Extracting the Rules of KPIs for Equipment Management Based on Rough Set Theory

Jia Wen Wang; Ching Hsue Cheng

doi:10.4028/www.scientific.net/AMR.314-316.2358

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 314-316Extracting the Rules of KPIs for Equipment...

Extracting the Rules of KPIs for Equipment Management Based on Rough Set Theory

Abstract:

This paper practically collects manufacturing supplier dataset in Taiwan. The dataset includes production records, and there are 18 attributes such as production scheduling, scheduled downtime, process, etc. For comparison, decision tree, naive bayesian, and multi-layer perceiving are utilized to compare with the proposed procedure in classification accuracy. The results show that the correct rate of rough set theory is not only superior to decision trees, naive bayesian, and multi-layer perceiving (MLP), and the proposed procedure can be easy to understand and produce fewer rules. In managerial implication, the results can generate predictive model, and classifiable rule which can help manufacturing to find out key related factors in equipment throughput and use the rules generated as a capacity planning assessment.

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Periodical:

Advanced Materials Research (Volumes 314-316)

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2358-2361

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.314-316.2358

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Online since:

August 2011

Authors:

Jia Wen Wang, Ching Hsue Cheng

Keywords:

Key Performance Indicators, Rough Set Theory (RST)

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References

[1] M.E. McGrath: Next Generation Product Development How to Increase Productivity, Cut Costs, and Reduce Cycle Times. McGraw Hill. (2005).

DOI: 10.1111/j.0737-6782.2005.133_2.x

Google Scholar

[2] P.R. Niven: Balanced Scorecard Step-by-Step : Maximizing performance and maintaining results, New York: Jo Sons (2002).

DOI: 10.1002/9781119205081

Google Scholar

[3] J.R. Quinlan: Introduction of decision trees. Machine Learning. Vol.1(1) (1986), pp.81-106.

Google Scholar

[4] T. Bayes: Essay towards solving a problem in the doctrine of chances. The Phylosophical Transactions of the Royal Society of London. (1764).

Google Scholar

[5] Z. Pawlak: Rough Sets. International Journal of Computer and Information Sciences. vol.11 (1982), pp.341-356.

Google Scholar

[6] Z. Pawlak: Rough Sets and Intelligent Data Analysis. International Sciences. Vol.147 (2002), pp.1-12.

Google Scholar

[7] P. Jonsson, M. Lesshammar: Evaluation and improvement of manufacturing performance measurement systems-The role of OEE. International Journal of Operations AND Production Management. Vol. 19(1) (1999), pp.55-78.

DOI: 10.1108/01443579910244223

Google Scholar

[8] Sheu, D. Daniel: Overall Input Efficiency and Total Equipment Efficiency. IEEE Transaction on SEMI. Vol.19(4) (2006).

Google Scholar