18
C. J. Thomas Renald et al.
4 Conclusion
In this work, the reaction improvement in the business on assembling of submersible
siphon by utilizing Cellular Manufacturing Systems has been inspected. Uncommon
elements, voids, group efficiency and group efficacy measures were utilized to assess
the three bunching calculations ROC-2, DCA, MOD-SLC. MOD-SLC brought about
fewer uncommon components (2), voids (3) higher gathering productivity of 91.33%
and higher gathering viability of 82.14%. The consequences of bunching demonstrate that MOD-SLC is more viable than ROC-2 and DCA dependent on the broke
down exhibition measures. Cell layout model has been planned dependent on the
chose MOD-SLC calculation. The current regular framework model and the phone
fabricating framework model were analyzed under various criteria, for example, efficiency and material dealing with separation. The outcomes from the correlation show
that the Cellular Manufacturing System can diminish material taking care of separation by 51.25% and can improve profitability by 29.04%. This prompts a quicker
reaction than the present framework. Cell manufacturing additionally expands generation precision that yields all the more opportune reactions and progressively focused
business capacity.
References
1. King JR (1980) Machine-component group formation in group technology. OMEGA 8:193–
199, and also Machine-component grouping in production flow analysis: an approach using a
rank order clustering algorithm. Int J Prod Res 18:213–232
2. King JR, Nakornchai V (1982) Machine-component group formation in group technology:
review and extension. Int J Prod Res 20:117–133
3. Chan HM, Milner DA (1982) Direct clustering algorithm for group formation in cellular
manufacture. J Manuf Syst 1:65–75
4. Gongaware TA, Ham I (1991) Cluster analysis applications for group technology manufacturing
systems. In: Proceedings: Ninth North American manufacturing research conference, pp 503–
508
5. Mosier CT, Taube L (1985) The facets of group technology and their impacts on implementation—a state of the art survey. Omega 13(5):381–391
6. Mosier CT, Taube L (1985) Weighted similarity measure heuristics for the group technology
machine clustering problem. Omega 13(6):577–583
7. Gupta T, Seifoddini H (1990) Production data based similarity coefficient for machinecomponent grouping decision in the design of a cellular manufacturing system. Int J Prod
Res 28(7):247–269
8. Seifoddini HK (1989) A note on the similarity coefficient method and the problem of improper
machine assignment in group technology problem. Int J Prod Res 27(7):1161–1165
9. Seifoddini HK (1989) Duplication process in machine cells formation in group technology. IIE
Trans 21(4):382–388
10. Khan M, Islam S, Sarker B (2000) A similarity coefficient measure and machine-parts grouping
in cellular manufacturing systems. Int J Prod Res 38(3):699–720
11. Yasuda K, Yin Y (2001) A dissimilarity measure for solving the cell formation problem in
cellular manufacturing. Comput Ind Eng 39(1):1–17
C. J. Thomas Renald et al.
4 Conclusion
In this work, the reaction improvement in the business on assembling of submersible
siphon by utilizing Cellular Manufacturing Systems has been inspected. Uncommon
elements, voids, group efficiency and group efficacy measures were utilized to assess
the three bunching calculations ROC-2, DCA, MOD-SLC. MOD-SLC brought about
fewer uncommon components (2), voids (3) higher gathering productivity of 91.33%
and higher gathering viability of 82.14%. The consequences of bunching demonstrate that MOD-SLC is more viable than ROC-2 and DCA dependent on the broke
down exhibition measures. Cell layout model has been planned dependent on the
chose MOD-SLC calculation. The current regular framework model and the phone
fabricating framework model were analyzed under various criteria, for example, efficiency and material dealing with separation. The outcomes from the correlation show
that the Cellular Manufacturing System can diminish material taking care of separation by 51.25% and can improve profitability by 29.04%. This prompts a quicker
reaction than the present framework. Cell manufacturing additionally expands generation precision that yields all the more opportune reactions and progressively focused
business capacity.
References
1. King JR (1980) Machine-component group formation in group technology. OMEGA 8:193–
199, and also Machine-component grouping in production flow analysis: an approach using a
rank order clustering algorithm. Int J Prod Res 18:213–232
2. King JR, Nakornchai V (1982) Machine-component group formation in group technology:
review and extension. Int J Prod Res 20:117–133
3. Chan HM, Milner DA (1982) Direct clustering algorithm for group formation in cellular
manufacture. J Manuf Syst 1:65–75
4. Gongaware TA, Ham I (1991) Cluster analysis applications for group technology manufacturing
systems. In: Proceedings: Ninth North American manufacturing research conference, pp 503–
508
5. Mosier CT, Taube L (1985) The facets of group technology and their impacts on implementation—a state of the art survey. Omega 13(5):381–391
6. Mosier CT, Taube L (1985) Weighted similarity measure heuristics for the group technology
machine clustering problem. Omega 13(6):577–583
7. Gupta T, Seifoddini H (1990) Production data based similarity coefficient for machinecomponent grouping decision in the design of a cellular manufacturing system. Int J Prod
Res 28(7):247–269
8. Seifoddini HK (1989) A note on the similarity coefficient method and the problem of improper
machine assignment in group technology problem. Int J Prod Res 27(7):1161–1165
9. Seifoddini HK (1989) Duplication process in machine cells formation in group technology. IIE
Trans 21(4):382–388
10. Khan M, Islam S, Sarker B (2000) A similarity coefficient measure and machine-parts grouping
in cellular manufacturing systems. Int J Prod Res 38(3):699–720
11. Yasuda K, Yin Y (2001) A dissimilarity measure for solving the cell formation problem in
cellular manufacturing. Comput Ind Eng 39(1):1–17