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The simulation-based performance measurement in an evaluation module

for Faborg-Sim simulation software

Halil Ibrahim Koruca a,*, Gultekin Ozdemir a, Erdal Aydemir a, Muhammed Cayirli b

a Suleyman Demirel University, Engineering & Architecture Faculty, Department of Industrial Engineering, Isparta, Turkeyb Suleyman Demirel University, Keciborlu MYO, Department of Computer Technology and Programming, Isparta, Turkey

a r t i c l e i n f o

Keywords:

Simulation

Performance measurement

Manufacturing systems

Faborg-Sim

a b s t r a c t

It is possible to evaluate production system performance measurement and organizational alternativestructures with dynamic simulation methods for efficiency of cost and productivity. The parameters

which are vary for different organizational structures and scope of production systems are examinedin a mutual simulation-based performance measurement system. In general, the simulation-based per-

formance measurement criteria are lead time, lead time deviation, utilization rate, and work-in-process

as goal achievements of logistics and delivery rate in performance evaluation module.In this study, an evaluation module is developed and integrated to system simulation results as a part

of the research project of Development of Simulation Software for Facility Organizing, Production Sys-

tem Structuring, and Performance Measuring (Faborg-Sim) at Suleyman Demirel University Industrial

Engineering Department which is supported by The Scientific and Technological Research Council of Tur-key (TUBITAK).

2010 Elsevier Ltd. All rights reserved.

1. Introduction

While a critical performance measures can have secondaryimportance after a period of time or they can become not critical(Bititci, Suwignjo, & Carrie, 2001). Since production systems arevery complex, system controlling and monitoring become difficult.

Therefore, performance measurement systems are developed forall production resources and parameters of customer orders. Thus,

customer orders, products, process and production resources(especially, machines and personnel) are evaluated together in aproduction system.

The most important goal achievement parameters in a produc-

tion system are productivity of production resources, competition,profitability, bottleneck of process, increase of delivery rate, andcapacity utilization as performance criteria.

In general, performance evaluates results of an activity with

specified measurement parameters in general. The performancemeasurement can aim to personnel, organizational and technical

activities. Table 1 shows some of performance measurement crite-ria for this purpose (Groth, 1992).

The performance criteria for personnel are utilization rate, qual-ification, advanced training, worker flexibility, work uncertainty,

work stress degree and overtime rate. The performance criteriafor organizational can be classified as lead/cycle time, delivery rate,

inventory levels, scrap rate and work organization degree. Then theperformance criteria for technical activities can be classified as

machine usage rate, material and tool usage rate, degree of ma-chine flexibility and levels of machine principles.

In this study, a simulation-based performance measurementand evaluation module is integrated to Faborg-Sim simulation soft-

ware in system data and analysis of process. Thus, users can seebottleneck of the production systems, utilization and other systemperformance parameters to monitor dynamic behaviors of system

and can create alternative organizational structures.

2. Literature review

Alternative organizational structures or systems are evaluatedwith performance measurement criteria for production systems

in literature. Commonly, mathematical modeling or simulationmethods are used for this purpose. Table 2 shows a review of liter-ature for performance measurement and evaluation in productionsystems.

Chandra and Grabis (2008) studied a single-stage variable leadtime inventory system with lead time dependent procurementcost. Selection of the lead time value represented finding the

trade-off between benefits of lead time reduction and increase inthe procurement cost. A model for joint optimization of inventoryand procurement costs is developed. Nyhuis (2006) considered the

task of production logistics which is the positioning of worksystems in the field of conflict between the logistic performance

0957-4174/$ - see front matter 2010 Elsevier Ltd. All rights reserved.doi:10.1016/j.eswa.2010.05.065

*

Corresponding author. Tel.: +90 246 211 1233.E-mail address: [email protected] (H.I. Koruca).

Expert Systems with Applications 37 (2010) 82118220

Contents lists available at ScienceDirect

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measures throughput time, work-in progress (WIP) level, machine

utilization and schedule reliability. The interdependencies of theselogistic performance measures can be simulated by the LogisticProduction Operating Curves in qualitative and quantitative terms.

Kabaday (2002) interested in the development of performance

measurement systems, the characteristics and classifications ofperformance measures, and the relationship between continuousimprovement and performance measures.

