Advertisement

Global Journal of Flexible Systems Management

, Volume 18, Issue 4, pp 331–351 | Cite as

Modified ISM/TISM Process with Simultaneous Transitivity Checks for Reducing Direct Pair Comparisons

Original Research

Abstract

Both interpretive structural modeling (ISM) and total interpretive structural modeling (TISM) are pair comparison methods to evolve hierarchical relationships among a set of elements. These methods help to convert ill-structured mental models into well-articulated models that act as base for conceptualization and theory building. One major challenge in applications of these methods is the number of pair comparisons to be made that increase exponentially with the increase in number of elements. Another challenge is the transitivity check on reachability matrix. This paper proposes a modified ISM/TISM process that addresses both these challenges by simultaneously carrying out transitivity checks along with the successive pair-wise comparisons. The pairs having transitive relationships in the process need not to be compared further. This reduces the expert-based pair comparisons drastically and provides the fully transitive reachability matrix in one go, thereby enabling easy implementation. It provides a complete flow chart for the same and is illustrated with already reported examples in past literature.

Keywords

ISM Pair comparisons Reachability matrix TISM Transitivity check 

References

  1. Agarwal, A., & Vrat, P. (2015). A TISM based bionic model of organizational excellence. Global Journal of Flexible Systems Management, 16(4), 361–376.CrossRefGoogle Scholar
  2. Agrawal, U., Mangla, A., & Sagar, M. (2016). Company-cause-customer: Interaction architecture. Global Journal of Flexible Systems Management, 17(3), 307–319.CrossRefGoogle Scholar
  3. Baetzgen, A., & Tropp, J. (2015). How can brand-owned media be managed? Exploring the managerial success factors of the new interrelation between brands and media. JMM International Journal on Media Management, 17(3), 135–155.CrossRefGoogle Scholar
  4. Balaji, M., & Arshinder, K. (2016). Modeling the causes of food wastage in Indian perishable food supply chain. Resources, Conservation and Recycling, 114, 153–167.CrossRefGoogle Scholar
  5. Baykasoğlu, A., & Gölcük, İ. (2017). Development of a two-phase structural model for evaluating ERP critical success factors along with a case study. Computers & Industrial Engineering, 106, 256–274.CrossRefGoogle Scholar
  6. Bishwas, S. K., & Sushil. (2016). LIFE: An integrated view of meta organizational process for vitality. Journal of Management Development, 35(6), 747–764.CrossRefGoogle Scholar
  7. Chatterjee, S., Kar, A. K., & Gupta, M. P. (2017). Critical success factors to establish 5G network in smart cities: Inputs for security and privacy. Journal of Global Information Management, 25(2), 15–37.CrossRefGoogle Scholar
  8. Dalvi, M. V., & Kant, R. (2017). Modelling supplier development enablers: An integrated ISM–FMICMAC approach. International Journal of Management Science and Engineering Management. doi: 10.1080/17509653.2017.1312581.Google Scholar
  9. Dubey, R., & Ali, S. S. (2014). Identification of flexible manufacturing system dimensions and their interrelationship using total interpretive structural modelling and fuzzy MICMAC analysis. Global Journal of Flexible Systems Management, 15(2), 131–143.CrossRefGoogle Scholar
  10. Dubey, R., Gunasekaran, A., & Chakrabarty, A. (2017a). Ubiquitous manufacturing: overview, framework and further research directions. International Journal of Computer Integrated Manufacturing, 30(4–5), 381–394.Google Scholar
  11. Dubey, R., Gunasekaran, A., Childe, S. J., Fosso Wamba, S., & Papadopoulos, T. (2016). Enablers of six sigma: Contextual framework and its empirical validation. Total Quality Management and Business Excellence, 27(11–12), 1346–1372.CrossRefGoogle Scholar
  12. Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S. J., Shibin, K. T., & Wamba, S. F. (2017b). Sustainable supply chain management: Framework and further research directions. Journal of Cleaner Production, 142, 1119–1130.CrossRefGoogle Scholar
  13. Dubey, R., Gunasekaran, A., Sushil, & Tripti, S. (2015). Building theory of sustainable manufacturing using total interpretive structural modelling. International Journal of Systems Science: Operations & Logistics, 2(4), 231–247.Google Scholar
  14. Faisal, M. N., Jabeen, F., Katsioloudes, I., & Katsioloudes, M. (2017). Strategic interventions to improve women entrepreneurship in GCC countries: A relationship modeling approach. Journal of Entrepreneurship in Emerging Economies, 9(2), 161–180.CrossRefGoogle Scholar
