System Dynamics Simulation based on SCOR Performance at ...ieomsociety.org/ieom2020/papers/815.pdf• Best Practices Solutions are given by using best practices depends on the SCOR

Proceedings of the International Conference on Industrial Engineering and Operations Management Dubai, UAE, March 10-12, 2020

© IEOM Society International

System Dynamics Simulation based on SCOR Performance at Table Tennis Table Manufacturer in Indonesia

Iqbal Wahyu Saputra*, Yuniaristanto*, Muhammad Hisjam, Roni Zakaria Department of Industrial Engineering

Universitas Sebelas Maret Surakarta 57126 Indonesia

[email protected], [email protected], [email protected], [email protected]

Abstract

The growth in the industrial sector will make it easier for companies to offer their products and develop their markets more broadly, one of them are the sports industry. Every company is required to continue to make improvements in all aspects so they won’t be left behind. This research is expected to be an input to maintain the supply chain management of the company at the world level. The results of supply chain performance measurements using the SCOR® 11.0 performance attribute are then performed using system Dynamics simulations which built using Powersim software. Results shown that their lowest score are the Cost attribute, so it must be their main focus on improvement. This simulation results are expected to be a basis for improvements on the supply chain management especially at Table Tennis Table Manufacturer in Indonesia.

Keywords Performance Measurement, SCOR, Simulation, System Dynamics

1. IntroductionThe growth in the industrial sector will make it easier for companies to offer their products and develop their markets more broadly, one of them are sports industry. Therefore, every company is required to continue to make improvements in all aspects. If the company does not make continuous improvements and follow the standards in the world, it is feared that it will have an impact on the decline in the company's competitiveness. According to Hadiyati (2010) quality is the starting point in capturing market share so that the level of satisfaction not only needs to be maintained but must also be increased to face increasingly fierce competition. With this research it is expected to be an input for continuous improvement to maintain the quality of the company at the world level (Lima-Junior and Carpinetti, 2016)

To make continuous improvements, analysis is needed related to the supply chain that exists in a company. According to Liu et al. (2013) supply chain is a network of facilities that provides raw materials, converts them into final products and delivers products to customers through a distribution system. In case measuring a supply chain performance, it is needed a performance measurement to support overall supply chain improvement rather than measuring only certain functions (Huan et al., 2004)

The SCOR® 11.0 model is a set of practical guidelines that provides a methodology for managing supply chain activities and analyzing their management practices (Erkan & Ugur, 2011). The strength of the SCOR® 11.0 model compared to other models is that it is able to define the relationship between processes and influential elements and the scope of all existing demand elements (Supply Chain Council [SCC], 2012). Several previous studies have discussed the application of the SCOR ® 11.0 model to various study objects in the industry, among others, according to Ariani et al (2017) cheese supply chain in CV. Brawijaya Dairy Industry has adjusted the KPI based on the SCOR® 11.0 model and weighted the KPI using AHP. In addition, in the research Sutawijaya & Marlapa (2016) about the supply chain at PT. Indoturbine has a less efficient condition because the POF and OFCT metrics are below the benchmark data advantage value. Also Ikasari et al. (2019) research about SCOR Performance Measurement that combined with system Dynamics in Lithium Ion battery manufacturer.

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mailto:[email protected]



