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Non-linear identification of judgmental forecasts effects at SKU-level

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Non-linear identification of judgmental forecasts effects at SKU-level. / Trapero Arenas, J R; Fildes, R A; Davydenko, A.

In: Journal of Forecasting, Vol. 30, No. 5, 2011, p. 490–508.

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Trapero Arenas, J R ; Fildes, R A ; Davydenko, A. / Non-linear identification of judgmental forecasts effects at SKU-level. In: Journal of Forecasting. 2011 ; Vol. 30, No. 5. pp. 490–508.

Bibtex

@article{e0269d6c54c74dfc8c5ec2f587fee761,
title = "Non-linear identification of judgmental forecasts effects at SKU-level",
abstract = "Prediction of demand is a key component within supply chain management. Improved accuracy in forecasts directly affects all levels of the supply chain, reducing stock costs and increasing customer satisfaction. In many application areas, demand prediction relies on statistical software which provides an initial forecast subsequently modified by the expert's judgment. This paper outlines a new methodology based on state-dependent parameter (SDP) estimation techniques to identify the nonlinear behaviour of such managerial adjustments. This non-parametric SDP estimate is used as a guideline to propose a nonlinear model that corrects the bias introduced by the managerial adjustments. One-step-ahead forecasts of stock-keeping unit sales sampled monthly from a manufacturing company are utilized to test the proposed methodology. The results indicate that adjustments introduce a nonlinear pattern, undermining accuracy. This understanding can be used to enhance the design of the forecasting support system in order to help forecasters towards more efficient judgmental adjustments",
author = "{Trapero Arenas}, {J R} and Fildes, {R A} and A Davydenko",
year = "2011",
doi = "10.1002/for.1184",
language = "English",
volume = "30",
pages = "490–508",
journal = "Journal of Forecasting",
issn = "0277-6693",
publisher = "John Wiley and Sons Ltd",
number = "5",

}

RIS

TY - JOUR

T1 - Non-linear identification of judgmental forecasts effects at SKU-level

AU - Trapero Arenas, J R

AU - Fildes, R A

AU - Davydenko, A

PY - 2011

Y1 - 2011

N2 - Prediction of demand is a key component within supply chain management. Improved accuracy in forecasts directly affects all levels of the supply chain, reducing stock costs and increasing customer satisfaction. In many application areas, demand prediction relies on statistical software which provides an initial forecast subsequently modified by the expert's judgment. This paper outlines a new methodology based on state-dependent parameter (SDP) estimation techniques to identify the nonlinear behaviour of such managerial adjustments. This non-parametric SDP estimate is used as a guideline to propose a nonlinear model that corrects the bias introduced by the managerial adjustments. One-step-ahead forecasts of stock-keeping unit sales sampled monthly from a manufacturing company are utilized to test the proposed methodology. The results indicate that adjustments introduce a nonlinear pattern, undermining accuracy. This understanding can be used to enhance the design of the forecasting support system in order to help forecasters towards more efficient judgmental adjustments

AB - Prediction of demand is a key component within supply chain management. Improved accuracy in forecasts directly affects all levels of the supply chain, reducing stock costs and increasing customer satisfaction. In many application areas, demand prediction relies on statistical software which provides an initial forecast subsequently modified by the expert's judgment. This paper outlines a new methodology based on state-dependent parameter (SDP) estimation techniques to identify the nonlinear behaviour of such managerial adjustments. This non-parametric SDP estimate is used as a guideline to propose a nonlinear model that corrects the bias introduced by the managerial adjustments. One-step-ahead forecasts of stock-keeping unit sales sampled monthly from a manufacturing company are utilized to test the proposed methodology. The results indicate that adjustments introduce a nonlinear pattern, undermining accuracy. This understanding can be used to enhance the design of the forecasting support system in order to help forecasters towards more efficient judgmental adjustments

U2 - 10.1002/for.1184

DO - 10.1002/for.1184

M3 - Journal article

VL - 30

SP - 490

EP - 508

JO - Journal of Forecasting

JF - Journal of Forecasting

SN - 0277-6693

IS - 5

ER -