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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

—

Svazek periodika

2022

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

12

Strana od-do

24203-24214

Kód UT WoS článku

000766543100001

EID výsledku v databázi Scopus

2-s2.0-85124816007