{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:44:57Z","timestamp":1782841497941,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>Global supply chains (SCs) are increasingly vulnerable to disruptions due to, inter alia, geopolitical tensions or natural disasters. To remain competitive, companies must balance efficiency and resilience while leveraging digital technologies. This paper presents a framework for integrating Predictive Process Monitoring (PPM) into resilience-oriented Supply Chain Risk Management (SCRM) to achieve an anticipatory risk detection at process level. The framework links supply chain risk categories, standardized processes of the Supply Chain Operations Reference (SCOR) model, and operational resilience capabilities. We suggest four phases to operationalize PPM, in fact (1) risk &amp; process scoping, (2) event log engineering, (3) predictive modeling, and (4) monitoring, intervention &amp; learning. An illustrative procurement case focusing on supplier concentration risk shows how PPM can transform descriptive process transparency into proactive decision support. By systematically connecting risk, process, and resilience perspectives, the paper advances the operationalization of data-driven resilience improvements in supply chains.<\/jats:p>","DOI":"10.7148\/2026-0495","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:55:14Z","timestamp":1782838514000},"page":"495-501","source":"Crossref","is-referenced-by-count":0,"title":["The potential of predictive process monitoring for supply chain risk management"],"prefix":"10.7148","author":[{"given":"Frank","family":"Schaetter","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rebecca","family":"Bulander","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Haas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank","family":"Morelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robin","family":"Staebler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:55:18Z","timestamp":1782838518000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0495_simo_ecms2026_0021.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0495","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}