Demand forecasting under uncertainty: a holistic approach to drivers and barriers for successful forecasting projects.
Rausch, Alexander
Promotor(s) : Van Caillie, Didier
Date of defense : 23-Jun-2021/25-Jun-2021 • Permalink : http://hdl.handle.net/2268.2/11350
Details
Title : | Demand forecasting under uncertainty: a holistic approach to drivers and barriers for successful forecasting projects. |
Author : | Rausch, Alexander |
Date of defense : | 23-Jun-2021/25-Jun-2021 |
Advisor(s) : | Van Caillie, Didier |
Committee's member(s) : | Heuchenne, Cédric
François, Véronique |
Language : | English |
Number of pages : | 114 |
Keywords : | [en] Predictive Analytics [en] Demand Forecasting [en] Forecasting Support System [en] Performance Management |
Discipline(s) : | Business & economic sciences > Production, distribution & supply chain management |
Target public : | Researchers Professionals of domain |
Institution(s) : | Université de Liège, Liège, Belgique University of Hohenheim, Stuttgart, Germany |
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 |
Abstract
[en] This research thesis aims at analyzing the key elements that enable successful
forecasting projects in a company which operates within an interorganizational framework.
More precisely, it is about identifying essential factors which influence the effectiveness and
efficiency of the forecasting management process in a positive or negative way. The unexpected
global outbreak of the COVID-19 pandemic in 2020 has shaken supply chains and highlights
the need to address demand forecasting under uncertainty. Thus, to examine factors that are
fundamental to accurate forecasting and those that drive forecasting project deliverables.
Among others, methodical, technological and organizational factors will be identified in a
literature review. Further a holistic single-case study is conducted to analyze the identified
factors in an organizational environment. The findings will be interlinked to be then able to
derive recommendations for action and to be able to identify further fields of research based on
this thesis.
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Description: Master Thesis
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Description: Case Study - Survey Data and Analysis
Size: 131.01 kB
Format: Microsoft Excel XML
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