890119895= minus119896

119898

sum

119894=1

1

119898ln 1

119898= 119896 ln119898 (16)

If we suppose 119896 = 1 ln119898 there is 0 le 119890119895le 1

(3) Calculation of the first 119895 indicators of the differencecoefficient 119892

119895is as follows For a given 119895 the smaller the

difference of 119909119894119895is the greater the 119890

119895is when 119909

119894119895are all equal

119890119895= 119890max = 1 at this time there is no effect on indicators 119883

119895

for the program compared the greater the difference betweenthe index values is the smaller the 119890

119895is the bigger role of the

indicators for the program are compared

(4) Improving data is as follows It can be based on expertopinion efficacy coefficient method used for transformation

of data and consistency check taking 119883(ℎ)

119895= max119883

119895 119883(120582)

119895=

min119883119895 the transform using the following formula

119883lowast

119894119895=

119909119894119895minus 119909(120582)

119895

119909(ℎ)

119895minus 119909(120582)

119895

times 119860 + 119861 (17)

If you consider that the indicator weight should be greaterthe date differences in large-scale can be chosen larger andif the data differences are in a small area it can be chosensmaller Also the combination of expert scoring method andthe evaluator can add a certain degree of subjective factorsthus increasing the evaluation-oriented that is in formula

119883lowast

119894119895=

119909119894119895minus 119909120582

119895

119909(ℎ)

119895minus 119909(120582)

119895

times 120572 + (120572 minus 1) (18)

If you want to increase the weights 120572 can be increased whenthe data difference is large and it is large in a similar mannerif we want to reduce the weight of the indicators 120572 can bedecreased when the data difference is small and the weightcalculated with using the entropy weight method is small

(5) The definition of weights is as follows Consider

119886119895=

119892119895

sum119899

119895=1119892119895

(19)

(6) Calculating the value of comprehensive evaluation of119881119894is as follows

119881119894=

119899

sum

119895=1

119886119895119901119894119895 (20)

119881119894option for the first 119894 values of comprehensive evaluation

5 The Numerical Analysis

(1) The Initial Data of Customer Weight See Table 5

(2) Enterprise Weight See Table 6

(3) Strategic Weight See Table 7

(4) Weight of Functional Objectives on Manufacturing SystemIt is proposed that

119882119860= 08 119882

119878= 02 (21)

The Scientific World Journal 7

Table 5

Efficiency Cost Quality Time Individuation Environment(03 05 07) (01 03 05) (05 07 09) (03 05 07) (07 09 1) (09 1 1)Result (013 008 018 013 023 and 025)

Table 6

Efficiency Cost Quality Time Individuation EnvironmentEfficiency 1 12 2 13 13 12Cost 2 1 3 4 1 2Quality 12 13 1 12 14 13Time 3 14 2 1 13 12Individuation 3 1 4 3 1 2Environment 2 12 3 2 12 1Result (009 026 006 014 028 and 018)

Table 7

Efficiency Cost Quality Time Individuation Environment084 079 093 072 096 06408 072 064 083 09 076082 068 086 089 078 078076 08 072 093 09 085Result (013 009 028 018 011 and 021)

Table 8 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment008 012 011 012 032 025

Table 9 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment011 020 012 027 012 018

Table 10 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment017 027 018 015 010 013

The comprehensive result is illustrated in Table 8It is proposed that

119882119860= 05 119882

119878= 05 (22)

The comprehensive result is illustrated in Table 9 Consider

119882119860= 02 119882

119878= 08 (23)

The comprehensive result is illustrated in Table 10By Table 8 it can be concluded that the six key elements

for function objective decision on the manufacturing systemin the sequence are personalization being environmentfriendly prompt delivery low cost good quality and highproduction efficiency From the reality the decision resultsare up to the present market for personalized consumption

and premise of legal requirements associated with environ-mental protection That could be considered to be person-alized and environmental competition orientation in theoverall design the manufacturing system function objectiveoccupies 57 in total and the other four for the intensity ofcompetition occupy the proportion of 43

From Tables 9 and 10 we can find that this decisionmethod is based on the stability and consistency of weightjudgments if there are remarkable differences on the weightwhich need to use additional methods to integrate differ-ences

6 Conclusion

In this paper we established the three dimensional weightdetermination methods comprehensively considering the

8 The Scientific World Journal

customer needs enterprise decision-makers intent andenterprise competitive position It provides scientific andpractical guidance for enterprises in the objective selectionof manufacturing system function From the final calculationof each objective weight we could see that this method hadinteraction of human-machine and universal applicability ofenvironment and it was also easy to operate and computeprogrammingThe deficiency of this paper is that themethodstill needs a large number of actual inspections in order toprove it in line with the objective decision of the practicalneed of the manufacturing system function

In the future main job will be to determine the relation-ship between 119882

119860 and 119882119878 using the scientific method such

as statistics data envelopment analysis (DEA) method andregression method to determine the proportions of them

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by the National Natural ScienceFoundation of China under Grants nos 70172042 70472034and the Soft Science Project no F13-317-5-29 of ShenyangScience and Technology Bureau

References

[1] C-F Tsai and M-Y Chen ldquoVariable selection by associationrules for customer churn prediction ofmultimedia on demandrdquoExpert Systems with Applications vol 37 no 3 pp 2006ndash20152010

[2] A Haensel G Koole and J Erdman ldquoEstimating uncon-strained customer choice set demand a case study on airlinereservation datardquo Journal of Choice Modelling vol 4 no 3 pp75ndash87 2011