Ramachandran and Delen (2005) characterized probability lawsfor queue sizes at buffers for a kitting process and derived the dis-

tribution of remaining waiting time for kits feeding the down-stream process. They showed that queue sizes at the componentbuffers are stable under very mild conditions for the control func-tions. Koruca, Kurbanoglu, and Zlch (2004) presented efficient

structures in organizational structures for planning and flow chartsof manufacturing with personnel characteristics in a bicycle man-ufacturing factory. Zlch, Kruger, Schindele, and Rottinger (2003)

simulated different variants of human factors in production sys-tems and related to objectives of enterprise and workers. Results

show that reduction of mental and physical work will also reducehuman errors. Zlch and Bogus (2001) presented simulation mod-els of planning for flexible working time. For this purpose, newsimulation models were developed in working time models. Zlch,

Jonsson, and Fischer (2002) developed a new simulation tool OSim,which allows for the integration of variously detailed sub-modelsinto an overall global model of the investigated production system,

was under development with stochastic data. It concerned the sim-ulation of the overall winding-up of customer orders with theirunderlying manufacturing processes. The consistency of this simu-

lation approach would be demonstrated by evaluating logisticalkey data at various levels of detail. It involved the performanceparameters which were lead time, utilization rate, work-in-processand order rate with manufacturing costs. Sivakumar and Chong

(2001) provided analysis of release control, set-up time, machineup time, cycle time and cycle time spread on variables of selected

process with a simulation model. Silva, Ramos, and Vilarinho(1999) presented a simulation study with limitations and problemsin manufacturing processes. System usage rate and queue lengthwere used as performance parameters.

3. Integration of simulation-based performance measurement

and evaluation module for Faborg-Sim

The main goal of a performance evaluation system is to provide

efficient decisions for the system which is modeled for defined per-formance measurement criteria in the process of the performancemeasurement and evaluation process. In this paper, we concerned

about the development of performance measurement module forproduction system in Faborg-Sim simulation software by using

C# programming language. Fig. 1 shows the relationship betweena production system and goal achievement degrees.

The basis of methods in production, the parameters of logisticsare related on economically as strategically and they affect on eco-

nomical values. In terms of customer, due date, deviation of deliv-ery rate and accuracy of due date in make-to-order production orservice degree in production for stock as external factors are af-

fected on productivity of logistics. The internal factors as lead time,deviation of due date and accuracy of due date are calculated fromproduction systems. In this point, the cost of logistics is indepen-

dent from productivity of logistics and type of customer orders(Ldding, 2008).

The goal achievement degrees of outsourcing are quantitativeindicators, the relation between system and environment. They

are classified as market-focused and process-focused degrees(Wedemeyer, 1989). Then, the goal achievement degrees of inter-

nal-sourcing are called costing indicator. A single value is not en-ough for analyzing the system according to the type of system

relationship in goal achievement degree. Generally, average valuemeasures must be defined to measure effects of goal achievements

precisely (Fryer, 1976; Sculli, 1987).

When the logistic goal achievement degrees are evaluated, theproduction time and due date rate are defined as a quantitativeproductivity value of customer orders. Capacity usage and current

actual degree of time related capacity of customer orders are calcu-lated by using some of the goal achievement degrees of outsourc-

ing (Koruca, 2002).Consequently, classified logistics and completed rate of cus-

tomer orders as performance measurement goal achievement de-grees in Faborg-Sim simulation software are given in Table 3.

3.1. Lead time

The goal achievement degree of lead time/cycle time is a basiccriterion for evaluating the efficiency of organization structures.

