Supply chain information sharing under consideration of bullwhip effect and system robustness

Lina Tang, Taho Yang, Yiliu Tu and Yizhong Ma ()
Additional contact information
Lina Tang: Nanjing University of Science and Technology
Taho Yang: National Cheng Kung University
Yiliu Tu: University of Calgary
Yizhong Ma: Nanjing University of Science and Technology

Flexible Services and Manufacturing Journal, 2021, vol. 33, issue 2, No 3, 337-380

Abstract: Abstract Supply chain system experiences variance amplification in order replenishment and inventory level, leading to severe inefficiencies of the system. Information distortion is universally known as a fundamental reason for the variance amplification phenomenon. The purpose of this paper is to study the effect of demand information sharing in reducing bullwhip effect and improving the robustness of supply chain systems. The “automatic pipeline inventory and order-based production control system, APIOBPCS” is adopted to model supply chains with different information-sharing strategies. The stochastic factors in the supply chain system lead to poor performance in system robustness. Taguchi design is adopted to find out the optimal setting of ordering parameters in the APIOBPCS model for a robust supply chain. An extension of Taguchi design is adopted to solve the multi-response problems. The weighted signal-to-noise ratio is used as the performance index of the overall performance of the supply chain, including inventory cost, customer service level, and inventory variance amplification. The results show that full demand information transparency helps to improve the overall performance of supply chain. Furthermore, the sensitivity analysis of stochastic lead times verifies the results. This research gives some insights to improve the overall performance of supply chain via information sharing.

Keywords: Information sharing; Supply chain; Simulation; Bullwhip effect; Signal-to-noise ratio; Multi-response optimization (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10696-020-09384-6

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