Forecasting Seasonal Sales with Many Drivers : Shrinkage or Dimensionality Reduction?

• 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. 

Ämnesord

SAMHÄLLSVETENSKAP  -- Ekonomi och näringsliv -- Företagsekonomi (hsv//swe)

SOCIAL SCIENCES  -- Economics and Business -- Business Administration (hsv//eng)

Nyckelord

forecasting

principal components analysis

promotions

retailing

seasonality

shrinkage

Skövde Artificial Intelligence Lab (SAIL)

Skövde Artificial Intelligence Lab (SAIL)

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)