Developing Simulation-Based Decision Support Systems For Customer-Driven Manufacturing Operation Planning

Juhani Heilala, Jari Montonen, Paula Järvinen, Sauli Kivikunnas, Matti Maantila, Jarkko Sillanpää, Tero Jokinen

    Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

    41 Citations (Scopus)


    Discrete-event simulation (DES) has mainly been used as a production system analysis tool to evaluate new production system concepts, layout and control logic. Recent developments have made DES models feasible for use in the day-to-day operational production and planning of manufacturing facilities. Operative simulation models provide manufacturers with the ability to evaluate the capacity of the system for new orders, unforeseen events such as equipment downtime, and changes in operations. A simulation-based Decision Support System (DSS) can be used to help planners and schedulers organize production more efficiently in the turbulent global manufacturing. This paper presents the challenges for development and the efforts to overcome these challenges for the simulation-based DSS. The major challenges are: 1) data integration 2) automated simulation model creation and updates and 3) the visualization of results for interactive and effective decision making. A recent case study is also presented.
    Original languageEnglish
    Title of host publicationProceedings of the 2010 Winter Simulation Conference, WSC '10
    Subtitle of host publicationBaltimore Marriot Waterfront, Baltimore, Maryland, USA, December 5-8, 2010
    EditorsB. Johansson, S. Jain, J. Montoya-Torres, J. Hugan, E. Yücesan
    Place of PublicationBaltimore, USA
    PublisherIEEE Institute of Electrical and Electronic Engineers
    ISBN (Electronic)978-1-4244-9865-9
    ISBN (Print)978-1-4244-9864-2, 978-1-4244-9866-6
    Publication statusPublished - 2010
    MoE publication typeA4 Article in a conference publication


    • decision support system
    • manufacturing simulation
    • operation planning


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