Hierarchic change system dynamics supply chain model: impact of demand information sharing on holding cost and downstream project completion





downstream project completion, multi-echelon supply chain, demand information sharing, system dynamics, andesite aggregate stone


The interrelationships between system components are critical to improving the performance of a complex supply chain system. Thus, any improvement or development can be carried out systemically and comprehensively. The complexity of coordination grows as the number of echelons in a supply chain increases. In practice, coordination becomes more difficult to implement in a supply chain with more echelons. Through demand information sharing, this research attempts to figure out how coordination can have implications for complex multi-echelon supply chains with a modeling approach. The Aggregate Andesite Stone Supply Chain is used as an empirical model with four echelons. Changes in dimensions and values per ton of product in each echelon displacement add complexity. Total holding cost is not the only consideration. The timely completion of projects downstream is also a priority. So the system's behavior that runs and changes over time also needs to be observed. To accommodate this complexity, a system dynamics modeling approach is used. This modeling technique could capture fluctuations in volatile conditions that change in time sequences. The pattern of model behavior shows that demand information sharing in the andesite aggregate supply chain is faint, and the "bullwhip effect" occurs. The demand information sharing can eliminate this effect, reduce up to 73.5 % of total supply chain holding costs, and increase the percentage of project completion on time downstream of the supply chain. These results provide a scientific and practical understanding that although there are many obstacles, demand information sharing can significantly improve performance in multi-echelon complex supply chains and be worthwhile applied

Author Biographies

Ahmad Fatih Fudhla, Institut Teknologi Sepuluh Nopember; Universitas Maarif Hasyim Latif