[3] H G H TiemessenM Fleischmann G J VanHoutum J A EE VanNunen and E Pratsini ldquoDynamic demand fulfillment inspare parts networks with multiple customer classesrdquo EuropeanJournal of Operational Research vol 228 no 2 pp 367ndash3802013

[4] M Heijltjes and A van Witteloostuijn ldquoConfigurations ofmarket environments competitive strategies manufacturingtechnologies and human resource management policies a two-industry and two-country analysis of fitrdquo Scandinavian Journalof Management vol 19 no 1 pp 31ndash62 2003

[5] E Grigoroudis and Y Siskos ldquoA survey of customer sat-isfaction barometers some results from the transportation-communications sectorrdquo European Journal of OperationalResearch vol 152 no 2 pp 334ndash353 2004

[6] A A Levis and L G Papageorgiou ldquoCustomer demandforecasting via support vector regression analysisrdquo ChemicalEngineering Research and Design vol 83 no 8 A pp 1009ndash10182005

[7] K K B Hon ldquoPerformance and evaluation of manufacturingsystemsrdquo CIRP Annals Manufacturing Technology vol 54 no2 pp 139ndash154 2005

[8] J G Wacker and C Sheu ldquoEffectiveness of manufacturingplanning and controlsystems on manufacturing competitive-ness evidence from global manufacturing datardquo InternationalJournal of Production Research vol 44 no 5 pp 1015ndash10362006

[9] A M A Youssef A Mohib and H A Elmaraghy ldquoAvail-ability assessment of multi-state manufacturing systems usinguniversal generating functionrdquo CIRP Annals ManufacturingTechnology vol 55 no 1 pp 445ndash448 2006

[10] G S Dangayach and S G Deshmukh ldquoAn exploratory study ofmanufacturing strategy practices of machinery manufacturingcompanies in Indiardquo Omega vol 34 no 3 pp 254ndash273 2006

[11] B Menguc S Auh and E Shih ldquoTransformational leadershipand market orientation implications for the implementation ofcompetitive strategies and business unit performancerdquo Journalof Business Research vol 60 no 4 pp 314ndash321 2007

[12] M Acquaah andM Yasai-Ardekani ldquoDoes the implementationof a combination competitive strategy yield incremental perfor-mance benefits A new perspective from a transition economyin Sub-Saharan Africardquo Journal of Business Research vol 61 no4 pp 346ndash354 2008

[13] V Kalogeraki P MMelliar-Smith L E Moser and Y DrougasldquoResource management using multiple feedback loops in softreal-time distributed object systemsrdquo Journal of Systems andSoftware vol 81 no 7 pp 1144ndash1162 2008

[14] E Babakus U Yavas and N J Ashill ldquoThe role of cus-tomer orientation as a moderator of the job demand-burnout-performance relationship a surface-level trait perspectiverdquoJournal of Retailing vol 85 no 4 pp 480ndash492 2009

[15] P Vichare A Nassehi S Kumar and S T Newman ldquoA unifiedmanufacturing resource model for representing CNC machin-ing systemsrdquo Robotics and Computer-Integrated Manufacturingvol 25 no 6 pp 999ndash1007 2009

[16] I Um H Cheon and H Lee ldquoThe simulation design andanalysis of a flexible manufacturing system with automatedguided vehicle systemrdquo Journal of Manufacturing Systems vol28 no 4 pp 115ndash122 2009

[17] Z Deng Y Lu K K Wei and J Zhang ldquoUnderstandingcustomer satisfaction and loyalty an empirical study of mobileinstant messages in Chinardquo International Journal of InformationManagement vol 30 no 4 pp 289ndash300 2010

[18] M J R Ortega ldquoCompetitive strategies and firm performancetechnological capabilitiesrsquo moderating rolesrdquo Journal of BusinessResearch vol 63 no 12 pp 1273ndash1281 2010

[19] S Salunke J Weerawardena and J R McColl-KennedyldquoTowards a model of dynamic capabilities in innovation-basedcompetitive strategy insights from project-oriented servicefirmsrdquo Industrial Marketing Management vol 40 no 8 pp1251ndash1263 2011

[20] A Agus and Z Hassan ldquoEnhancing production performanceand customer performance through Total QualityManagement(TQM) strategies for competitive advantagerdquo in Proceedings ofthe 7th International Strategic Management Conference vol 24pp 1650ndash1662 July 2011

[21] HWang X ZhuHWang S J Hu Z Lin andGChen ldquoMulti-objective optimization of product variety and manufacturingcomplexity in mixed-model assembly systemsrdquo Journal of Man-ufacturing Systems vol 30 no 1 pp 16ndash27 2011

[22] E Claver-Cortes EM Pertusa-Ortega and J FMolina-AzorınldquoCharacteristics of organizational structure relating to hybridcompetitive strategy implications for performancerdquo Journal ofBusiness Research vol 65 no 7 pp 993ndash1002 2012

The Scientific World Journal 9

[23] W Wang and Y Koren ldquoScalability planning for reconfigurablemanufacturing systemsrdquo Journal of Manufacturing Systems vol31 no 2 pp 83ndash91 2012

[24] L Esbjerg B B Jensen T Bech-Larsen M D de BarcellosY Boztug and K G Grunert ldquoAn integrative conceptualframework for analyzing customer satisfaction with shoppingtrip experiences in grocery retailingrdquo Journal of Retailing andConsumer Services vol 19 no 4 pp 445ndash456 2012

