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"
}