Proactive Approach to Manufacturing Planning
Article Sidebar
Issue
Vol. 18 No. 1 (2014)
Submitted
10 October 2013
Published
17 June 2014
Keywords:
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data mining, production planning, proactive approach, knowledge-based system
Abstract
Proposed concept of proactive manufacturing planning uses sets of knowledge to create plan. These sets of knowledge are gained from transformation of historical data of selected indicators. This concept uses the analysis of occurred events, which is done by applying data mining methods to known historical data. Results of analysis are then recorded into knowledge-based system for further use. Application of data mining techniques helps to find hidden relationships with high influence on final decision of planner. This concept aims to navigate planner during creation of real plans resulting from real situations. Unknown situations are modelled using simulation module.
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Papadimitriou S., Sun J. and Yu, P., 2010. Local Correlation Tracking in Time Series. In: Proceedings of the Sixth International Conference on Data Mining (ICDM '06). IEEE Computer Society, Washington, DC, USA, 456-465. DOI 10.1109/ICDM.2006.99, Available at:
http://www.cs.cmu.edu/~spapadim/pdf/loco_icdm06.pdf.
Tanuska, P., Vazan, P., Kebisek, M., Moravcík, O. and Schreiber, P., 2012. Data Mining Model Building as a Support for Decision Making in Production Management, In: Advances in Intelligent and Soft Computing, Vol. 166. Advances in Computer Science, Engineering and Applications : Proceedings of the Second International Conference on Computer Science, Engineering and Applications (ICCSEA 2012), May 25-27, 2012, New Delhi, India, Volume 1.: Springer-Verlag Berlin Heidelberg, pp. 695-701.
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Zelenka, J., 2010. Discrete event dynamic systems framework for analysis and modeling of real manufacturing system. In: INES: proceedings of the 14th IEEE International Conference on Intelligent Engineering Systems 2010, ed. A. Szakál. - IEEE, pp. 287-291.
Authors
Bubeník, P., & Horák, F. (2014). Proactive Approach to Manufacturing Planning. Quality Innovation Prosperity, 18(1), 23–32. https://doi.org/10.12776/qip.v18i1.208
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