Publication Details
Screening Process Mining and Value Stream Techniques on Industrial Manufacturing Processes: Process Modelling and Bottleneck Analysis
Venkataschalam Hari Santosh
Essmann Roland
Hruška Tomáš, prof. Ing., CSc. (DIFS)
Colombo Armando Walter
bottleneck analysis, manufacturing process, process mining, process modelling,
information management system, value stream
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.
@article{BUT177411,
author="Julia {Rudnitckaia} and Hari Santosh {Venkataschalam} and Roland {Essmann} and Tomáš {Hruška} and Armando Walter {Colombo}",
title="Screening Process Mining and Value Stream Techniques on Industrial Manufacturing Processes: Process Modelling and Bottleneck Analysis",
journal="IEEE Access",
year="2022",
volume="2022",
number="10",
pages="24203--24214",
doi="10.1109/ACCESS.2022.3152211",
issn="2169-3536",
url="https://ieeexplore.ieee.org/document/9715073"
}