Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach
TL;DR: The purpose of this paper is to analyze the performance variables of flexible manufacturing system (FMS) by different approaches viz. interpretive structural modelling; Structural equation modelling (SEM); Graph Theory and Matrix Approach (GTMA) and a cross-sectional survey within manufacturing firms in India.
About: This article is published in International Journal of Production Economics.The article was published on 2016-01-01. It has received 78 citations till now. The article focuses on the topics: Exploratory factor analysis & Confirmatory factor analysis.
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TL;DR: Comparison of different parameters considered in the recent papers of modelling FMS has been provided in the form of Table, along with a clear vision related to those works that still need to be investigated.
Abstract: A flexible manufacturing system (FMS) due to its ability of being flexible in nature is concerned with automatic production of different parts in medium range. In short, it can be regarded as an au...
90 citations
Cites background from "Modeling and analysis of FMS perfor..."
...Jain and Raj (2016) studied the effect of different flexibility measures on the performance of FMS....
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TL;DR: The research outcomes reveal that strategic-based enablers are leading in nature, followed by environmental-based Enablers, which will help in planning and successful execution of environmental LSS in MSMEs.
83 citations
TL;DR: In this article, the authors analyze the interaction between distribution related challenges with a focus operational excellence and higher corporate green growth and sustainability viewpoints in food supply chains by considering the business example of four Indian dairy product based organizations using graph theory and matrix approach.
75 citations
TL;DR: In this paper, an Interpretive Structural Modeling (ISM) methodology was employed for establishing the mutual relationship between the determinants and the dependency of the global economy is significantly dependent on the Oil and Gas (OG).
69 citations
TL;DR: A TISM (Total Interpretive Structural Modelling) model has been developed to extract the key barriers influencing Health 4.0 adoption which will guide the healthcare managers and decision makers to explore the effect of each barrier on other barriers as well as the degree of relationships among them.
Abstract: In healthcare industry, the phenomenon of Industry 4.0 is popular as Health 4.0 where the modern technologies are integrated with available data along with the use of artificial intelligence. The main objective of this paper is to explore the barriers of Health 4.0 application in healthcare sector in India. Fifteen barriers which can affect the adoption of Health 4.0 in the Indian healthcare sector have been identified through extensive literature review and opinions of healthcare industry and academic experts. A TISM (Total Interpretive Structural Modelling) model has been developed to extract the key barriers influencing Health 4.0 adoption which will guide the healthcare managers and decision makers to explore the effect of each barrier on other barriers as well as the degree of relationships among them. The result shows that lack of top management support, exclusive and skilled workforce requirement, inadequate maintenance support systems and political support are the major barriers as they have strong driving power. Timely action taken by the management to remove these hurdles will not only reduce the cost of medical procedures but also improve the quality of treatment so that the true potential of Health 4.0 can be utilized.
49 citations
References
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TL;DR: In this paper, the statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined, and a drawback of the commonly applied chi square test, in additit...
Abstract: The statistical tests used in the analysis of structural equation models with unobservable variables and measurement error are examined. A drawback of the commonly applied chi square test, in addit...
56,555 citations
01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each.
Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations.
Table of Contents
1. Introduction.
I. PREPARING FOR A MULTIVARIATE ANALYSIS.
2. Examining Your Data.
3. Factor Analysis.
II. DEPENDENCE TECHNIQUES.
4. Multiple Regression.
5. Multiple Discriminant Analysis and Logistic Regression.
6. Multivariate Analysis of Variance.
7. Conjoint Analysis.
8. Canonical Correlation Analysis.
III. INTERDEPENDENCE TECHNIQUES.
9. Cluster Analysis.
10. Multidimensional Scaling.
IV. ADVANCED AND EMERGING TECHNIQUES.
11. Structural Equation Modeling.
12. Emerging Techniques in Multivariate Analysis.
Appendix A: Applications of Multivariate Data Analysis.
Index.
37,124 citations
TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.
Abstract: I Introduction 1 Introduction II Preparing For a MV Analysis 2 Examining Your Data 3 Factor Analysis III Dependence Techniques 4 Multiple Regression Analysis 5 Multiple Discriminate Analysis and Logistic Regression 6 Multivariate Analysis of Variance 7 Conjoint Analysis IV Interdependence Techniques 8 Cluster Analysis 9 Multidimensional Scaling and Correspondence Analysis V Moving Beyond the Basic Techniques 10 Structural Equation Modeling: Overview 10a Appendix -- SEM 11 CFA: Confirmatory Factor Analysis 11a Appendix -- CFA 12 SEM: Testing A Structural Model 12a Appendix -- SEM APPENDIX A Basic Stats
23,353 citations
Book•
21 Jul 2011
TL;DR: Structural Equation Models: The Basics using the EQS Program and testing for Construct Validity: The Multitrait-Multimethod Model and Change Over Time: The Latent Growth Curve Model.
