D 2017

Conversion of Real Data from Production Process of Automotive Company for Process Mining Analysis

DIŠEK, Miroslav, Roman ŠPERKA a Ján KOLESÁR

Základní údaje

Originální název

Conversion of Real Data from Production Process of Automotive Company for Process Mining Analysis

Autoři

DIŠEK, Miroslav (203 Česká republika, domácí), Roman ŠPERKA (703 Slovensko, domácí) a Ján KOLESÁR (703 Slovensko)

Vydání

Switzerland, Smart Innovation, Systems and Technologies. Agent and Multi-Agent Systems: Technologies and Applications. od s. 223-233, 11 s. 2017

Nakladatel

Springer International Publishing AG

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

paměťový nosič (CD, DVD, flash disk)

Odkazy

Kód RIV

RIV/47813059:19520/17:00010804

Organizační jednotka

Obchodně podnikatelská fakulta v Karviné

ISBN

978-3-319-59393-7

Klíčová slova anglicky

Process mining; Data cleaning; Data cleaning tools; DISCO
Změněno: 7. 2. 2020 10:57, RNDr. Daniel Jakubík

Anotace

V originále

The aim of this paper is to convert the real data from the raw format from different information systems (log files) to the format, which is suitable for process mining analysis of a production process in a large automotive company. The conversion process will start with the import from several relational databases. The motivation is to use the DISCO tool for importing real pre-processed data and to conduct process mining analysis of a production process. DISCO generates process models from imported data in a comprehensive graphical form and provides different statistical features to analyse the process. This makes it possible to examine the production process in detail, identify bottlenecks, and streamline the process. The paper firstly presents a brief introduction of a manufacturing process in a company. Secondly, it provides a description of a conversion and pre-processing of chosen real data structures for the DISCO import. Then, it briefly describes the DISCO tool and proper format of pre-processed log file, which serves as desired input data. This data will be the main source for all consecutive operations in generated process map. Finally, it provides a sample analysis description with emphasis on one production process (process map and few statistics). To conclude, the results obtained show high demands on pre-processing of real data for suitable import format into DISCO tool and vital possibilities of process mining methods to optimize a production process in an automotive company.