  15. Gardas, B. B., Raut, R. D., & Narkhede, B. E. (2017). A state-of the-art survey of interpretive structural modelling methodologies and applications. International Journal of Business Excellence, 11(4), 505–560.CrossRefGoogle Scholar
  16. Haleem, A., Sushil, Qadri, M. A., & Kumar, S. (2012). Analysis of critical success factors of world-class manufacturing practices: An application of interpretative structural modelling and interpretative ranking process. Production Planning & Control, 23(10–11), 722–734.CrossRefGoogle Scholar
  17. Iyengar, V., Pillai, S., Pednekar, J., & Abhyankar, M. (2017). Enablers for digital empowerment in technology using interpretive structural modeling (ISM) and MICMAC analysis. International Journal of Applied Business and Economic Research, 15(2), 161–176.Google Scholar
  18. Jain, V., & Raj, T. (2015). Modeling and analysis of FMS flexibility factors by TISM and fuzzy MICMAC. International Journal of Systems Assurance Engineering and Management, 6(3), 350–371.CrossRefGoogle Scholar
  19. Kedia, P. K., & Sushil. (2013). Total interpretive structural modelling of strategic technology management in automobile industry. In 2013 Proceedings of PICMET’13: technology management in the IT-driven services (PICMET) (pp. 62–71), IEEE.Google Scholar
  20. Khatwani, G., Singh, S. P., Trivedi, A., & Chauhan, A. (2015). Fuzzy-TISM: A fuzzy extension of TISM for group decision making. Global Journal of Flexible Systems Management, 16(1), 97–112.CrossRefGoogle Scholar
  21. Luo, Z., Dubey, R., Papadopoulos, T., Hazen, B., & Roubaud, D. (2017). Explaining environmental sustainability in supply chains using graph theory. Computational Economics. doi: 10.1007/s10614-017-9688-2.Google Scholar
  22. Madaan, J. K., & Choudhary, D. (2015). A flexible decision model for risk analysis in product recovery systems. Global Journal of Flexible Systems Management, 16(4), 313–329.CrossRefGoogle Scholar
  23. Mandal, A., & Deshmukh, S. G. (1994). Vendor selection using interpretive structural modelling (ISM). International Journal of Operations and Production Management, 14(6), 52–59.CrossRefGoogle Scholar
  24. Mangla, S. K., Kumar, P., & Barua, M. K. (2014). Flexible decision approach for analyzing performance of sustainable supply chains under risks/uncertainty. Global Journal of Flexible Systems Management, 15(2), 113–130.CrossRefGoogle Scholar
  25. Mani, V., Agrawal, R., & Sharma, V. (2016). Impediments to social sustainability adoption in the supply chain: An ISM and MICMAC analysis in Indian manufacturing industries. Global Journal of Flexible Systems Management, 17(2), 135–156.CrossRefGoogle Scholar
  26. Mohanty, M., & Shankar, R. (2017). Modelling uncertainty in sustainable integrated logistics using fuzzy-TISM. Transportation Research Part D: Transport and Environment, 53, 471–491.CrossRefGoogle Scholar
  27. Parkhi, S., Tamraparni, M., & Punjabi, L. (2016). Throughput accounting: An overview and framework. International Journal of Services and Operations Management, 25(1), 1–20.CrossRefGoogle Scholar
  28. Patri, R., & Suresh, M. (2017). Factors influencing lean implementation in healthcare organizations: An ISM approach. International Journal of Healthcare Management. doi: 10.1080/20479700.2017.1300380.Google Scholar
  29. Saxena, J. P., Sushil, & Vrat, P. (1990). Impact of indirect relationships in classification of variables: A MICMAC analysis for energy conservation. Systems Research (now named as Systems Research and Behavioral Science), 7(4), 245–253.Google Scholar
  30. Saxena, J. P., Sushil, & Vrat, P. (1992). Scenario building—A critical study of energy conservation in Indian cement industry. Technological Forecasting and Social Change, 41(2), 121–146.CrossRefGoogle Scholar
  31. Sharma, H. D., Gupta, A. D., & Sushil. (1995). The objectives of waste management in India: A futures inquiry. Technological Forecasting and Social Change, 48(3), 285–309.CrossRefGoogle Scholar
  32. Shibin, K. T., Gunasekaran, A., Papadopoulos, T., Dubey, R., Singh, M., & Wamba, S. F. (2016). Enablers and barriers of flexible green supply chain management: A total interpretive structural modeling approach. Global Journal of Flexible Systems Management, 17(2), 171–188.CrossRefGoogle Scholar
  33. Sindhwani, R., & Malhotra, V. (2017). A framework to enhance agile manufacturing system: A total interpretive structural modelling (TISM) approach. Benchmarking, 24(2), 467–487.CrossRefGoogle Scholar
