Rights statement: This is a pre-print of an article published in Journal of the Operational Research Society. The definitive publisher-authenticated version On the identification of sales forecasting models in the presence of promotions Juan R Trapero, Nikolaos Kourentzes & Robert Fildes Journal of the Operational Research Society (2014) 66, 299–307 doi:10.1057/jors.2013.174 is available online at: http://www.palgrave-journals.com/jors/journal/v66/n2/full/jors2013174a.html
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - On the identification of sales forecasting models in the presence of promotions
AU - Trapero, Juan R.
AU - Kourentzes, Nikos
AU - Fildes, Robert
N1 - This is a pre-print of an article published in Journal of the Operational Research Society. The definitive publisher-authenticated version On the identification of sales forecasting models in the presence of promotions Juan R Trapero, Nikolaos Kourentzes & Robert Fildes Journal of the Operational Research Society (2014) 66, 299–307 doi:10.1057/jors.2013.174 is available online at: http://www.palgrave-journals.com/jors/journal/v66/n2/full/jors2013174a.html
PY - 2014
Y1 - 2014
N2 - Shorter product life cycles and aggressive marketing, among other factors, have increased the complexity of sales forecasting. Forecasts are often produced using a Forecasting Support System that integrates univariate statistical forecasting with managerial judgment. Forecasting sales under promotional activity is one of the main reasons to use expert judgment. Alternatively, one can replace expert adjustments by regression models whose exogenous inputs are promotion features (price, display, etc.). However, these regression models may have large dimensionality as well as multicollinearity issues. We propose a novel promotional model that overcomes these limitations. It combines Principal Component Analysis to reduce the dimensionality of the problem and automatically identifies the demand dynamics. For items with limited history, the proposed model is capable of providing promotional forecasts by selectively pooling information across established products. The performance of the model is compared against forecasts provided by experts and statistical benchmarks, on weekly data; outperforming both substantially.
AB - Shorter product life cycles and aggressive marketing, among other factors, have increased the complexity of sales forecasting. Forecasts are often produced using a Forecasting Support System that integrates univariate statistical forecasting with managerial judgment. Forecasting sales under promotional activity is one of the main reasons to use expert judgment. Alternatively, one can replace expert adjustments by regression models whose exogenous inputs are promotion features (price, display, etc.). However, these regression models may have large dimensionality as well as multicollinearity issues. We propose a novel promotional model that overcomes these limitations. It combines Principal Component Analysis to reduce the dimensionality of the problem and automatically identifies the demand dynamics. For items with limited history, the proposed model is capable of providing promotional forecasts by selectively pooling information across established products. The performance of the model is compared against forecasts provided by experts and statistical benchmarks, on weekly data; outperforming both substantially.
KW - Promotional modelling
KW - demand forecasting
KW - judgmental adjustments
KW - principal components analysis
U2 - 10.1057/jors.2013.174
DO - 10.1057/jors.2013.174
M3 - Journal article
VL - 66
SP - 299
EP - 307
JO - Journal of the Operational Research Society
JF - Journal of the Operational Research Society
SN - 0160-5682
ER -