Detail publikace

Process Mining in a Manufacturing Company for Predictions and Planning

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

Originální název

Process Mining in a Manufacturing Company for Predictions and Planning

Anglický název

Process Mining in a Manufacturing Company for Predictions and Planning

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

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"
}