  34. Singh, R. K., Garg, S. K., & Deshmukh, S. G. (2007). Interpretive structural modelling of factors for improving competitiveness of SMEs. International Journal of Productivity and Quality Management, 2(4), 423–440.CrossRefGoogle Scholar
  35. Singh, A. K., & Sushil. (2013). Modeling enablers of TQM to improve airline performance. International Journal of Productivity and Performance Management, 62(3), 250–275.CrossRefGoogle Scholar
  36. Srivastava, A. K., & Sushil. (2013). Modeling strategic performance factors for effective strategy execution. International Journal of Productivity and Performance Management, 62(6), 554–582.CrossRefGoogle Scholar
  37. Srivastava, A. K., & Sushil. (2014). Modelling drivers of adapt for effective strategy execution. Learning Organization, 21(6), 369–391.CrossRefGoogle Scholar
  38. Srivastava, A. K., & Sushil. (2015). Modeling organizational and information systems for effective strategy execution. Journal of Enterprise Information Management, 28(4), 556–578.CrossRefGoogle Scholar
  39. Sushil. (2012). Interpreting the interpretive structural model. Global Journal of Flexible Systems Management, 13(2), 87–106.CrossRefGoogle Scholar
  40. Sushil. (2015). Diverse shades of flexibility and agility in business. In  Sushil & G. Chroust (Eds.), Systemic flexibility and business agility, flexible systems management (pp. 3–19). New Delhi: Springer.Google Scholar
  41. Sushil. (2016). How to check correctness of total interpretive structural models? Annals of Operations Research. doi: 10.1007/s10479-016-2312-3.Google Scholar
  42. Sushil. (2017). Multi-criteria valuation of flexibility initiatives using integrated TISM–IRP with a big data framework. Production Planning & Control, 28(11–12), 999–1010.CrossRefGoogle Scholar
  43. Thakkar, J., Deshmukh, S. G., Gupta, A. D., & Shankar, R. (2006). Development of a balanced scorecard: An integrated approach of interpretive structural modeling (ISM) and analytic network process (ANP). International Journal of Productivity and Performance Management, 56(1), 25–59.CrossRefGoogle Scholar
  44. Thakkar, J., Kanda, A., & Deshmukh, S. G. (2008). Interpretive structural modeling (ISM) of IT-enablers for Indian manufacturing SMEs. Information Management & Computer Security, 16(2), 113–136.CrossRefGoogle Scholar
  45. Valmohammadi, C., & Dashti, S. (2016). Using interpretive structural modeling and fuzzy analytical process to identify and prioritize the interactive barriers of e-commerce implementation. Information & Management, 53(2), 157–168.CrossRefGoogle Scholar
  46. Venkatesh, V. G., Rathi, S., & Patwa, S. (2015). Analysis on supply chain risks in Indian apparel retail chains and proposal of risk prioritization model using interpretive structural modeling. Journal of Retailing and Consumer Services, 26, 153–167.CrossRefGoogle Scholar
  47. Warfield, J. N. (1974). Toward interpretation of complex structural models. IEEE Transactions on Systems, Man and Cybernetics, 4(5), 405–417.CrossRefGoogle Scholar
  48. Wasuja, S., Sagar, M., & Sushil. (2012). Cognitive bias in salespersons in specialty drug selling of pharmaceutical industry. International Journal of Pharmaceutical and Healthcare Marketing, 6(4), 310–335.CrossRefGoogle Scholar
  49. Whetten, D. A. (1989). What constitutes a theoretical contribution? Academy of Management Review, 14(4), 490–495.CrossRefGoogle Scholar
  50. Yadav, D. K., & Barve, A. (2016). Modeling Post-disaster challenges of humanitarian supply chains: A TISM approach. Global Journal of Flexible Systems Management, 17(3), 321–340.CrossRefGoogle Scholar
  51. Yadav, M., Rangnekar, S., & Bamel, U. (2016). Workplace flexibility dimensions as enablers of organizational citizenship behavior. Global Journal of Flexible Systems Management, 17(1), 41–56.CrossRefGoogle Scholar
  52. Yadav, N., & Sushil. (2014). Total interpretive structural modelling (TISM) of strategic performance management for Indian telecom service providers. International Journal of Productivity and Performance Management, 63(4), 421–445.CrossRefGoogle Scholar
  53. Yadav, N., Sushil, & Sagar, M. (2015). Modeling strategic performance management of automobile manufacturing enterprises: An Indian context. Journal of Modelling in Management, 10(2), 198–225.CrossRefGoogle Scholar

Copyright information

© Global Institute of Flexible Systems Management 2017

Authors and Affiliations

  1. 1.Department of Management StudiesIndian Institute of Technology DelhiNew DelhiIndia

Personalised recommendations