Publication detail

Process Mining in a Manufacturing Company for Predictions and Planning

POSPÍŠIL, M. MATES, V. HRUŠKA, T. BARTÍK, V.

Original Title

Process Mining in a Manufacturing Company for Predictions and Planning

English Title

Process Mining in a Manufacturing Company for Predictions and Planning

Type

journal article - other

Language

en

Original Abstract

Simulation can be used for analysis, prediction and optimization of business processes. Nevertheless, process models often differ from reality. Data mining techniques can be used to improve these models based on observations of a process and resource behavior from detailed event logs. More accurate process models can be used not only for analysis and optimization, but also for prediction and recommendation as well. This paper analyses process models in a manufacturing company and its historical performance data. Based on the observation, a simulation model can be created and used for analysis, prediction, planning and for dynamic optimization. Focus of this paper is in different data mining problems that cannot be solved easily by well-known approaches like Regression Tree.

English abstract

Simulation can be used for analysis, prediction and optimization of business processes. Nevertheless, process models often differ from reality. Data mining techniques can be used to improve these models based on observations of a process and resource behavior from detailed event logs. More accurate process models can be used not only for analysis and optimization, but also for prediction and recommendation as well. This paper analyses process models in a manufacturing company and its historical performance data. Based on the observation, a simulation model can be created and used for analysis, prediction, planning and for dynamic optimization. Focus of this paper is in different data mining problems that cannot be solved easily by well-known approaches like Regression Tree.

Keywords

business process simulation, business process intelligence, data mining, process mining, prediction, optimization, recommendation, association rules, genetic algorithms.

RIV year

2013

Released

31.12.2013

Publisher

NEUVEDEN

Location

NEUVEDEN

ISBN

1942-2628

Periodical

International Journal on Advances in Software

Year of study

2013

Number

3

State

US

Pages from

283

Pages to

297

Pages count

16

URL

Documents

BibTex


@article{BUT106393,
  author="Milan {Pospíšil} and Vojtěch {Mates} and Tomáš {Hruška} and Vladimír {Bartík}",
  title="Process Mining in a Manufacturing Company for Predictions and Planning",
  annote="Simulation can be used for analysis, prediction and optimization of business
processes. Nevertheless, process models often differ from reality. Data mining
techniques can be used to improve these models based on observations of a process
and resource behavior from detailed event logs. More accurate process models can
be used not only for analysis and optimization, but also for prediction and
recommendation as well. This paper analyses process models in a manufacturing
company and its historical performance data. Based on the observation,
a simulation model can be created and used for analysis, prediction, planning and
for dynamic optimization. Focus of this paper is in different data mining
problems that cannot be solved easily by well-known approaches like Regression
Tree.",
  address="NEUVEDEN",
  chapter="106393",
  edition="NEUVEDEN",
  howpublished="print",
  institution="NEUVEDEN",
  number="3",
  volume="2013",
  year="2013",
  month="december",
  pages="283--297",
  publisher="NEUVEDEN",
  type="journal article - other"
}