Ansari, F., & Kohl, L. (2022). AI-Enhanced Maintenance for Building Resilience and Viability in Supply Chains. In A. Dolgui, D. Ivanov, & B. Sokolov (Eds.), Supply Network Dynamics and Control (Vol. 20, pp. 163–185). https://doi.org/10.1007/978-3-031-09179-7_8
In the era of Industry 4.0, supply chain management still faces the challenge of operating with increasingly complex networks under high uncertainty. These uncertainties influence decision-making processes and change the balance in the supply chain. Enterprise, therefore, strives to enable data-driven decision-making by increasing the digitalization and intelligentization of their processes. Artificial Intelligence (AI) approaches in particular can reinforce enterprises to proactively respond to changes and problems in the supply chain at an early stage and thus plan ahead. Utilizing predictive analytics and semantic modeling may improve target performance metrics, increases flexibility, and enables the development of a resilient and viable supply chain. This chapter provides an AI-enhanced approach for integrative modeling and analysis of related Key Performance Indicators (KPIs) toward building resilience and viability in manufacturing and supply chains, aided by Dynamic Bayesian Networks (DBN).
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Research Areas:
Digital Transformation in Manufacturing: 50% Sustainable Production and Technologies: 20% Mathematical and Algorithmic Foundations: 30%