Production/cycle time is appeared as an important rising factorin front of us in the market. In general, production/cycle time atlimit of modeling system is defined as part of time from comingcustomer orders to finishing the orders (Koruca, 2002). Produc-

tion/cycle/lead time which is given in Eq. (1) is calculated by sum-ming of all process times of all functions in the process ( REFA,

1985). The transportation time which is given in Eq. (2) in a pro-duction time (TzwS, aq) is divided into as transportation and flownecessary waiting time (Refa, 1991). In this study, fault sourcingnecessary break times and additional operation times are not eval-

uated. But flow necessary waiting time affects the results of anal-ysis. Additional operation times which are given in Eq. (3) areconsidered as additional tasks (Koruca, 2002). Production/cycle/lead time of a customer order is calculated on work flow plan by

following the longest path. Eqs. (4) and (5) give minimum produc-tion time for customer order (q) and path (w) length for customer

order q, respectively. At the end of simulation process, productiontime is defined as a quantitative value which is called simulatedproduction time (DLS). The minimum production time in an orderis calculated from process (operation) times and transportation

times. It can be provided as critical path long (DLMq) from workflow plan (DIN, 1989, 1989, S.86). The degree of production time

which is given in Eq. (6) can be calculated by the minimum produc-tion time in an order and simulated production time (Koruca,2002). In a real production system, the degree of production/leadtime is calculated only 10% rate (Gerlach & Bobenhausen, 1986).

The most reachable theoretical value is 100%. The goal achieve-ment degree of lead time which is given in Eq. (7) can be obtainedby average value of completed customer orders rate (Koruca,

2002).

3.2. Lead time deviation

If the indicator of goal achievements is defined the degree ofgoal achievements, the accuracy of due date can be calculated to

relate by delivery time. This time is defined on the minimum pro-duction time (Wedemeyer, 1989; Graf, 1991). As the degree of pro-duction time value is loss, the accuracy of due date goal

achievements degree value is partly loss. The dependence of thisis disrupted by analyzing of weighted production time and produc-tion time deviation and evaluating from the results (Koffler, 1987).

The revised-expected production time is involved the calculationof indicator of goal achievements and positive and negative simu-

lated production time deviation. Indicator of goal achievements iscalculated goal achievement degree of production time and return

of tardiness factor value with critical path for all customer orders.The production/lead deviation for customer order (q) is given in Eq.

8212 H.I. Koruca et al./ Expert Systems with Applications 37 (2010) 82118220
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(8). The norm-production time deviation which is given in Eq. (9) is

calculated the production time deviation and the revised-expectedproduction time. The degree of production time deviation which is

given in Eq. (10) can be derived from the norm-production timedeviation. The number of production time deviation is measuredas positive and negative deviations. The goal achievement degreeof production time deviation which is given in Eq. (11) can be cal-

culated on average value for all customer orders like production/lead time (Koruca, 2002).

3.3. Capacity utilization rate

In a production system, the capacity utilization rate is examinedthe capacities in a period of time. The goal achievement degree ofcapacity utilization rate which is given in Eq. (12) is calculated bydividing an organization unit capacity in report period time to the-

oretical capacity (REFA, 1985; Wedemeyer, 1989). In here, the usedcapacity (time) in a period report time zone which is given in Eq.

(13) is calculated by sum of realizing the functions operationtimes. The used capacity calculation is not included in the capacityneeded for transportation. Because, the transportation process isrealized by another organizational unit. The theoretical capacity

which is given in Eq. (14) is calculated by summing of theoretical

capacities of organizational unit (Koruca, 2002).

3.4. Work-in-process (WIP)

The quantity of orders in the process is defined by customer or-

ders. The goal achievement degree of present customer orders isprovided to evaluate the average actual time capacity of waitingcustomer orders. The minimum order quantity is the amount of

customer orders that must be met. This quantity levels are neces-sary for realizing the customer orders at simulation run (Wede-meyer, 1989). In the report period the minimum existing order

quantity level which is given in Eq. (15) includes operation timesand additional times in processing. The waiting order quantitywhich is given in Eq. (16) is the quantity of customer orders onthe queue in front of the organization unit. Then, the goal achieve-

ments degree of order quantity work-in-process is formulated inEq. (17). The current order quantity work-in-process does not eval-

uate the increase of value. It occurs with completion of all steps ofan order. Because, the perspective of logistic in a production sys-tem is a basis for existing order quantity work-in-process ( Koruca,2002). Consequently, Faborg-Sim simulation software provides

Table 1

Personnel, organizational and technical performance measurement systems.

