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Joint inventory and pricing decisions with reference effects

Author

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  • M. Güler
  • Taner Bi̇lgi̇ç
  • Refi̇k Güllü

Abstract

This article considers a periodic review joint replenishment and pricing problem of a single item with reference effects. The demand is random and is contingent on the price history as well as the current price. Randomness is introduced with both an additive and a multiplicative random term. Price history is captured by a reference price, which is developed by consumers that are frequent buyers of a product or a service. The common reference price acts as a benchmark against which the consumers compare the price of a product. They perceive the difference between the price and the reference price as a loss or a gain and have different attitudes to these perceptions, such as loss aversion, loss neutrality, or loss seeking. A general way to handle the nonconvexity of the holding cost for nonlinear demand models is to make a transformation and use the inverse demand function. However, in reference price-dependent demand models, this brings the problem of a nonconvex action space. This problem is circumvented by defining an action space that preserves the convexity after a transformation. For the transformed problem, it is shown that a state-dependent order-up-to policy is optimal for concave demand models and concave transformed expected revenue functions that are not necessarily differentiable. It is shown that there are demand models with relative difference reference effects and loss-averse customers that satisfy the considered concavity assumptions. A computational study is performed to highlight the effects of joint inventory and pricing decisions under reference effects.

Suggested Citation

  • M. Güler & Taner Bi̇lgi̇ç & Refi̇k Güllü, 2014. "Joint inventory and pricing decisions with reference effects," IISE Transactions, Taylor & Francis Journals, vol. 46(4), pages 330-343.
  • Handle: RePEc:taf:uiiexx:v:46:y:2014:i:4:p:330-343
    DOI: 10.1080/0740817X.2013.768782
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    Citations

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    Cited by:

    1. Ehsan Ardjmand & Gary R. Weckman & William A. Young & Omid Sanei Bajgiran & Bizhan Aminipour, 2016. "A robust optimisation model for production planning and pricing under demand uncertainty," International Journal of Production Research, Taylor & Francis Journals, vol. 54(13), pages 3885-3905, July.
    2. Peng Hu & Ye Lu & Miao Song, 2019. "Joint Pricing and Inventory Control with Fixed and Convex/Concave Variable Production Costs," Production and Operations Management, Production and Operations Management Society, vol. 28(4), pages 847-877, April.
    3. Li, Yang & Liu, Feng, 2021. "Joint inventory and pricing control with lagged price responses," International Journal of Production Economics, Elsevier, vol. 241(C).
    4. Wang, Qiang & Zhao, Nenggui & Wu, Jie & Zhu, Qingyuan, 2021. "Optimal pricing and inventory policies with reference price effect and loss-Averse customers," Omega, Elsevier, vol. 99(C).
    5. Guang Yang & Ying Wang & Mulin Liu, 2023. "Optimal Policy for Probabilistic Selling with Three-Way Revenue Sharing Contract under the Perspective of Sustainable Supply Chain," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    6. Shining Wu & Qian Liu & Rachel Q. Zhang, 2015. "The Reference Effects on a Retailer’s Dynamic Pricing and Inventory Strategies with Strategic Consumers," Operations Research, INFORMS, vol. 63(6), pages 1320-1335, December.
    7. M. Güler & Taner Bilgiç & Refik Güllü, 2015. "Joint pricing and inventory control for additive demand models with reference effects," Annals of Operations Research, Springer, vol. 226(1), pages 255-276, March.
    8. Junhai Ma & Zhanbing Guo, 2017. "Implications for Firms with Limited Information to Take Advantage of Reference Price Effect in Competitive Settings," Complexity, Hindawi, vol. 2017, pages 1-16, June.

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