Screening Process Mining and Value Stream Techniques on Industrial Manufacturing Processes: Process Modelling and Bottleneck Analysis
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU144164" target="_blank" >RIV/00216305:26230/22:PU144164 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9715073" target="_blank" >https://ieeexplore.ieee.org/document/9715073</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2022.3152211" target="_blank" >10.1109/ACCESS.2022.3152211</a>
Alternative languages
Result language
angličtina
Original language name
Screening Process Mining and Value Stream Techniques on Industrial Manufacturing Processes: Process Modelling and Bottleneck Analysis
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Access
ISSN
2169-3536
e-ISSN
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Volume of the periodical
2022
Issue of the periodical within the volume
10
Country of publishing house
US - UNITED STATES
Number of pages
12
Pages from-to
24203-24214
UT code for WoS article
000766543100001
EID of the result in the Scopus database
2-s2.0-85124816007