Skip to main content
SpringerLink
Log in

Modeling and analysis of FMS flexibility factors by TISM and fuzzy MICMAC

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

To increase the manufacturing flexibility, manufacturing organizations are looking at flexible manufacturing system (FMS) as a viable alternative to enhance their competitive edge. There are, however, some factors which affect the flexibility of FMS. Fifteen factors are identified from the literature and found their evaluation by interpretive structural modeling (ISM), exploratory factor analysis, confirmatory factor analysis and graph theory matrix approach. But, Interpretation of the mutual relationship of factors is comparatively weak in ISM. Thus, an upgraded version of ISM i.e. Total interpretive structural modeling (TISM) methodology is used to develop the model and the mutual relationship of factors is identified in the TISM. This paper is an application of TISM to interpret the mutual relationship with the ISM using the tool of interpretive matrix and leads to evolving the framework and find out driving and the dependence power of factors, using fuzzy MICMAC analysis. The result shows that use of reconfigurable machine tool, automation and flexible fixturing have strong driving power and weak dependence power and are at the lowest levels in hierarchy in the TISM model. Hence, superior performance of FMS can be achieved by improving the driving factors of flexibility.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Arya DS, Abbasi SA (2001) Identification and classification of key variables and their role in environmental impact assessment: methodology and software package intra. Environ Monit Assess 72:277–296. doi:10.1023/A:1012001326357

    Article  Google Scholar 

  • Bayazit O (2005) Use of AHP in decision-making for flexible manufacturing systems. J Manuf Technol Manag 16:808–819

    Article  Google Scholar 

  • Chan FTS (1999) Evaluations of operational control rules in scheduling a flexible manufacturing system. Robot Comput Integr Manuf 15:121–132

    Article  Google Scholar 

  • Chowdary B (2001) Flexibility and related issues in evaluation and selection of technological systems. Glob J Flex Syst Manag 2:11–20

    Google Scholar 

  • Dai JB, Lee NK (2012) Economic feasibility analysis of flexible material handling systems: a case study in the apparel industry. Int J Product Econ 136:28–36

    Article  Google Scholar 

  • Debata BR, Sree K, Patnaik B, Mahapatra SS (2013) Evaluating medical tourism enablers with interpretive structural modeling. Benchmarking 20:716–743

    Article  Google Scholar 

  • Dubey R, Ali SS (2014) Identification of flexible manufacturing system dimensions and their interrelationship using total interpretive structural modelling and fuzzy MICMAC analysis. Glob J Flex Syst Manag 15:131–143

    Article  Google Scholar 

  • Gorane S, Kant R (2013a) Modelling the SCM enablers: an integrated ISM-fuzzy MICMAC approach Asia Pacific. J Mark Logist 25:263–286

    Article  Google Scholar 

  • Gorane S, Kant R (2013b) Supply chain management: modelling the enablers using ISM and fuzzy MICMAC approach international. J Logist Syst Manag 16:147–166

    Google Scholar 

  • Groover MP (2006) Automation, production systems, and computer-integrated manufacturing, 2nd edn. Prentice Hall Press, Upper Saddle River

    Google Scholar 

  • Gultekin H (2012) Scheduling in flowshops with flexible operations: throughput optimization and benefits of flexibility. Int J Product Econ 140:900–911

    Article  Google Scholar 

  • Jain V, Raj T (2013a) Evaluating the variables affecting flexibility in FMS by exploratory and confirmatory factor analysis. Glob J Flex Syst Manag 14:181–193. doi:10.1007/s40171-013-0042-9

    Article  Google Scholar 

  • Jain V, Raj T (2013b) Evaluation of flexibility in FMS using SAW and WPM. Decis Sci Lett 2:223–230

    Article  Google Scholar 

  • Jain V, Raj T (2013c) Ranking of flexibility in flexible manufacturing system by using a combined multiple attribute decision making method. Glob J Flex Syst Manag 14:125–141. doi:10.1007/s40171-013-0038-5

    Article  Google Scholar 

  • Jain V, Raj T (2014a) Evaluation of flexibility in FMS by VIKOR methodology. Int J Ind Syst Eng 18:483–498

    Google Scholar 

  • Jain V, Raj T (2014b) Modelling and analysis of FMS productivity variables by ISM, SEM and GTMA approach. Front Mech Eng 9:218–232

    Article  Google Scholar 

  • Jain V, Raj T (2015a) Evaluating the intensity of variables affecting flexibility in FMS by graph theory and matrix approach. Int J Ind Syst Eng 19:137–154. doi:10.1504/IJISE.2015.067245

    Google Scholar 

  • Jain V, Raj T (2015b) A hybrid approach using ISM and modified TOPSIS for the evaluation of flexibility in FMS. Int J Ind Syst Eng 19:389–406

    Google Scholar 

  • Kaighobadi M, Venkatesh K (1994) Flexible manufacturing systems: an overview. International Journal of Operations & Production Management 14:26–49

    Article  Google Scholar 

  • Kandasamy WV, Smarandache F, Ilanthenral K (2007) Elementary fuzzy matrix theory and fuzzy models for social scientists. Infinite study. Automaton, Los Angeles

    Google Scholar 

  • Khan U, Haleem A (2012) Smart organisations: modelling of enablers using an integrated ISM and fuzzy–MICMAC approach. Int J Intell Enterp 1(3):248–269

    Article  Google Scholar 

  • Khurana M, Mishra P, Jain R, Singh A (2010) Modeling of information sharing enablers for building trust in Indian manufacturing industry: an integrated ISM and fuzzy MICMAC approach. Int J Eng Sci Technol 2:1651–1669

