System dynamics: an approach to modeling supply chain performance measurement
Abstract
The complexity of goods and services in the current world has caused individual companies that do not have the help and cooperation of other organizations to face many problems for their survival. In this paper, a system dynamics model was proposed by creating a cause-effect curve to increase supply chain (SC) performance with an emphasis on agility and flexibility (AAF) indicators. The proposed model aimed to reduce cost and delivery time and increase customer satisfaction by considering AAF indicators. To this end, the concepts used were first introduced. Afterward, the important goals were identified by reviewing the existing literatures and interviewing experts in the field of AAF indicators in the studied SC. In the next step, the model was constructed by determining the cause-and-effect (CAE) relationships between the variables. Finally, by developing and simulating different scenarios, the results showed that AAF alone and absolutely cannot enhance profitability. By implication, to increase profitability, AAF do not need to be enhanced to the highest level, but an optimal point must be found. Finally, an optimal level of AAF was estimated. by using this system and considering that this system supports the production line, the ability to respond to sudden demands is increased and as a result, the speed of covering these demands increases.
Keyword : supply chain, performance evaluation, modeling, system dynamics, agility, flexibility
How to Cite
Liu, P., Atifeh, M., Khorshidnia, M., & Taheri, S. G. . (2023). System dynamics: an approach to modeling supply chain performance measurement. Technological and Economic Development of Economy, 29(4), 1291–1317. https://doi.org/10.3846/tede.2023.19211
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Díaz-Reza, J. R., García-Alcaraz, J. L., Avelar-Sosa, L., & Mendoza-Fong, J. R. (2020). The role of employees’ performance and external knowledge transfer on the supply chain flexibility. In García-Alcaraz, J., Sánchez-Ramírez, C., Avelar-Sosa, L., Alor-Hernández, G. (Eds.), Intelligent systems reference library: Vol. 166. Techniques, tools and methodologies applied to global supply chain ecosystems (pp. 25–51). Springer, Cham. https://doi.org/10.1007/978-3-030-26488-8_2
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Gupta, S., Drave, V. A., Bag, S., & Luo, Z. (2019). Leveraging smart supply chain and information system agility for supply chain flexibility. Information Systems Frontiers, 21, 547–564. https://doi.org/10.1007/s10796-019-09901-5
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Haq, M. A., Hameed, I., & Raheem, A. (2020). An empirical analysis of behavioral flexibility, relationship integration and strategic flexibility in supply chain agility: Insights from SMEs sector of Pakistan. South Asian Journal of Management Sciences, 14(1), 104–121. https://doi.org/10.21621/sajms.2020141.06
Hasani, M., & Hosseini, S. H. (2011). Dynamic assessment of capacity investment in electricity market considering complementary capacity mechanisms. Energy, 36(1), 277–293. https://doi.org/10.1016/j.energy.2010.10.041
Helo, P. T. (2000). Dynamic modelling of surge effect and capacity limitation in supply chains. International Journal of Production Research, 38(17), 4521–4533. https://doi.org/10.1080/00207540050205271
Hendalianpour, A. (2020). Optimal lot-size and price of perishable goods: A novel game-theoretic model using double interval grey numbers. Computers & Industrial Engineering, 149, 106780. https://doi.org/10.1016/j.cie.2020.106780
Hendalianpour, A., Fakhrabadi, M., Sangari, M. S., & Razmi, J. (2020a). A combined benders decomposition and Lagrangian relaxation algorithm for optimizing a multi-product, multi-level omni-channel distribution system. Scientia Iranicam. https://doi.org/10.24200/sci.2020.53644.3349
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