Doctoral Student

Departement of Industrial and Systems Engineering

Departement of Industrial Engineering

Budisantoso Wirjodirdjo, Institut Teknologi Sepuluh Nopember

Doctor of Engineering, Professor

Departement of Industrial and Systems Engineering


  1. Gronwald, K.-D. (2020). SCM – Supply-Chain-Management. Integrierte Business-Informationssysteme, 25–69. doi: https://doi.org/10.1007/978-3-662-59815-3_3
  2. Pujawan, I. N., Mahendrawathi, E. (2017). Supply Chain Management (Edisi 3). Yogyakarta, 374.
  3. Kharsun, L., Kavun-Moshkovska, O., Kotova, M., Nechyporuk, A. (2022). Adaptation of risk management in the supply chains of e-commerce enterprises under the conditions of political instability. Eastern-European Journal of Enterprise Technologies, 5 (13 (119)), 6–20. doi: https://doi.org/10.15587/1729-4061.2022.265649
  4. Wankmüller, C., Reiner, G. (2021). Identifying Challenges and Improvement Approaches for More Efficient Procurement Coordination in Relief Supply Chains. Sustainability, 13 (4), 2204. doi: https://doi.org/10.3390/su13042204
  5. Li, S., Zhao, X., Huo, B. (2018). Supply chain coordination and innovativeness: A social contagion and learning perspective. International Journal of Production Economics, 205, 47–61. doi: https://doi.org/10.1016/j.ijpe.2018.07.033
  6. Nguyen, W. P. V., Dusadeerungsikul, P. O., Nof, S. Y. (2022). Collaborative Control, Task Administration, and Fault Tolerance for Supply Chain Network-Dynamics. Springer Series in Supply Chain Management, 43–78. doi: https://doi.org/10.1007/978-3-031-09179-7_3
  7. Arshinder, Kanda, A., Deshmukh, S. G. (2008). Supply chain coordination: Perspectives, empirical studies and research directions. International Journal of Production Economics, 115 (2), 316–335. doi: https://doi.org/10.1016/j.ijpe.2008.05.011
  8. Feizabadi, J., Gligor, D., Alibakhshi, S. (2021). Strategic supply chains: a configurational perspective. The International Journal of Logistics Management, 32 (4), 1093–1123. doi: https://doi.org/10.1108/ijlm-09-2020-0383
  9. Kurudzhy, Y., Mayorova, I., Moskvichenko, I. (2022). Building a model of supply chains duopoly taking into account the marketing and innovative activities of manufacturing enterprises. Eastern-European Journal of Enterprise Technologies, 2 (3 (116)), 15–21. doi: https://doi.org/10.15587/1729-4061.2022.253821
  10. Ren, J., Hao, Y., Liu, Y. (2010). Two-Echelon Supply Chain Coordination with Uncertain Demand. 2010 Third International Conference on Intelligent Networks and Intelligent Systems. doi: https://doi.org/10.1109/icinis.2010.79
  11. Du, R., Banerjee, A., Kim, S.-L. (2013). Coordination of two-echelon supply chains using wholesale price discount and credit option. International Journal of Production Economics, 143 (2), 327–334. doi: https://doi.org/10.1016/j.ijpe.2011.12.017
  12. Xu, Y., Wei, W. (2010). Coordinative of one-shot cooperation in two-echelon supply chain. 2010 IEEE International Conference on Software Engineering and Service Sciences. doi: https://doi.org/10.1109/icsess.2010.5552270
  13. Ren, J. Y., Liu, Y. X., Hao, Y. P. (2009). Two-Echelon Supply Chain Coordination with Vendor Managed Inventory. Applied Mechanics and Materials, 16-19, 1048–1052. doi: https://doi.org/10.4028/www.scientific.net/amm.16-19.1048
  14. Giri, B. C., Bardhan, S. (2017). Sub-supply chain coordination in a three-layer chain under demand uncertainty and random yield in production. International Journal of Production Economics, 191, 66–73. doi: https://doi.org/10.1016/j.ijpe.2017.04.012
  15. Seifert, R. W., Zequeira, R. I., Liao, S. (2012). A three-echelon supply chain with price-only contracts and sub-supply chain coordination. International Journal of Production Economics, 138 (2), 345–353. doi: https://doi.org/10.1016/j.ijpe.2012.04.006
  16. Yu, Z., Zu, S. (2011). Three-echelon supply chain coordination model based on option-buyback contract. 2011 International Conference on E-Business and E-Government (ICEE). doi: https://doi.org/10.1109/icebeg.2011.5887240
