A STUDY ON THE RELATIONSHIP AMONG SUPPLY CHAIN MANAGEMENT COMPONENTS, SUPPLY CHAIN PERFORMANCE AND ORGANIZATIONAL PERFORMANCE OF MANUFACTURING INDUSTRIES IN UNION TERRITORY OF PUDUCHERRY
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A STUDY ON THE RELATIONSHIP AMONG SUPPLY CHAIN MANAGEMENT COMPONENTS, SUPPLY CHAIN PERFORMANCE AND ORGANIZATIONAL PERFORMANCE OF MANUFACTURING INDUSTRIES IN UNION TERRITORY OF PUDUCHERRY

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A STUDY ON THE RELATIONSHIP AMONG SUPPLY CHAIN MANAGEMENT COMPONENTS, SUPPLY CHAIN PERFORMANCE AND ORGANIZATIONAL PERFORMANCE OF MANUFACTURING INDUSTRIES IN UNION TERRITORY OF PUDUCHERRY A STUDY ON THE RELATIONSHIP AMONG SUPPLY CHAIN MANAGEMENT COMPONENTS, SUPPLY CHAIN PERFORMANCE AND ORGANIZATIONAL PERFORMANCE OF MANUFACTURING INDUSTRIES IN UNION TERRITORY OF PUDUCHERRY Presentation Transcript

  • A STUDY ON THE RELATIONSHIP AMONG SUPPLY CHAIN MANAGEMENT COMPONENTS, SUPPLY CHAIN PERFORMANCE AND ORGANIZATIONAL PERFORMANCE OF MANUFACTURING INDUSTRIES IN UNION TERRITORY OF PUDUCHERRY C. GANESH KUMAR Research Scholar Department of Management Studies Pondicherry University Mail: gcganeshkumar@gmail.com 1
  • CONTENTS 1. Introduction 2. Review of Literature and Conceptual Framework 3. Research Methodology 4. Results and Discussions 4.1 Profile of Manufacturing Industries 4.2 Dimension Level Analysis 4.2.1 Supply Chain Concerns 4.2.3 Supply Chain Competence 4.2.4 Supply Chain Practices 4.2.5 Supply Chain Performance 4.2.6 Organizational Performance 4.3 Casual Model and Hypotheses Testing 5. Summary of Findings 6. Conclusion
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  • 1. 1 Introduction Today’s competition is moving from “among organizations” to “between supply Chains” (Koh et al., 2006) So there is a need for business executives and managers to have a knowledge of essential components of supply chain management and the impact of those components on the organizational performance . This knowledge will help the manager to give emphasis on those components to improve the productivity and efficiency of the organization (Chow et al., 2006). Thus, the following research question are explored in this research work: What is the impact of important components of SCM on the performance of the supply chain per se and also on the organizational performance? 4
  • 1.2 Challenges and Opportunities of supply chain in India Country Ratio of logistics cost with GDP (In %) Japan 8.7 United States 8.5 Korea 16.5 India 12.3 Taxation structure drives firm’s location decisions Poor state of logistics infrastructure –Both Warehouse and Transportation are in the hand of unorganized sector. Large number of customers at the bottom of the economic pyramid. 70 % of population are in Rural 37 % People are below poverty line Purchasing Power Parity(PPP) is $ 4.4 trillion, third in the world by IMF Amul, the Shakti project of Uniliver and the Dabbawalas of Mumbai are cases in point where Indian firms have come up with unique solutions to supply chain challenges in India. Firms like Asian Paints and Marico industries have achieved higher levels of profitability and growth than their competitors because of their superior supply chain capabilities 5
  • 1.3 Objectives of the Study • To study the relationship between supply chain management components and supply chain performance of manufacturing enterprises. • To study the impact of supply chain management components and supply chain performance on organizational performance of manufacturing enterprises 6
  • 2. REVIEW OF LITERATURE S.no 1 Authors Chow et al(2008) Objectives To study the association of supply chain management components and organizational performance through structural equation modeling Variables and Constructs used Supply chain concerns, Supply chain competence and organizational performance Methodology Findings Perceptions of middle-line managers in the US and Taiwan Supply chain competencies have positive effects on organizational performance. and Supply chain practices and competencies are significantly associated in both the US and Taiwan. 7
