Coordinating judgmental forecasting: coping with intentional biases

Clint Pennings, Jan Dalen, Laurens Rook

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)
44 Downloads (Pure)


Human judgment, an almost inextricable ingredient in demand forecasting, introduces many unintentional and intentional biases to the forecasting and operations planning process. In the present research, we isolate intentional biases from this process and relate them to heterogeneous departmental roles and incentives. Through a laboratory experiment, which simulates forecasting operations planning in an interdepartmental decision-making context, we examine the effects of departmental roles, incentives and various weighting schemes on forecasting behavior and performance. We find that departmental roles, even without role-specific incentives, entail intentional biases of 8% of the forecast, and that role-specific incentives increase these biases to 14%. We further test the claim that accuracy-weighted schemes can remove biases in forecasting, and conclude that they halve, but don’t fully remove them. Finally, a weighting scheme that explicitly corrects biased inputs shows great promise in reducing intentional and unintentional biases. In our experiment, this scheme reduces biases by 35%. This shows the importance of disentangling intentional and unintentional biases for more effective forecasting adjustments. Our insights have substantial ramifications for the design of the forecasting operations planning process in dynamic business environments determined by high levels of role- and incentive-based heterogeneity.
Original languageEnglish
Pages (from-to)46-56
Number of pages11
Publication statusPublished - 11 Aug 2018

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