OPERATIONS, TECHNOLOGY, AND INFORMATION MANAGEMENT


Predicting mean and variance in inventory order decisions


LI CHEN


EMERSON PROFESSOR OF MANUFACTURING MANAGEMENT


Samuel Curtis Johnson Graduate School of Management


Cornell SC Johnson College of Business Cornell University


Decision Sciences, 55, 4, March 2024 LINK TO PAPER LINK TO LI CHEN VIDEO LINK TO ANDREW DAVIS VIDEO


Co-authors • Li Chen


Emerson Professor of Manufacturing Management, Samuel Curtis


Johnson Graduate School of Management, Cornell SC Johnson College of Business, Cornell University • Andrew M. Davis


Professor, Breazzano Family Term Professor of Management,


Samuel Curtis Johnson Graduate School of Management, Cornell SC Johnson College of Business, Cornell University


• Dayoung Kim, National University of Singapore


Summary To explain and predict the mean and variance of observed inventory order


ANDREW M. DAVIS PROFESSOR


BREAZZANO FAMILY TERM PROFESSOR OF MANAGEMENT


Samuel Curtis Johnson Graduate School of Management


Cornell SC Johnson College of Business Cornell University


decisions in a newsvendor problem, the authors develop a simple forecast-an- choring model assuming that people employ a two-step decision heuristic. In the first step, a behavioral bias may gravitate the decision maker’s point forecast toward a random forecast versus a constant unbiased forecast. In the second step, a behavioral bias of the same magnitude may cause the decision maker to treat the point forecast as if it is the mean of potential demand, and then make an upward or downward adjustment depending on the underage and overage costs.


Te authors evaluate the performance of this descriptive forecast-anchoring model across five experimental newsvendor data sets. First, they fit the model to a setting with uniform demand, and then use the corresponding estimates to generate predictions in a secondary data set with uniform demand, as an out-of-sample test. Tey proceed to fit the model to three additional newsven- dor data sets, two with normal demand and one with asymmetric two-point demand. In all cases, the model predicts the mean and variance of inventory order decisions well, and can help upstream supply chain parties to anticipate inventory order decisions from buyers and improve profitability.


CONTENTS TO MAIN


| RESEARCH WITH IMPACT: CORNELL SC JOHNSON COLLEGE OF BUSINESS • 2024 EDITION


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