Personnel Organizational Technical

Utilization rate Material flow rate Machine usage

Degree of qualification Cycle/Lead time Machine utilization rate

Degree of machineutilizationDegree of advanced training Inventory levels

Waiting times

Work training

Courses

Information activities

Material utilization rate

Rejected rate Tool utilization rate

Delivery rate Degree of machine flexibility

Degree of work flexibility Quality rate Degree of machine flexibility

Degree of procedure flexibility

Degree of variation Degree of scrap

Rate of re-work

Exp: Retirement Levels of machine conditions

Degree of absence Degree of work organization

Rate of holiday

Rate of patient

Rate of other case

Work division

Degree of enterprise

Degree of hierarchy

Machine age

Rate of maintenance

Rate of seize up/idleDegree of work uncertainty

Numberof work and case of

Accident

Rate of overtime

Degree of work stress

Exp: Physical work,

Emotional/sensorial work

Table 2

A literature review of performance measurement and evaluation in production systems.

Production

management

Chandra andGrabis (2008), Garengo et al. (2007), Leeand Schwarz (2007), Zhouet al. (2006), Choi et al. (2007), Chen et al. (2005), Turner et al.

(2002), Inderfurth and Van Der Laan (2001), Wiendahl and Breithaupt (2000)

Performance

parameters

Lee (2009), Nyhuis (2006), Kim and Duffie (2006), Kabaday (2002), Ramachandran and Delen (2005), Qiu (2005), Bozer and Hsieh (2004),

Suwignjo et al. (2000), Bititci et al. (2001)

Scheduling and

sequencing

Oner and Bilgic (2008), Penn and Raviv (2009), Bhaskar and Lallement (2008), Shiue (2009), Guo et al. (2007), Chen (2008), Samaddar and Hill

(2007), Yildirim et al. (2006), Alenezi et al. (2008), Charnsirisakskul et al. (2006), Sarin et al. (2006), Tavakkoli-Moghaddam and Safei (2006),Tumbe and Ozbayrak (2006)

Human Factors Fowler et al. (2008), Koruca et al. (2004), Zlch et al. (2003), Zlch and Bogus (2001)Simulation and

Heuristics

Hsu et al.(2008),Liu et al.(2009), Zlchet al. (2002), Kost andZdanowicz(2005), Yuan andLiming(2005),Tavakkoli-Moghaddamet al.(2006),

Martinek et al. (2006), Sandikci and Sabuncuoglu (2005), Li (2003), Sivakumar and Chong (2001), Law and Mccomas (1999), Silva et al. (1999),

Cogun and Savsar (1996), Savsar (1989)

H.I. Koruca et al./ Expert Systems with Applications 37 (2010) 82118220 8213
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quantitative values for goal achievement degree of lead time, leadtime deviation, capacity utilization rate and work-in-process by

developing the performance measurement and evaluation modulefor evaluating the dynamic system behaviors. Data is defined the-oretically between 01. In this study, goal achievement degrees aregiven as percentage values.

3.5. Sum of logistics

The indicator of system logistics value is calculated withweighted average of goal achievement degree of lead time, lead

time deviation, capacity utilization rate and work-in-processwhich is called as goal achievement degree of Sum of Logistics.In this study, the default value of weighted factor is accepted asgi = 1 for all goal achievement degrees.

3.6. Goal achievement degree: Delivery rate

The completed customer order rate (delivery rate) which is gi-ven in Eq. (19) is calculated by dividing the finished customer or-der in a report time to the total customer order quantity. Thedelivery rate is an important performance parameter degree for

customer satisfaction value of production system. It provides the

accuracy of due date and increase of prestige of enterprise (Koruca,2002).

4. A case study: Performance evaluation of a machine

manufacturing job shop

Faborg-Sim simulation software is developed to include model

and to simulate all production systems. The last module is pro-cessed the performance evaluation module in Faborg-Sim. Thus,the simulation-based performance measurement module includeslead time, lead time deviation, utilization rate, and work-in-pro-

Organizasyon nitesi

retim Sistemi

Organizational Unit

Production System

M M M MCustomerOrders

Finishing the last

function of

Customer Orders

Productivity of Logistics Cost of Logistics

Make-to-Order Production

Due Date

Deviation of Delivery Rate

Accuracy of Due Date

Production for Stock

Service Degree

Lead Time

Deviation of Due Date

Accuracy of Due Date

Price

Inventory (WIP)

Capacity Usage

Tardiness Cost

External

Interna

l

Fig. 1. The relationship between a production system and goal achievement degrees (Weck, 1991; Schulte et al., 1991; Ldding, 2008).