[25] N Bolloju C Schneider and V Sugumaran ldquoA knowledge-based system for improving the consistency between objectmodels and use case narrativesrdquo Expert Systems with Applica-tions vol 39 no 10 pp 9398ndash9410 2012

[26] H Nouri and T S Hong ldquoDevelopment of bacteria foragingoptimization algorithm for cell formation in cellular manu-facturing system considering cell load variationsrdquo Journal ofManufacturing Systems vol 32 no 1 pp 20ndash31 2013

[27] Z Yang and Y Chen ldquoFuzzy soft set-based approach to pri-oritizing technical attributes in quality function deploymentrdquoNeural Computing and Applications vol 23 no 7-8 pp 2493ndash2500 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

6 The Scientific World Journal

Table 3 Relative importance for customer needs

Efficiency Cost Quality Time Individuation EnvironmentEfficiencyCostQualityTimeIndividuationEnvironment

Table 4 RI Value table

Verify items 1 2 3 4 5 6 7 8 9RI 0 0 058 090 112 124 132 141 145

119870119901

119894standardization 119870

119901

119894= 119870119901

119894sum119894119870119901

119894 that is the

important weights of customer needs(4) Verify the validity weight If CR lt 01 it shows

that the consistency of a judgment matrix can beaccepted if CR gt 01 it shows that data does notneed to modify the data consistency until satisfied asillustrated in Table 4

42 Strategic Weight (1) The indicators with the quantitativecalculation of the first indicators 119895 relation 119894 option value ofthe proportion of indicators 119901

119894119895 Consider

119901119894119895=

119909119894119895

sum119898

119894=1119909119894119895

(13)

(2) Calculation of the first indicators 119895 entropy 119890119895is as

follows

119890119895= minus119896

119898

sum

119894=1

119901119894119895ln119901119894119895 (14)

Among them 119896 gt 0 ln is natural logarithm 119890119895ge 0 If 119909

119894119895is

all equal to a given 119895 then

119901119894119895=

119909119894119895

sum119898

119894=1119909119894119895

=1

119898 (15)

119864119895gets the maximum at this time that is

119890119895= minus119896

119898

sum

119894=1

1

119898ln 1

119898= 119896 ln119898 (16)

If we suppose 119896 = 1 ln119898 there is 0 le 119890119895le 1

(3) Calculation of the first 119895 indicators of the differencecoefficient 119892

119895is as follows For a given 119895 the smaller the

difference of 119909119894119895is the greater the 119890

119895is when 119909

119894119895are all equal

119890119895= 119890max = 1 at this time there is no effect on indicators 119883

119895

for the program compared the greater the difference betweenthe index values is the smaller the 119890

119895is the bigger role of the

indicators for the program are compared

(4) Improving data is as follows It can be based on expertopinion efficacy coefficient method used for transformation

of data and consistency check taking 119883(ℎ)

119895= max119883

119895 119883(120582)

119895=

min119883119895 the transform using the following formula

119883lowast

119894119895=

119909119894119895minus 119909(120582)

119895

119909(ℎ)

119895minus 119909(120582)

119895

times 119860 + 119861 (17)

If you consider that the indicator weight should be greaterthe date differences in large-scale can be chosen larger andif the data differences are in a small area it can be chosensmaller Also the combination of expert scoring method andthe evaluator can add a certain degree of subjective factorsthus increasing the evaluation-oriented that is in formula

119883lowast

119894119895=

119909119894119895minus 119909120582

119895

119909(ℎ)

119895minus 119909(120582)

119895

times 120572 + (120572 minus 1) (18)

If you want to increase the weights 120572 can be increased whenthe data difference is large and it is large in a similar mannerif we want to reduce the weight of the indicators 120572 can bedecreased when the data difference is small and the weightcalculated with using the entropy weight method is small

(5) The definition of weights is as follows Consider

119886119895=

119892119895

sum119899

119895=1119892119895

(19)

(6) Calculating the value of comprehensive evaluation of119881119894is as follows

119881119894=

119899

sum

119895=1

119886119895119901119894119895 (20)

119881119894option for the first 119894 values of comprehensive evaluation

5 The Numerical Analysis

(1) The Initial Data of Customer Weight See Table 5

(2) Enterprise Weight See Table 6

(3) Strategic Weight See Table 7

(4) Weight of Functional Objectives on Manufacturing SystemIt is proposed that

119882119860= 08 119882

119878= 02 (21)

The Scientific World Journal 7

Table 5

Efficiency Cost Quality Time Individuation Environment(03 05 07) (01 03 05) (05 07 09) (03 05 07) (07 09 1) (09 1 1)Result (013 008 018 013 023 and 025)

Table 6

Efficiency Cost Quality Time Individuation EnvironmentEfficiency 1 12 2 13 13 12Cost 2 1 3 4 1 2Quality 12 13 1 12 14 13Time 3 14 2 1 13 12Individuation 3 1 4 3 1 2Environment 2 12 3 2 12 1Result (009 026 006 014 028 and 018)

Table 7

Efficiency Cost Quality Time Individuation Environment084 079 093 072 096 06408 072 064 083 09 076082 068 086 089 078 078076 08 072 093 09 085Result (013 009 028 018 011 and 021)

Table 8 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment008 012 011 012 032 025

Table 9 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment011 020 012 027 012 018