The results of performance measurements using the SCOR® 11.0 are then performed by system Dynamics simulations System Dynamics is a computer-aided approach to analyzing and designing a policy. This applies to Dynamics problems that arise in social, managerial, economic, or complex ecological systems or literally Dynamics systems characterized by interdependence of variables, mutual interaction, information feedback, and circular causality (Forrester, 1987). Sumari et al. (2013) stated that system Dynamics has advantages in strategic level that gaining more understanding on system behavior in long term and the Dynamics feedback behavior. Several previous studies have discussed the application of the system Dynamics simulation to various study objects, for example in Kumar and Kumar (2015), he’s using the system Dynamics approach for healthcare supply chain in rural parts of Uttarakhand, India. Georgiadis et al. (2004) use the system Dynamics as a long term plan in major Greek fast food chain. Thus, in this study the main objective discussed is performance measurement of supply chain performance using the Supply Chain Operations Reference (SCOR® 11.0) models as a basis which then implemented in the form of system Dynamics simulation models to improve the supply chain at Table Tennis table manufacturer in Indonesia. 2. Research Method Lockamy III and McCormack (2004) stated that SCOR model is designed as a tool to describe, measure and evaluate any supply-chain configuration. Thus, firms must implement specific supply-chain management practices based upon their unique set of competitive priorities and business conditions to achieve the desired level of performance. So, the data used for this research are primary data, that obtained from direct observations, can be in the form of observations or interviews and secondary data, as historical data obtained from the company. Data collection was carried out by identifying the table tennis table supply chain, which was then followed by mapping the SCOR® 11.0 model so that any data could be identified. Data processing is carried out after collecting the data needed for final project research, the points of the steps carried out in data processing are as follows • Performance Measurement of Supply Chain Performance using SCOR® 11.0 approach After supply chain mapping and data collection, then the supply chain performance is measured as a data needed to generate calculation for SCOR Performances later in System Dynamics Simulation • System Dynamics Simulation using Powersim software Simulation is based on the results of performance measurements using the Powersim software to find the results and points of problems according to real conditions. As the system is better understood, additional complexities could be added to the model to account for more feedback loops (Treadwell et al., 2018) • Best Practices Solutions are given by using best practices depends on the SCOR Performances, especially the attribute that have the lowest score. 3. Result and Discussion Table tennis table manufacturer in Indonesia have 4 chain of supply chain management, there are suppliers, manufacturer, distributors, and customers. Figure 1 is a table tennis table supply chain diagram that contains related entities

: Material flow (raw material, finished product, or return) : Cash flow (invoice, payment or credit) : Information flow (order, total order, or order schedule)

MDF Supplier

Steel Supplier

Alumunium Supplier

Tire Supplier

Table Tennis Table Manufacturer

1a

Distributors

Retail / Agent

End User

1b

1c

1d

5

3

2

4a

4b

1st factory

2nd factory

Suppliers Customers Distributors Manufacturer

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Figure 1. Supply chain of Table Tennis Table Manufacturer

After identifying the supply chain and its entities, SCOR Performances are arranged and verified by having questionnaire to 3 respondent in table tennis table manufacturer. The questionnaire results are showed in Table 1

Table 1. Metrics Verification SCOR® 11.0

Atribut Metrics Level 1 Metrics Level 2 R1 R2 R3 Validation

RL Reliability RL 1.1 Perfect

Order Fulfillment

% of Order Delivered in Full V V V Valid Delivery Performance to Customer Commit Date V V V Valid

Accurate Documentation V V V Valid Perfect Condition V V V Valid

RS Responsiveness

RS 1.1 Order Fulfillment Cycle Time

Source Cycle Time V V V Valid Make Cycle Time V V V Valid Deliver Cycle Time V V V Valid Deliver Retail Cycle Time V V V Valid

AG Agility

AG 1.1 Upside Supply Chain

Flexibility

Upside Source Flexibility - - - Not valid Upside Make Flexibility V V V Valid Upside Deliver Flexibility V V V Valid Upside Source Return - - - Not valid Upside Deliver Return Flexibility - - - Not valid

AG 1.2 Upside Supply Chain Adaptability

Upside Source Adaptability - - - Not valid Upside Make Adaptability V V V Valid Upside Deliver Adaptability V V V Valid Upside Source Return Adaptability - - - Not valid

Upside Deliver Return Adaptability - - - Not valid

AG 1.3 Downside

Supply Chain Adaptability

Downside Source Adaptability - - - Not valid Downside Make Adaptability - - - Not valid Downside Deliver Adaptability - - - Not valid

AG 1.4 Overall Value at Risk

Supplier/Customer/ Product Risk Rating - - - Not valid

Value at Risk (Plan) - - - Not valid Value at Risk (Source) - - - Not valid Value at Risk (Make) - - - Not valid Value at Risk (Deliver) - - - Not valid Value at Risk (Return) - - - Not valid