  • Cont… S.no Authors Objectives Variables and Constructs used 2 Koh et al (2007) To study the impact of supply chain management practices on performance of SMEs in Turkey SCM practices, Operational performance, organizational performance 3 Tan(2006) To investigates the contemporary practices and concerns of supply chain management and relate it to firm’s performance in United States Supply Chain Management Practices, Supply Chain Management Concerns and performance measure Methodology Findings Data collected using a self-administered questionnaire in the manufacture industries within the city of Istanbul in Turkey High levels of SCM practices have a high level of operational performance and High level of SCM practices have high level of organizational performance. Empirical survey in US, The supply chain The lack of consensus practices affected regarding a valid cross overall product industry measure of quality and organizational customer service performance, in this study levels. was operational zed by senior management’s perceptions of a firm’s performance in comparison to that of major competitors. 8
  • Cont… S.no Authors Objectives Variables and Constructs used Methodology Findings 4 Rohit Bhatnagar and Amrik S. Sohal(2008) Impact of location decisions on the competitiveness of global supply chains. Qualitative factors in plant location, Supply chain uncertainty, Manufacturing practices, Supply chain performance measures Empirical survey in developed country, regression analysis The impact of the location decision framework comprising location factors, supply chain uncertainty, and manufacturing practices, is outlined for a variety of performance measures 5 C.-C. HSU et (2007) To study the supply chain management practices mediate the relationship between operations capability and firm performance. Operations capability and supply chain management practices, Total quality management, Just-in-time capability and firm performance Survey instrument and data collection, structural equation modeling Direct influence of operations capability and supply chain management practices on firm performance and on the need to examine operations capability in a broader, supply chain context. 9
  • Cont… S.no Authors Objectives Variables and Constructs used To empirically tested the SCM Practices, SCM –IS related inhibiting factors and causal links among operational performance supply chain management (SCM) and information system s (IS) practices, SCM –IS related inhibiting factors and operational performance 6 Erkan Bayraktar et (2009) 7 Suhong Li To Develop and SCM practices and et al (2005) validation of a Performance outcomes measurement instrument for studying supply chain management practices Methodology Data collected using a selfadministered questionnaire , a sample of 203 manufacturing SMEs operating within the greater metropolitan area of Istanbul in Turkey Scale development, Empirical scale refinement and validation, Assessment of construct validities Findings SCM and IS practices positively and significantly influenced the operational performance conceptualize and develop measures of SCM practices and a parsimonious measurement instrument. 10
