Data-driven machine criticality assessment – maintenance decision support for increased productivity

Abstract

Data-driven decision support for maintenance management is necessary for modern digitalized production systems. The data-driven approach enables analyzing the dynamic production system in realtime. Common problems within maintenance management are that maintenance decisions are experience-driven, narrow-focussed and static. Specifically, machine criticality assessment is a tool that is used in manufacturing companies to plan and prioritize maintenance activities. The maintenance problems are well exemplified by this tool in industrial practice. The tool is not trustworthy, seldom updated and focuses on individual machines. Therefore, this paper aims at the development and validation of a framework for a data-driven machine criticality assessment tool. The tool supports prioritization and planning of maintenance decisions with a clear goal of increasing productivity. Four empirical cases were studied by employing a multiple case study methodology. The framework provides guidelines for maintenance decision-making by combining the Manufacturing Execution System (MES) and Computerized Maintenance Management System (CMMS) data with a systems perspective. The results show that by employing data-driven decision support within the maintenance organization, it can truly enable modern digitalized production systems to achieve higher levels of productivity.

An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development

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Multiobjective Lot Sizing and Scheduling of Multiproduct Switching Production in the Process Industry Considering Uncertain Market Information Under Mass Customization

“…Under this challenging background environment, this paper starts from the internal enterprise to process the uncertain demand orders from the market, and uses the manufacturing execution system (MES) to formulate the corresponding production plan and implement scheduling for the orders [5,7]. In contrast to previous studies, the novelty and contribution of this paper are in the implementation of product lot sizing and scheduling, in addition to consideration of uncertain demand information, as well as the novel proposition of the problem of product switching in multi-variety process industries.…”

An approach for mastering Carbon Footprint in the context of Distributed Maintenance

“…Thus, production assets require monitoring and scheduling the associated maintenance actions (preventive and corrective). Fortunately, the evolution of technologies has led to an Industry 4.0 where managers can predict failures and conduct maintenance operations remotely and in real-time [2]. However, they remain confronted with the limitation of budget and resources dedicated to maintenance [3].…”