CO Cost CO.1.001 Total Cost to Serve

Planning Cost - - - Not valid Sourcing Cost V V V Valid Material Landed Cost - - - Not valid Production Cost V V V Valid Order Management Cost - - - Not valid Fulfillment Cost V V V Valid Returns Cost - - - Not valid Cost of Goods Sold V V V Valid

AM Assets management

AM 1.1 Cash to Cash Cycle Time

Days Sales Outstanding V V V Valid Inventory Days of Supply V V V Valid Days Payable Outstanding V V V Valid

AM 1.2 Return on Fixed Assets Supply Chain Fixed Assets V V V Valid

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Atribut Metrics Level 1 Metrics Level 2 R1 R2 R3 Validation

AM 1.3 Return on Working Capital

Accounts Payable (Payables Outstanding) - - - Not valid

Accounts Receivable (Sales Outstanding) - - - Not valid

Inventory V V V Valid From the recapitulation above, it is found that level 1 metrics are validated, there are 8 metrics out of 10 metrics available. SCOR® level 1 metrics that can be measured are perfect order fulfillment (POF), order fulfillment cycle time (OFCT), upside supply chain flexibility (USCF), upside supply chain adaptability (USCA), total cost to serve (TC), cash- to cash cyle time (CCCT), return on supply chain fixed assets (ROFA), and return on working capital (ROWC). Verified level 1 metrics are then referred to as KPI (Key Performance Indicators). To determine the value of level 1 performance metrics, consideration is used to calculate level 2 metrics. SCOR® level 2 metrics that can be measured based on the results of the questionnaire there are 21 valid metrics out of the 43 metrics available. After validate the metrics and collecting the data, then the SCOR Performance performed using system Dynamics simulation on Powersim software as follows

Figure 2. System Dynamics Simulation Model of Table Tennis Table Manufacturer

At the level of Perfect Order Fulfillment, it is influenced by customer service and additional POF. If customer service is high, the percentage of Perfect Order Fulfillment will be lower. Customer complaints are affected by% Orders Delivered in Full, Delivery Performance to Customer Commit Date, Documentation Accuracy and Perfect Condition. Each value of% Orders Delivered in Full, Delivery Performance to Customer Commit Date, Documentation Accuracy and Perfect Condition has a maximum value of 100% so that customer service is obtained from 100% minus the percentage value of each level 2 metric. % Orders Delivered in Full are influenced by the amount of production and demand or demand by agents. Perfect Condition is influenced by the number of product defects from table tennis tables that have been produced. Documentation Accuracy is affected by total orders with complete documentation and the number of shipments. Delivery Performance to Customer Commit Date is influenced by the number of late delivery of table tennis tables. At the Total Cost to Serve level, it is influenced by Cost of Goods Sold and Outcomes. Cost of Goods Sold is intended as income obtained by the company. Cost of Goods Sold is influenced by sales. Sales are obtained from Production, namely the number of table tennis tables produced with Return, namely the number of table tennis table returns and the price or selling price of table tennis tables. Outcomes are influenced by Production Cost, Sourcing Cost and Fulfillment Cost. Fulfillment Cost is the shipping cost and is influenced by the number of vehicles, the monthly cost of the vehicle, and the cost of the expedition. Sourcing Cost is the cost of raw materials for production purposes and

