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Total Interpretive Structural Modeling (TISM) of the Enablers of a Flexible Control System for Industry

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Abstract

The incumbent trend in process industry is to deploy information and communication technology (ICT), enabled wired process control systems. Distributed control system, supervisory control and data acquisition systems with wireless open loop control systems are commonly used to facilitate the same. However, wireless closed loop control system is a flexible system, which is not yet been introduced in the process industry. The major theme of this research work is to model the enablers of implementing ICT enabled control system in the process industry based on their interrelationships, with the help of industrialists in an oil refinery in central Kerala. The relationships of enablers have been established effectively by interpretive structural modeling (ISM). However, the interpretation of links is comparatively weak in ISM. Qualitative criteria are often accompanied by obscurities and vagueness. To compensate for this, ISM is further modified by total ISM (TISM). TISM is a novel qualitative modeling technique that has been used by researchers in diverse fields of investigation. For this study, TISM is used to develop the performance model for the enablers of a flexible control system for industry. The structural model developed using this methodology helps to understand the interaction between the various elements of enablers. After the model is developed, it is further subjected to assessment by a different group of domain experts so as to enhance its validity.

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References

  • Attri, P., Dev, N., & Sharma, V. (2013). Interpretive structural modelling (ISM) approach: An overview. Research Journal of Management Sciences, 2(2), 3–8.

    Google Scholar 

  • Bidarian, S., Bidarian, S., & Davoudi, A. M. (2011). A model for application of ICT in the process of teaching and learning. Procedia: Social and Behavioral Sciences, 29, 1032–1041.

    Google Scholar 

  • Bohm, C. (2013). Cultural flexibility in ICT projects: A new perspective on managing diversity in project teams. Global Journal of Flexible Systems Management, 14(2), 115–122.

    Article  Google Scholar 

  • Dubey, R. S., & Ali, S. S. (2014). Identification of flexible manufacturing system dimensions and their interrelationship using total interpretive structural modeling and fuzzy MICMAC analysis. Global Journal of Flexible Systems Management, 15(2), 131–143.

  • Faizal, M. N., Banwet, D. K., & Shankar, R. (2006). Supply chain risk mitigation: Modeling the enablers. Business Process Management Journal, 12(4), 535–552.

    Article  Google Scholar 

  • Farris, D. R., & Sage, A. P. (1975). On the use of interpretive structural modelling for worth assessment. Computers and Electrical Engineering, 2(2/3), 149–174.

    Article  Google Scholar 

  • Hollenstein, H. (2004). Determinants of the adoption of ICT: An empirical analysis based on firm-level data for the Swiss business sector. Structural Change and Economic Dynamics, 15(3), 315–342. Retrieved January 15, 2014, from http://freewimaxinfo.com/point-to-point-wireless-networks.html.

  • Kumar, S., Luthra, S., & Haleem, A. (2013). Customer involvement in greening the supply chain: An interpretive structural modeling methodology. Journal of Industrial Engineering International, 9(6), 1–13.

    Article  Google Scholar 

  • Lepmets, M., Mesquida, A. L., Cater-Steel, A., Mas, A., & Ras, E. (2014). The evaluation of the IT service quality measurement framework in industry. Global Journal of Flexible Systems Management, 15(1), 39–57.

    Article  Google Scholar 

  • Mandal, A., & Deshmukh, S. G. (1994). Vendor selection using interpretive structural modeling (ISM). International Journal of Operations and Production Management, 14(6), 52–59.

    Article  Google Scholar 

  • Nasim, S. (2011). Total interpretive structural modeling of continuity and change forces in e-government. Journal of Enterprise Transformation, 1(2), 147–168.

    Article  Google Scholar 

  • Pramod, V. R., & Banwet, D. K. (2010). Interpretive structural modeling for understanding the inhibitors of a telecom service supply chain. In IEOM (Dhaka, Bangladesh), 9–10 January 2010.

  • Prasad, U. C., & Suri, R. K. (2011). Modeling of continuity and change forces in private higher technical education using total interpretive structural modeling (TISM). Global Journal of Flexible Systems Management, 12(3–4), 31–40.

    Google Scholar 

  • Sandbhor, S. S., & Botre, R. P. (2014). Applying total interpretive structural modeling to study factors affecting construction labour productivity. Australasian Journal of Construction Economics and Building, 14(1), 20–31.

