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A TISM Model for Structuring the Productivity Elements of Flexible Manufacturing System
Authors: Sandhya Dixit, Tilak Raj
Abstract:
Flexible Manufacturing System (FMS) is seen as an option for industries which want to boost productivity as well as respond quickly to an increasingly changing marketplace. FMS produces in mid variety, mid volume range and can meet the changing market demands very quickly. But still the impact of adoption of FMS on the productivity of any industry is not very clear. In this paper an attempt has been made to model the various factors affecting the productivity of FMS installation using Total Interpretive Structural Modelling (TISM) Technique.Keywords: Flexible manufacturing system, productivity, total interpretive structural modelling.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1314632
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[1] Stecke, K.E. (1983) ‘Formulation and solution of nonlinear integer production planning problems for flexible manufacturing systems’, Journal of Management Science, Vol. 29, No.3, pp. 273-287.
[2] Chan, F.T.S. and Chan, H.K. (2004) ‘Analysis of dynamic control strategies of an FMS under different scenarios’, International Journal of Robotics and Computers in Manufacturing, Vol. 20, pp. 423-437.
[3] Nagarjuna, N., Mahesh, O. and Rajagopal, K.(2006), ‘A heuristic based on multi-stage programming approach for machine –loading problem in a flexible manufacturing system’, International Journal of Robotics and Computer Integrated Manufacturing, Vol. 22, pp. 342-352.
[4] Kumar, A. Prakash, Tiwari, MK, Shankar, R., Baveja, A., (2006) ‘Solving machine-loading problem of a flexible manufacturing system with a constraint-based genetic algorithm’, European Journal of Operations Research, Vol. 175, No. 2, pp. 1043-1069.
[5] Rao, R.V., and Parnichkun, M. (2009) ‘Flexible manufacturing system selection using a combinatorial mathematics-based decision-making method’, .International Journal of Production Research, Vol. 47, No.24, pp.6981-6998.
[6] Dixit, S. and Raj, T. (2016)‘Identification and modelling of the various factors affecting the productivity of FMS’, International Journal of Productivity and Quality Management, Vol. 17, No. 3,pp.353–379.
[7] Sage, A.P. (1977) ‘Interpretive structural modeling: methodology for large-scale systems’, pp. 91-164, 1977 (McGraw-Hill: New York, NY).
[8] Raj, T., Shankar, R. and Suhaib, M., (2008) ‘An ISM approach for modeling the enablers of flexible manufacturing system: the case for India’, International Journal of Production Research, Vol. 46, No.24, pp. 6883-6912.
[9] Ravi, V. and Shankar, R.(2005) ‘Analysis of interactions among the barriers of reverse logistics’, Journal of Technical Forecast & Social Change, Vol. 72, No.8, pp. 1011-1029.
[10] Sushil (2012) ‘Interpreting the interpretive structural model’, Global Journal of Flexible Systems Management, Vol. 13, No. 2, pp- 87-106.
[11] Parker, R. P., Wirth, A. (1999) ‘Manufacturing flexibility: measures and relationships’, European Journal of Operations Research, Vol. 118, No. 3, pp.429-449.
[12] Sarkis, J. (1997) ‘An empirical analysis of productivity and complexity for flexible manufacturing system’, International Journal of Production Economics, Vol. 48, pp.39–48.
[13] Groover, M.P. (2008) ‘Automation, production system and computer integrated manufacturing’, 3rd Edition, Upper Saddle River, N.J.: Prentice-Hall, London.
[14] Raj, T., Shankar, R. and Suhaib, M. (2007) ‘A review of some issues and identification of some barriers in the implementation of FMS’, International Journal of Flexible Manufacturing Systems, Vol. 19, No.1, pp.1-40.
[15] Saloman, D.P. and Beigel, J.E. (1984) ‘Assessing economic attractiveness of FMS applications in small batch manufacturing , International Journal of Industrial Engineering, pp. 88-96.
