Innosuisse Procurement Intelligence: Data-driven total cost and resilience optimization for purchasing
Type
applied research project
Start Date
01 February 2021
End Date
01 August 2023
URI
Status
ongoing
Keywords
Procurement
Resilience
Total cost of ownership (TCO)
Machine learning
Web scraping
Description
Efforts to contain the spread of COVID-19 have kickstarted an economic crisis throwing off the balance of international supply chains. Swiss companies seeking to remain globally competitive will find themselves between conflicting priorities of resilience enhancement and cost reduction. Purchasers from various industries face increasingly complex decisions (e.g. supplier selection, make-or-buy, etc.) under aspects of value contributions, incl. risk, compliance, and sustainability issues.

Total cost of ownership (TCO) helps purchasers in these decisions by considering not only the purchasing price of a good, but by factoring in all associated costs that are directly or indirectly related to the purchase and the physical provision (incl. opportunity cost). However, total cost models are limited by the lack of nonmonetary metrics derived from external data (i.e. risk, compliance, and sustainability). Moreover, there is a lack of adequate IT infrastructure; the multifaceted use of a decision support tool and the large amount of data require a cloud-based solution. Within the project, these challenges are to be tackled by the following innovation contributions:

1. Developing a "total cost of resilience" concept: Primarily addressing the challenge of identifying, operationalizing, and weighting all relevant monetary and non-monetary drivers for purchasing decisions (e.g. supplier selection).

2. Identifying, accessing, and collecting data: Conceptualizing and developing a data model that integrates relevant internal and external data periodically.

3. Quantifying non-monetary metrics: Benchmarking, selecting, and deploying of adequate machine learning models to map unstructured web data to reliable procurement decision metrics incl. resilience (risk, compliance, and sustainability).

4. Augmenting decision-making: Integrating value contributions and total cost data for providing procurement intelligence.
Leader contributor(s)
Hofmann, Erik
Member contributor(s)
Partner(s)
SOLTAR AG
Stadler Rail AG
Wandfluh AG
SFS unimarket AG
Bucher Municipal AG
R&S International Holding AG
Industrielle Werke Basel
procure.ch
Swissmem
Funder(s)
Method(s)
Systems Engineering
Range
HSG + Partners
Range (De)
HSG + Partner
Principal
Innosuisse - Schweizer Agentur für Innovationsförderung
Division(s)
Eprints ID
247950
Reference Number
49829.1 IP-SBM

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Publication

Towards Predicting Supplier Resilience: A Tree-Based Model Approach

2022-01 , Enthoven, Maximilian , Blohm, Ivo , Hofmann, Erik , Philipp Gordetzki

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.

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Publication

Einkaufsoptimierung in Echtzeit

2021-06 , Enthoven, Maximilian , Hofmann, Erik , Locker, Alwin

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Publication

Resilienz-Benchmarking im Einkauf - Erwarte das Unerwartete

2023-02 , Auer, Tim , Locker, Alwin

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Publication

Supply-side resilience: a practice-based view

2022-04-10 , Enthoven, Maximilian

This study attempts to structure and define supply-side resilience for both researchers and practitioners. Through a systematic literature review, 135 supply chain resilience practices are identified. A survey with CPOs from manufacturing firms illustrates that around 68% of mentioned SCRES practices are relevant for purchasing and supply management. Finally, three cases of practice adoption are investigated in terms of ease and effectivity. A practice typology is developed to describe the adoption context. The Practice-Based View is used as a theoretical underpinning to provide a framework to structure and make sense of the results.

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