    Article  Google Scholar 

  • Saxena, J. P., Sushil, & Vrat, P. (2006). Policy and strategy formulation: An application of flexible systems methodology. New Delhi: Global Institute of Flexible Systems Management, GIFT Publishing.

  • Sharma, B. P., Singh, M. D., & Neha, M. (2012). Knowledge sharing barriers: An approach of interpretive structural modeling. The IUP Journal of Knowledge Management, 10(3), 35–52.

    Google Scholar 

  • Singh, A. N., Picot, A., Kranz, J., Gupta, M. P., & Ojha, A. (2013). Information security management (ISM) Practices: Lessons from select cases from India and Germany. Global Journal of Flexible Systems Management, 14(4), 225–239.

    Article  Google Scholar 

  • Singh, A. K., & Sushil (2013). Modeling enablers of TQM to improve airline performance. International Journal of Productivity and Performance Management, 62(3), 250–275.

  • Srivastava, A. K., & Sushil (2013). Modeling strategic performance factors for effective strategic execution. International Journal of Productivity and Performance Management, 62(6), 354–582.

  • Stoshikj, M., Kryvinska, N., & Strauss, N. (2014). Efficient managing of complex programs with project management services. Global Journal of Flexible Systems Management, 15(1), 25–38.

    Article  Google Scholar 

  • Sushil (2005a). Interpretive matrix: A tool to aid interpretation of management in social research. Global Journal of Flexible System Management, 6(2), 27–30.

  • Sushil (2005b). A flexible strategy framework for managing continuity and change. International Journal of Global Business and Competitiveness, 1(1), 22–32.

  • Sushil (2009). Interpretive ranking process. Global Journal of Flexible System Management, 10(4), 1–10.

  • Sushil (2012). Interpreting the interpretive structural model. Global Journal of Flexible Systems Management, 13(2), 87–106.

  • Thakkar, J., Deshmugh, S. G., Gupta, A. D., & Shankar, R. (2007). An integrated approach of interpretive structural modeling and analytic network process. International Journal of Productivity and Performance Management, 56(1), 25–59.

    Article  Google Scholar 

  • Thompson, H. A. (2004). Wireless and Internet communications technologies for monitoring and control. Control Engineering Practice, 12(6), 781–791.

    Article  Google Scholar 

  • Tripathy, S., Sahu, S., & Ray, P. K. (2013). Interpretive structural modelling for critical success factors of R&D performance in Indian manufacturing firms. Journal of Modelling in Management, 8(2), 212–240.

  • Warfield, J. N. (1974a). A Science of Generic design: Managing complexity through systems design. Ames, IA: Iowa State University Press.

    Google Scholar 

  • Warfield, J. N. (1974b). Towards interpretation of complex structural models. IEEE Transactions: System, Man and Cybernetics, 4(5), 405–417.

    Google Scholar 

  • Wasuja, S., Sagar, M., & Sushil (2012). Cognitive bias in salespersons in speciality drug selling of pharmaceutical industry. International Journal of Pharmaceutical and Healthcare Marketing, 6(4), 310–335.

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Authors and Affiliations

  1. Mechanical Engineering Department, Karpagam University, Coimbatore, Tamil Nadu, India

    B. Jayalakshmi

  2. I C E Dept, NSS College of Engineering, Palakkad, Kerala, India

    B. Jayalakshmi

  3. Mechanical Department, NSS College of Engineering, Palakkad, Kerala, India

    V. R. Pramod

Authors
  1. B. Jayalakshmi
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  2. V. R. Pramod
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Correspondence to B. Jayalakshmi.

Appendices

Appendix I

Table 6 Interpretive logic-knowledgebase
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Appendix II

Table 7 Level I
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Table 8 Level II
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Table 9 Level III
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Table 10 Level IV
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Table 11 Level V
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Appendix III

Table 12 Interaction matrix
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Jayalakshmi, B., Pramod, V.R. Total Interpretive Structural Modeling (TISM) of the Enablers of a Flexible Control System for Industry. Glob J Flex Syst Manag 16, 63–85 (2015). https://doi.org/10.1007/s40171-014-0080-y

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  • DOI: https://doi.org/10.1007/s40171-014-0080-y

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