COMPUTER-AIDED DESIGN & APPLICATIONS, 2017VOL. 14, NO. 4, 472–485http://dx.doi.org/10.1080/16864360.2016.1257189

An architecture design for smart manufacturing execution system

Byeong Woo Jeon a, Jumyung Um b, Soo Cheol Yoon b and Suh Suk-Hwan b

aGraduate School of Engineering Mastership, POSTECH, Korea; bCenter for Ubiquitous Manufacturing, POSTECH, Korea

ABSTRACTMES (Manufacturing Execution System) puts plans given by enterprise system such as ERP(EnterpriseResource Planning) into practice in the shop floor. As contemporary companies are faced with chal-lenge represented by customer-centric market environment and intense competitions, it becomesincreasingly important for companies to cope with dynamic customers’ demand and shop floorsituation rapidly. Even though current MESs gets some improvement on collecting raw data, theyfall short of expectation on analyzing data and takes action by cooperating various manufactur-ing management functions. Furthermore, even though many researches treat data collection, dataanalysis based on shop floor situation, little number of previous researches deals with collaborationamong various functionalities provided by MES. In this paper, we introduce the design of advancedMES (namely Smart MES) for helping MES collect, analyze, take measure by collaborating variousfunctions. This paper consists of two parts. In the first part, current MES problem analysis and corre-sponding design consideration are presented. In the second part, architecture design of Smart MESis proposed based on design consideration and an operation scenario of TO-BE model is providedfor comparison with AS-IS model.

KEYWORDSManufacturing executionsystem (MES); dataintegration; systemarchitecture; collaboration

1. Introduction

Contemporary manufacturing companies have beenconfronting with various demands such as fast responseto shop floor fault, balancing inventory while aimingat customized production, flexibly changing operationschedule according to products and shop floor status,which is hard to be satisfied concurrently [33]. Eventhough ERP (Enterprise Resource Planning) system sup-ports information flow between enterprise and outsidestakeholder, using ERP alone isn’t enough since ERPmainly concentrates on managerial level issues and itdoesn’t meticulously treat shop floor situation [34]. Todeal with above challenges, not only information fromenterprise systems such as ERP but also shop floor datashould be utilized so that appropriate judgement canbe brought with various demands from shop floor andenterprise being in harmony. The MES was introducedfor managing shop floor activities based on productionschedule and shop floor situation. MES is the systemwhich performs manufacturing execution from workorder to finished product with optimization of over-all manufacturing operation management [26]. Besidesmeans for information exchange between shop floor

CONTACT Byeong Woo Jeon jhs1728@postech.ac.kr

and enterprise information system, MES is in chargeof managing various shop floor activities like operationscheduling, production execution and control, equip-ment status management linking shop-floor supervisor,material delivery and consumption management [2].

It’s important to deal with shop floor event such asquality problem and facility failure in real-time.However,many of current manufacturing companies have a diffi-culty in handling manufacturing management activitiesusing MES mainly due to lack of infrastructure for datacollection, analysis, integration of manufacturing data[16] and collaboration and cooperation on each func-tionality of MES system. Cooperation work is mainlydone via call conversation or email etc., whichmeans thatworker should know in detail how to deal with or getaround manufacturing management issues. Lots of com-panies still have a hard time collecting shop floor datamainly due to inhibition on approaching detail protocolof control devices without paying money [19][27][28]. Itmeans that there’s no way to get useful shop floor dataexcept adopting sensor technology or other integrationmeans such as MTConnect or OPC-UA. MTConnectand OPC-UA can integrate different data protocols by

© 2016 POSTECH U-Manufacturing Center

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specifying the data types, attributes, protocol for unify-ing data model. Servers and Clients attached in facilitiesshould have the ability to convert different types of pro-tocols and data to the unified model and vice versa. Inthis sense, we will develop MES architecture which notonly can it collects and analyzes various manufacturingdata but also respond to shop floor situation throughcollaboration among various functionalities.