  • Cont… S.no Authors Objectives 8 Michael Tracey et al (2005) To empirically test the impact of supplychain management (SCM) capabilities on business performance 9 VijayR. Kannana To study the linkage and Keah Choon of Just in time, total Tanb (2004) quality management, and supply chain Practices and its impact on business performance Variables and Constructs used Methodology Findings Outside-in capabilities, Inside-out Data collected using capabilities, Spanning capabilities and a questionnaire , a organizational performance sample of 555 manufacturing in US Just in time, Quality management, Supply chain management practices and business performance This study found both direct and indirect effects of outside-in, inside-out, and spanning capabilities on the perceived product value, customer loyalty, market performance, and financial performance Empirical survey of linkages exist between 556 manufacturing in JIT, TQM, and SCM. US While some companies may understand the inherent relationships between the three and actively exploit their synergy, those that do not maybe inadvertently achieving the benefits of synergy 11
  • Cont… S.no Authors Objectives Variables and Constructs used Methodology Findings 10 Chinho Lin et al (2004) The impact of supply chain quality management on organizational performance Quality management practices, Supplier participation and Organizational performance SEM was used to empirically test the Proposed hypotheses. organizational performance can be optimized when the organization considers its suppliers as important trading partners and members of the value chain. 11 Keah-Choon Tan et al (1999) To determine quality management, supply base management, and customer relations practices can impact corporate Performance Total quality management practices, Supply base management practices, Customer relations practices and Performance measures Performance measures Questions were designed using a seven point Likert scale, sample is 313 This study support this notion and confirm that all three major components of a supply chain, suppliers, manufacturers, and customers, must be effectively integrated in order to achieve financial and growth objectives 12
  • Cont… S.no Authors Objectives 12 Archie Lockamy III and Kevin McCormack(20 04) To study the relationship between supply-chain management practices and supply chain performance 13 Suhong Li et al (2004) To study the impact of supply chain management practices on competitive advantage and organizational performance Variables and Constructs used Methodology Findings Supply-chain management practices and supply chain performance Data collected using a questionnaire , a sample of 55 manufacturing in US Supply chain management practices , competitive advantage and organizational performance Instrument development methods for SCM practices include four phases: (1) item generation, (2) prepilot study, (3) pilot study, and (4) large-scale data analysis. Collaboration variables were found to have a direct impact on SC performance in the PLAN, SOURCE, and MAKE areas of the SCOR Model do organizations with high levels of SCM practices have high levels of competitive advantage; (2) do organizations with high level of SCM practices have high levels of organizational performance; 13
  • Cont… S.no Authors Objectives Variables and Constructs used Methodology Findings 14 Inda Sukati et al (2010) To study of supply chain management practices: an empirical Investigation on consumer goods industry in malaysia. Supply chain Management practices, Supply Chain Responsiveness and Competitive advantage Data collected using a questionnaire , a sample of200 consumer goods industry in malaysia 15 Chin S. Ou et al (2012) To examine the relationships among supply chain management (SCM) practices and their impacts on firm financial and nonfinancial performance Supply chain management (SCM) practices and firm performance Data collected using a questionnaire , manufacturing industry in Taiwan supply chain management practices is term of strategic supplier partnership, customer relationship and information sharing are related to supply chain responsiveness in term of operation system responsiveness, logistic process responsiveness and supplier network responsiveness. external customerfirm-supplier relation management positively impacts firm internal contextual factors, which in turn have positive effects on firm performance. 14