Table 3

Calculation of the goal achievement degrees in Faborg-Sim performance evaluation module.

Goal achievement degree Formulasa

Lead time (GADLT) TD,aq = tdS,aq + tzwS,aq + tzuS,aq TzwS,aq = tztS,aq + tSAN,aq + tSAV,aq (1), (2)

tzuS;aq P

tSSaq P

tSZaq DLMq maxw2WfLWDwq g (3), (4)

LWDwq PAAVwq

a1 Tawq tzt;awq DLGq DLMqDLSq

(5), (6)

GADLT 1AAA :PAAA

q1DLGq(7)

Lead time deviation (GADLTD)PDTq DLSq

DLMqGADLT NPTDq

DLSDLMqGADLT

DLMqGADLT

DLSq

DLMq:GADLT 1

(8), (9)

DPTDq 1

1jNPTDq jGADLTD 1AAA :

PAAAq1

1

1DLSq

DLMq:GADLT1

(10), (11)

Capacity utilization rate (GADCUR) GADCUR NCTTC NCT PABA

q1

PCCFqa1 tdS;a;q tzuS;a;q

(12), (13)

TCSPNDR

r1 TCSr(14)

Work-in-process (GADWIP) MOQ 1RPTZPABA

q1

PCCFqaq tdS;aq tzuS;aq

2 WOQ 1RPTZ:PABA

q1

PCCFqi1 WTBa;q:tdS;aq tzuS;aq

(15), (16)

GADWIP MOQMOQWOQ(17)

Sum of logistics (GADSOL) GADSOL g1:

GADLTg2:

GADLTDg3:

GADCURg4:

GADWIPP4i1

gi(18)

Completed customer orders rate (GADCOR) CCO FCOTCO 100 (19)

a Legend is given in Appendix A.

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cess as goal achievements of logistics and delivery rate for more

efficiency decisions about the systems. The system modelingparameters are defined before simulation in a database and thesimulation results are obtained after simulation run for calculating

of all process performance measures in the evaluation module. Anexample for a production system model is developed for the per-

formance evaluation module in this section. The general model isgiven in Fig. 2.

Fig. 2 shows the layout of a machine manufacturing job shop

production system. In Faborg-Sim Modeling Module, the assess-ment matrices are the operation/process workplace/machineassessment matrix, the function-personnel type assessment matrix

and the workplace type-personnel type assessment matrix. Thepreparation and processing times of each operation in machines

and for personnel are given in Appendix A.The Faborg-Sim is run for five days simulation run-time in a re-

port period (20 workdays/month). Then, Fig. 3 shows scheduling of

Customer Orders

80 Orders for

4 Products

P01

P02

P03

P04

Products

Workstation /

Machine

Worker /

Personnal

M1_TORNA

3 Parallel Units

M2_FREZE

2 Parallel Units

A Machine Manufacturing Job ShopCustomers Customers

M3_MATKAP

1 Unit

M4_MONTAJ

1 Unit

Fig. 2. The layout of a machine manufacturing job shop production system.

Fig. 3. The machine and personnel Gantt charts of simulation results.

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80 customer orders of products on the simulation results on the

machine Gantt chart and the personnel Gantt chart.According to the simulation results on the performance mea-

sures, the performance measurement degrees of utilization rate

are classified as workplace and personnel.The workplace of M1_TORNA consists of three parallel capacity

units when the personnel of P1_TORNA consist of two parallel

capacity units. Therefore, the capacity utilization rates are 21.96%and 32.94%, respectively. The next criteria are lead time and leadtime deviation about products. The goal achievement degree oflead time is obtained as 92.93% and the goal achievement degree

of lead time deviation is obtained as 93.35%. The quantity of devi-ation is more important. Faborg-Sim calculated the work-in-pro-cess degree as 46.41% in research model. It is in an acceptable

level for this production system. Then, the customer orders deliv-ery rate is calculated as 100% for all customer orders which arecompleted in a report period.

