Application of ISM in the Manufacturing Sector

Application of ISM in the Manufacturing Sector

Abhinav Pal, Jisha Rajendran, Aastha Behl
Copyright: © 2020 |Pages: 18
DOI: 10.4018/978-1-7998-2216-5.ch002
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This chapter discusses the application of Interpretive Structural Modelling (ISM) in the manufacturing sector. Authors provide a brief about ISM and its usage in the current scenario, and a comprehensive literature review that sheds light on the methodology used over the years in the application of ISM. The chapter assesses the method's advantages and disadvantages. It introduces the technique of ISM in the manufacturing sector, and discusses the applications of ISM in the manufacturing sector along the review of literature.
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In the approach of ISM, there is a systematic application of various elements of graph theory in such a manner that conceptual, computational and theoretical aspects are fused together so as to explain certain complex pattern of an intricate system of various interlinked variables(Ravi, Shankar, & Tiwari, 2005; Janes, 1988; Singh, Garg, & Deshmukh, 2007; Singh, Shankar, Narain, & Agarwal, 2003). The idea behind the use of ISM stems from the desire to make use of structured and logical thinking in order to tackle a complex problem under consideration. The methodology is deemed as interpretive in the sense because the collective judgement of a group decides the various linkages that exist among the different variables of the system, it is structured on the various mutual relationships that exist among elements which leads to the determination of the holistic and the overall structure on the basis of all the elements that exists in the system(Bolaños, Fontela, Nenclares, & Pastor, 2005; Faisal, 2010; Warfield and Staley, 1996; Dubey and Ali, 2014). ISM as a modelling technique intends to determine and impose an order and direction on the relationships of various elements of the system. ISM is majorly a group exercise but can be used individually as well (Mangle, Phillips, Pitts, & Laver-Bradbury, 2014); Azevedo, Carvalho, & Cruz-Machado, 2013).

Key Terms in this Chapter

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS): TOPSIS is an analysis method used for deriving multi-criteria decisions and was developed in 1981 by Yoon and Ching-Lai Hwang. It is dependent on the fact that the alternative that is chosen is should ideally have the least possible geometric distance from the PIS, otherwise known as the positive ideal solution and should be the farthest from negative ideal solution (NIS).

Analytic Hierarchy Process (AHP): AHP is a structured technique that is used for the organisation and analysis of multifaceted decisions, depending on the psychology and mathematics. It helps in the representation of the most precise approach for the quantification of weights of each criteria. Here, the experiences of each expert is used for the estimation of the relative scale of the parameters by using the method of pair-wise correlation. Each respondent is expected to strike a comparison between the relative importance of the two items in a questionnaire designed specially for this purpose.

Structural Equation Modelling (SEM): SEM is a multivariate statistical analysis technique that is usually utilised for the analysis of structural relationships. It is usually regarded as an amalgamation of multiple regression and factor analysis and is used to assess the structural relationships that tend to exist between the latent constructs and the variables used to measure these latent constructs.

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