  • Cont… S.no 18 Authors Wai Peng Wong et al (2011) Objectives To investigate how supply chain management (SCM) practices and knowledge management (KM) capabilities affect firm performance Variables and Constructs used Supply chain management (SCM) practices and knowledge management (KM) capabilities and firm performance 19 Veera Pandiyan Kaliani Sundram et al (2011) To explore the effects of different dimensions of supply chain management practices (SCMP) on supply chain performance (SCP) in the electronics industry in Malaysia Supply chain management (SCM) practices and supply chain performance Methodology three-phase statistical analysis which comprised phase one (convergent validity, reliability, and discriminant validity), phase two (mediated regression analysis) and phase three (path analysis) was used to analyze the data The study employed the quantitative method where convenience sampling and selfadministrated survey questionnaires were sent to 125 electronics firms in Malaysia. The research framework was tested using variance-based structural equation model, the partial least squares (PLS) method. Findings he implementation of SCM practices will interact with KM capabilities to influence firm performance. The empirical results of PLS indicate that six of the seven dimensions of SCMP have a significant positive effect on SCP. Furthermore, agreed vision and goals shows a greater influence than other dimensions of SCMP. 15
  • Cont… S.no Authors Objectives Variables and Constructs used 20 Sang M. Lee et al (2011) To examine Supply chain (SC) innovation supply chain (SC) and organisational performance innovation for improving organisational performance in the healthcare industry 21 Alexander Ellinger et al (2012) The relationship between supply chain management (SCM) competency and firm performance Supply chain management (SCM) competency and firm performance Methodology The proposed research model and hypotheses were tested using structural equation modeling based on data collected from 243 hospitals. SCM competency is assessed with data from the expert opinion element of Gartner Supply Chain Group (formerly AMR Research)’s Supply Chain Top 25 rankings; Findings The results of the study support that organisational performance is positively associated with constructs of each SC innovation factor. Firms recognized by peers and experts for superior SCM competency exhibit higher levels of customer satisfaction and shareholder value than their respective industry averages 16
  • Cont… S.no 22 Authors Objectives R. Glenn Richey To examine Jr et al(2009) moderators impacting supply chain integration barriers 23 Lori S. Cook et al (2010) OTHER REVIEW To examine the relationships between specific supply chain practices and organizational performance Variables and Constructs used Supply chain integration and firm performance Supply chain practices and organizational performance Methodology survey was developed to measure levels of supply chain integration drivers, barriers to supply chain integration, and firm performance. The measures were validated using EFA, and the responses analyzed using multiple regression regression analysis and the relative weights method to analyze a set of survey data from respondents within the nonacademic, North American membership of the Institute of Supply Management. Findings barriers to supply chain integration can actually increase the firm’s ability to achieve firm performance as the firm is required to make greater efforts to overcome those barriers and develop effective supply chain linkages. The results show that the supply chain role for a company makes a difference in terms of the specific supply chain practices that lead to better performance. 17
  • 2.1 Research Gap Different supply chain components are studied separately There are studies on impact of supply chain components on Organizational performance There are studies on the linkage supply chain performance and Organizational performance There is no study on impact of supply chain components on supply chain performance and in turn its impact on organizational performance. 18
