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

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.

Automated summary

Supply chain systems experience variance amplification in order replenishment and inventory level, with information distortion being a fundamental reason for this phenomenon. Sharing demand information can reduce the bullwhip effect and improve system robustness. Through the APIOBPCS model and Taguchi design, optimal ordering parameters are identified to enhance overall supply chain performance.