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Strategic Capacity Management When Customers Have Boundedly Rational Expectations

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  • Tingliang Huang
  • Qian Liu

Abstract

type="main" xml:id="poms12420-abs-0001"> In retailing industries, such as apparel, sporting goods, customer electronics, and appliances, many firms deploy sophisticated modeling and optimization software to conduct dynamic pricing in response to uncertain and fluctuating market conditions. However, the possibility of markdown pricing creates an incentive for customers to strategize over the timing of their purchases. How should a retailing firm optimally account for customer behavior when making its pricing and stocking/capacity decisions? For example, is it optimal for a firm to create rationing risk by deliberately under stocking products? In this study, we develop a stylized modeling framework to answer these questions. In our model, customers strategize over the timing of their purchases. However, customers have boundedly rational expectations in the sense of anecdotal reasoning about the firm's fill rate, i.e., they have to rely on anecdotes, past experiences, or word-of-mouth to infer the firm's fill rate. In our modeling framework, we distinguish two settings: (i) capacity commitment, where the firm commits to its capacity level in the long run, or (ii) the firm dynamically changes it in each season. For both settings, within the simplest form of anecdotal reasoning, we prove that strategic capacity rationing is not optimal independent of customer risk preferences. Then, using a general form of anecdotal reasoning, we provide sufficient conditions for capacity rationing to be optimal for both settings, respectively. We show that the result of strategic capacity rationing being suboptimal is fairly robust to different valuation distributions and utility functions, heterogeneous sample size, and price optimization.

Suggested Citation

  • Tingliang Huang & Qian Liu, 2015. "Strategic Capacity Management When Customers Have Boundedly Rational Expectations," Production and Operations Management, Production and Operations Management Society, vol. 24(12), pages 1852-1869, December.
  • Handle: RePEc:bla:popmgt:v:24:y:2015:i:12:p:1852-1869
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    Citations

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    Cited by:

    1. Tingliang Huang & Zhe Yin & Ying-Ju Chen, 2017. "Managing Posterior Price Matching: The Role of Customer Boundedly Rational Expectations," Manufacturing & Service Operations Management, INFORMS, vol. 19(3), pages 385-402, July.
    2. Rajat Mishra & Randy Napier & Mahmut Yasar, 2019. "Do competitors respond to capacity changes? Evidence from U.S. manufacturers," Operations Management Research, Springer, vol. 12(3), pages 159-172, December.
    3. Tingliang Huang & Chao Liang & Jingqi Wang, 2018. "The Value of “Bespoke”: Demand Learning, Preference Learning, and Customer Behavior," Management Science, INFORMS, vol. 64(7), pages 3129-3145, July.
    4. Muhammad Tahir Jan & Johan de Jager & Naheed Sultan, 2020. "The Impact of Social Media Activity, Interactivity, and Content on Customer Satisfaction: A Study of Fashion Products," Eurasian Journal of Business and Management, Eurasian Publications, vol. 8(4), pages 336-347.
    5. Bin Dai & Yu Nu, 2020. "Pricing and capacity allocation strategies: Implications for manufacturers with product sharing," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(3), pages 201-222, April.
    6. Suresh P. Sethi & Sushil Gupta & Vipin K. Agrawal & Vijay K. Agrawal, 2022. "Nobel laureates’ contributions to and impacts on operations management," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4283-4303, December.
    7. Jinting Wang & Ke Sun, 2022. "Optimal pricing and capacity sizing for online service systems with free trials," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 57-86, March.
    8. Zhang, Ting & Choi, Tsan-Ming & (Edwin) Cheng, Tai-Chiu, 2024. "Competitive pricing and product strategies in the presence of consumers’ social comparisons," European Journal of Operational Research, Elsevier, vol. 312(2), pages 573-586.
    9. Jordan Tong & Daniel Feiler, 2017. "A Behavioral Model of Forecasting: Naive Statistics on Mental Samples," Management Science, INFORMS, vol. 63(11), pages 3609-3627, November.
    10. Xin Li & Pengfei Guo & Zhaotong Lian, 2017. "Price and capacity decisions of service systems with boundedly rational customers," Naval Research Logistics (NRL), John Wiley & Sons, vol. 64(6), pages 437-452, September.
    11. Dongling Cai & Li Jiang, 2020. "The Bright and Dark Sides of Customer Switching," Production and Operations Management, Production and Operations Management Society, vol. 29(6), pages 1381-1396, June.
    12. Han Zhu & Yimin Yu & Saibal Ray, 2021. "Quality Disclosure Strategy under Customer Learning Opportunities," Production and Operations Management, Production and Operations Management Society, vol. 30(4), pages 1136-1153, April.
    13. Andrei Bazhanov & Yuri Levin & Mikhail Nediak, 2019. "Resale Price Maintenance with Strategic Customers," Production and Operations Management, Production and Operations Management Society, vol. 28(3), pages 535-549, March.
    14. Li, Xin & Li, Qingying & Guo, Pengfei & Lian, Zhaotong, 2017. "On the uniqueness and stability of equilibrium in quality-speed competition with boundedly-rational customers: The case with general reward function and multiple servers," International Journal of Production Economics, Elsevier, vol. 193(C), pages 726-736.
    15. Xu, Jian & Duan, Yongrui, 2020. "Pricing, ordering, and quick response for online sellers in the presence of consumer disappointment aversion," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    16. Qiao‐Chu He & Ying‐Ju Chen & Rhonda Righter, 2020. "Learning with Projection Effects in Service Operations Systems," Production and Operations Management, Production and Operations Management Society, vol. 29(1), pages 90-100, January.
    17. Ye, Taofeng & Yang, Huiqiang, 2020. "Price and Quality Management with Strategic Consumers: Whether to Introduce a High or Low Product Variant," Applied Mathematics and Computation, Elsevier, vol. 386(C).

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