  17. Shaban, A., Costantino, F., Di Gravio, G., Tronci, M. (2020). Coordinating of multi-echelon supply chains through the generalized (R, S) policy. SIMULATION, 96 (9), 767–778. doi: https://doi.org/10.1177/0037549720920708
  18. Abdelsalam, H. M., Elassal, M. M. (2014). Joint economic lot sizing problem for a three – Layer supply chain with stochastic demand. International Journal of Production Economics, 155, 272–283. doi: https://doi.org/10.1016/j.ijpe.2014.01.015
  19. Hu, J., Zhang, J., Mei, M., Yang, W. min, Shen, Q. (2019). Quality control of a four-echelon agri-food supply chain with multiple strategies. Information Processing in Agriculture, 6(4), 425–437. doi: https://doi.org/10.1016/j.inpa.2019.05.002
  20. Purnomo, M. R. A., Wangsa, I. D., Rizky, N., Jauhari, W. A., Zahria, I. (2022). A multi-echelon fish closed-loop supply chain network problem with carbon emission and traceability. Expert Systems with Applications, 210, 118416. doi: https://doi.org/10.1016/j.eswa.2022.118416
  21. Khorshidvand, B., Soleimani, H., Sibdari, S., Seyyed Esfahani, M. M. (2021). Developing a two-stage model for a sustainable closed-loop supply chain with pricing and advertising decisions. Journal of Cleaner Production, 309, 127165. doi: https://doi.org/10.1016/j.jclepro.2021.127165
  22. Tantiwattanakul, P., Dumrongsiri, A. (2019). Supply chain coordination using wholesale prices with multiple products, multiple periods, and multiple retailers: Bi-level optimization approach. Computers & Industrial Engineering, 131, 391–407. doi: https://doi.org/10.1016/j.cie.2019.03.050
  23. Haque, M., Paul, S. K., Sarker, R., Essam, D. (2021). A combined approach for modeling multi-echelon multi-period decentralized supply chain. Annals of Operations Research, 315 (2), 1665–1702. doi: https://doi.org/10.1007/s10479-021-04121-0
  24. Marchi, B., Zavanella, L. E., Zanoni, S. (2020). Joint economic lot size models with warehouse financing and financial contracts for hedging stocks under different coordination policies. Journal of Business Economics, 90 (8), 1147–1169. doi: https://doi.org/10.1007/s11573-020-00975-1
  25. Liao, C.-J., Shyu, C.-C., Tseng, C.-T. (2009). A least flexibility first heuristic to coordinate setups in a two- or three-stage supply chain. International Journal of Production Economics, 117 (1), 127–135. doi: https://doi.org/10.1016/j.ijpe.2008.10.002
  26. Buhayenko, V., Ho, S. C., Thorstenson, A. (2018). A variable neighborhood search heuristic for supply chain coordination using dynamic price discounts. EURO Journal on Transportation and Logistics, 7 (4), 363–385. doi: https://doi.org/10.1007/s13676-018-0122-2
  27. Seeler, K. A. (2014). Introduction to System Dynamics. System Dynamics, 1–44. doi: https://doi.org/10.1007/978-1-4614-9152-1_1
  28. Dangerfield, B. (2020). System Dynamics: Introduction. System Dynamics, 3–7. doi: https://doi.org/10.1007/978-1-4939-8790-0_538
  29. Breitling, T. (2019). Inter-functional coordination of purchasing and logistics: impact on supply chain performance. Supply Chain Forum: An International Journal, 20 (2), 71–88. doi: https://doi.org/10.1080/16258312.2019.1612226
  30. Wirjodirdjo, B., Ghiffary Budianto, A., Widjanarka, A., Pujawan, I. N., Maflahah, I. (2021). Carrier and Freight Forwarders Strategies to Utilize the Immobile Shipping Capacity of Freight Forwarders and Maximize Profits. International Journal of Technology, 12 (4), 876. doi: https://doi.org/10.14716/ijtech.v12i4.4413
  31. Dogan, M., Cerci, H. S., Koyluoglu, A. S. (2022). The effect of green supply chain practices on the firm performance: an empirical research. Eastern-European Journal of Enterprise Technologies, 4 (13 (118)), 61–67. doi: https://doi.org/10.15587/1729-4061.2022.263634
  32. Large, R. O., Merminod, N. (2019). Special Dossier: inter-functional coordination in the supply chain: myth or reality? Supply Chain Forum: An International Journal, 20 (2), 69–70. doi: https://doi.org/10.1080/16258312.2019.1609276
  33. Qrunfleh, S., Tarafdar, M. (2014). Supply chain information systems strategy: Impacts on supply chain performance and firm performance. International Journal of Production Economics, 147, 340–350. doi: https://doi.org/10.1016/j.ijpe.2012.09.018
