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Judgmental Adjustments to Demand Forecasts: Accuracy Evaluation and Bias Correction

Research output: Working paper

Published

Standard

Judgmental Adjustments to Demand Forecasts: Accuracy Evaluation and Bias Correction. / Davydenko, A; Fildes, R A; Trapero Arenas, J R.

Lancaster University : The Department of Management Science, 2010. (Management Science Working Paper Series).

Research output: Working paper

Harvard

Davydenko, A, Fildes, RA & Trapero Arenas, JR 2010 'Judgmental Adjustments to Demand Forecasts: Accuracy Evaluation and Bias Correction' Management Science Working Paper Series, The Department of Management Science, Lancaster University.

APA

Davydenko, A., Fildes, R. A., & Trapero Arenas, J. R. (2010). Judgmental Adjustments to Demand Forecasts: Accuracy Evaluation and Bias Correction. (Management Science Working Paper Series). The Department of Management Science.

Vancouver

Davydenko A, Fildes RA, Trapero Arenas JR. Judgmental Adjustments to Demand Forecasts: Accuracy Evaluation and Bias Correction. Lancaster University: The Department of Management Science. 2010. (Management Science Working Paper Series).

Author

Davydenko, A ; Fildes, R A ; Trapero Arenas, J R. / Judgmental Adjustments to Demand Forecasts: Accuracy Evaluation and Bias Correction. Lancaster University : The Department of Management Science, 2010. (Management Science Working Paper Series).

Bibtex

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title = "Judgmental Adjustments to Demand Forecasts: Accuracy Evaluation and Bias Correction",
abstract = "Judgmental adjustments to statistically generated forecasts have become a standard practice in demand forecasting, especially at a stock keeping units level. However, due to the subjective nature of judgmental interventions this approach cannot guarantee optimal use of available information and can lead to substantial cognitive biases. It is therefore important to monitor the accuracy of adjustments and estimate persistent systematic errors in order to correct final forecast. This paper presents an appropriate methodology for such analysis and focuses on specific features of source data including time series heterogeneity, skewed distributions of errors, and generally nonlinear patterns of biases. Enhanced modelling and evaluation techniques are suggested to overcome some imperfections of well-known standard methods in the given context. Empirical analysis showed that a considerable proportion of final forecast error is formed by a systematic component which can be pre- dicted. Proposed bias correction procedures allowed to substantially improve the accuracy of final forecasts. In particular, one-factor mod- els of the relationship between forecast error and adjustment were found to be a simple, robust and efficient tool for the given purpose.",
keywords = "demand forecasting, judgmental adjustments, judgment under uncertainty, bias correction, accuracy measurement",
author = "A Davydenko and Fildes, {R A} and {Trapero Arenas}, {J R}",
year = "2010",
language = "English",
series = "Management Science Working Paper Series",
publisher = "The Department of Management Science",
type = "WorkingPaper",
institution = "The Department of Management Science",

}

RIS

TY - UNPB

T1 - Judgmental Adjustments to Demand Forecasts: Accuracy Evaluation and Bias Correction

AU - Davydenko, A

AU - Fildes, R A

AU - Trapero Arenas, J R

PY - 2010

Y1 - 2010

N2 - Judgmental adjustments to statistically generated forecasts have become a standard practice in demand forecasting, especially at a stock keeping units level. However, due to the subjective nature of judgmental interventions this approach cannot guarantee optimal use of available information and can lead to substantial cognitive biases. It is therefore important to monitor the accuracy of adjustments and estimate persistent systematic errors in order to correct final forecast. This paper presents an appropriate methodology for such analysis and focuses on specific features of source data including time series heterogeneity, skewed distributions of errors, and generally nonlinear patterns of biases. Enhanced modelling and evaluation techniques are suggested to overcome some imperfections of well-known standard methods in the given context. Empirical analysis showed that a considerable proportion of final forecast error is formed by a systematic component which can be pre- dicted. Proposed bias correction procedures allowed to substantially improve the accuracy of final forecasts. In particular, one-factor mod- els of the relationship between forecast error and adjustment were found to be a simple, robust and efficient tool for the given purpose.

AB - Judgmental adjustments to statistically generated forecasts have become a standard practice in demand forecasting, especially at a stock keeping units level. However, due to the subjective nature of judgmental interventions this approach cannot guarantee optimal use of available information and can lead to substantial cognitive biases. It is therefore important to monitor the accuracy of adjustments and estimate persistent systematic errors in order to correct final forecast. This paper presents an appropriate methodology for such analysis and focuses on specific features of source data including time series heterogeneity, skewed distributions of errors, and generally nonlinear patterns of biases. Enhanced modelling and evaluation techniques are suggested to overcome some imperfections of well-known standard methods in the given context. Empirical analysis showed that a considerable proportion of final forecast error is formed by a systematic component which can be pre- dicted. Proposed bias correction procedures allowed to substantially improve the accuracy of final forecasts. In particular, one-factor mod- els of the relationship between forecast error and adjustment were found to be a simple, robust and efficient tool for the given purpose.

KW - demand forecasting

KW - judgmental adjustments

KW - judgment under uncertainty

KW - bias correction

KW - accuracy measurement

M3 - Working paper

T3 - Management Science Working Paper Series

BT - Judgmental Adjustments to Demand Forecasts: Accuracy Evaluation and Bias Correction

PB - The Department of Management Science

CY - Lancaster University

ER -