Integrated Master Production Planning and Machine Learning-Based Failure Prediction Using Event-Based Time Series Data with Mathematical Modeling
Keywords:
Master production planning, Predictive maintenance, Machine learning, Failure predictionAbstract
This paper deals with the problem of integrated predictive maintenance (PdM) and master production planning (MPP) in a real multi-machine packaging system. In contrast to prior work in which environmental variables of machines and components or popular datasets were used, we employed event-based historical data of machine failures. Four machine learning (ML) models for failure prediction were built with the Deep Neural Network (DNN) outperforms the other. We proposed a dynamic linear programming (DLP) model to determine optimal production strategies while minimizing costs. While previous studies concentrate mainly on scheduling and planning, our research concentrates on the higher master production level. The framework was tested using real-world data from a one-year data collection, and analyses of three scenarios revealed different trade-offs between production strategies. This study provides practical evaluation in the area of maintenance for professionals using failure prediction analysis. Moreover, the approach proposed in this framework can help planners to decide which strategy they would like to implement based on the key production and cost-related parameters specific to their business. In conclusion, this paper as a strong methodology provides managerial insights for decision-makers and highlights future directions to advance the adaptability of manufacturing processes in the Industry 4.0 environment.
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