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Dynamic order replenishment policy in internet-based supply chains

Author

Listed:
  • Oded Berman
  • Eungab Kim

Abstract

We consider a problem of dynamic replenishment of parts in the supply chain consisting of single class of customers, company, and supplier. Customers request a service via the WEB-based ordering system and the company supports service using parts which are procured from the supplier. The replenishment process of parts possesses an Erlang distribution. With Poisson customer arrival process and exponential service times, the model is formulated as a Markov decision problem. The goal of this paper is to identify an order replenishment policy which minimizes the customer waiting, inventory holding, and order replenishment costs under both the discounted cost and average cost criteria. The main result is that the optimal ordering policy has a monotonic threshold structure. Computational results demonstrate that the replenishment model with Erlang lead times is more stable than that of exponential lead times in terms of the cost and dynamic policies with a variable reorder point is more cost-effective than (Q, r) policy under the Erlang lead time model. Copyright Springer-Verlag Berlin Heidelberg 2001

Suggested Citation

  • Oded Berman & Eungab Kim, 2001. "Dynamic order replenishment policy in internet-based supply chains," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 53(3), pages 371-390, July.
    • Handle: RePEc:spr:mathme:v:53:y:2001:i:3:p:371-390
    DOI: 10.1007/s001860100116
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    Cited by:

    1. Manafzadeh Dizbin, Nima & Tan, Barış, 2020. "Optimal control of production-inventory systems with correlated demand inter-arrival and processing times," International Journal of Production Economics, Elsevier, vol. 228(C).
    2. Song, Dong-Ping & Dong, Jing-Xin & Xu, Jingjing, 2014. "Integrated inventory management and supplier base reduction in a supply chain with multiple uncertainties," European Journal of Operational Research, Elsevier, vol. 232(3), pages 522-536.
    3. Jeganathan, K. & Abdul Reiyas, M. & Prasanna Lakshmi, K. & Saravanan, S., 2019. "Two server Markovian inventory systems with server interruptions: Heterogeneous vs. homogeneous servers," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 177-200.
    4. Maike Schwarz & Hans Daduna, 2006. "Queueing systems with inventory management with random lead times and with backordering," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 64(3), pages 383-414, December.
    5. Zhao, Ning & Lian, Zhaotong, 2011. "A queueing-inventory system with two classes of customers," International Journal of Production Economics, Elsevier, vol. 129(1), pages 225-231, January.
    6. Wei Xu & Dong-Ping Song, 2022. "Integrated optimisation for production capacity, raw material ordering and production planning under time and quantity uncertainties based on two case studies," Operational Research, Springer, vol. 22(3), pages 2343-2371, July.
    7. Jalili Marand, Ata & Li, Hongyan & Thorstenson, Anders, 2019. "Joint inventory control and pricing in a service-inventory system," International Journal of Production Economics, Elsevier, vol. 209(C), pages 78-91.

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