Before solving this, literature survey on improvingMES system is conducted. Data collection research ismainly done with AUTO-ID and sensor network tech-nology. Hua, et al. [12] presented RFID based data col-lection. It tries to integrate with current MES by ref-erencing a cotton spinning company. Hori, et al.[11]suggest scalable MES based on distributed object com-puting because of vendor independent, scalability. It usedCORBA as a means for object computing and data inte-gration. It mainly focuses on production control as illus-tration. Huang, et al.[13] suggest management of jobshop by using RFID tag. It’s mainly focusing on gather-ing WIP inventory information. It points out the currentjob shopmanagement problem as paper-work based datacollection. RFID technology is also used in repair workin car production industries [15]. Kadri, et al.[17] usedWSN(wireless sensor network) to monitor air-qualityby integrating sensor network with web environment.Many of research treated are mainly focusing on utiliz-ing one-or two sensor types to get data. Since typicalmanufacturing ground has many types of sensors, somemeans to integrate them are needed. To get around, web-based open sensor web architecture is suggested to over-come the obstacle of connecting heterogeneous sensorresources [6]. Fumagalli, et al. [9] use ontology as ameansfor integrating various shop floor data. Picking systemis used as a use case for ontology-based modeling ofmanufacturing system.

Even though there are many use case for data anal-ysis, MES data analysis is mainly related to scheduling,production control. Zhong, et al. [34] used RFID dataand production order to derive appropriate operationschedule. The schedule is modified according to RFIDdata which denote whether worker is operating machineor not which could change an original schedule. Rolon,et al. [25] suggest using agent-based modeling and simu-lation for efficient scheduling by using order agent andresource agent and their communication. Each agentdoes monitor-analyze-plan-execute cycle. Order agentmonitors overall schedule/order monitoring, specifiesprocess route, books appropriate resource, executes pro-duction control. Resource agent monitors whether corre-sponding resource can be operated or not and its opera-tion schedule, registers task schedule by communicating

with order agent, executes task. Chen, et al. [3] pro-poses intelligent MES which integrates MES with datawarehouse, OLAP (On-Line Analytic Processing), Data-mining to extract useful information from collected data.It describes only delivering extracted information toenterprise information system such as ERP. Chen, et al.[4] apply context-aware computing to MES so that eventcan be interpreted through shop floor data and thenappropriate services which can handle correspondingevent can be called. Likewise, it doesn’t clearly show top-down information flow. NEXUS platform [8] combinestop-down application execution which shop floor dataare used and bottom-up event-delivering. Literatures oncollaboration among functionalities in MES system werehard to find.

Abovementioned research leaves threemajor implica-tions. We need (1) infrastructure which can collect shopfloor data overcoming various protocol, analyze data andextract some useful information, (2) various functionsother than just operation scheduling, production control,part tracking, some function needs to be included such asquality management, maintenance management[20]. (3)Collaboration among various functions to optimizeman-ufacturing management activity in shop floor. (4) Datasynchronization among various functions to optimize theeffect of cooperation.

We will solve these issues by making architecturefor MES dealing with shop floor situation or enterprisedemand in real-time by cooperating among various MESfunctions. Collaboration among functions is importantbecause each function is correlated to other functions.However, there are little researches that treat intercon-nection or association amongMES functions. SmartMESis the MES that can recognize and deal with shop floorsituation in real-time manner with having 4 things men-tioned above. Collaboration among various functionsbased on shop floor event and enterprise system infor-mation is the main point. By implementing this system,it can contribute to: (1) shorten response timewhen deal-ing with shop floor situation or stakeholder requirement,(2) provide user convenience with consistent responseto shop floor situation because user doesn’t need toknow all of details on collaboration of functionalities,(3) cope with varying operation schedule flexibly. Fur-thermore, it can be one of approaches on contempo-rary industrial innovation campaign other than Ameri-can initiative “Industrial Internet” and German initiative“Industry 4.0”.

This paper is organized as follows. Section 2 givesrequirement the SmartMES will satisfy. Section 3 derivesdesign consideration from requirement. Section 4 sug-gests architecture for Smart MES by reflecting on design

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consideration. Section 5 suggests an operation scenarioapplied to car door assembly company.