Cash to cash CycleTime Days Payable

Outstanding

Material ShifftInventory

Inventory Days ofSupply

Papan MDF Price

Besi Price

Papan MDFQuantities

Besi Quantities

ConditionalMaterial

Papan MDF

Besi

Days SalesOutstanding

n AssetManagement

min CCCT

x CCCTmax CCCT

Normalized SCORAsset

Management

SCOR AssetManagement

Return on FixedAsset

AlumuniumQuantities

Alumunium Price

Alumunium

Roda Price

Roda Quantities

Roda

Papan MDFSchedule

Papan MDF Cost

Besi Schedule

Besi Cost

AlumuniumSchedule

Alumunium Cost

Roda ScheduleRoda Cost

Sourcing Cost

SCOR

OutcomeTotal Cost to Serve

Cost of GoodsSold

Production Cost

Daily ProductionOverhead Cost

Fulfillment Cost

Expedition Rate

Paytime

Additional POFPerfect Order

Fulfillment Customer Service

SCOR Reliability

n Reliability

DeliveryPerformance to

Customer CommitDate

Delivery CommitDate

Perfect Condition

min TCx TC

max TC

Normalized SCORCost

SCOR Cost

Defect

DocumentationAccuracy Total Delivery

Total Deliver withAccurate

Documentation

% of OrderDelivered in Full

x POF

Sales

Return Rate

TC Percentage

Return

Price

Prod Cost

Cost per Product

Production

AdditionalProduction

Upside MakeAdaptability Additional

Machine Capacity

AdditionalProduction Day

Required

Upside MakeFlexibility Conditional

Production Rate

Additional DeliverUpside DeliverAdaptability Additional Load

Capacity

Additional DeliverDay Required

Upside DeliverFlexibillity Conditional

Delivery Rate

Machine Capacity

Vehicle CapacityConditionalDelivery

DeliveryDifferences

Delivery CycleTime

x UDF

x UDA

x UMF

x UMA

Buffer Stock

Norm SCORAgility

n Agility

Additional OFCTOrder Fulfillment

Cycle Time Lot Order

Conditional OFCT

Additional Product

ProductionDifference

Total Cycle Time

Deliver RetailCycle Time

max OFCT

min OFCT x OFCT

Normalized SCORResponsiveness

SCORResponsiveness

n Responsiveness

Demand

n Cost

Delivery CycleTime

Source Cycle Time

Papan MDF CT

Besi CT

Alumunium CT

Roda CT

SCOR AssetManagement

SCOR Cost

SCOR Reliability

SCORResponsiveness

Source Cycle Time

Make Cycle Time

Total Delivery

Distance

Make Cycle Time

Packed Item

min UMFmax UMF

max UDFmin UDF

SCOR Agi

SCOR Agi

Norm SCORReliability

TC

Additional CycleTime

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is influenced by the total cost of each raw material. Each total raw material cost is obtained from the amount of material purchased and the price. Production Cost is the production costs incurred by the company to produce table tennis tables obtained from Prod Cost, which are production costs and Production Overhead Cost, which is other production overhead costs. The Upside Make Flexibility level is affected by the additional days needed to complete additional tasks and changes in production rates. Conditional Production Rate is a change in production rate due to additional production. Additional Production Day Required is the number of days needed to complete additional production obtained from Make Cycle Time, which is the cycle time of the production process and the Machine Capacity, which is the capacity of the machine used At the Upside Deliver Flexibility level is influenced by the additional days needed to complete additional delivery tasks and changes in the delivery rate. Conditional Delivery Rate is a change in shipping rate due to additional shipments. Additional Deliver Day Required is the number of days needed to complete additional shipments obtained from Deliver Cycle Time, which is the delivery process cycle time and Vehicle Capacity, which is the capacity of the vehicle used At the Upside Make Adaptability level is influenced by the increase in engine capacity and increase in the number of production. Additional Capacity Machine is an additional capacity of the machine which is obtained from the information of the existing engine capacity. Additional Production is an increase in the amount of production obtained from Product Differences, namely the difference between normal and additional production and Machine Capacity, which is the capacity of the machine used. At the Upside Deliver Adaptability level, it is influenced by the increase in vehicle capacity and the increase in the number of loads. Additional Load Capacity is an additional load capacity obtained from other modes of transportation. Additional Deliver is an addition to the number of shipments obtained from Delivery Differences, which is the difference between the number of normal shipments with additional shipments and Vehicle Capacity The Order Fulfillment Cycle Time level is influenced by Lot of Order and Additional OFCT. Additional OFCT is the time required by the company to meet the increase in the amount of production from before. The time needed to meet the additional production amount is obtained from Total Cycle Time, which is the total cycle time needed by the company obtained from Sourcing Cycle Time, Make Cycle Time, Delivery Cycle Time, and Deliver Retail Cycle Time. Additional OFCT is influenced by Conditional OFCT which is to state if there is an increase in production, because not every time there is an increase in the number of table tennis table production. Lot of Order is a lot of the number of table tennis tables that are produced based on the time available. This amount is obtained from the Order Fulfillment Cycle Time and Production or the number of table tennis table products that are produced and if there is an addition, additional products are added. At the level of Cash to Cash Cycle Time influenced by Additional CT and Days Payable Outstanding and added with Supply Chain Fixed Assets. Days Payable Outstanding is the time required from material purchase until the material deadline must be paid. Cash to Cash Cycle Time is affected by additional cycle times (Additional CT) based on Days Sales Oustanding and Days of Inventory Supply. Days Sales Oustanding is the time between products sold until the company receives money from the sale of these products. Days of Inventory Supply is time of material stock so Days of Inventory Supply is influenced by Inventory. Days of Inventory Supply is obtained from the Inventory level. Inventory is the level of material storage used for the production process. Inventory is obtained from the time of purchase of raw materials. For Return on Fixed Assets added from the company's decision on the fixed assets used.