The last performance measurement degree is Sum of Logistics

and is given in Fig. 4. According to the Fig. 4, the degree of sumof logistics is calculated as 67.35 %. It shows that there is moreopportunity for improving system performance for system users.

The detailed performance evaluation values are given Appendix Bfrom Faborg-Sim Evaluation Module.

5. Summary and conclusions

The simulation results can be evaluated by using different crite-

ria for modeling of production systems and development of alter-native organization structures. All of this criteria show theperformance of system. At this point, the simulation methods can

be more important for following the dynamic behaviors and varia-tion of the systems. For this purpose, the performance evaluation

module in Faborg-Sim simulation software is developed to analyzeperformance of production systems.

The production systems are very complex in the sense of oper-ations and machines. A simulation model must include more

parameters about the system to reduce errors on system modeling.

Faborg-Sim simulation software provides different indicators ofthe system performance for machines and personnel about utiliza-tion rate, cycle/lead time of products, work-in-process of customer

orders, delivery rate and sum of logistics for a production system.Thus, sum of logistics goal achievement degree and delivery rateare important indicators of continuous improvements and cus-

tomer satisfaction for production systems.In this paper, a simulation-based performance measurement

and evaluation module is developed and is integrated to Faborg-Sim simulation software. Thus, users can monitor bottlenecks of

production systems and dynamic behaviors of systems. Finally,users are directed to improve the actual system under consider-ation for more efficiency and alternative organizational structures.

6. Further research

The simulation-based performance evaluation system is devel-

oped with personnel, organizational and technical criteria. In thispoint, material and costing modeling can be added to the softwareas a further research area. Thus, Faborg-Sim will be more compat-ible and flexible to analyze production system performances.

Acknowledgments

Part of this research has been supported by the Scientific and

Technology Research Council of Turkey (TUBITAK). The authorsthank to TUBITAK for supporting of this project.

Appendix A. Nomenclature for formulas of goal achievement

degrees.

a Process or functionq Customer orderw PathTDS,aq production/cycle/lead time

Fig. 4. The goal achievement degree: Sum of logistics.

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tdS,aq process timetzwS travel time between operations or time of transition,tzuS additional time.TzwS,aq travel time between operations or time of transition,tztS,aq transportation time

tSAV,aq waiting timetSAN,aq transfer timetzuS,aq additional timetSS,aq system fault sourcing necessary break (waiting) timetSZ,aq additional operation timeDLMq the minimum production timeLWDwq path (w) lengthAAVwd count of process,Tawq customer order timetzt,awq necessary waiting timew,W count of path in an orderDLGq the degree of production timeDLSq the simulated production timeGADLT goal achievement degree of lead time,AAA completed customer orders in reporting time zone,

AEA all customer orders in system, AAA 6 AEA.PTDq the production/lead deviation for customer order (q).NPTDq the norm-production/lead deviation for customer order

(q).DPTDq the degree of production/lead deviation for customer order

(q).GADLTD the goal achievement degree of production/lead time devi-

ation.

GADCUR the goal achievement degree of capacity utilization rate,

NCT the used capacity (time) in a period report time zone,

TC the theoretical capacity.

ABA count of started customer orders in report period, ABA 6AEA,

CCFq count of completed function in work flow plan for cus-tomer order (q),

TCS the theoretical capacity of system,

NDR count of organizational units,

r the indicator of organizational units, r2 IN.

MOQ the minimum exist order quantity,

RPTZ the report period of time zone.

WOQ the waiting order quantity,

WTBa,q the waiting time before simulation run for customer order(q) function (a).

GADSOL the goal achievement degree of Sum of Logistics,

gi the weighted-factor, i = 1,2,3,4.

CCO the completed customer orders (%),

FCO the finished customer orders,

TCO the total customer orders

Appendix B. The model data: Workflow plans, assessment

matrices and operation times.

See Fig. B1

B2. Preparation times (PT) and operation times (OT) of all products for example model.

