2019/11/26 by Alberto Redondo, Redondo, Alberto, Alberto Torres‐Barrán +7
Business, Management and Accounting · Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Supply Chain Resilience and Risk Management #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1911.11652
arxiv created 2019/11/26 · openalex publication_date 2019/11/26 · arxiv updated 2019/11/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Risk assessment is a major challenge for supply chain managers, as it potentially affects business factors such as service costs, supplier competition and customer expectations. The increasing interconnectivity between organisations has put into focus methods for supply chain cyber risk management. We introduce a general approach to support such activity taking into account various techniques of attacking an organisation and its suppliers, as well as the impacts of such attacks. Since data is lacking in many respects, we use structured expert judgment methods to facilitate its implementation. We couple a family of forecasting models to enrich risk monitoring. The approach may be used to set up risk alarms, negotiate service level agreements, rank suppliers and identify insurance needs, among other management possibilities.