Process automation in the area of manufacturability analysis using machine learning

Seibold, Johannes; Hentsch, Maximilian; Kharitonov, Aleksei; Eber, Rainer; Schwarzer, Steffen

doi:10.1016/j.procs.2022.08.024

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…This goal supports data-driven risk management and datadriven decision-making by providing real-time data for professionals [27]. Not only do professionals benefit from the increased volume of data, but sophisticated tools such as regression modeling (or machine-learning tools) will also be available, and patterns can be identified [28]. Furthermore, one of the most crucial fields, production scheduling, can use the outcome of the accumulated information [29,30].…”

Section: Risk Management and Decision-making Under Industry 40 Domainmentioning

confidence: 99%

A Risk Management Framework for Industry 4.0 Environment

2023

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In past decades, manufacturing companies have paid considerable attention to using their available resources in the most efficient way to satisfy customer demands. This endeavor is supported by many Industry 4.0 methods. One of these is called MES (Manufacturing Execution System), which is applied for monitoring and controlling manufacturing by recording and processing production-related data. This article presents a possible method of implementation of a risk-adjusted production schedule in a data-rich environment. The framework is based on production datasets of multiple workshops, which is followed by statistical analysis, and its results are used in stochastic network models. The outcome of the simulation is implemented in a production scheduling model to determine how to assign the production among workshops. After collecting the necessary data, the reliability indicator-based stochastic critical path method was applied in the case study. Two cases were presented based on the importance of inventory cost and two different scheduling results were created and presented. With the objective of the least inventory cost, the production was postponed to the latest time possible, which means that workshops had more time to finish their previous work on the first day due to the small production quantity. When the cost was not relevant, the production started on the first day of each workshop, and the production was completed before the deadline. These are optimal solutions, but alternative solutions can also be performed by the decision maker based on the results. The use of the modified stochastic critical path method and its analysis shed light on the deficiency of the production, which is a merit in the continuous improvement process and the estimation of the total project time.

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