Data does not always trump intuition - Evaluating performance of statistical forecasting methods in ecommerce
Promotor(s) : Ittoo, Ashwin
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|
|Date of defense :||20-Jun-2016|
|Advisor(s) :||Ittoo, Ashwin|
|Committee's member(s) :||Ghilissen, Michael
|Number of pages :||73|
|Keywords :||[en] demand forecasting|
[en] predictive methods
|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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