Table 10 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment017 027 018 015 010 013

The comprehensive result is illustrated in Table 8It is proposed that

119882119860= 05 119882

119878= 05 (22)

The comprehensive result is illustrated in Table 9 Consider

119882119860= 02 119882

119878= 08 (23)

The comprehensive result is illustrated in Table 10By Table 8 it can be concluded that the six key elements

for function objective decision on the manufacturing systemin the sequence are personalization being environmentfriendly prompt delivery low cost good quality and highproduction efficiency From the reality the decision resultsare up to the present market for personalized consumption

and premise of legal requirements associated with environ-mental protection That could be considered to be person-alized and environmental competition orientation in theoverall design the manufacturing system function objectiveoccupies 57 in total and the other four for the intensity ofcompetition occupy the proportion of 43

From Tables 9 and 10 we can find that this decisionmethod is based on the stability and consistency of weightjudgments if there are remarkable differences on the weightwhich need to use additional methods to integrate differ-ences

6 Conclusion

In this paper we established the three dimensional weightdetermination methods comprehensively considering the

8 The Scientific World Journal

customer needs enterprise decision-makers intent andenterprise competitive position It provides scientific andpractical guidance for enterprises in the objective selectionof manufacturing system function From the final calculationof each objective weight we could see that this method hadinteraction of human-machine and universal applicability ofenvironment and it was also easy to operate and computeprogrammingThe deficiency of this paper is that themethodstill needs a large number of actual inspections in order toprove it in line with the objective decision of the practicalneed of the manufacturing system function

In the future main job will be to determine the relation-ship between 119882

119860 and 119882119878 using the scientific method such

as statistics data envelopment analysis (DEA) method andregression method to determine the proportions of them

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by the National Natural ScienceFoundation of China under Grants nos 70172042 70472034and the Soft Science Project no F13-317-5-29 of ShenyangScience and Technology Bureau

References

[1] C-F Tsai and M-Y Chen ldquoVariable selection by associationrules for customer churn prediction ofmultimedia on demandrdquoExpert Systems with Applications vol 37 no 3 pp 2006ndash20152010

[2] A Haensel G Koole and J Erdman ldquoEstimating uncon-strained customer choice set demand a case study on airlinereservation datardquo Journal of Choice Modelling vol 4 no 3 pp75ndash87 2011

[3] H G H TiemessenM Fleischmann G J VanHoutum J A EE VanNunen and E Pratsini ldquoDynamic demand fulfillment inspare parts networks with multiple customer classesrdquo EuropeanJournal of Operational Research vol 228 no 2 pp 367ndash3802013

[4] M Heijltjes and A van Witteloostuijn ldquoConfigurations ofmarket environments competitive strategies manufacturingtechnologies and human resource management policies a two-industry and two-country analysis of fitrdquo Scandinavian Journalof Management vol 19 no 1 pp 31ndash62 2003

[5] E Grigoroudis and Y Siskos ldquoA survey of customer sat-isfaction barometers some results from the transportation-communications sectorrdquo European Journal of OperationalResearch vol 152 no 2 pp 334ndash353 2004

[6] A A Levis and L G Papageorgiou ldquoCustomer demandforecasting via support vector regression analysisrdquo ChemicalEngineering Research and Design vol 83 no 8 A pp 1009ndash10182005

[7] K K B Hon ldquoPerformance and evaluation of manufacturingsystemsrdquo CIRP Annals Manufacturing Technology vol 54 no2 pp 139ndash154 2005

[8] J G Wacker and C Sheu ldquoEffectiveness of manufacturingplanning and controlsystems on manufacturing competitive-ness evidence from global manufacturing datardquo InternationalJournal of Production Research vol 44 no 5 pp 1015ndash10362006

[9] A M A Youssef A Mohib and H A Elmaraghy ldquoAvail-ability assessment of multi-state manufacturing systems usinguniversal generating functionrdquo CIRP Annals ManufacturingTechnology vol 55 no 1 pp 445ndash448 2006

[10] G S Dangayach and S G Deshmukh ldquoAn exploratory study ofmanufacturing strategy practices of machinery manufacturingcompanies in Indiardquo Omega vol 34 no 3 pp 254ndash273 2006

[11] B Menguc S Auh and E Shih ldquoTransformational leadershipand market orientation implications for the implementation ofcompetitive strategies and business unit performancerdquo Journalof Business Research vol 60 no 4 pp 314ndash321 2007

[12] M Acquaah andM Yasai-Ardekani ldquoDoes the implementationof a combination competitive strategy yield incremental perfor-mance benefits A new perspective from a transition economyin Sub-Saharan Africardquo Journal of Business Research vol 61 no4 pp 346ndash354 2008

[13] V Kalogeraki P MMelliar-Smith L E Moser and Y DrougasldquoResource management using multiple feedback loops in softreal-time distributed object systemsrdquo Journal of Systems andSoftware vol 81 no 7 pp 1144ndash1162 2008

[14] E Babakus U Yavas and N J Ashill ldquoThe role of cus-tomer orientation as a moderator of the job demand-burnout-performance relationship a surface-level trait perspectiverdquoJournal of Retailing vol 85 no 4 pp 480ndash492 2009

[15] P Vichare A Nassehi S Kumar and S T Newman ldquoA unifiedmanufacturing resource model for representing CNC machin-ing systemsrdquo Robotics and Computer-Integrated Manufacturingvol 25 no 6 pp 999ndash1007 2009