  • 2.2 Conceptual Framework 2.4 HYPOTHESES H1 Supply chain competencies and supply chain concerns are associated. H2 Supply chain practices and supply chain competencies are associated. H3 Supply chain practices and supply chain concerns are associated. H4 The level of supply chain concerns positively influences the degree of supply chain performance 19
  • Cont… H5 Hypotheses The level of supply chain competence positively influences the degree of supply chain performance H6 The level of supply chain practice positively influences the degree of supply chain performance. H7 The level of supply chain concerns positively influences the degree of organizational performance. H8 The level of supply chain competence positively influences the degree of organizational performance H9 The level of supply chain practice positively influences the degree of organizational performance H10 The level of supply chain performance positively influences the degree of organizational performance
  • 3.RESEARCH METHODOLOGY Research design- Descriptive Nature of data :Supply chain concerns, competence, practices, performance, Organizational performance and Profile of manufacturing industries. Source of data- Primary data Data collection instrument : Structured Questionnaire 3.1 Sampling Design Sample unit = Manufacturing Industries in Union Territory of Puducherry. Sample technique = Simple random sampling Sample frame: Dept of Industry and commerce Sample size = 255 (Israel, 2009) Where, Z=1.96 , σ = 8.12, e = 1 = 253.29 21
  • 3.2 Research tools used : . Simple Mean, Chi-square test, Independent Sample T-Test, Analysis of Variance (ANOVA), Factor Analysis, Cluster Analysis, Discriminate Analysis, Correspondence Analysis, Canonical correlation, Structural Equation Modeling (SEM)- Confirmatory Factor Analysis (CFI) and Path or Structural Model. 3.3 Software packagesSPSS, LISREL, Microsoft-Excel and STATA 22
  • 3.4 Development and Purification of the Questionnaire Comprehensive review of Literature Items were generated PHASE I (PRE PILOT STUDY) • CONTENT VALIDITY Interview with Practitioners and Academicians Based on their feedback, redundancies and ambiguousness were removed from the questionnaire 23
  • Pilot Study with 30 sample Initial Reliability •Reliability, Assumptions PHASE and II Construct validity Final Questionnaire Final survey Final Reliability 1. Normality 2. Homogeneity Checking Assumptions 3. Multicollinearity 4. Linearity 1. Convergent Validity 2. Discriminate Validity 3. Uni dimensionality Assessment of construct validity 24
  • 3.5 Reliability Test for Data Collection Instrument S.no Variables Cronbach’s- Alpha value Pilot Study 1 Supply Chain Concerns 0.6863 2 Supply Chain Competence 0.8447 3 Supply Chain practices 0.6990 4 Supply Chain Performance 0.7524 5 Organizational Performance 0.7234 25
  • 3.6 Checking Assumptions Normality, Homogeneity, Multicollinearity and Linearity 26
  • 3.7 Normality CON Normal Parametersa Mean Std. Deviation Most Extreme Differences PRA PER OP 255 N COM 255 255 255 255 2.9708 3.3521 3.2062 3.3034 3.2162 0.69022 0.55976 0.65503 0.60684 0.70241 Absolute 0.069 0.066 0.066 0.059 0.061 Positive 0.069 0.066 0.066 0.059 0.054 Negative -0.067 -0.053 -0.041 -0.038 -0.061 Kolmogorov-Smirnov Z 1.099 1.061 1.046 .947 .979 Asymp. Sig. (2-tailed) 0.179 0.210 0.224 0.332 0.294 27
  • 3.8 Homogeneity Constructs Levene Statistic df1 df2 Sig. CON 1.952 11 243 0.054 COM 1.204 11 243 0.285 PRA 1.502 11 243 0.131 PER 0.956 11 243 0.488 OP 1.449 11 243 0.152 28
  • 3.9 Multicollinearity Unstandardized Coefficients Model Std. Error (Constant) 0.171 0.284 CON 0.188 0.055 COM 0.236 PRA PER 1 B Standardized Coefficients Beta Collinearity Statistics t Sig. Tolerance VIF .603 0.547 0.184 3.420 0.001 0.919 1.088 0.074 0.188 3.167 0.002 0.760 1.316 0.150 0.066 0.140 2.278 0.024 0.707 1.415 0.368 0.069 0.318 5.362 0.000 0.759 1.318 a. Dependent Variable: OP 29