Process

code

10 20 30 40 50 60 70 80 90 100

Product

01

Workplace Workplace M1_TORNA M1_TORNA M2_FREZE M3_MATKAP M2_FREZE M4_KAYNAK M3_MATKAP M2_FREZE M4_KAYNAK M5_MONTAJ

PT (s) 400 400 450 900 450 500 900 450 500 1000

OT (s) 400 400 450 900 450 500 900 450 500 1000

Personnel Personnel P1_TORNA P1_TORNA P2_FREZE P3_MATKAP P2_FREZE P4_KAYNAK P3_MATKAP P2_FREZE P4_KAYNAK P5_MONTAJ

PT (s) 400 400 450 900 450 500 900 450 500 1000

OT (s) 400 400 450 900 450 500 900 450 500 1000

Product

02

Workplace Workplace M1_TORNA M1_TORNA M2_FREZE M3_MATKAP P4_KAYNAK M1_TORNA M2_FREZE M3_MATKAP M3_MATKAP M5_MONTAJ

PT (s) 200 200 300 100 250 200 300 100 100 500

OT (s) 200 200 300 100 250 200 300 100 100 500

Personnel Personnel P1_TORNA P1_TORNA P2_FREZE P3_MATKAP P4_KAYNAK P1_TORNA P2_FREZE P3_MATKAP P3_MATKAP P5_MONTAJ

PT (s) 200 200 300 100 250 200 300 100 100 500

OT (s) 200 200 300 100 250 200 300 100 100 500

Product

03

Workplace Workplace M1_TORNA M1_TORNA M2_FREZE M2_FREZE M3_MATKAP M4_KAYNAK M1_TORNA M2_FREZE M3_MATKAP M5_MONTAJ

PT (s) 400 400 500 500 300 600 400 500 300 800

OT (s) 400 400 500 500 300 600 400 500 300 800

Personnel Personnel P1_TORNA P1_TORNA P2_FREZE P2_FREZE P3_MATKAP P4_KAYNAK P1_TORNA P2_FREZE P3_MATKAP P5_MONTAJPT (s) 400 400 500 500 300 600 400 500 300 800

OT (s) 400 400 500 500 300 600 400 500 300 800

Product

04

Workplace Workplace M1_TORNA FREZE M2_FREZE M3_MATKAP M2_FREZE M3_MATKAP M3_MATKAP M4_KAYNAK M4_KAYNAK M5_MONTAJ

PT (s) 200 300 300 400 300 400 400 500 500 600

OT (s) 200 300 300 400 300 400 400 500 500 600

Personnel Personnel P1_TORNA P1_FREZE P2_FREZE P3_MATKAP P2_FREZE P3_MATKAP P3_MATKAP P4_KAYNAK P4_KAYNAK P5_MONTAJPT (s) 200 300 300 400 300 400 400 500 500 600

OT (s) 200 300 300 400 300 400 400 500 500 600

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Appendix C. The performance evaluation values

See Fig. C1

Fig. C1. The simulation results on Faborg-Sim Performance Evaluation frameworks.

Fig. B1. Workflow plans and assessment matrices for example model.

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C2. The detailed simulation results on the performance measure-ment criteria.

Workplace Parallel

units

Utilization rate

(%)

Personnel Parallel

units

Utilization

rate (%)

Capacity utilization rate (Workplace and Personnel)

M1_TORNA 3 21.96P1_TORNA 2 32.94

M2_FREZE 2 49.41P2_FREZE 2 49.41

M3_MATKAP2 22.26P3_MATKAP 2 24.48

M4_KAYNAK1 38.28P4_KAYNAK 1 33.83

M5_MONTAJ 1 51.64P5_MONTAJ 1 51.64

Average (%) - 36.71Average 38.46

Products Customer

orders

Lead time (s) Products Lead time

deviation (%)

Products(Cycle/lead time and lead time deviation)

P01 20 1163937.5 P01 93.35

P02 20 1185907.5 P02

P03 20 1,202,935 P03

P04 20 1175962.5 P04

Average (%) 92.93Average (%) 93.35

WIP

indicators

Work-in-

processvalue (s)

Products Finished

orders

Delivery

rate (%)

Customer orders (Work-in-process and delivery rate)

Minimum

WIP

134,064,000 P01 20/20 100.00

Waiting WIP 154,812,000 P02 20/20 100.00P03 20/20 100.00

P04 20/20 100.00

Rate (%) 46.41 Total/

Average

80/80 100.00

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