The role of contextual information in demand forecasting

The paper deals with clarifying the role of contextual information in demand forecasting. Both judgemental and statistical forecasting methods are often needed to provide accurate forecasts. However, in practice it is often difficult to tell when judgemental intervention is needed and when it is not. This paper presents a case example about judgemental forecasting, in which the forecaster has different pieces of information available for the basis of a forecast. The paper provides some guidelines on how to evaluate the value of contextual information with probability calculations. The calculations show that in some situations, it is impossible to improve forecast accuracy, even though the contextual information is seemingly valuable. With probability calculations, it is possible to give more objective and specific rules on when contextual information is useful in forecasting and when it is not. This can help in selecting proper forecasting methods, and setting more realistic accuracy targets.

[1]  S. French,et al.  Forecasting with judgment , 1998 .

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

[3]  M. O'Connor,et al.  Judgemental adjustment of initial forecasts: Its effectiveness and biases , 1995 .

[4]  John T. Mentzer,et al.  Sales Forecasting Management: A Demand Management Approach , 2004 .

[5]  John T. Mentzer,et al.  Forecasting Technique Familiarity, Satisfaction, Usage, and Application , 1995 .

[6]  Alan E. Pankratz.  Time series forecasts and extra‐model information , 1989 .

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

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

[9]  J. Boylan,et al.  On the bias of intermittent demand estimates , 2001 .

[10]  Nada R. Sanders,et al.  The efficacy of using judgmental versus quantitative forecasting methods in practice , 2003 .

[11]  G. Miragliotta,et al.  Exploiting information sharing, stock management and capacity oversizing in the management of lumpy demand , 2004 .

[12]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[13]  Marcus O'Connor,et al.  The use of non‐time series information in sales forecasting: A case study , 1988 .

[14]  Philip Hans Franses,et al.  Averaging Model Forecasts and Expert Forecasts: Why Does It Work? , 2011, Interfaces.

[15]  Kai H. Lim,et al.  Improving judgmental forecasts with judgmental bootstrapping and task feedback support , 2005 .

[16]  Joa Sang Lim,et al.  Judgmental forecasting with time series and causal information , 1996 .

[17]  Fintan Costello,et al.  Fallacies in probability judgments for conjunctions and disjunctions of everyday events , 2009 .

[18]  Michael Lawrence,et al.  A field study of sales forecasting accuracy and processes , 2000, Eur. J. Oper. Res..

[19]  Dean P. Foster,et al.  Precision and Accuracy of Judgmental Estimation , 1997 .

[20]  Larry P. Ritzman,et al.  Bringing judgment into combination forecasts , 1995 .

[21]  Sébastien Thomassey,et al.  A hybrid sales forecasting system based on clustering and decision trees , 2006, Decis. Support Syst..

[22]  Derek W. Bunn,et al.  Setting accuracy targets for short-term judgemental sales forecasting , 2001 .

[23]  Paul Goodwin,et al.  Correct or combine? Mechanically integrating judgmental forecasts with statistical methods , 2000 .

[24]  Adamantios Diamantopoulos,et al.  Factors affecting the nature and effectiveness of subjective revision in sales forecasting: An empirical study , 1989 .

[25]  R. Fildes,et al.  The effects of integrating management judgement into intermittent demand forecasts , 2009 .

[26]  Robert Fildes,et al.  Judgmental forecasts of time series affected by special events: does providing a statistical forecast improve accuracy? , 1999 .

[27]  Roberto Verganti,et al.  Forecasting demand from heterogeneous customers , 2006 .

[28]  Helena Forslund,et al.  The impact of forecast information quality on supply chain performance , 2007 .

[29]  R. Fildes,et al.  Providing support for the use of analogies in demand forecasting tasks , 2007 .

[30]  Stephen J. Hoch,et al.  A psychological approach to decision support systems , 1996 .

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

[32]  E. Mohebbi.  An unreliable production system with fluctuating production and demand rates , 2010 .

[33]  Larry P. Ritzman,et al.  Integrating judgmental and quantitative forecasts: methodologies for pooling marketing and operations information , 2004 .

[34]  Roberto Verganti,et al.  Managing demand uncertainty through order overplanning , 1995 .

[35]  Roberto Verganti,et al.  A simulation framework for forecasting uncertain lumpy demand , 1999 .

[36]  M. O'Connor,et al.  Judgemental and statistical time series forecasting: a review of the literature , 1996 .

[37]  Stefano Ronchi,et al.  Clustering customers to forecast demand , 2005 .

[38]  Teresa M. McCarthy,et al.  The Evolution of Sales Forecasting Management: A 20-year Longitudinal Study of Forecasting Practices , 2006 .

[39]  J. Scott Armstrong,et al.  Structured Analogies for Forecasting , 2007 .

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

[41]  Saeed Zolfaghari,et al.  Synergy of chaos theory and artificial neural networks in chaotic time series forecasting , 2011 .