Development of a central order processing system for optimizing demand-driven textile supply chains: a real case based simulation study

  • Ke MaEmail author
  • Sébastien Thomassey
  • Xianyi Zeng
S.I.: RealCaseOR


Nowadays, the demand of small-series production and quick response become more and more important in textile supply chains. To meet the increasing trend of customization in garment production, forecast based supply chain model is not suitable any more. Demand-driven garment supply chain is developed and employed more and more. However, there are still many defects in current model for demand-driven supply chains, e.g. long lead time, low efficiency etc. Therefore, in this study we proposed a new collaborative model with central order processing system (COPS) to optimize current demand-driven garment supply chain and improve multiple supply chain performances. Common and important supply chain collaboration strategies, including resource sharing, information sharing, joint-decision making and profit sharing, were merged into this system. Discrete-event simulation technology was utilized to experiment and evaluate the new collaborative model under different conditions based on a real case in France. Multiple key performance indicators (KPIs) were examined for the whole supply chain and also for individual companies. Based on the simulation experiment results, we found that new proposed collaborative model gain improvements in all examined KPIs. New model with COPS performed better under high workload condition than under low workload condition. It can not only increase overall profit level of the whole supply chain but also individual profit level of each company.


Supply chain collaboration Demand-driven supply chain Textile supply chain Non-preemptive priority queue Discrete-event simulation Case study Operations research 



Funding was provided by Erasmus Mundus SMDTex programme. This work is supported by the joint doctorate programme “Sustainable Management and Design for Textiles” which is funded by the European Commission’s Erasmus Mundus programme.


