The Assessment of Big Data Analytics Based Supply Chain Resilience

A comprehensive tool to assess and benchmark the level of supply chain resilience based on big data analytics enablers in the FMCG industry

Master thesis (2022)

Authors

W.D.H. de Wilt Civil Engineering & Geosciences

Contributors

M.W. Ludema Transport and Logistics - TPM (mentor)
W.W.A. Beelaerts van Blokland Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (mentor)
Jotham Hensen (mentor)
M. Comes System Engineering - TPM (mentor)
Faculty
Civil Engineering & Geosciences, Civil Engineering & Geosciences
Copyright: © 2022 Wouter de Wilt
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Published Date

08-09-2022

Language

English

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Faculty

Civil Engineering & Geosciences

Abstract

Big data analytics (BDA) and supply chain resilience are both present and important topics, but research on the relation between the subjects is limited. This also holds for the translation of the subject for a specific industry. This research therefore addresses this relation and translates it to a comprehensive partial resilience assessment tool that provides a benchmark for the Fast Moving Consumer Goods (FMCG) industry. The tool is based on a deterministic model that incorporated 14 resilience enablers and their corresponding interdependence. Results show that current industry BDA based resilience levels are, compared to a theoretical optimum, on average 48% and have a better practice of 66%. It is recommended that the tool is further implemented within the industry to gain more reliable and substantiated results.