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.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (Nos. 71931006, 71871119 and 71771121), the Natural Sciences and Engineering Research Council of Canada (No. RGPIN-2018-03862), the Fundamental Research Funds for the Central Universities (No. 3091511102), and China Scholarship Council.
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Appendices
Appendices
1.1 Appendix 1: Nomenclature
1. Variables in simulation model.
AINV | Actual inventory |
AVCON | Average consumption |
AWIP | Actual work-in-progress |
COMRATE | Completion rate |
CONS | Consumption |
TINV | Target inventory |
EINV | Error of inventory |
TWIP | Target WIP |
EWIP | Error of WIP |
ORATE | Order rate |
DRATE | Demand rate of customer |
TBA | Time between arrivals of customer demand |
TQ | Transportation quantity |
OWD | Products on way of delivery |
QP | Quality passed products |
QF | Quality failed products |
2. Parameters in simulation model.
CAPCON | Capacity constraint |
---|---|
QRATE | Quality pass rate of products |
To | Order lead time |
Td | Delivery lead time |
Tp | Production lead time |
Tr | Rework time of unqualified products |
Kw | Constant multiplier to determine target WIP |
Ta | Time to smooth consumption |
Ti | Time to recover inventory |
Tw | Time to recover WIP |
1.2 Appendix 2: Equations for the APIOBPCS based simulation model
1. Determine the order/production rate
2. Ordering delay and transportation delay
3. Production delay and rework delay
4. Fulfilling backorders
1.3 Appendix 3: Taguchi design for three responses with 10 replicates
1.4 Appendix 4: Experiment data for sensitivity analysis of stochastic lead time
See Table 13
.
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Tang, L., Yang, T., Tu, Y. et al. Supply chain information sharing under consideration of bullwhip effect and system robustness. Flex Serv Manuf J 33, 337–380 (2021). https://doi.org/10.1007/s10696-020-09384-6
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DOI: https://doi.org/10.1007/s10696-020-09384-6