[16] I Um H Cheon and H Lee ldquoThe simulation design andanalysis of a flexible manufacturing system with automatedguided vehicle systemrdquo Journal of Manufacturing Systems vol28 no 4 pp 115ndash122 2009

[17] Z Deng Y Lu K K Wei and J Zhang ldquoUnderstandingcustomer satisfaction and loyalty an empirical study of mobileinstant messages in Chinardquo International Journal of InformationManagement vol 30 no 4 pp 289ndash300 2010

[18] M J R Ortega ldquoCompetitive strategies and firm performancetechnological capabilitiesrsquo moderating rolesrdquo Journal of BusinessResearch vol 63 no 12 pp 1273ndash1281 2010

[19] S Salunke J Weerawardena and J R McColl-KennedyldquoTowards a model of dynamic capabilities in innovation-basedcompetitive strategy insights from project-oriented servicefirmsrdquo Industrial Marketing Management vol 40 no 8 pp1251ndash1263 2011

[20] A Agus and Z Hassan ldquoEnhancing production performanceand customer performance through Total QualityManagement(TQM) strategies for competitive advantagerdquo in Proceedings ofthe 7th International Strategic Management Conference vol 24pp 1650ndash1662 July 2011

[21] HWang X ZhuHWang S J Hu Z Lin andGChen ldquoMulti-objective optimization of product variety and manufacturingcomplexity in mixed-model assembly systemsrdquo Journal of Man-ufacturing Systems vol 30 no 1 pp 16ndash27 2011

[22] E Claver-Cortes EM Pertusa-Ortega and J FMolina-AzorınldquoCharacteristics of organizational structure relating to hybridcompetitive strategy implications for performancerdquo Journal ofBusiness Research vol 65 no 7 pp 993ndash1002 2012

The Scientific World Journal 9

[23] W Wang and Y Koren ldquoScalability planning for reconfigurablemanufacturing systemsrdquo Journal of Manufacturing Systems vol31 no 2 pp 83ndash91 2012

[24] L Esbjerg B B Jensen T Bech-Larsen M D de BarcellosY Boztug and K G Grunert ldquoAn integrative conceptualframework for analyzing customer satisfaction with shoppingtrip experiences in grocery retailingrdquo Journal of Retailing andConsumer Services vol 19 no 4 pp 445ndash456 2012

[25] N Bolloju C Schneider and V Sugumaran ldquoA knowledge-based system for improving the consistency between objectmodels and use case narrativesrdquo Expert Systems with Applica-tions vol 39 no 10 pp 9398ndash9410 2012

[26] H Nouri and T S Hong ldquoDevelopment of bacteria foragingoptimization algorithm for cell formation in cellular manu-facturing system considering cell load variationsrdquo Journal ofManufacturing Systems vol 32 no 1 pp 20ndash31 2013

[27] Z Yang and Y Chen ldquoFuzzy soft set-based approach to pri-oritizing technical attributes in quality function deploymentrdquoNeural Computing and Applications vol 23 no 7-8 pp 2493ndash2500 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

The Scientific World Journal 7

Table 5

Efficiency Cost Quality Time Individuation Environment(03 05 07) (01 03 05) (05 07 09) (03 05 07) (07 09 1) (09 1 1)Result (013 008 018 013 023 and 025)

Table 6

Efficiency Cost Quality Time Individuation EnvironmentEfficiency 1 12 2 13 13 12Cost 2 1 3 4 1 2Quality 12 13 1 12 14 13Time 3 14 2 1 13 12Individuation 3 1 4 3 1 2Environment 2 12 3 2 12 1Result (009 026 006 014 028 and 018)

Table 7

Efficiency Cost Quality Time Individuation Environment084 079 093 072 096 06408 072 064 083 09 076082 068 086 089 078 078076 08 072 093 09 085Result (013 009 028 018 011 and 021)

Table 8 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment008 012 011 012 032 025

Table 9 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment011 020 012 027 012 018

Table 10 The final weight of functional objectives on manufacturing system

Efficiency Cost Quality Time Individuation Environment017 027 018 015 010 013

The comprehensive result is illustrated in Table 8It is proposed that

119882119860= 05 119882

119878= 05 (22)

The comprehensive result is illustrated in Table 9 Consider

119882119860= 02 119882

119878= 08 (23)

The comprehensive result is illustrated in Table 10By Table 8 it can be concluded that the six key elements

for function objective decision on the manufacturing systemin the sequence are personalization being environmentfriendly prompt delivery low cost good quality and highproduction efficiency From the reality the decision resultsare up to the present market for personalized consumption

and premise of legal requirements associated with environ-mental protection That could be considered to be person-alized and environmental competition orientation in theoverall design the manufacturing system function objectiveoccupies 57 in total and the other four for the intensity ofcompetition occupy the proportion of 43

From Tables 9 and 10 we can find that this decisionmethod is based on the stability and consistency of weightjudgments if there are remarkable differences on the weightwhich need to use additional methods to integrate differ-ences

6 Conclusion

In this paper we established the three dimensional weightdetermination methods comprehensively considering the

8 The Scientific World Journal

customer needs enterprise decision-makers intent andenterprise competitive position It provides scientific andpractical guidance for enterprises in the objective selectionof manufacturing system function From the final calculationof each objective weight we could see that this method hadinteraction of human-machine and universal applicability ofenvironment and it was also easy to operate and computeprogrammingThe deficiency of this paper is that themethodstill needs a large number of actual inspections in order toprove it in line with the objective decision of the practicalneed of the manufacturing system function

