2023-04-132023-04-13https://www.alexandria.unisg.ch/handle/20.500.14171/57617Efforts 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.Procurement Resilience Total cost of ownership (TCO) Machine learning Web scrapingapplied research project