  • 3.10 Linearity Model Summary Equation R Square F df1 df2 Sig. Linear 0.035 6.438 1 253 0.002 Logarithmic 0.016 4.068 1 253 0.024 Inverse 0.008 2.087 1 253 0.150 Quadratic 0.022 6.859 2 252 0.034 Cubic 0.017 5.035 3 251 0.021 Compound 0.027 6.925 1 253 0.021 Power 0.019 4.873 1 253 0.042 S 0.011 2.911 1 253 0.089 Growth 0.023 6.925 1 253 0.022 Exponential 0.024 6.925 1 253 0.035 Logistic 0.024 6.925 1 253 0.045 30
  • 3.11 Construct Validity Convergent Validity Discriminate Validity Unidimensional validity 31
  • 3.12 Assessment of Composite Reliability CONSTRUCT INDICATORS RELIABILITY (α) CONSTRUCT RELIABILIY (ρc) Supply Chain Concerns 9 0.7153 0.75 Supply Chain Competence Supply Chain practices 14 0.8595 0.84 12 0.7458 0.73 Supply Chain Performance Organizational Performance 8 0.7694 0.75 8 0.7854 0.75 32
  • 3.13 Assessment of Unidimensionality and Convergent Validity CONSTRUCT INDICATORS X2(DF) p-VALUE Goodness of Fit RMR AVE 0.052 0.47 0.054 0.46 0.080 0.55 0.041 0.54 0.042 0.49 Index (GFI) Supply Chain Concerns 9 81.49(27) 0.000 Supply Chain Competence 14 206.97(77) 0.000 Supply Chain Management 12 353.58(54) 0.000 0.93 0.90 0.81 practices Supply Chain Performance 8 50.20(20) 0.002 Organizational Performance 8 71.86(20) 0.000 0.93 0.93 33
  • 3.14 Discriminant Validity CONSTRUCT VE Organizational Performance versus Supply Chain Performance 0.595 Organizational Performance versus Supply Chain practices SQUARE CORRELATION 0.59 0.5429 0.4489 Organizational Performance versus Supply Chain Competence 0.53 0.2601 Organizational Performance versus Supply Chain concern 0.46 0.3844 Supply Chain Performance versus Supply Chain Competence 0.45 0.5476 Supply Chain Performance versus Supply Chain concern 0.51 0.1521 Supply Chain Performance versus Supply Chain Practices 0.48 0.4489 Supply Chain Practices versus Supply Chain concern 0.58 0.5476 Supply Chain Practices versus Supply Chain Competence 0.47 0.1521 Supply Chain concern versus Supply Chain Competence 0.51 0.1521 34
  • 4. RESULTS AND DISCUSSION 4. 1Profile of Manufacturing Industries Questionnaire CAPITAL INVESTED Capital Invested (in Rs.) Frequency Percent Less than 50 Lakhs 87 34.1 50 Lakhs to 1 Crore 73 28.6 1 Crore to 50 Crores 60 23.5 More than 50 Crores 35 13.7 255 100.00 Total POSITION IN SUPPLY CHAIN Position Frequency Percent Raw Material Manufacturer 45 17.6 Sub product/Assemble Manufacturer 90 35.3 Final Product Manufacturer 76 29.8 Assemble and Distribution 44 17.3 255 100.00 Total 35
  • DATA ANALYSIS PROCESS SC_CON SC_COM Confirmatory Factor Analysis(CFA) Path Analysis Factor Analysis Cluster Analysis Discriminate Analysis SC_PRA Demographical SC_PER Demographical ANOVA and T-test Canonical Correlation OP Chi-Square and Correspondence analysis
  • 4.2 Dimension Wise Analysis – Supply Chain Concerns Very Low 1 1 2 3 4 5 6 7 8 9 Low Moderate 2 3 High Very High 4 5 Lack of sophisticated information system 1 2 3 4 5 Lack of ability in managing Supply chain inventories 1 2 3 4 5 Lack of cooperation among supply chain members 1 2 3 4 5 Lack of trust among supply chain members 1 2 3 4 5 Lack of interest among your suppliers or customers 1 2 3 4 5 Competition from other supply chains 1 2 3 4 5 Your firm’s lack of leverage within your supply chain 1 2 3 4 5 Your suppliers’ geographical distance 1 2 3 4 5 Your customers’ geographical distance 1 2 3 4 5 37
  • 4.2.1 Priorities of Competition Oriented Concerns SUPPLY CHAIN CONCERN Mean Value Rank Competition from other supply chains 3.27 I Lack of sophisticated information system 3.14 II Customers’ geographical distance 3.04 III Lack of interest among suppliers or customers 3.01 IV Suppliers’ geographical distance 2.96 V Firm’s lack of leverage within its supply chain 2.88 VI Lack of ability in managing Supply chain inventories 2.87 VII Lack of trust among supply chain members 2.79 VIII Lack of cooperation among supply chain members 2.77 IX 38
