Towards Predicting Supplier Resilience: A Tree-Based Model Approach

Item Type Conference or Workshop Item (Paper)
Abstract With looming uncertainties and disruptions in today’s global supply chains, such as lockdown measures to contain COVID-19, supply chain resilience has gained considerable attention recently. While decision-makers in procurement have emphasized the importance of traditional risk assessment, its shortcomings can be complemented by resilience. However, while most resilience studies are too qualitative in nature and abstract to inform supplier decisions, many quantitative resilience studies frequently rely on complex and impractical operations research models fed with simulated supplier data. Thus there is the need for an integrative, intermediate way for the practical and automated prediction of resilience with real-world data. We therefore propose a random forest-based supervised learning method to predict supplier resilience, outperforming the current human benchmark evaluation by 139 percent. The model is trained on both internal ERP data and publicly available secondary data to help assess suppliers in a pre-screening step, before deciding which supplier to select for a specific product. The results of this study are to be integrated into a software tool developed for measuring and tracking the total cost of supply chain resilience from the perspective of purchasing decisions.
Authors Enthoven, Maximilian; Blohm, Ivo; Hofmann, Erik & Gordetzki, Philipp
Projects Hofmann, Prof. Dr. Erik; Enthoven, M.Sc. ETH Maximilian & Blohm, Prof. Dr. Ivo (2021) Innosuisse Procurement Intelligence: Data-driven total cost and resilience optimization for purchasing [applied research project] Official URL
Journal or Publication Title Proceedings of the Hawaii International Conference on System Sciences
Language English
Subjects business studies
information management
HSG Classification contribution to scientific community
HSG Profile Area SoM - Business Innovation
Refereed Yes
Date January 2022
Page Range 1686-1695
Number of Pages 10
ISSN 978-0-9981331-2-6
Depositing User M.Sc. ETH Maximilian Enthoven
Date Deposited 04 Oct 2021 10:49
Last Modified 20 Jul 2022 17:46


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Enthoven, Maximilian; Blohm, Ivo; Hofmann, Erik & Gordetzki, Philipp: Towards Predicting Supplier Resilience: A Tree-Based Model Approach. 2022.

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