  1. 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. Scholar
  2. Bahinipati, B. K., Kanda, A., & Deshmukh, S. G. (2009). Horizontal collaboration in semiconductor manufacturing industry supply chain: An evaluation of collaboration intensity index. Computers & Industrial Engineering, 57(3), 880–895. Scholar
  3. Bian, W., Shang, J., & Zhang, J. (2016). Two-way information sharing under supply chain competition. International Journal of Production Economics, 178, 82–94. Scholar
  4. Boza, A., Alemany, M. M. E., Alarcon, F., & Cuenca, L. (2014). A model-driven DSS architecture for delivery management in collaborative supply chains with lack of homogeneity in products. Production Planning & Control, 25(8), 650–661. Scholar
  5. Brandt, A., & Brandt, M. (1999). On a two-queue priority system with impatience and its application to a call center*. Methodology And Computing in Applied Probability, 1(2), 191–210. Scholar
  6. Broadbent, A. D. (2001). Basic principles of textile coloration. Society of Dyers and Colorists West Yorkshire.Google Scholar
  7. Buijs, P., & Wortmann, J. C. (2014). Joint operational decision-making in collaborative transportation networks: The role of IT. Supply Chain Management-an International Journal, 19(2), 200–210. Scholar
  8. Cao, M., Vonderembse, M. A., Zhang, Q. Y., & Ragu-Nathan, T. S. (2010). Supply chain collaboration: Conceptualisation and instrument development. International Journal of Production Research, 48(22), 6613–6635. Scholar
  9. Cao, B.-B., Xiao, Z.-D., & Sun, J.-N. (2017). A study of the bullwhip effect in supply- and demand-driven supply chain. Journal of Industrial and Production Engineering, 34(2), 124–134. Scholar
  10. Cao, M., & Zhang, Q. Y. (2011). Supply chain collaboration: Impact on collaborative advantage and firm performance. Journal of Operations Management, 29(3), 163–180. Scholar
  11. Chaharsooghi, S. K., & Heydari, J. (2010). Supply chain coordination for the joint determination of order quantity and reorder point using credit option. European Journal of Operational Research, 204(1), 86–95. Scholar
  12. Chan, F. T. S., & Prakash, A. (2012). Inventory management in a lateral collaborative manufacturing supply chain: A simulation study. International Journal of Production Research, 50(16), 4670–4685.CrossRefGoogle Scholar
  13. Chan, H.-L., Shen, B., & Cai, Y. (2017). Quick response strategy with cleaner technology in a supply chain: Coordination and win-win situation analysis. International Journal of Production Research, 7543, 1–12. Scholar
  14. Choi, T.M., Cheng, T.C.E., & Zhao, X. (2016). Multi-methodological research in operations management. Production and Operations Management, 25(3), 379–389.CrossRefGoogle Scholar
  15. Choi, T.-M., Govindan, K., Li, X., & Li, Y. (2017). Innovative supply chain optimization models with multiple uncertainty factors. Annals of Operations Research, 257(1), 1–14. Scholar
  16. Choi, T. M., & Guo, S. (2017). Responsive supply in fashion mass customisation systems with consumer returns. International Journal of Production Research, 7543, 1–14. Scholar
  17. Choi, T. M., Yeung, W. K., Cheng, T. C. E., & Yue, X. (2018). Optimal scheduling, coordination, and the value of RFID technology in garment manufacturing supply chains. IEEE Transactions on Engineering Management, 65(1), 72–84. Scholar
  18. Deniz, B., Karaesmen, I., & Scheller-Wolf, A. (2010). Managing perishables with substitution: Inventory issuance and replenishment heuristics. Manufacturing & Service Operations Management, 12(2), 319–329. Scholar
  19. Gündoğdu, K., & Çalhan, A. (2016). An implementation of wireless body area networks for improving priority data transmission delay. Journal of Medical Systems, 40(3), 75. Scholar
  20. Hadaya, P., & Cassivi, L. (2007). The role of joint collaboration planning actions in a demand-driven supply chain. Industrial Management & Data Systems, 107(7), 954–978. Scholar
  21. Henry, A., & Wernz, C. (2014). A multiscale decision theory analysis for revenue sharing in three-stage supply chains. Annals of Operations Research, 226(1), 277–300. Scholar
  22. Heydari, J. (2014). Lead time variation control using reliable shipment equipment: An incentive scheme for supply chain coordination. Transportation Research Part E-Logistics and Transportation Review, 63, 44–58. Scholar
  23. Hlioui, R., Gharbi, A., & Hajji, A. (2017). Joint supplier selection, production and replenishment of an unreliable manufacturing-oriented supply chain. International Journal of Production Economics, 187, 53–67. Scholar
  24. Hsueh, C. F. (2014). Improving corporate social responsibility in a supply chain through a new revenue sharing contract. International Journal of Production Economics, 151, 214–222. Scholar
  25. Hvolby, H.-H., & Steger-Jensen, K. (2010). Technical and industrial issues of Advanced Planning and Scheduling (APS) systems. Computers in Industry, 61(9), 845–851. Scholar
  26. Inderfurth, K., Sadrieh, A., & Voigt, G. (2013). The impact of information sharing on supply chain performance under asymmetric information. Production and Operations Management, 22(2), 410–425. Scholar
  27. Kao, E. P. C., & Wilson, S. D. (1999). Analysis of nonpreemptive priority queues with multiple servers and two priority classes. European Journal of Operational Research, 118(1), 181–193. Scholar
  28. Keiser, S., & Garner, M. (2012). Beyond design: The synergy of apparel product development. A&C Black.Google Scholar