[16] Koren Y. and Shpitalni, M. (2011) ‘Design of reconfigurable manufacturing systems’, Journal of Manufacturing Systems, Vol. 29, No. 4, pp. 130-141.
[17] Chan, F.T.S. (2003) ‘Effects of dispatching and routing decisions on the performance of a flexible manufacturing system’, International Journal of Manufacturing Technology, Vol. 21, pp. 328- 338.
[18] Wadhwa, S., Rao, K.S. and Chan, F.T.S. (2005) ‘Flexibility- enabled lead- time reduction in flexible systems. International Journal of Production Research, Vol. 43, No.15, pp. 3131-3163.
[19] Gola A. and Swic A. (2012) ‘Directions of manufacturing systems, evolution from the flexible level point of view’, Innovations in Management and Production Engineering. Oficyna Wyd. Polskiego Towarzystwa zarzadzania produkcja, Opole: 226-238.
[20] Kaighobadi, Mehdi and Venkatesh, K. (1994) ‘FMS: an overview’, International Journal of Operations and Production Management, Vol.14, No. 4, pp-26-49.
[21] Bayazit, O.(2005) ‘Use of AHP in decision-making for flexible manufacturing systems’, Journal of Manufacturing Technology Management, Vol. 16, No.7, pp. 808-819.
[22] El-Tamimi, A.M., Abidi, M.H., Mian, S.H. and Aalam, J. (2012) ‘ Analysis of performance measures of flexible manufacturing system’, Journal of King Saud University- Engineering Services, Vol. 24, No.2, pp. 115-129.
[23] Singholi, A., Ali, M. and Sharma, C. (2013) ‘ Evaluating the effect of machine and routing flexibility on flexible manufacturing system performance’, International Journal of Services and Operations Management, Vol. 16, No. 2, pp- 240-261.
[24] Chan, F.T.S., Bhagwat, R.and Wadhwa, S. (2006) ‘Increase in flexibility: productive or counterproductive? A study on the physical and operating characteristics of a flexible manufacturing system’, International Journal of Production Research, Vol. 44, No. 7, pp.1431-1445.
[25] Singholi, A., Chhabra, D. and Ali, M. (2010) ‘Towards improving the performance of flexible manufacturing system: a case study’, Journal of Industrial Engineering and Management, Vol. 3, No.1, pp. 87-115.
[26] Keong, O.C., Ahmad, M.M.H.M., Sulaiman, N.I.S. and Ismail, M.Y. (2005) ‘Proposing a non traditional ordering methodology in achieving optimal flexibility with minimal inventory risk’, Asia Pacific Journal of Marketing and Logistics, Vol. 17, No. 2.
[27] Koren Y. (2010) ‘The global manufacturing revolution. Product-process-business integration & reconfigurable manufacturing’, Willey, New Yersey.
[28] Mahadevan, B. and Narendran, T. T. (1990) ‘Design of an automated guided vehicle-based material handling system for a flexible manufacturing system’, International Journal of Production Research, Vol. 28, No. 9, pp.1611–1622.
[29] Singh, S., Kulkarni, K., and Saroop, V. (2016) ‘Selection of material handling system for flexible manufacturing cell using hybrid multi attribute decision making approach: a case study’, International Journal of Latest Trends in Engineering and Technology, Vol. 6, No.3, pp. 361-366.
[30] Singh, M.D., Shankar, R., Narain, R. and Agarwal, A.(2003) ‘An interpretive structural modelling of knowledge management in engineering industries’, Journal of Advances in Management Research, Vol. 1, No.1, pp. 28-40.
[31] Warfield, J. N. (1974) ‘An interim look at uses of interpretive structural modeling’, Research Futures, Third Quarter, 1974.
[32] Saxena, A. and Seth, N. (2012) ‘Supply chain risk and security management: an interpretive structural modelling approach’, International. Journal of Logistics Economics and Globalisation, Vol. 4, Nos. 1/2, pp.117–132.
[33] Rao, R.V. (2007) ‘Decision making in manufacturing environment: using graph theory and fuzzy multiple attribute decision making methods’, Springer- Verlag, London.