2. Problem on current manufacturing executionsystem

To design Smart MES, problem derived from currentMES solution and previous research should be identifiedand should be reflected.We used this approach for recog-nizing field problems in the perspective of implicationsand reflecting into the design of architecture. We orga-nize problem based on literature review, some interviewwith shop floor workers, engineers in car door makingcompany and refractory-brick making company. Theseare substantial companies that most of automation onfacility is completed but have something to be desiredon information management and handling. The majorrequirements are summarized as follows.

• [Problem (PR) #1] Workers should record data byhand which coming frommachine controller and typeinto MES or DB (Database). This means that uti-lization of the data will be less helpful due to timegap.

• [PR #2] Sensor technology is often used as a meansfor getting data from shop floor. However, sincethere’re many existing communication standard onsensors [23] and the PLC (Programmable Logic Con-troller) supports different types of sensor dependingon vendors, integrating these data is hard. It causesloss of opportunity for maximizing of current datautilization.

• [PR #3] Even though some companies invest moneyto extract data from facilities by utilizing controllerprotocol, it’s hard to integrate data because of dif-ferent protocol controller supports. It also causesloss of opportunity for maximizing of current datautilization.

• [PR #4] It’s hard to modify operation schedule basedon stakeholder requirement or shop floor situationonce manufacturing schedule is initially determined.So, if some situation happens it brings about lots ofcommunication betweenMESoperator and shopfloorworkers, which cause lots of time consumption inrescheduling.

• [PR #5] It’s difficult for MES operator to track theWIP (work-in-piece) in real-time. When some partsgo to wrong route, manual search is usually done butit causes time delay.

• [PR #6] Allocating right preventive maintenanceschedule is challenging one since it should considerresource status and production operation schedule. It

causes delay on making both operation schedule andpreventive maintenance schedule and requires lots ofcommunication load.

• [PR #7] Generally, it’s hard to recognize what kindof failure happened in machine. It mostly depends onworkers’ tacit knowledge to get around. If user doesn’thave experience and insight, then it’ll take lots of timeto figure out problem. That is, there exists deviationamong workers.

• [PR #8] Most of information processing in manymanufacturing companies was conducted in simpleway such as facility on/off check or x-R chart. Sincethere are some cases that the problem is compositivelycaused by a number of factors other than one variable,it’s hard to figure out that problem if we use the sim-ple data processing. It mostly depends on insight ofworkers.

• [PR #9] It’s hard to figure out what kind of qualityproblem on product happened, what brought aboutthat problem and when that problem started to hap-pen. Even though manual inspection is conducted, italso depends on insight or expertise of workers so itcan cause deviation among workers.

• [PR #10] Estimating material consumption for pro-duction is barely linked with SCM(supply chain man-agement), which means that demand prediction relieson experience of manager and feedback work ismainly done call conversation, which causes lots ofcommunication load and reduction of work efficiency.

• [PR #11] In manufacturing company, production per-formance analysis mainlymanages the amount of pro-duction. Since there are lots of perspectives on theperformance evaluation of manufacturing manage-ment, mainly evaluating the amount of productionloses the other opportunity for feedback in variousperspectives.

• [PR #12] Data integration /synchronization betweenMES and Enterprise information system isn’t fullyestablished. It can pose lots of communication loadsand manual work since one system can’t utilize thedata from other systems, which causes reduction ofwork efficiency.

• [PR #13] There is a lack of collaboration betweenMES functions. In general, every function ofMES isn’tindependent. Each of them in some sense is closelyrelated. However, due to lack of it, lots of communi-cation and waiting for call conversation or email isrequired, which causes reduction of work efficiency.Furthermore, user should have all knowledge on com-municating with different workers when cooperationis needed, which means that deviation happen amongworkers.

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3. Design consideration for smartmanufacturing execution system

To design a field-centric Smart MES with consid-ering previous research, requirements presented insection 2 should be reflected into design considera-tion in the perspective of data collection, integration,analysis, and collaboration among various MES func-tions. We adopted this approach to reflect requirementsderived in previous section into the design of SmartMES architecture. Detailed consideration is given asfollows.

