Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -