Scenario development for improving supply chain performance using the system dynamics approach

  • Authors

    • Imam Santoso Universitas Brawijaya
    • Miftahus Sa'adah Universitas Brawijaya
    • Siti Asmaul Mustaniroh Universitas Brawijaya
    2019-11-10
    https://doi.org/10.14419/ijet.v8i4.29796
  • Bell Pepper, Policy, Profit, Simulation, Dynamics Model.

  • Abstract

    Supply chain management integrates the entire business process of a product from upstream to downstream with the aim of delivering products to consumers in a timely manner and precise quantity without overriding profit. The application of dynamics systems is aimed to provide a holistic view of the system and to identify how interrelationship affects the system as a whole. System dynamics approach is used to analyze the efforts to improve performance. Besides being used as an analysis related to the model, system dynamics can also be used to formulate effective policy related to the profit distribution in case study of bell pepper supply chain. In this study, several scenarios were used as references to improve bell pepper supply chain performance in Regency X. The stake holders in the supply chain were farmer, middleman, and wholesaler. There were three sub-models used in system dynamics, namely the sub-model farmer, middleman, and wholesaler. The model in the system dynamics was then developed to find out the best scenario in improving the performance of bell pepper supply chain. The scenario developed consisted of 4 scenarios in which scenario 1 became the basic scenario as the comparison of simulation results. Then, Scenario 2 was a Supply-Demand arrangement to reduce losses obtained at the level of middleman and wholesaler. The policy of Scenario 3 was warehouse procurement, which was a model improvement scenario in Scenario 3. Lastly, Scenario 4 was an increase in the level of demand / market expansion without an increase in the number of production. The highest total supply chain profit from the four scenarios was in Scenario 3, while the lowest was Scenario 2.

     

     

     

  • References

    1. [1] Ministry of Industry. Overview of Cocoa Industry. Jakarta: Secretariat General of the Ministry of Industry, 2007 (Text in Indonesia.

      [2] Directorat General of Plantation. Plantation Statistics Indonesia Cocoa Commodity 2013-2015. Jakarta: Directorat General of Plantation, 2014 (Text in Indonesia).

      [3] T. Wahyudi, T. R. Panggabean, and Pujiyanto. Cocoa Complete Guide. Jakarta: Penebar Swadaya, 2008 (Text in Indonesia).

      [4] P. Singhal, G. Agarwal, and M.L. Mittal, “Supply Chain Risk Management: Review, Classification and Future Research Directions†International Journal of Business Science and Applied Management, vol. 6, pp. 15-42, 2011. https://doi.org/10.4018/978-1-60960-135-5.ch002.

      [5] K. O. Sarpong, O.F. Alexander, and E.K. Anin, “An Assesssment of Supply Chain Risk in the Cocoa Industry in the Ashanti Region, Ghana†International Journal of Humanities and Social Science, vol. 3, pp. 191-201, 2013.

      [6] H. Aini, M. Syamsun, and A. Setiawan, “Risk of Cocoa Supply Chain in Indonesia with Analytic Network Process and Failure Mode Effect Analysis Integrated Method†Journal of Management & Agribusiness., vol. 11, pp. 209-219, 2014 (Text in Indonesia).

      [7] Y. R. Perdana, “Logistics Information System for Supply Chain of Agricultural Commodity†Social and Behavioral Sciences. vol. 65, pp. 608-613, 2012. https://doi.org/10.1016/j.sbspro.2012.11.172.

      [8] W. Ho, T. Zheng, H. Yildiz, and S. Talluri, “Supply Chain Risk Management: A Literature Review†International Journal of Production Research, vol. 53, pp. 5031-5069, 2015. https://doi.org/10.1080/00207543.2015.1030467.

      [9] I. Etikan, S. A. Musa, and R. S. Alkassim, “Comparison of Convenience Sampling and Purposive Sampling†American Journal of Theoretical and Applied Statistics., vol. 5, pp. 1-4, 2016.

      [10] I. N. Pujawan and L. H. Geraldin, “House of Risk: A Model for Proactive Supply Chain Risk Management†Business Process Management, vol. 15, pp. 963-967, 2009. https://doi.org/10.1108/14637150911003801.

      [11] J. A. P. Mendoza, “Analytical Hierarchy Process and SCOR Model to Support Supply Chain Re-design†International Journal of Information Management, vol. 34, pp. 634-638, 2014. https://doi.org/10.1016/j.ijinfomgt.2014.06.002.

      [12] A. Lutfi and H. Irawan. 2012. Analysis of Supply Chain Risk Using House of Risk Model (Case Study at PT. XXX). Journal of Indonesia Management, vol. 12, pp 1-11, 2012 (Text in Indonesia).

