Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach
The purpose of this paper is to analyze the performance variables of flexible manufacturing system (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...
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Veröffentlicht in: | International journal of production economics 2016-01, Vol.171, p.84-96 |
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description | The purpose of this paper is to analyze the performance variables of flexible manufacturing system (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 hierarchical structure of performance variables, and to find the driving and the dependence power of the variables. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are powerful statistical techniques. By performing EFA, factor structure is placed. Whereas CFA verified the factor structure of a set of observed variables. CFA is carried by SEM statistical technique. 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 fifteen performance variables are identified through literature, and three factors extracted, which involves the performance of FMS. The three factors are Quality, Productivity and Flexibility. SEM using AMOS 20 was used to perform the first order three factor structure. GTMA is a Multiple Attribute Decision Making (MADM) Methodology used to find intensity/quantification of performance variables in an organization. The FMS Performance Index has purposed to intensify the factors which affect FMS. |
doi_str_mv | 10.1016/j.ijpe.2015.10.024 |
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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 hierarchical structure of performance variables, and to find the driving and the dependence power of the variables. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are powerful statistical techniques. By performing EFA, factor structure is placed. Whereas CFA verified the factor structure of a set of observed variables. CFA is carried by SEM statistical technique. 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 fifteen performance variables are identified through literature, and three factors extracted, which involves the performance of FMS. The three factors are Quality, Productivity and Flexibility. SEM using AMOS 20 was used to perform the first order three factor structure. GTMA is a Multiple Attribute Decision Making (MADM) Methodology used to find intensity/quantification of performance variables in an organization. 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The fifteen performance variables are identified through literature, and three factors extracted, which involves the performance of FMS. The three factors are Quality, Productivity and Flexibility. SEM using AMOS 20 was used to perform the first order three factor structure. GTMA is a Multiple Attribute Decision Making (MADM) Methodology used to find intensity/quantification of performance variables in an organization. The FMS Performance Index has purposed to intensify the factors which affect FMS.</description><subject>Discriminant analysis</subject><subject>EFA</subject><subject>Flexible manufacturing systems</subject><subject>FMS</subject><subject>GTMA</subject><subject>ISM</subject><subject>SEM</subject><subject>Software</subject><subject>Studies</subject><subject>Variables</subject><issn>0925-5273</issn><issn>1873-7579</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNp9kFFLwzAUhYMoOKd_wKeAr7YmTdsk4MsY2xRWFDafQ5reaMq21mQb7N-bOp99uFw4nHPv4UPonpKUElo-talre0gzQosopCTLL9CICs4SXnB5iUZEZkVSZJxdo5sQWkIIp0KM0HvVNbBxu0-sd00cvTkFF3Bn8bxa4R687fxW7wzgo_ZO1xsIuD7h11X1iFez6je1WFcTrPved9p83aIrqzcB7v72GH3MZ-vpS7J8W7xOJ8vE5ETuEw5WNwAiLw0lXAI1NRESrDXSMFFIWzQgWM44L0TNdK5LkZXCSKlJZpi0bIweznfj2-8DhL1qu4OP_YOiPOclkYTQ6MrOLuO7EDxY1Xu31f6kKFEDOdWqgZwayA1aJBdDz-cQxP5HB14F4yAyaJwHs1dN5_6L_wDGO3VJ</recordid><startdate>201601</startdate><enddate>201601</enddate><creator>Jain, Vineet</creator><creator>Raj, Tilak</creator><general>Elsevier B.V</general><general>Elsevier Sequoia S.A</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TA</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JG9</scope><scope>KR7</scope></search><sort><creationdate>201601</creationdate><title>Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach</title><author>Jain, Vineet ; Raj, Tilak</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c409t-7efadee846c1079e1cb089effc9c3859f5de83437758b3a4a68268c99a02c39f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Discriminant analysis</topic><topic>EFA</topic><topic>Flexible manufacturing systems</topic><topic>FMS</topic><topic>GTMA</topic><topic>ISM</topic><topic>SEM</topic><topic>Software</topic><topic>Studies</topic><topic>Variables</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jain, Vineet</creatorcontrib><creatorcontrib>Raj, Tilak</creatorcontrib><collection>CrossRef</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Materials Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>International journal of production economics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jain, Vineet</au><au>Raj, Tilak</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach</atitle><jtitle>International journal of production economics</jtitle><date>2016-01</date><risdate>2016</risdate><volume>171</volume><spage>84</spage><epage>96</epage><pages>84-96</pages><issn>0925-5273</issn><eissn>1873-7579</eissn><coden>IJPCEY</coden><abstract>The purpose of this paper is to analyze the performance variables of flexible manufacturing system (FMS). 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The fifteen performance variables are identified through literature, and three factors extracted, which involves the performance of FMS. The three factors are Quality, Productivity and Flexibility. SEM using AMOS 20 was used to perform the first order three factor structure. GTMA is a Multiple Attribute Decision Making (MADM) Methodology used to find intensity/quantification of performance variables in an organization. The FMS Performance Index has purposed to intensify the factors which affect FMS.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.ijpe.2015.10.024</doi><tpages>13</tpages></addata></record> |
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subjects | Discriminant analysis EFA Flexible manufacturing systems FMS GTMA ISM SEM Software Studies Variables |
title | Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach |
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