Predicting Order Variability in Inventory Decisions: A Model of Forecast Anchoring

33 Pages Posted: 15 Aug 2013 Last revised: 18 Jul 2018

See all articles by Li Chen

Li Chen

Cornell University - Samuel Curtis Johnson Graduate School of Management

Andrew M. Davis

Cornell University - Samuel Curtis Johnson Graduate School of Management

Dayoung Kim

National University of Singapore (NUS) - Global Asia Institute

Date Written: March 15, 2017

Abstract

Problem Definition: In this paper, we develop a forecast anchoring model that explains and predicts order variability behavior in a multi-period newsvendor problem. Our model assumes that people anchor on random point forecast (hence the name) and insufficiently adjust toward the profit-maximizing quantity.

Academic/Practical Relevance: We show that this model is capable of predicting both the mean and variance of order quantities observed in experimental studies, and thus extends the existing literature that focus on predicting the mean order quantities only. It also helps identify possible behavioral causes that lead to excessive order variability (or the bullwhip effect) in supply chains.

Methodology: We apply the generalized method of moments to fit the forecast anchoring model to an experimental data set, and then use these estimates to generate predictions for order decisions in a separate data set, as an out-of-sample test. As a further robustness check, we conduct our own experiment, which considers a unique setting with asymmetric two-point demand, and fit the model to its data as well.

Results: Our model fits both the mean and variance of order quantities well across the different data sets. We also find a consistent pattern of subjects anchoring heavily on their demand forecast when the product profit margin is high. Moreover, our results show that the main cause of order variability at the individual level is random point forecasts, whereas the order variability at the aggregate level is driven by both random point forecasts and variable adjustment levels.

Managerial Implications: Our study provides a new perspective for explaining and predicting order variability in inventory decisions. By providing better predictions, our model can help the upstream supply chain parties better anticipate the order variability from the downstream buyers and thus improve overall profitability.

Keywords: Behavioral operations, forecast anchoring, newsvendor problem, decision heuristics, order variability

Suggested Citation

Chen, Li and Davis, Andrew M. and Kim, Dayoung, Predicting Order Variability in Inventory Decisions: A Model of Forecast Anchoring (March 15, 2017). Johnson School Research Paper Series, Available at SSRN: https://ssrn.com/abstract=2310619 or http://dx.doi.org/10.2139/ssrn.2310619

Li Chen (Contact Author)

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

Andrew M. Davis

Cornell University - Samuel Curtis Johnson Graduate School of Management ( email )

Ithaca, NY 14853
United States

Dayoung Kim

National University of Singapore (NUS) - Global Asia Institute ( email )

10 Lower Kent Ridge Road
Block S17 #03-01
Singapore
Singapore

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