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Collaborative pricing and replenishment policy for hi-tech industry

Author

Listed:
  • P C Yang

    (St John's University)

  • H M Wee

    (Chung Yuan Christian University)

  • J C P Yu

    (Takming College)

Abstract

Owing to rapid technological innovation and severe competition, the upstream component price and the downstream product cost of hi-tech industries like computers and communication consumer's products usually decline significantly with time. From a practical viewpoint, there is a need to develop a collaborative pricing and replenishing model with finite horizon when the vendor's purchase cost and the end-consumer's market price are reduced simultaneously. To entice collaboration, the vendor may offer some price discount to the buyer using a negotiation factor to balance the net profit for each player. A numerical example and sensitivity analysis are carried out to illustrate the model. Our results indicate that higher decline-rate in the vendor's purchase cost leads to a smaller vendor lot size, and the higher decline-rate in the market price leads to a larger buyer lot size. The percentage increase in the net profit is approximately 6.57% when cost/price reduction is considered. Therefore, it is significant to consider the effect of the cost/price reduction, especially in hi-tech industries.

Suggested Citation

  • P C Yang & H M Wee & J C P Yu, 2007. "Collaborative pricing and replenishment policy for hi-tech industry," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(7), pages 894-900, July.
  • Handle: RePEc:pal:jorsoc:v:58:y:2007:i:7:d:10.1057_palgrave.jors.2602196
    DOI: 10.1057/palgrave.jors.2602196
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    References listed on IDEAS

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    1. Erel, E, 1992. "The effect of continuous price change in the EOQ," Omega, Elsevier, vol. 20(4), pages 523-527, July.
    2. Khouja, Moutaz & Park, Sungjune, 2003. "Optimal lot sizing under continuous price decrease," Omega, Elsevier, vol. 31(6), pages 539-545, December.
    3. S. K. Goyal, 1992. "A Note on “Inventory Models with Cost Increases”," Operations Research, INFORMS, vol. 40(2), pages 414-415, April.
    4. Benjamin Lev & Howard J. Weiss, 1990. "Inventory Models with Cost Changes," Operations Research, INFORMS, vol. 38(1), pages 53-63, February.
    5. Wee, H. M. & Yang, P. C., 2004. "The optimal and heuristic solutions of a distribution network," European Journal of Operational Research, Elsevier, vol. 158(3), pages 626-632, November.
    6. Andrew J. Clark & Herbert Scarf, 2004. "Optimal Policies for a Multi-Echelon Inventory Problem," Management Science, INFORMS, vol. 50(12_supple), pages 1782-1790, December.
    7. André Gascon, 1995. "On the Finite Horizon EOQ Model with Cost Changes," Operations Research, INFORMS, vol. 43(4), pages 716-717, August.
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    Cited by:

    1. Xu, Xiaoyan & Choi, Tsan-Ming & Chung, Sai-Ho & Guo, Shu, 2023. "Collaborative-commerce in supply chains: A review and classification of analytical models," International Journal of Production Economics, Elsevier, vol. 263(C).
    2. Yang, P.C. & Wee, H.M. & Liu, B.S. & Fong, O.K., 2011. "Mitigating Hi-tech products risks due to rapid technological innovation," Omega, Elsevier, vol. 39(4), pages 456-463, August.
    3. Wang, Dongfan & He, Zhen & He, Shuguang & Zhang, Zhaomin & Zhang, Yiwen, 2021. "Dynamic pricing of two-dimensional extended warranty considering the impacts of product price fluctuations and repair learning," Reliability Engineering and System Safety, Elsevier, vol. 210(C).
    4. repec:spr:compst:v:72:y:2010:i:1:p:107-127 is not listed on IDEAS
    5. Kung-Jeng Wang & Yu-Siang Lin, 2012. "Optimal inventory replenishment strategy for deteriorating items in a demand-declining market with the retailer’s price manipulation," Annals of Operations Research, Springer, vol. 201(1), pages 475-494, December.
    6. Yu-Chung Tsao, 2010. "Two-phase pricing and inventory management for deteriorating and fashion goods under trade credit," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 72(1), pages 107-127, August.
    7. Ya Gao & Guangquan Zhang & Jie Lu & Hui-Ming Wee, 2011. "Particle swarm optimization for bi-level pricing problems in supply chains," Journal of Global Optimization, Springer, vol. 51(2), pages 245-254, October.
    8. Udayan Chanda & Alok Kumar, 2019. "Optimization of EOQ Model for New Products Under Multi-Stage Adoption Process," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 16(02), pages 1-25, April.
    9. Yang, P.C. & Chung, S.L. & Wee, H.M. & Zahara, E. & Peng, C.Y., 2013. "Collaboration for a closed-loop deteriorating inventory supply chain with multi-retailer and price-sensitive demand," International Journal of Production Economics, Elsevier, vol. 143(2), pages 557-566.
    10. Chen, Liang-Hsuan & Kang, Fu-Sen, 2010. "Integrated inventory models considering the two-level trade credit policy and a price-negotiation scheme," European Journal of Operational Research, Elsevier, vol. 205(1), pages 47-58, August.

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