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 - The value of competitive information in forecasting FMCG retail product sales and the variable selection problem
AU - Huang, Tao
AU - Fildes, Robert
AU - Soopramanien, Didier
PY - 2014/9/1
Y1 - 2014/9/1
N2 - Sales forecasting at the UPC level is important for retailers to manage inventory. In this paper, we propose more effective methods to forecast retail UPC sales by incorporating competitive information including prices and promotions. The impact of these competitive marketing activities on the sales of the focal product has been extensively documented. However, competitive information has been surprisingly overlooked by previous studies in forecasting UPC sales, probably because of the problem of too many competitive explanatory variables. That is, each FMCG product category typically contains a large number of UPCs and is consequently associated with a large number of competitive explanatory variables. Under such a circumstance, time series models can easily become over-fitted and thus generate poor forecasting results.Our forecasting methods consist of two stages. In the first stage, we refine the competitive information. We identify the most relevant explanatory variables using variable selection methods, or alternatively, pool information across all variables using factor analysis to construct a small number of diffusion indexes. In the second stage, we specify the Autoregressive Distributed Lag (ADL) model following a general to specific modelling strategy with the identified most relevant competitive explanatory variables and the constructed diffusion indexes.We compare the forecasting performance of our proposed methods with the industrial practice method and the ADL model specified exclusively with the price and promotion information of the focal product. The results show that our proposed methods generate substantially more accurate forecasts across a range of product categories. (C) 2014 Elsevier B.V. All rights reserved.
AB - Sales forecasting at the UPC level is important for retailers to manage inventory. In this paper, we propose more effective methods to forecast retail UPC sales by incorporating competitive information including prices and promotions. The impact of these competitive marketing activities on the sales of the focal product has been extensively documented. However, competitive information has been surprisingly overlooked by previous studies in forecasting UPC sales, probably because of the problem of too many competitive explanatory variables. That is, each FMCG product category typically contains a large number of UPCs and is consequently associated with a large number of competitive explanatory variables. Under such a circumstance, time series models can easily become over-fitted and thus generate poor forecasting results.Our forecasting methods consist of two stages. In the first stage, we refine the competitive information. We identify the most relevant explanatory variables using variable selection methods, or alternatively, pool information across all variables using factor analysis to construct a small number of diffusion indexes. In the second stage, we specify the Autoregressive Distributed Lag (ADL) model following a general to specific modelling strategy with the identified most relevant competitive explanatory variables and the constructed diffusion indexes.We compare the forecasting performance of our proposed methods with the industrial practice method and the ADL model specified exclusively with the price and promotion information of the focal product. The results show that our proposed methods generate substantially more accurate forecasts across a range of product categories. (C) 2014 Elsevier B.V. All rights reserved.
KW - Forecasting
KW - Business analytics
KW - OR in marketing
KW - Retailing
KW - Promotions
KW - Competitive information
KW - DECISION-SUPPORT-SYSTEM
KW - CATEGORY MANAGEMENT
KW - PRICE PROMOTIONS
KW - ERROR MEASURES
KW - SCANNER DATA
KW - TIME-SERIES
KW - STORE
KW - MODEL
KW - BRAND
KW - ACCURACY
U2 - 10.1016/j.ejor.2014.02.022
DO - 10.1016/j.ejor.2014.02.022
M3 - Journal article
VL - 237
SP - 738
EP - 748
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 2
ER -