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A multiple replenishment contract with ARIMA demand processes

Author

Listed:
  • Jong Soo Kim

    (Hanyang University, Ansan Campus)

  • K Y Shin

    (Hanyang University, Ansan Campus)

  • S E Ahn

    (Hanyang University, Ansan Campus)

Abstract

This paper is concerned with a multiple replenishment contract with a purchase price discount in a supply chain. The chain is composed of one supplier, one buyer and consumers for a product. The replenishment contract is based upon the well-known (s, Q) policy, but allows us to contract replenishments at a future time with a price discount. Owing to the larger forecast error of future demand, the buyer should keep a higher level of safety stock to provide the same level of service as the usual (s, Q) policy. However, the buyer can reduce his purchase cost by ordering a larger quantity at a discounted price. Hence, there exists a trade-off between the price discount and the inventory holding cost. For the ARIMA demand processes, we present a model for the contract and an algorithm to find the number of the future replenishments. Computational experiments show that the algorithm finds the global optimum solution very quickly.

Suggested Citation

  • Jong Soo Kim & K Y Shin & S E Ahn, 2003. "A multiple replenishment contract with ARIMA demand processes," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(11), pages 1189-1197, November.
    • Handle: RePEc:pal:jorsoc:v:54:y:2003:i:11:d:10.1057_palgrave.jors.2601620
    DOI: 10.1057/palgrave.jors.2601620
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    References listed on IDEAS

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    Cited by:

    1. J S Kim & T C Kwak, 2007. "Game theoretic analysis of the bargaining process over a long-term replenishment contract," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(6), pages 769-778, June.
    2. Barros, Júlio & Cortez, Paulo & Carvalho, M. Sameiro, 2021. "A systematic literature review about dimensioning safety stock under uncertainties and risks in the procurement process," Operations Research Perspectives, Elsevier, vol. 8(C).
    3. Jiseong Noh & Hyun-Ji Park & Jong Soo Kim & Seung-June Hwang, 2020. "Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management," Mathematics, MDPI, vol. 8(4), pages 1-14, April.

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