• [Design Consideration(DC) #1] Real-time data acqui-sition via sensor technology: To realize real-time dataanalysis and response, it’s essential to gather shopfloor data to recognize shop floor situation. It cancontribute to reduce time gap on data value betweenshop floor and Smart MES. Furthermore it can help toextract data from some facilities which are prohibitedto access data in controller for some reasons. AdoptingOPC-UA technology will help to collect sensor datato the upper system with platform independence (Tosolve PR #1)

• [DC #2] Reliability of data generated from shop floor:Since analysis and judgement are based on shop floordata, it’s crucial to ensure the reliability of data. Ingeneral, accuracy and reliability is two important fac-tors for measurement. Reliability has precedence overaccuracy because error can be adjusted using severalsoftware filter such as Kalman filter [29]. (To solvePR #1)

• [DC #3] Communication means for various sensorsand controllers: Even though PLC can retrieve sensordata from sensor, controller and then send toMES, notall types of sensors are supported. For example, RFIDdata isn’t easy to input to PLC. OPC-UA will help toget around as mentioned in [DC #1]. Furthermore,since each PLC vendors support different types of sen-sors, it needs various communicationmeans to receivedata. Controller data can be gathered through variousmeans such as RS-232, RS-485, Ethernet/IP. (To solvePR #1, #2, #3)

• [DC #4] Close connection with enterprise infor-mation system: Communication between enterpriseinformation system andMES should be established sothat information can be exchanged effectively betweenthem. (To solve PR #12)

• [DC #5] Data storage in distributed database: Notall data collected from shop floor can’t be handledimmediately due to processing capability of system.To get around, temporary storage space is needed.Using relational database such as MySQL is not good

option because manufacturing data is complex thatrelational database can’t deal with it efficiently. Dis-tributed database is an appropriate option because ofhigh performance, efficiency, scalability. (To solve PR#1, #5, #7, #8, #9, #10, #11)

• [DC #6] Data analysis methodology: Gathering shopfloor data only isn’t enough. Some methodology likedata mining is needed to extract some useful infor-mation. Supporting functions of data mining includeprediction, classification, clustering etc [21]. (To solvePR #7, #8, #9, #10, #11)

• [DC #7] Visualization / Report for analysis result: Bigdata analysis result is needed to be organized so thatSmartMES operator or other users can understand thebehavior of shop floor. (To solve PR #7, #8, #9, #10,#11)

• [DC #8] Collaboration among production manage-ment functions: It’s desired to be linked among MESfunctions to share data generated or stored in eachfunction. Especially, various information is needed tocompose operation schedule.(To solve PR#4, #5, #6,#10, #11, #13)

• [DC #9] Pervasive access to analysis result and execu-tion result of MES functions from shop floor and userdevices: Pervasive access to analysis result is a basis foraccomplishing pervasive use for the result. (To solvePR #4, #5, #6, #7, #9, #10, #13)

• [DC #10] Unified data model: Each facility, eventhough it supports same function, has differentattributes set. That means that it requires unified datamodel to integrate data from shop floor. Moreover,since MES plays a role as a broker between enter-prise information system and shop floor, there mustexists unified data model that accommodate enter-prise information system and shop floor data. (Tosolve PR #2, #3, #12)

• [DC #11] Data transformation between shop floorand SmartMES: For transforming data between SmartMES and shop floor using unified data model, theremust exists some means to deal with.(To solve PR#2, #3)

• [DC #12] Data transformation between Smart MESand Enterprise information system: For transformingdata between Smart MES and enterprise informationsystem using unified data model, there must existssome means to deal with. (To solve PR #12)

4. Smart MES architecture

In this section, architecture of Smart MES is providedbased on system concept which is derived from designconsideration.

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4.1. SmartMES architecture and description ofmain components

Before proposing architecture, system concept whichapplies to design of reference architecture are defined.This comes from the design consideration presented andthese are big block of architecture. System concept isformed by reflecting design consideration. Details arepresented below.

