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

Ke Ma (), Sébastien Thomassey () and Xianyi Zeng ()
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Ke Ma: ENSAIT
Sébastien Thomassey: ENSAIT
Xianyi Zeng: ENSAIT

Annals of Operations Research, 2020, vol. 291, issue 1, No 24, 627-656

Abstract: Abstract 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.

Keywords: Supply chain collaboration; Demand-driven supply chain; Textile supply chain; Non-preemptive priority queue; Discrete-event simulation; Case study; Operations research (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10479-018-3000-2

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