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Abstrakty
In the process of unit and small batch production a very important aspect is the amount of time from production setup to availability to the customer. In spite of applying modern management techniques, setup time still plays an important part in the production cycle time. In the examined companies the relationship between changeover time to processing time was significant. The above research inspired the author to prepare a method of setup time reduction through the appropriate arrangement of tasks in the operational production plan. The appropriate arrangement meant considering the similarity of parts from the point of view of carried out operation. The similarity of parts facilitates setup time reduction, which translate into smaller lot sizes, reduced in-process inventories, shorter lead time and higher throughput. The method was validated in conditions of the production practice for unit and small batch production. The presented method is one of the elements of a computer aided management system for small and medium enterprises (SME).
Wydawca
Czasopismo
Rocznik
Tom
Strony
41--55
Opis fizyczny
Bibliogr. 22 poz, rys., tab.
Twórcy
autor
- University of Bielsko-Biała, Department of Industrial Engineering, Willowa 2, 43-309 Bielsko-Biała, Poland, phone: +48 33 827253, jmleczko@ath.bielsko.pl
Bibliografia
- [1] Tralix M.T., From mass production to mass customization, Journal of Textile and Apparel, Technology and Management, 1, 2, 2001.
- [2] Goldratt E., Cox J., The Goal: A Process of Ongoing Improvement, Werbel, Warszawa, 2002 (in Polish).
- [3] Davies J., Mabin V.J., Balderstone S.J., The theory of constraints: a methodology apart? - a comparison with selected OR/MS methodologies, The International Journal of Management Science Omega, 33, 506-524, 2005.
- [4] Houtzeel A., Group Technology. Maynard’s Industrial Engineering Handbook (5th Edition), Edited by: Zandin, Kjell B, McGraw-Hill, 2001.
- [5] Ben-Arieh D., Analysis of a distributed group technology methodology, Computers Industry Engineering, 35, 69-72, 1998.
- [6] Everitt B., Cluster analysis (3rd edition), London, Arnold, 1993.
- [7] Tan P., Steinbach M., Kumar V., Introduction to Data Mining, Addison-Wesley, 2006.
- [8] Tatikonda M.V., Wemmerlow U., Adoption and implementation of group technology classification and coding systems: insight from seven case studies, Int. J. Prod. Res., 30, 2087-2110, 1992.
- [9] Adenso-Diaz B., Lozano S., Eguia I., Part-machine grouping using weighted similarity coeficients, Computers & Industrial Engineering, 48, 553-570, 2005.
- [10] Jeon G., Broering M., Leep H.R., Parsaei H.R., Wong J.P., Part family formation based on alternative routes during machine failure, Computers Industry Engineering, 35, 73-76, 1998.
- [11] Kulkarni U.R., Kiang Y.M., Dynamic grouping of parts in flexible manufacturing systems - A selforganizing neutral networks approach, European Journal of Operational Research, 84, 192-212, 1995.
- [12] Owsiński J.W., Machine-part grouping and cluster analysis: similarities, distances and grouping criteria, Bulletin of the Polish Academy of Science, Technical Sciences, 57, 3, 217-228, 2009.
- [13] Knosala R., Pilot T., The application of neutral networks in group technology, Journal of Materials Processing Technology, 78, 150-155, 1998.
- [14] Kukkurainen P., Luukka P., Classification method using fuzzy level set subgrouping, Expert Systems with Applications, 34, 859-865, 2008.
- [15] Shirahama S., Setup Time Reduction, Maynard’s Industrial Engineering Handbook (5th Edition), Edited by: Zandin, Kjell B, McGraw-Hill, 2001.
- [16] Matuszek J., Koˇsturiak J., Gregor M., Chal J., Krištiak J., Lean company, ATH, Bielsko-Biała, 2003.
- [17] Jacobs F.R., Bendoly E., Enterprise resource planning: Developments and directions for operations management research, European Journal of Operational Research, 146, 233-240, 2003.
- [18] Matuszek J., Mleczko J., Production control in moving bottlenecks in conditions of unit and small-batch production, Bulletin of the Polish Academy of Science, Technical Sciences, 57, 3, 229-239, 2009.
- [19] Fayeda H.A., Hashema S.R., Atiyab A.F., Selfgenerating prototypes for pattern classification, Pattern Recognition, 40, 1498-1509, 2007.
- [20] Abu-Abbas O., Comparisons Between Data Clustering Algorithms, The International Arab Journal of Information Technology, 5, 3, 2008.
- [21] Kusiak A., Vannelli A., Kumar K.R., Clustering analysis: models and algorithms, Control and Cybernetics, 15, (2), 139-154, 1986.
- [22] Liao T.W., Celmins A.K., Hammell R.J., A fuzzy c-means variant for the generation of fuzzy term sets, Fuzzy Sets and Systems, 135, 241-257, 2003.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAR0-0065-0028