  29. Kurata, H. (2014). How does inventory pooling work when product availability influences customers’ purchasing decisions? International Journal of Production Research, 52(22), 6739–6759. Scholar
  30. Law, A. M., & Kelton, W. D. (2000). Simulation Modeling and Analysis (McGraw Hill Series in Industrial Engineering and Management Science).Google Scholar
  31. Leng, M. M., & Parlar, M. (2009). Lead-time reduction in a two-level supply chain: Non-cooperative equilibria vs. coordination with a profit-sharing contract. International Journal of Production Economics, 118(2), 521–544. Scholar
  32. Li, D. C., & Dai, W. L. (2009). Determining the optimal collaborative benchmarks in a supply chain. International Journal of Production Research, 47(16), 4457–4471. Scholar
  33. Li, G., Fan, H., Lee, P. K. C., & Cheng, T. C. E. (2015). Joint supply chain risk management: An agency and collaboration perspective. International Journal of Production Economics, 164, 83–94. Scholar
  34. Li, Y. N., Xu, X. J., Zhao, X. D., Van Yeung, J. H., & Ye, F. (2012). Supply chain coordination with controllable lead time and asymmetric information. European Journal of Operational Research, 217(1), 108–119. Scholar
  35. Lin, C., Hwang, S., & Min-Yang Wang, E. (2007). A reappraisal on advanced planning and scheduling systems. Industrial Management & Data Systems, 107(8), 1212–1226. Scholar
  36. Ma, K., Wang, L., & Chen, Y. (2017). A resource sharing mechanism for sustainable production in the garment industry. Sustainability, 10(1), 52. Scholar
  37. Ma, K., Wang, L., & Chen, Y. (2018). A collaborative cloud service platform for realizing sustainable make-to-order apparel supply chain. Sustainability, 10(1), 11. Scholar
  38. Mendes, P., Leal, J. E., & Thomé, A. M. T. (2016). A maturity model for demand-driven supply chains in the consumer product goods industry. International Journal of Production Economics, 179, 153–165. Scholar
  39. Naesens, K., Gelders, L., & Pintelon, L. (2009). A swift response framework for measuring the strategic fit for a horizontal collaborative initiative. International Journal of Production Economics, 121(2), 550–561. Scholar
  40. Ormerod, A., & Sondhelm, W. S. (1995). Weaving: Technology and operations. Boca Raton: CRC.Google Scholar
  41. Ramanathan, U., & Gunasekaran, A. (2014). Supply chain collaboration: Impact of success in long-term partnerships. International Journal of Production Economics, 147, 252–259. Scholar
  42. Rönngren, R., & Ayani, R. (1997). A comparative study of parallel and sequential priority queue algorithms. ACM Transactions on Modeling and Computer Simulation ACM, 7(2), 157–209. Scholar
  43. Selen, W., & Soliman, F. (2002). Operations in today’s demand chain management framework. Journal of Operations Management. Scholar
  44. Sharif, A. Bin, Stanford, D. A., Taylor, P., & Ziedins, I. (2014). A multi-class multi-server accumulating priority queue with application to health care. Operations Research for Health Care, 3(2), 73–79. Scholar
  45. Shen, B., Ding, X., Chen, L., & Chan, H. L. (2017). Low carbon supply chain with energy consumption constraints: Case studies from China’s textile industry and simple analytical model. Supply Chain Management: An International Journal, 22(3), 258–269. Scholar
  46. Takagi, H. (2016). Waiting time in the M/M/m LCFS nonpreemptive priority queue with impatient customers. Annals of Operations Research, 247(1), 257–289. Scholar
  47. Vafa Arani, H., Rabbani, M., & Rafiei, H. (2016). A revenue-sharing option contract toward coordination of supply chains. International Journal of Production Economics, 178, 42–56. Scholar
  48. Verdouw, C. N., Beulens, A. J. M., Trienekens, J. H., & van der Vorst, J. G. A. J. (2011). A framework for modelling business processes in demand-driven supply chains. Production Planning & Control, 22(4), 365–388. Scholar
  49. Vilkelis, A., & Jakovlev, S. (2014). Outbound supply chain collaboration modelling based on the automotive industry. Transport, 29(2), 223–230. Scholar
  50. Wang, K., Gou, Q., Sun, J. W., & Yue, X. H. (2012). Coordination of a fashion and textile supply chain with demand variations. Journal of Systems Science and Systems Engineering, 21(4), 461–479. Scholar
  51. Wang, S. D., Zhou, Y. W., & Wang, J. P. (2010). Supply chain coordination with two production modes and random demand depending on advertising expenditure and selling price. International Journal of Systems Science, 41(10), 1257–1272. Scholar
  52. Wee, H. M., & Wang, W. T. (2013). Supply chain coordination for short-life-cycle products with option contract and partial backorders. European Journal of Industrial Engineering, 7(1), 78–99. Scholar
  53. Williams, T. M. (1980). Nonpreemptive multi-server priority queues. The Journal of the Operational Research Society, 31(12), 1105. Scholar
  54. Xiao, T. J., & Xu, T. T. (2013). Coordinating price and service level decisions for a supply chain with deteriorating item under vendor managed inventory. International Journal of Production Economics, 145(2), 743–752. Scholar
  55. Zhu, X. (2017). Outsourcing management under various demand Information Sharing scenarios. Annals of Operations Research, 257(1–2), 449–467. Scholar

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Authors and Affiliations

  1. 1.GEMTEXENSAITRoubaixFrance
  2. 2.Department of Business Administration and Textile ManagementUniversity of BoråsBoråsSweden
  3. 3.Department of Textile and Clothing EngineeringSoochow UniversitySuzhouChina

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