• [System Concept(SC) #1] Adoption of real-time com-munication environment: It’s an infrastructure thatconnects Smart MES, shop floor, enterprise informa-tion system, ERP, user. It is necessary to have thisinfrastructure so that shop floor data and enterprisesystem information can be flowed lively. (ReflectedDC #1, #2, #3, #4, #9)

• [SC #2] Data integration & transformation: Data inte-gration and transformation should be mounted forgathering information efficiently from different typeof systems and efficient analysis. (Reflected DC #2, #3,#5, #10, #11, #12)

• [SC #3] Adoption of Big data module: As platformfor storing, analysis, visualization of large amount ofdata, it plays a critical role for Smart MES since sev-eral MES functions utilize output of big data module

for conducting each own duty. In this module, thereexists some tools to change existing analysis model sothat whenever shop floor data behavior changed, rele-vant model can be changed to cope with. And if thereare some changes in certain applications such as inputdata coming from analysis result, then correspondinganalysis model can be modified. (Reflected DC #5, #6,#7)

• [SC #4] SmartMES functionality as application: SmartMES functionality modules utilize analysis data tojudge what they should do and conduct accordingly.Furthermore, each function can collaborate each otherto optimize shop floor production activity. We adoptform of Smart MES functions as applications becauseapplication can be modified and added flexibly so thatwhenever new functionalities are required to MES,then it’ll be configured and reflected with flexibility.Moreover, data synchronization and module for sup-porting application collaboration are needed to opti-mize overall manufacturing operation management.(Reflected #8)

Overall traceability among PR, DC, SC is shown inFig. 1 and based on above system concept and designconsideration, we provide the reference architecture for

Figure 1. PR-DC-SC traceability diagram.

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Smart MES as shown in Fig. 2.We give description onoverall architecture including main components in thesmart MES. Detailed description is given as follows.

• Devicemiddleware:Devicemiddleware integrates dif-ferent communication protocol existing in the shopfloor. As shown in figure, there are several communi-cation means in the shop floor. To gather data fromdifferent communication protocols, there must existsa mean to integrate different protocols.

• Enterprise informationDB: Information coming fromenterprise information system and information cre-ated fromSmartMES application are stored and trans-formed. It functions two ways: (1): it can play a role asa temporal storage area if either of them can’t receivethe message due to processing capability limit. (3) itcan transform data so that the data can be

• utilized in both systems. Nowadays, many of databasesupport data transformation function [14].

• Enterprise Service Bus: In general, each of enterprisesystem is constructed independently, which meansthat it needs additional work for each of enterprisesystem to be able to communicate with each other[10][22][24].

• Data storage & integration: Data coming from shopfloor needs to be integrated in unified format to beable to analyze efficiently. Traditionally, transforma-tion is normally done in specific module other thanstorage module. However, due to processing speed,Some approaches have been developed so that dataprocessing can be done in data storage area [31].

• Data analysis Engine: After storage & integration, dataismoved to data analysis engine to extract useful infor-mation for application. Overall process of analysis isshown in Fig. 3. The set of data that are transferred aredetermined by model builder. Model builder buildsanalysis model and specifies output by connectingappropriate pre-processing and data mining methodwith parameters for specific analysis problem. Pre-processing methods and data mining methods areextracted from pre-processing module and data min-ing module, respectively. Data behavior from shopfloor can be changed because different criteria forquality, manufacturing process, WIP (work-in-piece)tracking on new product are proposed. Furthermore,they can be changed when new sensors or facilities areintroduced.Model builder can deal with by creating orupdating model. After analysis, visualization & report

Figure 2. Smart MES architecture

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Figure 3. Data analysis procedure.

module processes result of data analysis so that usercan understand what the result stands for. Visualizedmaterial or report can be transferred via web interfaceso that every user is available to access.

• Event Processing: Analysis result coming out fromdata analysis comes to this module. Each result of dataanalysis is regarded as an event. Event filter sees eachof events and records whether this denote abnormalor normal situation. It hands over to event abstrac-tion. Then event abstraction transforms the filteredevent so that application of smart MES can under-stand. Abstracted event goes to application collabora-tionmanager via application interface. Rule repositorydefines filtering rule and abstraction rule. Event dataformat is given in Fig. 4.