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Result for each metrics level 1 or KPI are explained in graph below. The following chart is the result of a one-year simulation using Powersim software based on Perfect Order Fulfillment, the initial value is 93.67% and at the end value 100%. While the average yield for one year is 99.95%. The higher the value of Perfect Order Fulfillment, the better.

Figure 3. Simulation Result of Perfect Order Fulfillment

The following chart is the result of a one-year simulation using Powersim software based on the Order Fulfillment Cycle Time, the following has an initial input value of 2 hours and at the end of 2 hours 50 minutes. While the average yield for one year is 2 hours and 52 minutes. The lower the value of the Order Fulfillment Cycle Time, the better.

Figure 4. Simulation Result of Order Fulfillment Cycle Time

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The following chart is the result of a one-year simulation using Powersim software based on Upside Supply Chain Flexibility graph. For upside make flexibility the initial input value 34 minutes and the final value is 482,8 minutes. While the average yield for one year is 8 hour and 3 minutes. For upside deliver flexibility the initial input value is 6 hour and the final value is 8 hour and 18 minutes. While the average yield for one year is 8 hour and 17 minutes. The higher the value of upside supply chain flexibility, the better.

Figure 5. Simulation Result of Upside Supply Chain Flexibility

The following chart is the result of a one-year simulation using Powersim software based on Upside Supply Chain Adaptability, for upside make adaptability, the initial input value is 40% and the final value is 101,43%. While the average yield for one year is 101,4% while for upside deliver adaptability, the initial input value is 40% and the final value is 102,88%. While the average yield for one year is 102,8%. The higher the value of upside adaptability, the better.

Figure 6. Simulation Result of Upside Supply Chain Adaptability


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Upside Deliver Flexibillity Upside Make Flexibility

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Upside Deliver Adaptability Upside Make Adaptability

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The following chart is the result of a one-year simulation using Powersim software based on the Total Cost to Serve chart below, the initial input value is IDR 182.718.918 then the final value is IDR 171.540.000. While the average yield for one year is worth IDR 171.540.000. The lower the Total Cost to Serve value, the better.

Figure 7. Simulation Result of Total Cost to Serve

The following chart is the result of a one-year simulation using Powersim based on the Cash-to-Cash Cycle Time, it has value at the beginning of the year 25 days and at the end of the year of 39 days. While the average yield for one year is worth 39 days. The lower the Cash-to-Cash Cycle Time value, the better.

Figure 8. Supply chain of Cash to Cash Cycle Time

Based on the results from system Dynamics simulations, it is known that the attributes cost, responsiveness and assets management have different units so it needs to be normalized first before calculate the overall SCOR result. The normalization results are showed in Table 2.


