Modelling and analysis of FMS productivity variables by ISM, SEM and GTMA approach

doi:10.1007/s11465-014-0309-7

Front. Mech. Eng.

2014, Vol. 9

Issue (3) : 218-232 https://doi.org/10.1007/s11465-014-0309-7

RESEARCH ARTICLE

Modelling and analysis of FMS productivity variables by ISM, SEM and GTMA approach

Vineet JAIN^1,^*(

),Tilak RAJ²

1. Department of Mechanical Engineering, Amity University Haryana, Gurgaon 122413, India
2. Department of Mechanical Engineering, YMCA University of Science and Technology, Faridabad 121006, India

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Abstract

Productivity has often been cited as a key factor in a flexible manufacturing system (FMS) performance, and actions to increase it are said to improve profitability and the wage earning capacity of employees. Improving productivity is seen as a key issue for survival and success in the long term of a manufacturing system. The purpose of this paper is to make a model and analysis of the productivity variables of FMS. This study was performed by different approaches viz. interpretive structural modelling (ISM), structural equation modelling (SEM), graph theory and matrix approach (GTMA) and a cross-sectional survey within manufacturing firms in India. ISM has been used to develop a model of productivity variables, and then it has been analyzed. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are powerful statistical techniques. CFA is carried by SEM. EFA is applied to extract the factors in FMS by the statistical package for social sciences (SPSS 20) software and confirming these factors by CFA through analysis of moment structures (AMOS 20) software. The twenty productivity variables are identified through literature and four factors extracted, which involves the productivity of FMS. The four factors are people, quality, machine and flexibility. SEM using AMOS 20 was used to perform the first order four-factor structures. GTMA is a multiple attribute decision making (MADM) methodology used to find intensity/quantification of productivity variables in an organization. The FMS productivity index has purposed to intensify the factors which affect FMS.

Keywords FMS ISM EFA SEM GTMA

Corresponding Author(s): Vineet JAIN

Online First Date: 02 September 2014 Issue Date: 10 October 2014

Cite this article:

Vineet JAIN,Tilak RAJ. Modelling and analysis of FMS productivity variables by ISM, SEM and GTMA approach[J]. Front. Mech. Eng., 2014, 9(3): 218-232.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-014-0309-7
https://academic.hep.com.cn/fme/EN/Y2014/V9/I3/218

Tab.1 Data of the responding companies

Tab.2 Inheritance of FMS factor

Tab.3 Values of interdependence of FMS factor

Tab.4 Structural self-interactive matrix

Tab.5 Initial reachability matrix

Tab.6 Final reachability matrix

Tab.7 Iterations (Level of Variable)

Tab.8 Conical matrix

Fig.1 An interpretive structural model showing the levels of FMS productivity variables

Tab.9 Extraction method: Principal Component Analysis, Rotation Method: Varimax with Kaiser Normalization with Reliability Statistics (EFA result)

Fig.2 Path diagram of SEM

Tab.10 Confirmatory factor analysis results

Tab.11 Discriminant Validity

Fig.3 Digraph for factors

Tab.12 Maximum and minimum values of the permanent function

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