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

Trapero Arenas, J R and Fildes, R A and Davydenko, A (2011) Non-linear identification of judgmental forecasts effects at SKU-level. Journal of Forecasting, 30 (5). 490–508. ISSN 0277-6693

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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

Item Type:
Journal Article
Journal or Publication Title:
Journal of Forecasting
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/aacsb/disciplinebasedresearch
Subjects:
ID Code:
45812
Deposited By:
Deposited On:
11 Jul 2011 18:38
Refereed?:
Yes
Published?:
Published
Last Modified:
20 May 2020 02:16