Rp130.000.000

Rp140.000.000

Rp150.000.000

Rp160.000.000

Rp170.000.000

Rp180.000.000

To

tal C

ost

to

Se

rve



20

30

40

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60da

Ca

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o C

ash

Cyc

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Table 2. Normalized SCOR Performance

Attribute Existing Condition Reliability 99,95 % Responsiveness 75,2 % Agility 99,96 % Cost 34,65 % Asset Management 50,7 %

Based on the results above the SCOR performance for reliability attribute are 99,95% and for agility are 99,96% while the calculation results of normalization for the responsiveness attribute are 75,2%. As for the cost attribute is 34.65%. And the asset management attribute is 50,7%. And the SCOR Performance results for 1 year is showed in Figure 9

Figure 9. SCOR Performance Simulation Result

To calculate the overall SCOR score, attribute weighted evenly given to each SCOR performances that have

been normalized as stated on Table 3 below

Table 3. SCOR Performance Result Attribute Weight Score Weighted Score

Reliability 0,2 99,95 19,99 Responsiveness 0,2 75,2 15,04 Agility 0,2 99,96 19,99 Cost 0,2 34,65 6,93 Asset Management 0,2 50,7 10,14

SCOR Performance Score 72,09 Based on Trienekens and Holby (2020) monitoring system of the performance indicators, the performance of reliability, responsiveness and agility is in good category, asset management is in average category while cost is included in the bad category. Company performance can be improved by implementing the suitable best practices on SCOR 11.0. The lowest score of SCOR Performance in Table Tennis Table Manufacturer are the cost attribute, so it must be the main focus point to improve. There are the best practices in cost attribute: • Risk Management Strategies, • Bowtie Risk Management, • Supply Network Planning, • Enterprise Level Spend Analysis, • Convergence of SCOR with Lean and Six Sigma,


40

50

60

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Normalized SCOR Cost Normalized SCOR AgilityNormalized SCOR Reliability Normalized SCOR Asset ManagementNormalized SCOR Responsiveness

SC

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Non-commercial use only!

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4. Conclusion Based on the performance measurements and simulation result, the supply chain performance of the Table Tennis Table Manufacturer hasn’t maximized yet because the overall SCOR obtained is 72,09%. Even there are 2 perfect attributes (reliability and agility), but there is attributes with very low scores as cost with weighted score 6,93%, this value is far from their target on 10% and require improvements to increase their supply chain performance. In further research, it is possible to combine this method with multi criteria decision making so the output will be ranked among the importance of the problems itself Acknowledgements This research was supported by Penelitian Unggulan Terapan (PUT) Grant (Contract No. 516 / UN27.21 / PP / 2019) References Ariani, A., Ulya, M., and Jakfar, A. Weighted Analysis of Key Performance Indicator (KPI) with SCOR model using

Analitical Hierarchy Process (AHP) for Mozarella Cheese Production at CV Brawijaya Dairy Industry, Junrejo Kota Batu, Agrointek, vol. 11, no. 1, pp. 27-36, 2017.

Erkan, T., and Ugur, B., Supply Chain Performance Measurement: A Case Study about Applicability of SCOR Model in A Manufacturing Industry firm, International Journal of Business and Management Studies, vol. 3, issue 1, pp. 381-390, 2011.

Georgiadis, P., Vlachos, D., and Iakovou, E., A System Dynamics Modeling Framework for the Strategic Supply Chain Management of Food Chains, Journal of Food Engineering, vol. 70, no. 3, pp. 351-364, 2004.

Hadiyati, E., Analysis of Service Quality and its Influences on Customer Loyalty (Case study on PT. Pos Indonesia (Persero) Lawang Post Office, Jurnal Manajemen Pemasaran Modern, vol. 2, no. 2, pp. 81-90, 2010.

Huan, S., Sheoran, S., and Wang, G., A review and analysis of supply chain operations reference (SCOR) model, Supply Chain Management, vol. 9, no. 1, pp. 23-29, 2004.