Abstract: Psychology is a science that advances by leaps and bounds The impulse of new mathematical models along with the incorporation of computers to research has drawn a new reality with many methodological progresses that only a few people could imagine not too long ago Such progress has no doubt revolutionized the panorama of research in the behavioral sciences Structural Equation Models are a clear example of this Under this label are usually included a series of state-of-the-art multivariate statistical procedures that allow the researcher to test theoryguided hypotheses with clearly confi rmatory ends as well as to establish causal relations among variables Confi rmatory factor analysis, the study of measurement invariance, or the multitraitmultimethod models are some of the procedures that stem from this methodology In this sense, it would be diffi cult to fi nd a scientifi c journal that publishes empirical works in psychology that does not address some of these issues, so their current transcendence is undeniable The manual written by the Full Professor of the University of Ottawa, Barbara M Byrne, is a link in a series of books that address this topic Throughout her long academic trajectory, Professor Byrne developed interesting and popular work focused on bringing the researcher and the professional layman—and not so layman—closer to the diverse statistical programs available on the market for data analysis from the perspective of structural equation models (ie, LISREL, AMOS, EQS) (Byrne, 1998, 2001, 2006) Bearing this in mind, the main goal of this work is to introduce the reader to the basic concepts of this methodology, in a simple and entertaining way, avoiding mathematical technicisms and statistical jargon For this purpose, we used the statistical program Mplus 60 (Muthen & Muthen, 2007-2010), an extremely suggestive software that incorporates interesting applications The authoress provides a practical guide that leads the reader through illustrative examples of how to proceed step by step with the Mplus, from the initial specifi cations of the model to the interpretation of the output fi les On the one hand, we underline that the data used proceed from prior investigations and can be consulted in the Internet, offering the reader the possibility of practicing with them (http://wwwpsypresscom/sem-with-mplus/ datasets/); on the other hand, updating the information with novel and apt bibliographic references allows the reader to study in more depth the diverse topics that are presented in the manual, if he or she so desires The book consists of four sections, with a total of 12 chapters The fi rst section, Chapters 1 and 2, addresses introductory terms related to structural equation models and working with the Mplus program at a user-level The second unit focuses on data analysis with a single group In Chapter 3, the factor validity of the self-concept is tested by means of confi rmatory factor analysis In Chapter 4, the authoress performs a fi rst-order confi rmatory factor analysis, in which she examines the validity of the scores of the Maslach Burnout Inventory (MBI) in a sample of teachers In Chapter 5, the internal structure of the scores on the Beck Depression Inventory-II is analyzed by means of second-order confi rmatory factor analysis in a sample of Chinese adolescents In the next chapter, the complete model of structural equations is tested, and the authoress examines the causal relation established between diverse variables (ie, work climate, self-esteem, social support) and Burnout The third section of the manual is, in my opinion, the most interesting, not only because of the expansion of the study of measurement invariance in recent years but also because of the expansion it may possibly have in the future In this section, Professor Byrne goes into multigroup comparisons Specifi cally, in Chapter 7, she examines the factor equivalence of the MBI in two samples of teachers by means of the analysis of covariance structures In this chapter, she introduces relevant concepts, such as types of invariance (confi gural, metric, and strict) or the invariance of partial measurement In Chapter 8, she also analyzes measurement invariance, using for this purpose the analysis of mean and covariance structures This analysis, in comparison to the analysis of covariance structures, allows contrasting the latent means of two or more groups With this goal, she verifi es whether there is measurement invariance between the scores on the Self-description Questionnaire-I in Nigerian and Australian adolescents In Chapter 9, she proposes a complete model of structural equations in which she tests the causal structure through the procedure of cross validation Lastly, in the fourth section, she reveals three very interesting topics, that are also up-to-date and that, to some degree, go beyond the initial goal of the book, such as the multitrait-multimethod models, latent growth curves, and multilevel models Summing up, the work “Structural Equation Modeling with Mplus: Basic concepts, applications, and programming” is of enormous interest and utility for all professionals of psychology and related sciences who, without having exhaustive knowledge of the details of structural equation models, wish to test their hypothetical models by means of the Mplus program No doubt, this is a reference manual, a must-read that is accessible and that has a high degree of methodological rigor We hope that the readers
16,616 citations
Book•
01 Nov 2000TL;DR: In this article, the EQS program is used to test the factorial verifiability of a theoretical construct and its invariance to a Causal Structure using the First-Order CFA model.
Abstract: Contents: Part I: Introduction. Structural Equation Models: The Basics. Using the EQS Program. Part II: Single-Group Analyses. Application 1: Testing for the Factorial Validity of a Theoretical Construct (First-Order CFA Model). Application 2: Testing for the Factorial Validity of Scores From a Measuring Instrument (First-Order CFA Model). Application 3: Testing for the Factorial Validity of Scores from a Measuring Instrument (Second-Order CFA Model). Application 4: Testing for the Validity of a Causal Structure. Part III: Multiple-Group Analyses. Application 5: Testing for the Factorial Invariance of a Measuring Instrument. Application 6: Testing for the Invariance of a Causal Structure. Application 7: Testing for Latent Mean Differences (First-Order CFA Model). Application 8: Testing for Latent Mean Differences (Second-Order CFA Model). Part IV: Other Important Topics. Application 9: Testing for Construct Validity: The Multitrait-Multimethod Model. Application 10: Testing for Change Over Time: The Latent Growth Curve Model. Application 11: Testing for Within- and Between-Level Variance: The Multilevel Model.
13,439 citations