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Analysis of optimal defective allowance policies for reducing product returns under alternative modeling of consumer behavior

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  • Zikopoulos, Christos

Abstract

Motivated by the challenges of managing returns of products with cosmetic defects at a home appliances manufacturer, we investigate the establishment of a defective allowance policy as a strategy to encourage consumers to avoid returning defective products. By offering a discount to consumers, the manufacturer can achieve cost savings by avoiding replacement expenses, including those related to returns handling and transportation. To our knowledge, this study is the first to analytically determine the optimal discount rate that a manufacturer should offer is such cases. We explore various models of consumer response to discounts and analyze their impact on both the optimal decisions and the financial outcomes of the defective allowance policy. Our findings indicate that simplistic consumer behavior models may reduce policy's effectiveness and underestimate its benefits, potentially deterring manufacturers from adopting such policies. Additionally, we examine differences in the optimal discount policy across product categories that differ in manufacturing cost, remaining value, handling and transportation costs, and price. Our analysis indicates that the ratio of replacement cost to product price plays a critical role in determining the optimal discount. Also, we find that product price exerts a greater influence on the optimal discount rate, whereas the impact of replacement costs depends on the price level: for high-priced products, these costs have negligible effect on the discount, whereas for low-priced products, these costs affect the optimal discount rate. Additionally, for lower-priced products, improved financial benefits are observed. Our analysis further suggests that accurately modeling consumer behavior and precisely determining the optimal discount rate are essential for maximizing the benefits of a defective allowance policy.

Suggested Citation

  • Zikopoulos, Christos, 2025. "Analysis of optimal defective allowance policies for reducing product returns under alternative modeling of consumer behavior," International Journal of Production Economics, Elsevier, vol. 284(C).
  • Handle: RePEc:eee:proeco:v:284:y:2025:i:c:s0925527325000945
    DOI: 10.1016/j.ijpe.2025.109609
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    1. Nima Kazemi & Nikunja Mohan Modak & Kannan Govindan, 2019. "A review of reverse logistics and closed loop supply chain management studies published in IJPR: a bibliometric and content analysis," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4937-4960, August.
    2. Farshbaf-Geranmayeh, Amir & Zaccour, Georges, 2021. "Pricing and advertising in a supply chain in the presence of strategic consumers," Omega, Elsevier, vol. 101(C).
    3. Yogesh Mani Tripathi & Amulya Kumar Mahto & Sanku Dey, 2017. "Efficient Estimation of the PDF and the CDF of a Generalized Logistic Distribution," Annals of Data Science, Springer, vol. 4(1), pages 63-81, March.
    4. Difrancesco, Rita Maria & Huchzermeier, Arnd & Schröder, David, 2018. "Optimizing the return window for online fashion retailers with closed-loop refurbishment," Omega, Elsevier, vol. 78(C), pages 205-221.
    5. Duong, Quang Huy & Zhou, Li & Meng, Meng & Nguyen, Truong Van & Ieromonachou, Petros & Nguyen, Duy Tiep, 2022. "Understanding product returns: A systematic literature review using machine learning and bibliometric analysis," International Journal of Production Economics, Elsevier, vol. 243(C).
    6. Zike Cao & Kai-Lung Hui & Hong Xu, 2018. "When Discounts Hurt Sales: The Case of Daily-Deal Markets," Information Systems Research, INFORMS, vol. 29(3), pages 567-591, September.
    7. Das, Debabrata & Dutta, Pankaj, 2022. "Product return management through promotional offers: The role of consumers’ loss aversion," International Journal of Production Economics, Elsevier, vol. 251(C).
    8. Yang, Guangyong & Ji, Guojun, 2022. "The impact of cross-selling on managing consumer returns in omnichannel operations," Omega, Elsevier, vol. 111(C).
    9. Danni Zhang & Regina Frei & Gary Wills & Enrico Gerding & Steffen Bayer & Prince Kwame Senyo, 2023. "Strategies and practices to reduce the ecological impact of product returns: An environmental sustainability framework for multichannel retail," Business Strategy and the Environment, Wiley Blackwell, vol. 32(7), pages 4636-4661, November.
    10. Li, Dan & Chen, Jing & Chen, Bintong & Liao, Yi, 2022. "Manufacturer’s contract choice and retailer’s returns management strategy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    11. Chen, Pingping & Chen, Huiru & Zhao, Ruiqing, 2022. "Price promotions in vertically-related market: Instant discount vs. gift card," Omega, Elsevier, vol. 108(C).
    12. Mehmet Sekip Altug & Tolga Aydinliyim, 2016. "Counteracting Strategic Purchase Deferrals: The Impact of Online Retailers’ Return Policy Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 18(3), pages 376-392, July.
    13. Fan, Huirong & Khouja, Moutaz & Zhou, Jing, 2022. "Design of win-win return policies for online retailers," European Journal of Operational Research, Elsevier, vol. 301(2), pages 675-693.
    14. James D. Abbey & Rainer Kleber & Gilvan C. Souza & Guido Voigt, 2017. "The Role of Perceived Quality Risk in Pricing Remanufactured Products," Production and Operations Management, Production and Operations Management Society, vol. 26(1), pages 100-115, January.
    15. Eric T. Anderson & James D. Dana, Jr., 2009. "When Is Price Discrimination Profitable?," Management Science, INFORMS, vol. 55(6), pages 980-989, June.
    16. Zikopoulos, Christos, 2022. "On the effect of upgradable products design on circular economy," International Journal of Production Economics, Elsevier, vol. 254(C).
    17. Khouja, Moutaz & Hammami, Ramzi, 2023. "Optimizing price, order quantity, and return policy in the presence of consumer opportunistic behavior for online retailers," European Journal of Operational Research, Elsevier, vol. 309(2), pages 683-703.
    18. Chang, Hsiu-Hua & Lu, Long-Chuan & Kuo, Tzu-Chiao, 2024. "Are discounts useful in promoting suboptimal foods for sustainable consumption and production? The interaction effects of original prices, discount presentation modes, and product types," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    19. repec:oup:jconrs:v:47:y:2021:i:5:p:772-786. is not listed on IDEAS
    20. Xiang Li & Shu Zhou & Guojun Ji & Weina Shi, 2022. "Optimal Return Freight Insurance Policies in a Competitive Environment," Sustainability, MDPI, vol. 14(18), pages 1-38, September.
    21. Mehmet Sekip Altug & Tolga Aydinliyim & Aditya Jain, 2021. "Managing Opportunistic Consumer Returns in Retail Operations," Management Science, INFORMS, vol. 67(9), pages 5660-5678, September.
    22. Akturk, M. Serkan & Ketzenberg, Michael & Yıldız, Barış, 2021. "Managing consumer returns with technology-enabled countermeasures," Omega, Elsevier, vol. 102(C).
    23. Peter Tait & Caroline Saunders & Paul Dalziel & Paul Rutherford & Timothy Driver & Meike Guenther, 2024. "How much less? Estimating price discounts for suboptimal food with environmental and social credence attributes," Applied Economics, Taylor & Francis Journals, vol. 56(13), pages 1581-1594, March.
    Full references (including those not matched with items on IDEAS)

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