    Google Scholar 

  • Koren Y, Heisel U, Jovane F, Moriwaki T, Pritschow G, Ulsoy G, Van Brussel H (1999) Reconfigurable manufacturing systems. CIRP Ann Manuf Technol 48:527–540

    Article  Google Scholar 

  • Mangla SK, Kumar P, Barua MK (2014) Flexible decision approach for analysing performance of sustainable supply chains under risks/uncertainty. Glob J Flex Syst Manag 15:113–130

    Article  Google Scholar 

  • Nasim S (2011) Total interpretive structural modeling of continuity and change forces in e-government. J Enterp Transform 1:147–168. doi:10.1080/19488289.2011.579229

    Article  Google Scholar 

  • Nasim S, Sushil (2014) Flexible strategy framework for managing continuity and change in e-government. In: Sushil, Stohr EA (eds) The flexible enterprise. Flexible systems management. Springer, India, pp 47–66. doi:10.1007/978-81-322-1560-8_4

    Chapter  Google Scholar 

  • Nayak NC, Ray PK (2011) Flexibility adoption in Indian manufacturing firms: a current perspective. Int J Process Syst Eng 1:266–287

    Article  Google Scholar 

  • Prasad UC, Suri R (2011) Modeling of continuity and change forces in private higher technical education using total interpretive structural modeling (TISM). Glob J Flex Syst Manag 12:31–40

    Google Scholar 

  • Primrose PL (1996) Do companies need to measure their production flexibility? Int J Oper Product Manag 16:4–11

    Article  Google Scholar 

  • Qureshi MN, Kumar D, Kumar P (2008) An integrated model to identify and classify the key criteria and their role in the assessment of 3PL services providers. Asia Pac J Mark Logist 20:227–249. doi:10.1108/13555850810864579

    Article  Google Scholar 

  • Raj T, Shankar R, Suhaib M (2008) An ISM approach for modelling the enablers of flexible manufacturing system: the case for India. IJPR 46:6883–6912

    Article  Google Scholar 

  • Raj T, Attri R, Jain V (2012) Modelling the factors affecting flexibility in FMS. Int J Ind Syst Eng 11:350–374

    Google Scholar 

  • Rao RV (2006) Machine group selection in a flexible manufacturing cell using digraph and matrix methods. Int J Ind Syst Eng 1:502–518

    Google Scholar 

  • Sagar M, Bora S, Gangwal A, Gupta P, Kumar A, Agarwal A (2013) Factors affecting customer loyalty in cloud computing: a customer defection-centric view to develop a void-in-customer loyalty amplification model. Glob J Flex Syst Manag 14:143–156

    Article  Google Scholar 

  • Sandbhor SS, Botre RP (2014) Applying total interpretive structural modeling to study factors affecting construction labour productivity. Australas J Constr Econ Build 14:20–31

    Article  Google Scholar 

  • Shafiq SI, Faheem M, Ali M (2010) Effect of scheduling and manufacturing flexibility on the performance of FMS. Glob J Flex Syst Manag 11:21–38

    Google Scholar 

  • Singh AK, Sushil (2013) Modeling enablers of TQM to improve airline performance. International Journal of Productivity and Performance Management 62:250–275. doi:10.1108/17410401311309177

    Article  Google Scholar 

  • Srivastava AK, Sushil (2013) Modeling strategic performance factors for effective strategy execution. Int J Product Perform Manag 62:554–582. doi:10.1108/IJPPM-11-2012-0121

    Article  Google Scholar 

  • Srivastava A, Sushil (2014) Adapt: a critical pillar of strategy execution process. In: Nandakumar MK, Jharkharia S, Nair AS (eds) Organisational flexibility and competitiveness. Flexible systems management. Springer, India, pp 9–24. doi:10.1007/978-81-322-1668-1_2

    Chapter  Google Scholar 

  • Sujono S, Lashkari R (2007) A multi-objective model of operation allocation and material handling system selection in FMS design. Int J Product Econ 105:116–133

    Article  Google Scholar 

  • Sushil (2005) Interpretive matrix: a tool to aid interpretation of management and social research. Glob J Flex Syst Manag 6:27–30

    Google Scholar 

  • Sushil (2012) Interpreting the interpretive structural model. Glob J Flex Syst Manag 13:87–106. doi:10.1007/s40171-012-0008-3

    Article  Google Scholar 

  • Wasuja S, Sagar M (2012) Cognitive bias in salespersons in specialty drug selling of pharmaceutical industry. Int J Pharm Healthc Mark 6:310–335

    Article  Google Scholar 

  • Yadav N (2014) Total interpretive structural modelling (TISM) of strategic performance management for Indian telecom service providers. Int J Product Perform Manag 63:421–445. doi:10.1108/IJPPM-04-2013-0081

    Article  Google Scholar 

Download references

Acknowledgments

The authors acknowledge the anonymous referee of this paper for his/her valuable suggestions, which have helped to improve the quality of this paper.

Appendix

See Exhibits 9, 10 and 11.

Exhibit 9 Interpretive logic—knowledge base
Full size table
Exhibit 10 Binary direct reachability matrix
Full size table
Exhibit 11 Possibility of numerical value of the reachability
Full size table

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Amity University Gurgaon, Gurgaon, 122413, India

    Vineet Jain

  2. Department of Mechanical Engineering, YMCA University of Science and Technology, Faridabad, India

    Tilak Raj

Authors
  1. Vineet Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tilak Raj
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vineet Jain.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jain, V., Raj, T. Modeling and analysis of FMS flexibility factors by TISM and fuzzy MICMAC. Int J Syst Assur Eng Manag 6, 350–371 (2015). https://doi.org/10.1007/s13198-015-0368-0

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-015-0368-0

Keywords

Search

Navigation