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A Behavioral Remedy for the Censorship Bias

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  • Jordan Tong
  • Daniel Feiler
  • Richard Larrick

Abstract

Existing evidence suggests that managers exhibit a censorship bias: demand beliefs tend to be biased low when lost sales from stockouts are unobservable (censored demand) compared to when they are observable (uncensored demand). We develop a non†constraining, easily implementable behavioral debias technique to help mitigate this tendency in demand forecasting and inventory decision†making settings. The debiasing technique has individuals record estimates of demand outcomes (REDO): participants explicitly record a self†generated estimate of every demand realization, allowing them to record a different value than the number of sales in periods with stockouts. In doing so, they construct a more representative sample of demand realizations (that differs from the sales sample). In three laboratory experiments with MBA and undergraduate students, this remedy significantly reduces downward bias in demand beliefs under censorship and leads to higher inventory order decisions.

Suggested Citation

  • Jordan Tong & Daniel Feiler & Richard Larrick, 2018. "A Behavioral Remedy for the Censorship Bias," Production and Operations Management, Production and Operations Management Society, vol. 27(4), pages 624-643, April.
    • Handle: RePEc:bla:popmgt:v:27:y:2018:i:4:p:624-643
    DOI: 10.1111/poms.12823
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    Cited by:

    1. Perera, H. Niles & Hurley, Jason & Fahimnia, Behnam & Reisi, Mohsen, 2019. "The human factor in supply chain forecasting: A systematic review," European Journal of Operational Research, Elsevier, vol. 274(2), pages 574-600.
    2. repec:cup:judgdm:v:16:y:2021:i:6:p:1439-1463 is not listed on IDEAS
    3. George Lifchits & Ashton Anderson & Daniel G. Goldstein & Jake M. Hofman & Duncan J. Watts, 2021. "Success stories cause false beliefs about success," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 16(6), pages 1439-1463, November.
    4. Ho Cheung Brian Lee & Jan Stallaert & Ming Fan, 2020. "Anomalies in Probability Estimates for Event Forecasting on Prediction Markets," Production and Operations Management, Production and Operations Management Society, vol. 29(9), pages 2077-2095, September.
    5. Tinglong Dai & Sridhar Tayur, 2022. "Designing AI‐augmented healthcare delivery systems for physician buy‐in and patient acceptance," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4443-4451, December.
    6. Song-Hee Kim & Jordan Tong & Carol Peden, 2020. "Admission Control Biases in Hospital Unit Capacity Management: How Occupancy Information Hurdles and Decision Noise Impact Utilization," Management Science, INFORMS, vol. 66(11), pages 5151-5170, November.
    7. Rajesh Bagchi & Sung H. Ham & Chuan He, 2020. "Strategic Implications of Confirmation Bias‐Inducing Advertising," Production and Operations Management, Production and Operations Management Society, vol. 29(6), pages 1573-1596, June.
    8. Abolghasemi, Mahdi & Hurley, Jason & Eshragh, Ali & Fahimnia, Behnam, 2020. "Demand forecasting in the presence of systematic events: Cases in capturing sales promotions," International Journal of Production Economics, Elsevier, vol. 230(C).

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