Home > Research > Publications & Outputs > Topics in retail forecasting

Electronic data

  • 2020wallerphd

    Final published version, 872 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Text available via DOI:

View graph of relations

Topics in retail forecasting

Research output: ThesisDoctoral Thesis

Published
Publication date2020
Number of pages161
QualificationPhD
Awarding Institution
Supervisors/Advisors
Publisher
  • Lancaster University
Original languageEnglish

Abstract

Retail forecasting is a diverse and dynamic research area encompassing a variety of
different topics. The advent of online channels, the increasing complexity of product ranges, and the shortening lifespan of many items are as examples of some of the new challenges that maintain the importance of improving forecasting in this domain. This thesis aims to address questions in retail forecasting that are closely linked with relevant problems faced in the industry. As such, the problems have been identified through a combination of reviewing the academic literature, discussion, and engagement with practitioners.

This thesis starts by considering the situation where demand series are influenced by multiple seasonal and calendar effects. This is a challenge which is widespread due to high frequency sampling and decision making in retailing. We develop a new model to accommodate flexibility in modelling complex seasonal patterns, which also aids with mitigating the effect of short demand histories on forecasting performance. The new model is embedded in an innovations state-space formulation and it is demonstrated empirically using wholesale food data to provide competitive forecasting accuracy to established benchmarks.

Next, the dual problems of SKU-level model parameter estimation and forecasting are considered. For retailers experiencing frequent promotional activities, this is a principal issue. The parameter estimates provide insights about the elasticity of different factors on demand for the SKU, and therefore inform marketing planning. Accurate forecasts, for both promotional and baseline periods, support other functions such as replenishment and inventory management. First, a geometric parameter inheritance procedure is proposed, which uses aggregate information within a product hierarchy to improve parameter estimates under certain assumptions. At brand level, it is typically easier to better estimate elasticity effects, making this strategy preferable. Second, a debiasing approximation is derived for the forecasting procedure, which is demonstrated to reduce bias, whilst remaining competitive in terms
of forecast accuracy, as shown in a simulation study. The debiasing approximation is then evaluated with an inventory simulation study, which examines the conditions under which improvements in inventory performance can be gained. The conclusions give useful insights for inventory managers, and demonstrate that bias is a significant factor in inventory performance.