A Framework for Integrated Proactive Maintenance Decision Making and Supplier Selection

Abstract

The increasing use of sensors in manufacturing enterprises has led to the need for real-time data-driven information systems capable of processing huge amounts of data in order to provide meaningful insights about the actual and the predicted business performance. We propose a framework for real-time, event-driven proactive supplier selection driven by Condition Based Maintenance (CBM). The proposed framework was tested in a real in automotive lighting equipment scenario.

A Review of Data-Driven Decision-Making Methods for Industry 4.0 Maintenance Applications

“…Bousdekis et al [44] propose an approach for real-time, event-driven proactive supplier selection with the use of Markowitz portfolio optimization theory, on the basis of the optimal times for replacement and ordering the spare parts. The algorithm is triggered by real-time, sensor-driven predictions about future failures and future spare parts prices.…”

“…Bousdekis et al [43] propose a proactive event-driven decision model for joint predictive maintenance and spare parts inventory optimization in the frame of the emaintenance concept. Bousdekis et al [44] propose a proactive event-driven model for joint maintenance and logistics optimization for recommending optimal (perfect or imperfect) maintenance actions and associated spare parts orders along with optimal timing.…”