Analyzing the impact of maintenance on profitability using dynamic bayesian networks.

In the era of Industry 4.0, predictive maintenance is regarded as a key factor for reaching business objectives. Cost-effective deployment of Cyber Physical Production Systems raises the question whether data-driven and knowledge-based maintenance affects profitability of smart factories. Several studies reveal that an appropriate data-driven maintenance strategy should not only focus on increasing availability but also should consider economic parameters. This paper presents a novel approach and a proof-of-concept demonstrator using Dynamic Bayesian Networks (DBN). The proposed DBN model enables identifying and predicting the economic impact of maintenance on profitability as well as support planning and monitoring of maintenance activities. [ABSTRACT FROM AUTHOR]