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Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning

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Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. / Fildes, R A; Goodwin, P; Lawrence, M et al.
In: International Journal of Forecasting, Vol. 25, No. 1, 2009, p. 3-23.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Fildes RA, Goodwin P, Lawrence M, Nikolopoulos K. Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting. 2009;25(1):3-23. doi: 10.1016/j.ijforecast.2008.11.010

Author

Fildes, R A ; Goodwin, P ; Lawrence, M et al. / Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. In: International Journal of Forecasting. 2009 ; Vol. 25, No. 1. pp. 3-23.

Bibtex

@article{68bf5eb846d44d5f93a61a387801779c,
title = "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning",
abstract = "Demand forecasting is a crucial aspect of the planning process in supply-chain companies. The most common approach to forecasting demand in these companies involves the use of a computerized forecasting system to produce initial forecasts and the subsequent judgmental adjustment of these forecasts by the company{\textquoteright}s demand planners, ostensibly to take into account exceptional circumstances expected over the planning horizon. Making these adjustments can involve considerable management effort and time, but do they improve accuracy, and are some types of adjustment more effective than others? To investigate this, we collected data on more than 60,000 forecasts and outcomes from four supply-chain companies. In three of the companies, on average, judgmental adjustments increased accuracy. However, a detailed analysis revealed that, while the relatively larger adjustments tended to lead to greater average improvements in accuracy, the smaller adjustments often damaged accuracy. In addition, positive adjustments, which involved adjusting the forecast upwards, were much less likely to improve accuracy than negative adjustments. They were also made in the wrong direction more frequently, suggesting a general bias towards optimism. Models were then developed to eradicate such biases. Based on both this statistical analysis and organisational observation, the paper goes on to analyse strategies designed to enhance the effectiveness of judgmental adjustments directly.",
author = "Fildes, {R A} and P Goodwin and M Lawrence and K Nikolopoulos",
year = "2009",
doi = "10.1016/j.ijforecast.2008.11.010",
language = "English",
volume = "25",
pages = "3--23",
journal = "International Journal of Forecasting",
publisher = "Elsevier Science B.V.",
number = "1",

}

RIS

TY - JOUR

T1 - Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning

AU - Fildes, R A

AU - Goodwin, P

AU - Lawrence, M

AU - Nikolopoulos, K

PY - 2009

Y1 - 2009

N2 - Demand forecasting is a crucial aspect of the planning process in supply-chain companies. The most common approach to forecasting demand in these companies involves the use of a computerized forecasting system to produce initial forecasts and the subsequent judgmental adjustment of these forecasts by the company’s demand planners, ostensibly to take into account exceptional circumstances expected over the planning horizon. Making these adjustments can involve considerable management effort and time, but do they improve accuracy, and are some types of adjustment more effective than others? To investigate this, we collected data on more than 60,000 forecasts and outcomes from four supply-chain companies. In three of the companies, on average, judgmental adjustments increased accuracy. However, a detailed analysis revealed that, while the relatively larger adjustments tended to lead to greater average improvements in accuracy, the smaller adjustments often damaged accuracy. In addition, positive adjustments, which involved adjusting the forecast upwards, were much less likely to improve accuracy than negative adjustments. They were also made in the wrong direction more frequently, suggesting a general bias towards optimism. Models were then developed to eradicate such biases. Based on both this statistical analysis and organisational observation, the paper goes on to analyse strategies designed to enhance the effectiveness of judgmental adjustments directly.

AB - Demand forecasting is a crucial aspect of the planning process in supply-chain companies. The most common approach to forecasting demand in these companies involves the use of a computerized forecasting system to produce initial forecasts and the subsequent judgmental adjustment of these forecasts by the company’s demand planners, ostensibly to take into account exceptional circumstances expected over the planning horizon. Making these adjustments can involve considerable management effort and time, but do they improve accuracy, and are some types of adjustment more effective than others? To investigate this, we collected data on more than 60,000 forecasts and outcomes from four supply-chain companies. In three of the companies, on average, judgmental adjustments increased accuracy. However, a detailed analysis revealed that, while the relatively larger adjustments tended to lead to greater average improvements in accuracy, the smaller adjustments often damaged accuracy. In addition, positive adjustments, which involved adjusting the forecast upwards, were much less likely to improve accuracy than negative adjustments. They were also made in the wrong direction more frequently, suggesting a general bias towards optimism. Models were then developed to eradicate such biases. Based on both this statistical analysis and organisational observation, the paper goes on to analyse strategies designed to enhance the effectiveness of judgmental adjustments directly.

U2 - 10.1016/j.ijforecast.2008.11.010

DO - 10.1016/j.ijforecast.2008.11.010

M3 - Journal article

VL - 25

SP - 3

EP - 23

JO - International Journal of Forecasting

JF - International Journal of Forecasting

IS - 1

ER -