Data does not always trump intuition - Evaluating performance of statistical forecasting methods in ecommerce

Abstract

As a global end-to-end eCommerce solutions provider, PFSweb deals everyday with thousands of shipping orders to all over Europe. Warehouse management is indeed an essential function of the organization. eCommerce is moreover a new market that is entering everyone’s day-to-day life. Logistics organizations therefore have to serve more and more customers, which leads to distribution issues like out-of-stock items, manpower shortage, and financial impacts, due to a lack of a demand forecast. In this thesis, we evaluate the performance of several state-of-the-art quantitative methods in demand forecasting, in opposition to methods based on judgment, in order to optimize and formalize forecasting in eCommerce. A systematic framework is therefore considered, which could: • Document the forecasting process for sharing purposes among PFSweb’s agents. • Allow the use of several statistical methods. • Facilitate traceability. The development of this structure is based on pertinent scientific readings and a rigorous methodology. The scientific literature brought the necessary knowledge and understanding of the extent of the issue: the importance of forecasting in Business, the challenges in eCommerce forecasting, and the state-of-the-art quantitative time-series methods. On the other hand, the methodology gives an overview of PFSweb’s forecasting approach, before the application of a series of steps that compose the proposed framework. Based on our findings, we show that while these methods work reasonably well, their use in real-life situations characterized by a fluctuating and double-seasonal demand, do not allow for optimal predictions. The performance of these methods particularly deteriorates in the absence of human domain-knowledge, intuition and experience. We thus recommend that these methods should not be applied on their own; rather, they should be used to complement the skills and expertise of human business analyst.