Integrating data analytics and simulation methods to support manufacturing decision making

Deogratias Kibira, Qais Hatim, Soundar Kumara, Guodong Shao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations


Modern manufacturing systems are installed with smart devices such as sensors that monitor system performance and collect data to manage uncertainties in their operations. However, multiple parameters and variables affect system performance, making it impossible for a human to make informed decisions without systematic methodologies and tools. Further, the large volume and variety of streaming data collected is beyond simulation analysis alone. Simulation models are run with well-prepared data. Novel approaches, combining different methods, are needed to use this data for making guided decisions. This paper proposes a methodology whereby parameters that most affect system performance are extracted from the data using data analytics methods. These parameters are used to develop scenarios for simulation inputs; system optimizations are performed on simulation data outputs. A case study of a machine shop demonstrates the proposed methodology. This paper also reviews candidate standards for data collection, simulation, and systems interfaces.

Original languageEnglish (US)
Title of host publication2015 Winter Simulation Conference, WSC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages12
ISBN (Electronic)9781467397438
StatePublished - Feb 16 2016
EventWinter Simulation Conference, WSC 2015 - Huntington Beach, United States
Duration: Dec 6 2015Dec 9 2015

Publication series

NameProceedings - Winter Simulation Conference
ISSN (Print)0891-7736


OtherWinter Simulation Conference, WSC 2015
Country/TerritoryUnited States
CityHuntington Beach

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Computer Science Applications


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