Optimizing Sustainable Supply Chains in Apparel Production through System Dynamics Modeling using Vensim
Abstract
Supply chain management plays a crucial role in manufacturing processes, where the flow of goods begins with raw materials and continues to the end consumer, involving various complexities and uncertainties at each stage. Computer-based modeling and simulation are highly useful methods for addressing operational issues within the supply chain, as they are capable of solving complex problems that would otherwise be time-consuming and challenging to analyze manually. On the other hand, manufacturing companies are often concerned about losing valuable time and resources during production; inaccurate estimations of raw materials, labor, and equipment not only result in financial losses but also cause adverse environmental impacts. The purpose of this study is to demonstrate that system dynamics modeling in sustainable supply chain management can be applied to apparel production in order to optimize the use of materials, labor, and equipment. This research employs Vensim as a modeling and simulation tool due to its ability to represent causal relationships, manage complex feedback loops, and visualize system dynamics over time. By utilizing Vensim, manufacturing companies can simulate various supply chain scenarios in a controlled environment, thereby minimizing resource waste and enhancing sustainability through the reduction of energy consumption, material use, and labor. Shirt manufacturing was selected as the case study because its production process is relatively straightforward, while this garment type is also widely used across the globe. The findings of this study indicate that simulation using system dynamics modeling with Vensim is an efficient method for optimizing the utilization of materials, labor, and equipment in shirt production. This leads to a more sustainable manufacturing process, as the developed model allows for the evaluation of supply chain policies and their impacts before implementation in real systems.
Downloads
References
Dyllick, T., & Hockerts, K. (2002). Beyond the business case for corporate sustainability. Business Strategy and the Environment, 11(2), 130–141. https://doi.org/10.1002/bse.323
Forrester, J. W. (1961). Industrial Dynamics. M.I.T. Press.
Marino, S., Hogue, I. B., Ray, C. J., & Kirschner, D. E. (2008). A methodology for performing global uncertainty and sensitivity analysis in systems biology. Journal of Theoretical Biology, 254(1), 178–196. https://doi.org/10.1016/j.jtbi.2008.04.011
Merschmann, U., & Thonemann, U. W. (2011). Supply chain flexibility, uncertainty and firm performance: An empirical analysis of German manufacturing firms. International Journal of Production Economics, 130(1), 43–53. https://doi.org/10.1016/j.ijpe.2010.10.013
Muthu, S. S. (Ed.). (2015). Handbook of Sustainable Apparel Production. CRC Press. https://doi.org/10.1201/b18428
Oelze, N. (2017). Sustainable Supply Chain Management Implementation–Enablers and Barriers in the Textile Industry. Sustainability, 9(8), 1435. https://doi.org/10.3390/su9081435
Pérez-Pérez, J. F., Parra, J. F., & Serrano-García, J. (2021). A system dynamics model: Transition to sustainable processes. Technology in Society, 65, 101579. https://doi.org/10.1016/j.techsoc.2021.101579
Rathore, D. B. (2023). Future of Textile: Sustainable Manufacturing & Prediction via ChatGPT. Eduzone : International Peer Reviewed/Refereed Academic Multidisciplinary Journal, 12(01), 52–62. https://doi.org/10.56614/eiprmj.v12i1y23.253
Richardson, G. P. (2020). Core of System Dynamics. In System Dynamics (pp. 11–20). Springer US. https://doi.org/10.1007/978-1-4939-8790-0_536
Schunk, D., & Plott, B. (n.d.). Using simulation to analyze supply chains. 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165), 2, 1095–1100. https://doi.org/10.1109/WSC.2000.899070
Shen, B., Li, Q., Dong, C., & Perry, P. (2017). Sustainability Issues in Textile and Apparel Supply Chains. Sustainability, 9(9), 1592. https://doi.org/10.3390/su9091592
