When providing optimistic and pessimistic scenarios can be detrimental to judgmental demand forecasts and production decisions

This paper examines the accuracy of judgmental forecasts of product demand and the quality of subsequent production level decisions under two different conditions: (i) the availability of only time series information on past demand; (ii) the availability of time series information together with scenarios that outline possible prospects for the product in the forthcoming period. An experiment indicated that production level decisions made by participants had a greater deviation from optimality when they also received optimistic and pessimistic scenarios. This resulted from less accurate point forecasts made by these participants. Further analysis suggested that participants focussed on the scenario that was congruent with the position of the latest observation relative to the series mean and discounted the opposing scenario. This led to greater weight being attached to this observation, thereby exacerbating the tendency of judgmental forecasters to see systematic changes in random movements in time series.

[1]  P. Schoemaker MULTIPLE SCENARIO DEVELOPMENT: ITS CONCEPTUAL AND BEHAVIORAL FOUNDATION , 1993 .

[2]  Robert Fildes,et al.  Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting , 2007, Interfaces.

[3]  P Slovic,et al.  Cue-consistency and cue-utilization in judgment. , 1966, The American journal of psychology.

[4]  Juan R. Trapero,et al.  On the identification of sales forecasting models in the presence of promotions , 2015, J. Oper. Res. Soc..

[5]  Kristine M. Kuhn,et al.  Confidence and Uncertainty in Judgmental Forecasting: Differential Effects of Scenario Presentation , 1996 .

[6]  Nigel Harvey,et al.  Judging the probability that the next point in an observed time series will be below, or above, a given value , 1995 .

[7]  Frank Steiner,et al.  Leveraging the Sustainability Potential of Mass Customization through Product Service Systems in the Consumer Electronics Industry , 2015 .

[8]  P. Goodwin,et al.  Judgmental forecasting: A review of progress over the last 25 years , 2006 .

[9]  Dilek Önkal,et al.  Scenarios as channels of forecast advice , 2013 .

[10]  Nigel Harvey,et al.  Sensitivity to autocorrelation in judgmental time series forecasting , 2011 .

[11]  Ilan Yaniv,et al.  The Benefit of Additional Opinions , 2004 .

[12]  Joshua Klayman,et al.  Overconfidence in interval estimates. , 2004, Journal of experimental psychology. Learning, memory, and cognition.

[13]  Eric J. Johnson,et al.  The adaptive decision maker , 1993 .

[14]  Fei-Fei Cheng,et al.  The joint effect of framing and anchoring on internet buyers' decision-making , 2011, Electron. Commer. Res. Appl..

[15]  Lutz E. Schlange Scenarios: The art of strategic conversation , 1997 .

[16]  Derek J. Koehler,et al.  People focus on optimistic scenarios and disregard pessimistic scenarios while predicting task completion times. , 2000, Journal of experimental psychology. Applied.

[17]  Anders Winman,et al.  Subjective probability intervals: how to reduce overconfidence by interval evaluation. , 2004, Journal of experimental psychology. Learning, memory, and cognition.

[18]  Robin Gregory,et al.  Narrative valuation in a policy judgment context , 2000 .

[19]  Steven P. Schnaars How to develop and use scenarios , 1987 .

[20]  Nada R. Sanders,et al.  Forecasting Software in Practice: Use, Satisfaction, and Performance , 2003, Interfaces.

[21]  Paul Goodwin,et al.  Enhancing Strategy Evaluation in Scenario Planning: a Role for Decision Analysis , 2001 .

[22]  Yunjie Calvin Xu,et al.  Cue consistency and page value perception: Implications for web-based catalog design , 2013, Inf. Manag..

[23]  Michael Lawrence,et al.  Judgmental Adjustments of Previously Adjusted Forecasts , 2008, Decis. Sci..

[24]  A. Gunasekaran,et al.  Performance measures and metrics in a supply chain environment , 2001 .

[25]  Mary E. Thomson,et al.  The relative influence of advice from human experts and statistical methods on forecast adjustments , 2009 .

[26]  R. Fildes,et al.  Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning , 2009 .

[27]  Spyros Makridakis,et al.  Factors affecting judgmental forecasts and confidence intervals , 1989 .

[28]  A. Tversky,et al.  Judgment under Uncertainty: Heuristics and Biases , 1974, Science.

[29]  Don A. Moore,et al.  Wide of the Mark: Evidence on the Underlying Causes of Overprecision in Judgment , 2015 .

[30]  Fred D. Davis,et al.  Harmful effects of seemingly helpful information on forecasts of stock earnings , 1994 .

[31]  Steven P. Schnaars,et al.  The use of multiple scenarios in sales forecasting: An empirical test , 1987 .

[32]  Hal R. Arkes,et al.  Overconfidence in Judgmental Forecasting , 2001 .

[33]  Robert D. Klassen,et al.  Forecasting practices of Canadian firms: Survey results and comparisons , 2001 .

[34]  Nigel Harvey,et al.  Context-Sensitive Heuristics in Statistical Reasoning , 1993 .

[35]  P. Juslin,et al.  Format dependence in subjective probability calibration , 1999 .

[36]  P. Yelland Bayesian forecasting of parts demand , 2010 .

[37]  Matteo Giacomo Maria Kalchschmidt,et al.  The role of the forecasting process in improving forecast accuracy and operational performance , 2011 .

[38]  Marcus O'Connor,et al.  Exploring judgemental forecasting , 1992 .

[39]  João Vitor Tomotani,et al.  Lot sizing and scheduling: a survey of practices in Brazilian companies , 2018 .

[40]  Paul Goodwin,et al.  Decision Analysis for Management Judgment , 1998 .

[41]  L. Ross,et al.  The role of construal processes in overconfident predictions about the self and others. , 1990, Journal of personality and social psychology.

[42]  Michael Lawrence,et al.  The effects of structural characteristics of explanations on use of a DSS , 2006, Decis. Support Syst..