In the future main job will be to determine the relation-ship between 119882

119860 and 119882119878 using the scientific method such

as statistics data envelopment analysis (DEA) method andregression method to determine the proportions of them

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by the National Natural ScienceFoundation of China under Grants nos 70172042 70472034and the Soft Science Project no F13-317-5-29 of ShenyangScience and Technology Bureau

References

[1] C-F Tsai and M-Y Chen ldquoVariable selection by associationrules for customer churn prediction ofmultimedia on demandrdquoExpert Systems with Applications vol 37 no 3 pp 2006ndash20152010

[2] A Haensel G Koole and J Erdman ldquoEstimating uncon-strained customer choice set demand a case study on airlinereservation datardquo Journal of Choice Modelling vol 4 no 3 pp75ndash87 2011

[3] H G H TiemessenM Fleischmann G J VanHoutum J A EE VanNunen and E Pratsini ldquoDynamic demand fulfillment inspare parts networks with multiple customer classesrdquo EuropeanJournal of Operational Research vol 228 no 2 pp 367ndash3802013

[4] M Heijltjes and A van Witteloostuijn ldquoConfigurations ofmarket environments competitive strategies manufacturingtechnologies and human resource management policies a two-industry and two-country analysis of fitrdquo Scandinavian Journalof Management vol 19 no 1 pp 31ndash62 2003

[5] E Grigoroudis and Y Siskos ldquoA survey of customer sat-isfaction barometers some results from the transportation-communications sectorrdquo European Journal of OperationalResearch vol 152 no 2 pp 334ndash353 2004

[6] A A Levis and L G Papageorgiou ldquoCustomer demandforecasting via support vector regression analysisrdquo ChemicalEngineering Research and Design vol 83 no 8 A pp 1009ndash10182005

[7] K K B Hon ldquoPerformance and evaluation of manufacturingsystemsrdquo CIRP Annals Manufacturing Technology vol 54 no2 pp 139ndash154 2005

[8] J G Wacker and C Sheu ldquoEffectiveness of manufacturingplanning and controlsystems on manufacturing competitive-ness evidence from global manufacturing datardquo InternationalJournal of Production Research vol 44 no 5 pp 1015ndash10362006

[9] A M A Youssef A Mohib and H A Elmaraghy ldquoAvail-ability assessment of multi-state manufacturing systems usinguniversal generating functionrdquo CIRP Annals ManufacturingTechnology vol 55 no 1 pp 445ndash448 2006

[10] G S Dangayach and S G Deshmukh ldquoAn exploratory study ofmanufacturing strategy practices of machinery manufacturingcompanies in Indiardquo Omega vol 34 no 3 pp 254ndash273 2006

[11] B Menguc S Auh and E Shih ldquoTransformational leadershipand market orientation implications for the implementation ofcompetitive strategies and business unit performancerdquo Journalof Business Research vol 60 no 4 pp 314ndash321 2007

[12] M Acquaah andM Yasai-Ardekani ldquoDoes the implementationof a combination competitive strategy yield incremental perfor-mance benefits A new perspective from a transition economyin Sub-Saharan Africardquo Journal of Business Research vol 61 no4 pp 346ndash354 2008

[13] V Kalogeraki P MMelliar-Smith L E Moser and Y DrougasldquoResource management using multiple feedback loops in softreal-time distributed object systemsrdquo Journal of Systems andSoftware vol 81 no 7 pp 1144ndash1162 2008

[14] E Babakus U Yavas and N J Ashill ldquoThe role of cus-tomer orientation as a moderator of the job demand-burnout-performance relationship a surface-level trait perspectiverdquoJournal of Retailing vol 85 no 4 pp 480ndash492 2009

[15] P Vichare A Nassehi S Kumar and S T Newman ldquoA unifiedmanufacturing resource model for representing CNC machin-ing systemsrdquo Robotics and Computer-Integrated Manufacturingvol 25 no 6 pp 999ndash1007 2009

[16] I Um H Cheon and H Lee ldquoThe simulation design andanalysis of a flexible manufacturing system with automatedguided vehicle systemrdquo Journal of Manufacturing Systems vol28 no 4 pp 115ndash122 2009

[17] Z Deng Y Lu K K Wei and J Zhang ldquoUnderstandingcustomer satisfaction and loyalty an empirical study of mobileinstant messages in Chinardquo International Journal of InformationManagement vol 30 no 4 pp 289ndash300 2010

[18] M J R Ortega ldquoCompetitive strategies and firm performancetechnological capabilitiesrsquo moderating rolesrdquo Journal of BusinessResearch vol 63 no 12 pp 1273ndash1281 2010

[19] S Salunke J Weerawardena and J R McColl-KennedyldquoTowards a model of dynamic capabilities in innovation-basedcompetitive strategy insights from project-oriented servicefirmsrdquo Industrial Marketing Management vol 40 no 8 pp1251ndash1263 2011

[20] A Agus and Z Hassan ldquoEnhancing production performanceand customer performance through Total QualityManagement(TQM) strategies for competitive advantagerdquo in Proceedings ofthe 7th International Strategic Management Conference vol 24pp 1650ndash1662 July 2011

[21] HWang X ZhuHWang S J Hu Z Lin andGChen ldquoMulti-objective optimization of product variety and manufacturingcomplexity in mixed-model assembly systemsrdquo Journal of Man-ufacturing Systems vol 30 no 1 pp 16ndash27 2011