• Smart MES application and its management mod-ule: Each function in this module utilizes result of

data analysis to determine which action should betaken to shop floor and what kind of informationshould be delivered to enterprise information system.Function like operation detailed scheduling can alsouse information from enterprise information system.Application can be handled and monitored by uservia user device such as smart phone. Authorized per-sonnel are only allowed to use. Data synchronizationis handled by Data sync. Manager. Along with datasynchronization, collaboration between applicationsand initial event reception is handled by ApplicationCollaboration Manager. Both of them will be elabo-rated on following section. When new application isinstalled or modified, then there are 3 things shouldbe done. : (1) If new application uses data attributesthat doesn’t exist in previous master data set or havedifferent names, then it will be reflected and enrolled

Figure 4. Event data format.

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in master data model. Even though it’s easy to con-figure application so that application should abide bypre-defined master data model through unified datamodel, we emphasize flexibility rather than speed anddesign simplicity. (2) Collaboration information ofvarious applications for newly installed applicationshould be enrolled in Application Collaborationman-ager. (3) Data synchronization should be modified toembrace new or modified data attributes.

• Data transformation rule repository: This reposi-tory specifies rules that transform different data for-mats. The rule defines the transformation relationshipamong enterprise information system format fromenterprise service bus, Smart MES application, Shopfloor data in distributed database in data analysisengine, shop floor data format.

4.2. Data synchronizationmanager and applicationcollaborationmanager

For right decision based on shop floor situation andenterprise system, data synchronization among appli-cations and application collaboration can play impor-tant role. Data synchronization should exist because it’smeaningless when different applications use differentdata value to judge and take action. Each applicationuses its own database to avoid access collision which canhappen when central DB is used. Furthermore, usingcentral DB causes concentration of the number of com-munication related to central DB access, which causelow speed. To get around, it’s desired to place databasein each application and for manager to manage datasynchronization [7].

Collaboration among applications should be treatedbecause each application presented in the architecturecan’t act independently. Each function needs to collabo-rate with each other to optimize overall shop floor man-ufacturing management activities. Even though over-all structure can be configured such that each applica-tion has its own logic to determine where to send, wechoose to make centralized collaboration manager. If weadopt the structure that each application handles its ownforwarding logic, then the number of change of inter-face will be huge if some of applications interface needto be updated due to some reason. In this subsection,we develop top-level architecture and sequence diagramfor Data Sync. Manager, App. Collaboration Manager.Sequence diagram is frequently used tool to clarify work-flow among various systems. Fig. 5 shows top-level archi-tecture for Data Sync. Manager and App. CollaborationManager.

Data Sync. Manager consists of data interface, datagrouping module, data routing module, data collectionDB, conflict resolver, and data attribute repository. Datainterface is a passage that updated data comes in and thencorrected data which go through data grouping mod-ule and conflict resolver goes out. Data collection DBtemporally stores data which comes to Data Sync. Man-ager. Data grouping module groups data which denotessame attributes but may have different value or numberof updates. More than two elements in a group meanstwo or more applications update same data concurrently.Data attribute repository stores attributes defined in App.master data model. Groups in which there are more thantwo data elements are sent to conflict resolver to handle.Data whose number of update is recorded to the high-est is selected to send data. That is, each data attribute in

Figure 5. Data sync. manager and application collaboration manager architecture.

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Figure 6. Data sync. manager sequence diagram.

each MES application DB accompanies its own numberof update so that latest information can be reflected in theapplication and increased number of update is recordedin application. After conflict resolver, data routing mod-ule determineswhere they should be transferred based onrule specified in it. Sequence diagram representing dataflow is provided in Fig. 6. It shows data flow from send-ing updated data in application #1, #2 to sending updateddata to application #3 after some update conflict check-ing. It includes two cases: (1) when there’s no conflict. (2):exists conflict.

Application Collaboration Manager consists of mes-sage interface, message queue, message content extrac-tion module, message generator, rule-based engine forcollaboration rule. Whenever an application recognizesthat it needs to send some request or information to otherapplications, it makes and sends message to applicationcollaboration manager to decide where to send thatmessage.