  • 4.2.2 Factorisation of Supply Chain Concerns Kaiser-Meyer-Olkin (KMO)Measure of Sampling Adequacy. Bartlett's Test of Sphericity Approx. Chi-Square 0.773 Cronbach’s alpha 445.538 0.758 DF 36 Sig. 0.000 Variance Explained by Factor Initial Eigen values % of Variance Cumulative % 34.964 34.964 12.379 47.343 11.388 58.730 9.108 67.839 Compon ent 1 2 3 4 Eigen 3.147 1.114 1.025 0.820 5 0.755 8.385 76.224 6 0.668 7.417 83.641 7 0.563 6.260 89.901 8 0.525 5.835 95.736 9 0.384 4.264 Rotation Sums of Squared Loadings Total % of Variance Cumulative % 2.352 26.134 26.134 1.545 17.168 43.302 1.389 15.429 58.730 100.000 39
  • Rotated Component Matrix Sl.No Competition Oriented Concerns Component 1 1 Lack of trust among supply chain members 2 Lack of ability in managing Supply chain inventories 3 Lack of cooperation among supply chain members 4 Lack of sophisticated information system 5 Your firm’s lack of leverage within your supply chain 6 Your customers’ geographical distance 7 Your suppliers’ geographical distance 8 Competition from other supply chains 9 Lack of interest among your suppliers or customers 2 3 0.758 0.725 Supply Chain Coherence 0.699 0.590 0.547 0.822 Geographical Proximity 0.753 0.878 Competition 0.532 40
  • 4.2.3 Segmentation of Supply Chain Concerns FINAL CLUSTER CENTERS Supply Chain Concerns Supply Chain Coherence Geographical Proximity Competition Average Cluster 1 2 3 2.55(III) 3.51(I) 2.58(II) 1.88(III) 3.68(I) 3.61(II) 2.80(II) 3.96(I) 2.58(III) 2.41 3.71 2.91 Low Supply Cluster name and number of Units in each cluster Chain Concerned Units-93 High Supply Chain Concerned Units-89 Moderate Supply Chain Concerned Units-73 41
  • 4.2.4 Testing Suitability of Segmentation using Discriminating Analysis 42
  • 4.2.5 Association between Business Demographic Variables and Supply Chain Concerns Segmentation Demographical Variables Nature of Industry Type of Industry Number of Employees Total Capital Invested Supply Chain Position Side of Supply Chain Type of Goods Produced Type of Business Organization Type of Ownership Type of Listing Kind of Manufacturing Manufacturing Pattern Type of process Annual Turnover Market Coverage Area of Market Business years Software Usage P-Value is > 0.05 Chi-Square value 16.070 22.283 22.763 9.823 11.830 0.829 4.287 7.858 10.112 24.059 6.596 17.811 5.275 11.606 9.686 21.180 1.468 7.332 Sig. Value Significance or not 0.003 0.443 0.012 0.132 0.066 0.661 0.117 0.249 0.120 0.001 0.159 0.007 0.509 0.312 0.046 0.007 0.962 0.026 Significant Not Significant Significant Not Significant Not Significant Not Significant Not Significant Not Significant Not Significant Significant Not Significant Significant Not Significant Not Significant Significant Significant Not Significant Significant 43
  • 4.2.6 Nature of Industry and Supply Chain Concerns Correspondence Analysis 44
  • 4.2.7 Relationship between Nature of Industry and Supply Chain Concerns Supply Chain Concerns Supply Chain Coherence F 3.466 Sig. 0.033 Geographical Proximity Competition 7.936 8.802 0.000 0.000 Relationship between nature of industry and supply chain coherence Nature of Industry Medium Scale Large Scale Small Scale N 1 2.7426 2.8435 94 46 115 2 3.0313 Relationship between nature of industry and geographical proximity Nature of Industry Medium Scale Small Scale Large Scale N 94 115 46 1 2.6702 2 3.1609 3.2826 Relationship between nature of industry and competition Nature of Industry Medium Scale Large Scale Small Scale N 94 46 115 1 2.8989 2.9891 2 3.3957 45