[22] E Claver-Cortes EM Pertusa-Ortega and J FMolina-AzorınldquoCharacteristics of organizational structure relating to hybridcompetitive strategy implications for performancerdquo Journal ofBusiness Research vol 65 no 7 pp 993ndash1002 2012

The Scientific World Journal 9

[23] W Wang and Y Koren ldquoScalability planning for reconfigurablemanufacturing systemsrdquo Journal of Manufacturing Systems vol31 no 2 pp 83ndash91 2012

[24] L Esbjerg B B Jensen T Bech-Larsen M D de BarcellosY Boztug and K G Grunert ldquoAn integrative conceptualframework for analyzing customer satisfaction with shoppingtrip experiences in grocery retailingrdquo Journal of Retailing andConsumer Services vol 19 no 4 pp 445ndash456 2012

[25] N Bolloju C Schneider and V Sugumaran ldquoA knowledge-based system for improving the consistency between objectmodels and use case narrativesrdquo Expert Systems with Applica-tions vol 39 no 10 pp 9398ndash9410 2012

[26] H Nouri and T S Hong ldquoDevelopment of bacteria foragingoptimization algorithm for cell formation in cellular manu-facturing system considering cell load variationsrdquo Journal ofManufacturing Systems vol 32 no 1 pp 20ndash31 2013

[27] Z Yang and Y Chen ldquoFuzzy soft set-based approach to pri-oritizing technical attributes in quality function deploymentrdquoNeural Computing and Applications vol 23 no 7-8 pp 2493ndash2500 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

8 The Scientific World Journal

customer needs enterprise decision-makers intent andenterprise competitive position It provides scientific andpractical guidance for enterprises in the objective selectionof manufacturing system function From the final calculationof each objective weight we could see that this method hadinteraction of human-machine and universal applicability ofenvironment and it was also easy to operate and computeprogrammingThe deficiency of this paper is that themethodstill needs a large number of actual inspections in order toprove it in line with the objective decision of the practicalneed of the manufacturing system function

In the future main job will be to determine the relation-ship between 119882

119860 and 119882119878 using the scientific method such

as statistics data envelopment analysis (DEA) method andregression method to determine the proportions of them

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

This research was supported by the National Natural ScienceFoundation of China under Grants nos 70172042 70472034and the Soft Science Project no F13-317-5-29 of ShenyangScience and Technology Bureau

References

[1] C-F Tsai and M-Y Chen ldquoVariable selection by associationrules for customer churn prediction ofmultimedia on demandrdquoExpert Systems with Applications vol 37 no 3 pp 2006ndash20152010

[2] A Haensel G Koole and J Erdman ldquoEstimating uncon-strained customer choice set demand a case study on airlinereservation datardquo Journal of Choice Modelling vol 4 no 3 pp75ndash87 2011

[3] H G H TiemessenM Fleischmann G J VanHoutum J A EE VanNunen and E Pratsini ldquoDynamic demand fulfillment inspare parts networks with multiple customer classesrdquo EuropeanJournal of Operational Research vol 228 no 2 pp 367ndash3802013

[4] M Heijltjes and A van Witteloostuijn ldquoConfigurations ofmarket environments competitive strategies manufacturingtechnologies and human resource management policies a two-industry and two-country analysis of fitrdquo Scandinavian Journalof Management vol 19 no 1 pp 31ndash62 2003

[5] E Grigoroudis and Y Siskos ldquoA survey of customer sat-isfaction barometers some results from the transportation-communications sectorrdquo European Journal of OperationalResearch vol 152 no 2 pp 334ndash353 2004

[6] A A Levis and L G Papageorgiou ldquoCustomer demandforecasting via support vector regression analysisrdquo ChemicalEngineering Research and Design vol 83 no 8 A pp 1009ndash10182005

[7] K K B Hon ldquoPerformance and evaluation of manufacturingsystemsrdquo CIRP Annals Manufacturing Technology vol 54 no2 pp 139ndash154 2005

[8] J G Wacker and C Sheu ldquoEffectiveness of manufacturingplanning and controlsystems on manufacturing competitive-ness evidence from global manufacturing datardquo InternationalJournal of Production Research vol 44 no 5 pp 1015ndash10362006

[9] A M A Youssef A Mohib and H A Elmaraghy ldquoAvail-ability assessment of multi-state manufacturing systems usinguniversal generating functionrdquo CIRP Annals ManufacturingTechnology vol 55 no 1 pp 445ndash448 2006

[10] G S Dangayach and S G Deshmukh ldquoAn exploratory study ofmanufacturing strategy practices of machinery manufacturingcompanies in Indiardquo Omega vol 34 no 3 pp 254ndash273 2006

[11] B Menguc S Auh and E Shih ldquoTransformational leadershipand market orientation implications for the implementation ofcompetitive strategies and business unit performancerdquo Journalof Business Research vol 60 no 4 pp 314ndash321 2007

[12] M Acquaah andM Yasai-Ardekani ldquoDoes the implementationof a combination competitive strategy yield incremental perfor-mance benefits A new perspective from a transition economyin Sub-Saharan Africardquo Journal of Business Research vol 61 no4 pp 346ndash354 2008

[13] V Kalogeraki P MMelliar-Smith L E Moser and Y DrougasldquoResource management using multiple feedback loops in softreal-time distributed object systemsrdquo Journal of Systems andSoftware vol 81 no 7 pp 1144ndash1162 2008