  34. Cokins, G., Pohlen, T., Klammer, T. (2021). Why Supply Chain Cost Systems Differ from Traditional Cost Systems. Supply Chain Costing and Performance Management, 59–74. doi: https://doi.org/10.1002/9781119793663.ch5
  35. Iida, T. (2012). Coordination of cooperative cost-reduction efforts in a supply chain partnership. European Journal of Operational Research, 222 (2), 180–190. doi: https://doi.org/10.1016/j.ejor.2012.03.029
  36. Kim, M., Chai, S. (2017). The impact of supplier innovativeness, information sharing and strategic sourcing on improving supply chain agility: Global supply chain perspective. International Journal of Production Economics, 187, 42–52. doi: https://doi.org/10.1016/j.ijpe.2017.02.007
  37. Huang, R., Yao, X. (2021). An analysis of sustainability and channel coordination in a three-echelon supply chain. Journal of Enterprise Information Management, 34 (1), 490–505. doi: https://doi.org/10.1108/jeim-12-2019-0413
  38. Niemsakul, J., Islam, S. M. N., Singkarin, D., Somboonwiwat, T. (2018). Cost-benefit sharing in healthcare supply chain collaboration. International Journal of Logistics Systems and Management, 30 (3), 406. doi: https://doi.org/10.1504/ijlsm.2018.092624
  39. Mouaky, M., Berrado, A., Benabbou, L. (2019). Using a kanban system for multi-echelon inventory management: the case of pharmaceutical supply chains. International Journal of Logistics Systems and Management, 32 (3/4), 496. doi: https://doi.org/10.1504/ijlsm.2019.098333
  40. Drakaki, M., Tzionas, P. (2019). Investigating the impact of inventory inaccuracy on the bullwhip effect in RFID-enabled supply chains using colored petri nets. Journal of Modelling in Management, 14 (2), 360–384. doi: https://doi.org/10.1108/jm2-08-2017-0081
  41. Khedlekar, U. K., Singh, P. (2019). Three-layer supply chain policy under sharing recycling responsibility. Journal of Advances in Management Research, 16 (5), 734–762. doi: https://doi.org/10.1108/jamr-01-2019-0002
  42. Rached, M. (2020). Genetic Algorithm to Evaluate Downstream and Upstream Information Sharing. Current Signal Transduction Therapy, 15 (1), 24–33. doi: https://doi.org/10.2174/1574362413666180830105740
  43. Haque, M., Paul, S. K., Sarker, R., Essam, D. (2020). Managing decentralized supply chain using bilevel with Nash game approach. Journal of Cleaner Production, 266, 121865. doi: https://doi.org/10.1016/j.jclepro.2020.121865
  44. Van Belle, J., Guns, T., Verbeke, W. (2021). Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains. European Journal of Operational Research, 288 (2), 466–479. doi: https://doi.org/10.1016/j.ejor.2020.05.059
  45. Ibrahim, A., Daniyal, H., Asmawaty, T., Kamaludin, A. (2021). Potential Data Collections Methods for System Dynamics Modelling: A Brief Overview. International Journal of Advanced Computer Science and Applications, 12 (3). doi: https://doi.org/10.14569/ijacsa.2021.0120332
  46. Rebs, T., Brandenburg, M., Seuring, S. (2019). System dynamics modeling for sustainable supply chain management: A literature review and systems thinking approach. Journal of Cleaner Production, 208, 1265–1280. doi: https://doi.org/10.1016/j.jclepro.2018.10.100
  47. Liu, J., Teng, Y., Wang, D., Gong, E. (2019). System dynamic analysis of construction waste recycling industry chain in China. Environmental Science and Pollution Research, 27 (30), 37260–37277. doi: https://doi.org/10.1007/s11356-019-06739-x
  48. Sundarakani, B., Sikdar, A., Balasubramanian, S. (2014). System dynamics-based modelling and analysis of greening the construction industry supply chain. International Journal of Logistics Systems and Management, 18 (4), 517. doi: https://doi.org/10.1504/ijlsm.2014.063983
  49. Wang, J., Li, Z., Tam, V. W. Y. (2015). Identifying best design strategies for construction waste minimization. Journal of Cleaner Production, 92, 237–247. doi: https://doi.org/10.1016/j.jclepro.2014.12.076
