HEC-Ecole de gestion de l'Université de Liège
HEC-Ecole de gestion de l'Université de Liège

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

Zagaria, Delphine ULiège
Promotor(s) : Ittoo, Ashwin ULiège
Date of defense : 20-Jun-2016 • Permalink :
Title : Data does not always trump intuition - Evaluating performance of statistical forecasting methods in ecommerce
Author : Zagaria, Delphine ULiège
Date of defense  : 20-Jun-2016
Advisor(s) : Ittoo, Ashwin ULiège
Committee's member(s) : Ghilissen, Michael ULiège
Deneye, Pierre ULiège
Mottroule, Michaël 
Language : English
Number of pages : 73
Keywords : [en] demand forecasting
[en] predictive methods
[en] Double-Seasonality
[en] Holt-Winters
[en] ARIMA
[en] Regression
[en] eCommerce
[en] logistics
Discipline(s) : Business & economic sciences > Quantitative methods in economics & management
Target public : Researchers
Professionals of domain
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur de gestion, à finalité spécialisée en Supply Chain Management and Business Analytics
Faculty: Master thesis of the HEC-Ecole de gestion de l'Université de Liège


[en] 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.



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  • Zagaria, Delphine ULiège Université de Liège > Master ingé. gest., fin. spéc. supply chain man. (ex 2e ma.)


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