Message interface is a passage that message sent froman application comes in and goes out after decision onwhere to send. Message queue is a temporary storage

Figure 7. Application collaboration manager sequence diagram.

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area that stores incoming datawhen computing resourcesin App. Collaboration Manager aren’t available. Whencomputing resources are available for deciding messagedestination, then that message is transferred to messagecontent extraction module. It extracts content in mes-sage so that App. Collaboration Manager can know whatthe message stands for. It is usually done by mappinggiven message to pre-specified message structure. Fromthis procedure, we can find some useful information suchas where this message comes from, which request themessage includes etc.

Based on this content, rule-based engine clarifieswhere this message should be transferred by using ruleswhich is stored in collaboration rule repository. Afterdeciding where to send, then it goes through mes-sage generator that generates messages that delivered torelated applications. Sequence diagram representing dataflow is provided in Fig. 7. It shows data flow from send-ing a message generated from app #1 to send messages tosome applications specified by rule-based engine. Here,the event includes the message from enterprise systemand application interface in the event processing.

5. Operation scenario and prototype based onarchitecture

Even though the best way to prove the validity of thearchitecture that suggested above section is implemen-tation, we don’t adopt that approach since MES itselfconsists of so many components that one can’t handleall of them. Thus, operation scenario and correspondingprototype based on that architecture is presented. Amongvarious scenarios that can be proposed and derived, wechoose a scenario onWIP quality diagnosing and repair-ing facilities in a car door assembly company A. In mak-ing car door, tens of welding should be gone through.It’s often conducted by spot welding robots. However,a WIP (work-in-piece) sometimes isn’t welded in rightposition due to several reasons. It’s sometimes caused bywrong position of jig or spot robot. However, considerthat problem is caused by facility failure such as wornbearing or servo driver malfunction in this operationscenario.

According to the interview with IT engineer, most ofall components of shop floor are connected to MES with

Figure 8. Comparison between As-Is and To-Be process.

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Figure 9. Prototype implementation architecture for the operation scenario.

Figure 10. ECMiner interface.

1:n structure. Enterprise systems such as ERP are alsoconnected to MES with 1: n structure. This originatesfrom different types of protocols and interfaces, data for-mat. The process to deal with is shown asAS-IS process inFig. 8.Quality inspection and recording onMES system isdone by manual work and accuracy of them depends onthe expertise. Even though workers get some clue fromcollected shop floor data such as current value etc., ittakes some time for judgement because there are lots ofthings to be concerned. If it needs to be repaired urgently

due to some reason such as servo driver malfunction,then call to mechanic to deal with the spot welding robotfirst.

Now, we describe improved scenario and advantagesof this system which is also shown in Fig. 8. Fig. 9shows the prototype implementation architecture for theoperation scenario. When data is collected, then it goesthrough data mining for classification of quality fault. Weused the data mining software named ECMiner1. Here,we adopted the SVM(support vectormachine) technique

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Figure 11. Data flow from quality management to other modules.

Figure 12. Change of maintenance schedule.

for classification. SVM can classify by drawing hyper-planes in the n-dimensional vector space if the numberof x variables is n. Fig. 10 shows the ECMiner interface.After analysis, event processing is conducted for judgingwhether there exists quality issue or not. If there existsproduct quality issue, then it sends the message to appli-cation collaboration manager to deal with. Applicationcollaboration manager gets the message, interpret it andthen send new message to quality management. If thatquality fault is related to facility issue, then it generates thenewmessage and then sends to application collaborationmanager for sending it to the maintenance management.Meanwhile, when resource status is updated in quality

management, then it is reflected in the resource alloca-tion & status management via data sync manager. Fig. 11shows the flow mentioned above. After the message issent to maintenance management, then change of sched-ule is done. Fig. 12 shows the change of schedule dueto the problem of part grapping robot deployed afterpress machine. Fig. 12(a) denotes the normal case andFig. 12(b) denotes the case when press machine causedthe quality issue. So, themaintenance schedule is updatedso that press machines are inspected first.

Based on operation scenario, we find out the advan-tages of the proposed Smart MES compared with currentMES operation scenario are summarized into Tab. 1. It

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Table 1. Advantage of the Smart MES compared with current MES.

