| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 558.99 KB | Adobe PDF |
Advisor(s)
Abstract(s)
Retailers depend on accurate forecasts of product sales at the Store SKU level to efficiently
manage their inventory. Consequently, there has been increasing interest in identifying more
advanced statistical techniques that lead to accuracy improvements. However, the inclusion of multiple
drivers affecting demand into commonly used ARIMA and ETS models is not straightforward,
particularly when many explanatory variables are available. Moreover, regularization regression
models that shrink the model’s parameters allow for the inclusion of a lot of relevant information but
do not intrinsically handle the dynamics of the demand. These problems have not been addressed by
previous studies. Nevertheless, multiple simultaneous effects interacting are common in retailing. To
be successful, any approach needs to be automatic, robust and efficiently scaleable. In this study, we
design novel approaches to forecast retailer product sales taking into account the main drivers which
affect SKU demand at store level. To address the variable selection challenge, the use of dimensionality
reduction via principal components analysis (PCA) and shrinkage estimators was investigated.
The empirical results, using a case study of supermarket sales in Portugal, show that both PCA and
shrinkage are useful and result in gains in forecast accuracy in the order of 10% over benchmarks
while offering insights on the impact of promotions. Focusing on the promotional periods, PCA-based
models perform strongly, while shrinkage estimators over-shrink. For the non-promotional periods,
shrinkage estimators significantly outperform the alternatives.
Description
Keywords
Retailing Forecasting Promotions Seasonality Shrinkage Principal components analysis