[14] E Babakus U Yavas and N J Ashill ldquoThe role of cus-tomer orientation as a moderator of the job demand-burnout-performance relationship a surface-level trait perspectiverdquoJournal of Retailing vol 85 no 4 pp 480ndash492 2009

[15] P Vichare A Nassehi S Kumar and S T Newman ldquoA unifiedmanufacturing resource model for representing CNC machin-ing systemsrdquo Robotics and Computer-Integrated Manufacturingvol 25 no 6 pp 999ndash1007 2009

[16] I Um H Cheon and H Lee ldquoThe simulation design andanalysis of a flexible manufacturing system with automatedguided vehicle systemrdquo Journal of Manufacturing Systems vol28 no 4 pp 115ndash122 2009

[17] Z Deng Y Lu K K Wei and J Zhang ldquoUnderstandingcustomer satisfaction and loyalty an empirical study of mobileinstant messages in Chinardquo International Journal of InformationManagement vol 30 no 4 pp 289ndash300 2010

[18] M J R Ortega ldquoCompetitive strategies and firm performancetechnological capabilitiesrsquo moderating rolesrdquo Journal of BusinessResearch vol 63 no 12 pp 1273ndash1281 2010

[19] S Salunke J Weerawardena and J R McColl-KennedyldquoTowards a model of dynamic capabilities in innovation-basedcompetitive strategy insights from project-oriented servicefirmsrdquo Industrial Marketing Management vol 40 no 8 pp1251ndash1263 2011

[20] A Agus and Z Hassan ldquoEnhancing production performanceand customer performance through Total QualityManagement(TQM) strategies for competitive advantagerdquo in Proceedings ofthe 7th International Strategic Management Conference vol 24pp 1650ndash1662 July 2011

[21] HWang X ZhuHWang S J Hu Z Lin andGChen ldquoMulti-objective optimization of product variety and manufacturingcomplexity in mixed-model assembly systemsrdquo Journal of Man-ufacturing Systems vol 30 no 1 pp 16ndash27 2011

[22] E Claver-Cortes EM Pertusa-Ortega and J FMolina-AzorınldquoCharacteristics of organizational structure relating to hybridcompetitive strategy implications for performancerdquo Journal ofBusiness Research vol 65 no 7 pp 993ndash1002 2012

The Scientific World Journal 9

[23] W Wang and Y Koren ldquoScalability planning for reconfigurablemanufacturing systemsrdquo Journal of Manufacturing Systems vol31 no 2 pp 83ndash91 2012

[24] L Esbjerg B B Jensen T Bech-Larsen M D de BarcellosY Boztug and K G Grunert ldquoAn integrative conceptualframework for analyzing customer satisfaction with shoppingtrip experiences in grocery retailingrdquo Journal of Retailing andConsumer Services vol 19 no 4 pp 445ndash456 2012

[25] N Bolloju C Schneider and V Sugumaran ldquoA knowledge-based system for improving the consistency between objectmodels and use case narrativesrdquo Expert Systems with Applica-tions vol 39 no 10 pp 9398ndash9410 2012

[26] H Nouri and T S Hong ldquoDevelopment of bacteria foragingoptimization algorithm for cell formation in cellular manu-facturing system considering cell load variationsrdquo Journal ofManufacturing Systems vol 32 no 1 pp 20ndash31 2013

[27] Z Yang and Y Chen ldquoFuzzy soft set-based approach to pri-oritizing technical attributes in quality function deploymentrdquoNeural Computing and Applications vol 23 no 7-8 pp 2493ndash2500 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

The Scientific World Journal 9

[23] W Wang and Y Koren ldquoScalability planning for reconfigurablemanufacturing systemsrdquo Journal of Manufacturing Systems vol31 no 2 pp 83ndash91 2012

[24] L Esbjerg B B Jensen T Bech-Larsen M D de BarcellosY Boztug and K G Grunert ldquoAn integrative conceptualframework for analyzing customer satisfaction with shoppingtrip experiences in grocery retailingrdquo Journal of Retailing andConsumer Services vol 19 no 4 pp 445ndash456 2012

[25] N Bolloju C Schneider and V Sugumaran ldquoA knowledge-based system for improving the consistency between objectmodels and use case narrativesrdquo Expert Systems with Applica-tions vol 39 no 10 pp 9398ndash9410 2012

[26] H Nouri and T S Hong ldquoDevelopment of bacteria foragingoptimization algorithm for cell formation in cellular manu-facturing system considering cell load variationsrdquo Journal ofManufacturing Systems vol 32 no 1 pp 20ndash31 2013

[27] Z Yang and Y Chen ldquoFuzzy soft set-based approach to pri-oritizing technical attributes in quality function deploymentrdquoNeural Computing and Applications vol 23 no 7-8 pp 2493ndash2500 2013

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of

International Journal of

AerospaceEngineeringHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

RoboticsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Active and Passive Electronic Components

Control Scienceand Engineering

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

RotatingMachinery

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporation httpwwwhindawicom

Journal ofEngineeringVolume 2014

Submit your manuscripts athttpwwwhindawicom

VLSI Design

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Shock and Vibration

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Civil EngineeringAdvances in

Acoustics and VibrationAdvances in

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawi Publishing Corporation httpwwwhindawicom

Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Chemical EngineeringInternational Journal of Antennas and

Propagation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Navigation and Observation

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

DistributedSensor Networks

International Journal of