Task Current-MES(As-Is) Smart MES(To-Be) Advantages

Recording inspection result Inspector records result by manuallywriting to database in MES.

Inspector doesn’t have to manuallywrite that result. If event happens,then that event is recorded byquality management service.

Recording work is automatically doneby service in smart MES module. Itmeans that MES can recognize thequality result faster.

Find out the cause ofproblem

Inspector relies on his/her expertiseknowledge on product and collecteddata expressed by graph to find outproblem onWIP.

Inspector doesn’t have to rely only onhis/her expertise knowledge. Dataanalysis can deal with it and givelots of help.

Even non-skilled inspector can handlea variety of WIP defect in real-timemanner. It means the detection timeis reduced.

Notify to Maintenancedepartment

MES operator should give a call tomaintenance worker to repair thatmachine. Usually, maintenance workerdoesn’t willing to change originalschedule flexibly if it’s not urgent suchas facility failure.

Quality management service candeliver message to maintenancemanagement service via applicationcollaboration manager.

Notification to Maintenance manage-ment service is done automaticmanner.

Deciding what kind ofaction should be taken

Inspector should remember manuals torespond to each different quality issue.

Application collaboration manager candeal with it.

Worker can handle various issueswithout memorizing all details ofmanual.

Notify to mechanic onchange of maintenanceschedule.

Since the contact for maintenance isusually done by call, so mechanicdoesn’t know unless it receives call.

Mechanics receive change of schedulegenerated from maintenancemanagement service automatically.

Mechanics can receive necessaryinformation in real-time. It impliesthat response time is reduced.

Facility status update After repair work is done, resource statusupdate is done by manual input.

After repair work is done, then it’snot necessary to manually inputbecause status update is done bydata synchronizationmanager whenrepair work is done.

Status update is done automaticmanner. So, corresponding facilitycan be reflected in next operationschedule faster than as-is model.

turns out that overall response time is reduced whilequality inspection is systematically approached. Further-more, since facility update can be automatically updatedand reflected in next scheduling or process management,Smart MES can contribute to flexibly adjust operationschedule according to shop floor situation. Furthermore,from the user perspective, they don’t need to knowall of details of cooperation work on each shop floorissues, which gives user sort of convenience. These pointsare written as contribution in the end of introductionsection.

6. Conclusion

In this paper, we described the problem for current MESsystem by pointing out lack of environment for analysingand interpreting, collecting shop floor data in real-timemanner. Even though many researches try to solve theseproblems, they didn’t deal with collaboration amongvarious MES functions. To solve above issues, Systemof interest named Smart MES is defined and require-ment and design consideration is defined for architecturedesign. Operation scenario making use of Smart MES isintroduced to show the validity and usefulness of smartMES against current MES. Through the operation sce-nario, we showed that Smart MES can comprehensivelyand effectively deal with shop floor situation in real-timemanner. The scenario introduced is only one exampleand many cases can be derived.

The developed works in this paper can be utilizedas base framework for development. Further research isneeded to establish and apply data model to the Smart

MES architecture to ensure that data flow among var-ious components can be shown. Detailed functions ofsmart MES can be elaborated by developing data modelin Smart MES since data model itself represents physicalentity, logical relationship among data, concept. Further-more, since the Smart MES system is a huge system, fullimplementation of it will take time to realize. So, fullfunctional system of smart MES is under development.

Note

1. ECMiner is the big data analysis solution developed byECMiner.

Acknowledgement

This research was in part supported by Ministry of Trade,Industry and Energy of Korea (Development of IntegratedOperational Technologies for Smart Factory Application withManufacturing Big Data), and Institute for Information &Communications Technology Promotion of Korea(Establish-ment of the Testbed for a Convergence of IoTs and Manufac-turing Technology).

ORCID

Byeong Woo Jeon http://orcid.org/0000-0002-3162-9296Jumyung Um http://orcid.org/0000-0002-8040-6144Soo Cheol Yoon http://orcid.org/0000-0002-1066-7867Suh Suk-Hwan http://orcid.org/0000-0002-3176-1692

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