Asset Optimization and Predictive Maintenance in Discrete Manufacturing Industry
Tutor / director / evaluatorKiritsis, Dimitris
Document typeMaster thesis (pre-Bologna period)
Rights accessOpen Access
Nowadays, the current challenging issue in production is to deliver products in a more efficient manner by controlling, monitoring and centralising all intra-logistical processes. With the growing focus on sustainability, complexity grows even further as productions managers have to manage energy and material consumption, carbon footprint, and waste output in addition to Key Performance Indicators like process efficiency, asset utilization, quality, scrap rate and costs. Efforts to find the optimum for yield, quality, and speed or energy consumption individually often result in local optima, far from the ideal solution. Optimization must start at global bottlenecks within the plant or supply network, which can only be identified if overall process transparency is given. This project is included in a much larger project called PLANTCockpit (Production Logistics and Sustainability Cockpit) which is a FP7 FoF ICT Collaborative Project. Here the aim of this project is to ensure the optimized use of available resource (personal, equipment, material and energy) for a scheduled product plan with continuous asset monitoring in discrete manufacturing industry. This issue is closed to real-world manufacturing problems and demands awareness from production managers on the holistic aspect of engineering assets availability. It includes the reliable detection and anticipation of performance deviations via monitoring the production and product related process, diagnostic of possible causes and predicting the time of occurrence. In such a context, PLANTCockpit project has been specially proposed to provide a decision support mechanism for an integrated maintenance and production management and consequently for asset optimization. In this project, a study of the existing methodologies for asset management and optimization will be performed. The outcome of this study will provide a novel approach for asset management and optimization. Furthermore, predictive maintenance and MIMOSA (Machinery Information Management Open System Alliance) standard will be one of the current issued to be tackled in order to prove improvement in the optimization for asset utilization. Keywords: Asset management, Asset optimization, Predictive Maintenance, Product Lifecycle Management (PLM), MIMOSA.