      [13] I. Alwi, “Empirical Criteria in Determining Sample Size on Testing Statistical Hypothesis and Grain Analysis†Formatif Journal., vol. 2, pp. 140-148, 2015 (Text in Indonesia).

      [14] Rahmatin, N., Santoso, I., Indriani, C., Rahayu, S., Widyaningtyas, S., Integration of the Fuzzy Failure Mode and Effect Analysis (Fuzzy FMEA) and the Analytical Network Process (ANP) in Marketing Risk Analysis and Mitigation. International Journal of Technology. vol.9(4), pp.809-81, 2018. https://doi.org/10.14716/ijtech.v9i4.2197.

      [15] Santoso, I., Sa’adah,M., Wijana, S.,. QFD and Fuzzy AHP for Formulating Product Concept of Probiotic Beverages for Diabetic. Telkomnika, 15(1), pp. 391-398, 2017. https://doi.org/10.12928/telkomnika.v15i1.3555.

      [16] E. N. Hayati, “Supply Chain Management (SCM) dan Logistic Management†Journal of engineering dynamics., vol 8, pp. 25-34, 2014 (Text in Indonesia).

      [17] B. Utoyo. Geography Opens the World's Horizon. Bandung: Setia Purna Inves, 2009 (Text in Indonesia).

      [18] P. Mensah and Y. Merkurvey, “Developing a Resilient Supply Chain†Social and Behavioral Sciences., vol. 110, pp. 309-319, 2014. https://doi.org/10.1016/j.sbspro.2013.12.875.

      [19] S. I. Tjipto, “Performance Analysis on Supply Chain Management of Construction Services Company†Journal of Management, pp. 1-12, 2014 (Text in Indonesia).

      [20] N. E. Wahyudin and I. Santoso, “Modelling of Risk Management for Product Development of Yogurt Drink Using House of Risk (HOR) Method†The Asian Journal of Technology Management, vol. 2, pp. 98-108, 2016. https://doi.org/10.12695/ajtm.2016.9.2.4.

      [21] L. Laricha, Rosehan, and Cynthia, “Proposed Quality Improvement with Six Sigma and FMEA Method (Mode of Failure and Effect Analysis) On MBC Roller Conveyor Production Process at PT. XYZ†Scientific Journal of industrial engineering., vol. 1, pp. 88-94, 2013 (Text in Indonesia)

      [22] T. Kusmantini, A. D. Guritno, and H. C. Rustamaji, “Mapping of Supply Chain Risk in Industrial Furniture Base on House of Risk Framework†European Journal of Business and Management., vol. 7, pp. 104-115, 2015.

      [23] F. Tampubolon, A. Bahaudin, and P. F. Ferdinant, “Management of Supply Chain Risk Using House of Risk Method†Industrial Engineering Journal., vol. 1, pp. 222-226, 2013 (Text in Indonesia).

      [24] S. J. Benjamin, M. S. Marathamuthu, and U. Murugalah, “The Use of 5-WHYs Technique to Eliminate OEE’s Speed Loss in A Manufacturing Firm†Journal of Quality in Maintenance Engineering., vol. 21, pp. 29-39, 2015. https://doi.org/10.1108/JQME-09-2013-0062.

      [25] D. I. Handayani “A Review: Potensi Risiko Pada Supply Chain Risk Managementâ€. Jounal of Industrial Spectrum, vol. 14, pp. 1-108, 2016 (Text in Indonesia). https://doi.org/10.12928/si.v14i1.3701.

      [26] R. Firdausa, N. W. Setyanto, and R. Yuniarti, “Risk Analysis of Risk Analysis Project Tool Queue C2000 Using House of Risk (Case Study at PT Cendana Teknika Utama)†Journal of Engineering and Industrial System Management, vol. 3, pp. 431-442, 2015 (Text in Indonesia).

      [27] B. R. Kristanto and N. L. P. Hariastuti, “Application of House of Risk (HOR) Model for Risk Mitigation on Leather Raw Materials Supply Chain†Scientific Journal of Industrial Engineering., vol. 13, pp. 149-157, 2014 (Text in Indonesia).

  • Downloads

  • How to Cite

    Santoso, I., Sa'adah, M., & Asmaul Mustaniroh, S. (2019). Scenario development for improving supply chain performance using the system dynamics approach. International Journal of Engineering & Technology, 8(4), 535-542. https://doi.org/10.14419/ijet.v8i4.29796

    Received date: 2019-09-12

    Accepted date: 2019-10-05

    Published date: 2019-11-10