  50. Malik, A., Khan, K. I. A., Qayyum, S., Ullah, F., Maqsoom, A. (2022). Resilient Capabilities to Tackle Supply Chain Risks: Managing Integration Complexities in Construction Projects. Buildings, 12 (9), 1322. doi: https://doi.org/10.3390/buildings12091322
  51. Ghufran, M., Khan, K. I. A., Ullah, F., Nasir, A. R., Al Alahmadi, A. A., Alzaed, A. N., Alwetaishi, M. (2022). Circular Economy in the Construction Industry: A Step towards Sustainable Development. Buildings, 12 (7), 1004. doi: https://doi.org/10.3390/buildings12071004
  52. Nasir, M. H. A., Genovese, A., Acquaye, A. A., Koh, S. C. L., Yamoah, F. (2017). Comparing linear and circular supply chains: A case study from the construction industry. International Journal of Production Economics, 183, 443–457. doi: https://doi.org/10.1016/j.ijpe.2016.06.008
  53. Wang, X., Du, Q., Lu, C., Li, J. (2022). Exploration in carbon emission reduction effect of low-carbon practices in prefabricated building supply chain. Journal of Cleaner Production, 368, 133153. doi: https://doi.org/10.1016/j.jclepro.2022.133153
  54. Yin, Y., Zhang, Y., Jin, K. (2021). System Dynamics Modeling of the Supply Chain Performance of Prefabricated Construction Based on the Stakeholder Analysis. Journal of Physics: Conference Series, 1827 (1), 012109. doi: https://doi.org/10.1088/1742-6596/1827/1/012109
  55. Vanteddu, G., Nicholls, G. (2020). Supply Chain Network Design and Tactical Planning in the Dimension Stone Industry. Operations and Supply Chain Management: An International Journal, 13 (4), 320–335. doi: https://doi.org/10.31387/oscm0430273
  56. Forrester, J. W., Collins, F. (1972). World Dynamics. Journal of Dynamic Systems, Measurement, and Control, 94 (4), 339–339. doi: https://doi.org/10.1115/1.3426619
  57. Forrester, J. W. (2016). Learning through System Dynamics as Preparation for the 21st Century. System Dynamics Review, 32 (3-4), 187–203. doi: https://doi.org/10.1002/sdr.1571
  58. Fudhlaa, A. F., Rachmawati, W., Retnowati, D. (2021). Analysis of sugar import policy effects on sugar cane farmer’s income in East Java: A system dynamic approach. IOP Conference Series: Materials Science and Engineering, 1072 (1), 012023. doi: https://doi.org/10.1088/1757-899x/1072/1/012023
  59. Barlas, Y. (1989). Multiple tests for validation of system dynamics type of simulation models. European Journal of Operational Research, 42 (1), 59–87. doi: https://doi.org/10.1016/0377-2217(89)90059-3
  60. Shreckengost, R. C. (1985). Dynamic simulation models: how valid are they? PsycEXTRA Dataset. doi: https://doi.org/10.1037/e496952006-007
  61. Olaya, C. (2020). System Dynamics: Engineering Roots of Model Validation. System Dynamics, 109–117. doi: https://doi.org/10.1007/978-1-4939-8790-0_544
  62. Ghali, A., Favre, R., Elbadry, M. (2020). Concrete Structures. CRC Press, 672. doi: https://doi.org/10.1201/9781003061274
  63. Nihad, Z., Sarsam, S. I. (2020). Variation of Asphalt Requirement and Strength Properties among Hot Mix (HMA) and Warm Mix (WMA) Asphalt Concrete. Association of Arab Universities Journal of Engineering Sciences, 27 (2), 24–33. doi: https://doi.org/10.33261/jaaru.2020.27.2.003
  64. Amin, F., Khan, K. I. A., Ullah, F., Alqurashi, M., Alsulami, B. T. (2022). Key Adoption Factors for Collaborative Technologies and Barriers to Information Management in Construction Supply Chains: A System Dynamics Approach. Buildings, 12 (6), 766. doi: https://doi.org/10.3390/buildings12060766
  65. Chopra, S. (2018). Supply Chain Management: Strategy, Planning, and Operation (What’s New in Operations Management). Pearson, 528.
Hierarchic change system dynamics supply chain model: impact of demand information sharing on holding cost and downstream project completion




How to Cite

Fudhla, A. F., & Wirjodirdjo, B. (2023). Hierarchic change system dynamics supply chain model: impact of demand information sharing on holding cost and downstream project completion. Eastern-European Journal of Enterprise Technologies, 1(3 (121), 25–37. https://doi.org/10.15587/1729-4061.2023.269284



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