Dynamic Scheduling of Machines Towards the Vision of Industry 4.0 Studio—A Case Study

Ayvarnam, Nagarajan; Mayurappriyan, P. S.

doi:10.1007/978-981-10-1645-5_9

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Value streams can be presented as an ongoing data handling, data pre-processing for further analysis that is one of the assumptions of the Industrial Digitalization following the major specifications of the Reference Architecture Model for Industry 4.0 (RAMI 4.0) [18,19]. It helps to avoid the loss of information throughout the process [20]. Therefore, integration of all steps and their automatization brings insights for companies.…”

Section: Related Workmentioning

confidence: 99%

Screening Process Mining and Value Stream Techniques on Industrial Manufacturing Processes: Process Modelling and Bottleneck Analysis

et al. 2022

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One major result of the Industrial Digitalization is the access to a large set of digitalized data and information, i.e. Big Data. The market of analytic tools offers a huge variety of algorithms and software to exploit big datasets.Implementing their advantages into one approach brings better results and empower possibilities for process analysis. Its application in the manufacturing industry requires a high level of effort and remains to be challenging due to product complexity, human-centric processes, and data quality. In this manuscript, the authors combine process mining and value streams methods for analyzing the data from the information management system, applying the approach to the data delivered by one specific manufacturing system. The manufacturing process to be examined is the process of assembling gas meters in the manufacture. This specific and important part of the whole supply-chain process was taken as suitable for the study due to almost full-automated line with data about each process activity of the value-stream in the information system. The paper applies process mining algorithms in discovering a descriptive process model that plays the main role as a basis for further analysis. At the same time, modern techniques of the bottleneck analysis are described, and two new comprehensible methods of bottlenecks detection (TimeLag and Confidence intervals methods), as well as their advantages, will be discussed. Achieved results can be subsequently used for other sources of big data and industrial-compliant Information Management Systems.

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Section: Related Workmentioning

confidence: 99%

Screening Process Mining and Value Stream Techniques on Industrial Manufacturing Processes: Process Modelling and Bottleneck Analysis

et al. 2022

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“…Finally, the study of Ayvarnam and Mayurappriyan (2017) showed that a 12% increase in productivity was attained in a company producing a wide variety of pumps for agricultural use by implementing some CPSF capabilities. This increase in productivity was reached by using the supervisory capabilities of a Manufacturing Execution System (MES) to carry out a well-informed process of order release and by the aid of a display to better convey all the scheduling instructions to the stations and workers in the shop-floor.…”

Section: Previous Research Supporting the Proposed Fitmentioning

confidence: 99%

Shop-floor scheduling as a competitive advantage: A study on the relevance of cyber-physical systems in different manufacturing contexts

Romero-Silva

Hernández-López

2020

International Journal of Production Economics

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Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing

2018

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Dynamic Scheduling of Machines Towards the Vision of Industry 4.0 Studio—A Case Study

Cited by 3 publications

References 3 publications

Screening Process Mining and Value Stream Techniques on Industrial Manufacturing Processes: Process Modelling and Bottleneck Analysis

Screening Process Mining and Value Stream Techniques on Industrial Manufacturing Processes: Process Modelling and Bottleneck Analysis

Shop-floor scheduling as a competitive advantage: A study on the relevance of cyber-physical systems in different manufacturing contexts

Establishment of maintenance inspection intervals: an application of process mining techniques in manufacturing

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