Ikasari, N., Yuniaristanto, and Sutopo, W., Supply Chain Performance Measurement using Hybrid SCOR Model and System Dynamics, Advances in Intelligent System Research, volume 171, 2019

Forrester J.W., Lessons from system Dynamics modeling, System Dynamics Review, vol.3, issue 2, 1987 Kumar D. and Kumar D., SD Modelling of Healthcare SC in Rural Parts of Uttarakhand, India, Proceedings of the

2015 IEEE IEEM, 2005. Lima-Junior, F., and Carpinetti, L., Combining SCOR® Model and Fuzzy TOPSIS for Supplier Evaluation and

Management, International Journal of Production Economics, vol. 174, pp. 128-141, 2016. Liu, P., Huang, S., Mokasdar, A., Zhou, H., and Hou, L., The Impact of Additive Manufacturing in the Aircraft Spare

Parts Supply Chain: Supply Chain Operation Reference (SCOR) Model Based Analysis, Production Planning and Control: The Management of Operations, vol. 25, issue 13-14, pp. 1169-1181, 2013.

Lockamy III, A., and McCormack, K., The Development of a Supply Chain Management Process Maturity Model Using the Concepts of Business Process Orientation, Supply Chain Management, vol. 9, no. 4, pp. 272-278, 2004.

Sumari, S., Ibrahim, R., Zakaria, N., and Hamid, A., Comparing Three Simulation Model Using Taxonomy: System Dynamics Simulation, Discrete Event Simulation and Agent Based Simulation, International Journal of Management Excellence, vol. 1, no. 3, pp. 54, 2013.

Supply Chain Council, Supply Chain Operation Reference Model Revision 11.0, Supply Chain Council, Inc. United States of America, 2012.

Sutawijaya, A., and Marlapa, E., Supply Chain Management: SCOR Analysis and Implementation at PT.Indoturbine, MIX: Jurnal Ilmiah Manajemen, vol. 6, no. 1, pp. 121-138, 2016.

Treadwell, J., Clark, O., and Bennett, E., Dynamics simulation of phosphorus flows through Montreal’s food and waste systems, Resources, Conservation and Recycling, vol. 131, pp. 122-133, 2018.

Trienekens, J. H., and Hvolby, H. H., Performance Measurement and Improvement in Supply Chains, Proceedings of the 3rd CINet Conference p 399-408, 2000.

Biographies Yuniaristanto is a lecturer at Department of Industrial Engineering, Faculty of Engineering, Universitas Sebelas Maret, He received his Bachelor degree from Institut Teknologi Sepuluh November Surabaya in 1999, and a Master degree from Institut Teknologi Bandung in 2002. His research interests are in supply chain, facility location, city logistics, simulation and modeling. He has published some papers in his research area.

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Muhammad Hisjam is a lecturer at Department of Industrial Engineering, Faculty of Engineering, Universitas Sebelas Maret, where he has been since 1998. He received his Bachelor degree from Universitas Gadjah Mada in 1986, and a Master degree from Institut Teknologi Bandung in 2002. He received his Ph.D. in Environmental Science from Universitas Gadjah Mada in 2016, with his dissertation title is “Sustainable Supply Chain Model in Export Oriented Furniture Industry in Indonesia (Case in Perum Perhutani)”. His research interests are in supply chain, logistics, business and sustainable development. He has published some papers in his research area. He and his colleagues have initiated and maintain some collaborations between his institution with some abroad universities, such as Ehime University, Japan and Universiti Teknologi Malaysia. Iqbal Wahyu Saputra is an undergraduate student in Industrial Engineering Program at Universitas Sebelas Maret. He’s the assistant member on Business and Logistic System Laboratory. He has internship experience in Telkomsel and SKF Indonesia. He’s followed summer school program in Universiti Teknologi Malaysia and research program in Lawrence Technological University. His final project is about hybrid simulation based on performance measurement in table tennis table manufacturer in Indonesia. Roni Zakaria is a lecturer at Department of Industrial Engineering, Faculty of Engineering, Universitas Sebelas Maret. He received his Bachelor degree from Institut Teknologi Bandung in 1998, and a Master degree from Institut Teknologi Bandung in 2002. He’s the research member under Penelitian Unggulan Terapan (PUT) Grant (Contract No. 516 / UN27.21 / PP / 2019)

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System Dynamics Simulation based on SCOR Performance at ...ieomsociety.org/ieom2020/papers/815.pdf• Best Practices Solutions are given by using best practices depends on the SCOR

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