  • 4.2.8 Canonical correlation between selected demographical variables and supply chain Concerns Coef. Std. Err. t P>|t| [95% Conf. Interval] u1 coh_con geo_con comp_con . 2 14 4 7 5 4 - . 1 27 9 9 3 2 - 1 . 10 7 2 3 1 . 2 8 88 0 6 1 . 2 0 93 9 8 7 . 2 4 51 2 8 8 0.74 -0.61 -4.52 0.458 0.542 0.000 -. 3 5 4 2 8 4 2 -. 5 4 0 3 7 2 1 -1 . 5 8 9 9 7 5 .783235 .2843856 -. 6 2 4 4 8 7 5 emply ind_nature listing manf_pattern mark_cover mark_area software . 3 69 0 4 2 7 . 9 87 0 7 2 2 . 2 30 0 0 8 5 .1 4 3 8 8 . 3 01 4 7 9 8 - . 8 20 7 2 5 2 - . 1 66 4 3 1 8 . 1 9 20 9 7 8 . 3 6 78 7 4 2 . 2 0 83 3 0 1 . 2 0 61 7 1 6 . 3 6 33 6 4 9 . 2 65 0 6 7 . 5 4 66 7 1 1 1.92 2.68 1.10 0.70 0.83 -3.10 -0.30 0.056 0.008 0.271 0.486 0.407 0.002 0.761 -. 0 0 9 2 6 4 6 .2626001 -. 1 8 0 2 6 5 8 -. 2 6 2 1 4 3 5 -.414112 -1 . 3 4 2 7 3 4 -1 . 2 4 3 0 1 7 .7473501 1.711544 .6402828 .5499035 1.017072 -. 2 9 8 7 1 6 1 .9101536 coh_con geo_con comp_con . 4 40 0 7 7 2 . 8 38 3 3 4 7 - . 4 53 5 2 9 1 . 3 1 36 5 9 8 . 2 2 74 1 8 9 . 2 6 62 2 3 8 1.40 3.69 -1.70 0.162 0.000 0.090 -. 1 7 7 6 2 7 9 .3904679 -. 9 7 7 8 1 6 3 1.057782 1.286201 .070758 emply ind_nature listing manf_pattern mark_cover mark_area software . 4 83 8 4 3 4 - . 8 23 0 7 6 8 . 3 56 4 2 5 5 . 0 39 9 3 4 3 . 3 41 2 6 2 1 . 3 84 6 4 8 1 . 2 49 8 6 0 2 . 2 0 86 2 9 1 . 3 9 95 3 2 2 . 2 2 62 5 8 2 . 2 23 9 1 4 . 3 9 46 3 4 8 . 2 8 78 7 7 8 . 5 9 37 1 5 8 2.32 -2.06 1.58 0.18 0.86 1.34 0.42 0.021 0.040 0.116 0.859 0.388 0.183 0.674 .0729801 -1 . 6 0 9 8 9 5 -. 0 8 9 1 5 5 7 -. 4 0 1 0 3 0 2 -. 4 3 5 9 1 1 1 -. 1 8 2 2 8 3 3 -. 9 1 9 3 7 2 5 .8947066 -. 0 3 6 2 5 9 1 .8020066 .4808989 1.118435 .9515794 1.419093 coh_con geo_con comp_con 1 . 32 7 9 2 3 - . 5 77 7 6 1 2 - . 1 06 5 5 0 9 . 4 4 29 0 4 4 . 3 2 11 2 7 6 . 3 7 59 2 2 2 3.00 -1.80 -0.28 0.003 0.073 0.777 .4556904 -1 . 2 1 0 1 7 3 -. 8 4 6 8 7 2 3 2.200156 .0546506 .6337706 emply ind_nature listing manf_pattern mark_cover mark_area software . 5 64 8 5 4 6 - . 8 30 5 5 3 2 - . 3 79 1 7 3 1 . 3 19 7 4 2 3 - . 0 65 0 7 2 1 - . 2 77 2 7 5 1 . 8 50 1 0 1 7 . 2 9 45 9 5 4 . 5 6 41 6 0 8 . 3 1 94 8 8 7 . 3 1 61 7 8 6 . 5 5 72 4 5 5 . 4 0 64 9 8 8 . 8 3 83 5 8 4 1.92 -1.47 -1.19 1.01 -0.12 -0.68 1.01 0.056 0.142 0.236 0.313 0.907 0.496 0.312 -. 0 1 5 3 0 6 3 -1 . 9 4 1 5 8 2 -1 . 0 0 8 3 5 7 -. 3 0 2 9 2 3 2 -1 . 1 6 2 4 8 2 -1 . 0 7 7 8 1 2 -. 8 0 0 9 1 7 4 1.145015 .2804755 .2500113 .9424077 1.032338 .5232622 2.501121 v1 u2 v2 u3 v3 (Standard Canonical 0 . 2 9 55 Tests of errors estimated conditionally) correlations: 0.2739 0 .1 9 7 7 significance Wilks' lambda Pillai's trace Lawley-Hotelling trace Roy's largest root of all canonical correlations Statistic . 8 11 2 . 2 0 1 44 2 . 2 1 7 47 3 . 0 9 5 6 76 4 e = exact, df1 21 21 21 7 a = df2 704.058 741 731 247 approximate, F 2.5343 2.5399 2.5234 3.3760 u = upper Prob>F 0.0002 0.0002 0.0002 0.0019 bound on 46 F a a a u
  • 5.1Summary of Factor, Cluster and Discriminant Analysis Results Constructs Supply Chain concerns Supply Chain Competence Supply chain Practices Supply Chain Performance Factors Analysis Coherence Discriminant analysis Z1 = 0.915 * Geographical Geographical proximity High supply chain concerned units Proximity, Z2 = 0.732 * Competition + Competition Moderate supply chain concerned 0.467*Supply Chain Coherence. units Design Effectiveness Low supply chain competence units Z1 = 0.941* Design Effectiveness, Quality and Services High supply chain competence units Z2 = 0.850 * Operations and Distribution + 0.498* Quality and Operations and Distribution Moderate supply chain competence Service. units Procurement Moderate supply chain practices Z1 = .817* Procurement Practices units Z2 = 0.790 * Strategic Planning Strategic planning and lean Partnership practices units and Lean Practices + 0.704* Close Close partnership High supply chain practices units Partnership Practices. Lead Time and Inventory Responsiveness Organizational Performance Cluster analysis Low supply chain concerned units Financial Performance Marketing Performance Low supply chain performance units Moderate supply chain performance units High supply chain performance units Average performance units High performance units Z1 =0.861* Responsiveness Z2 = 0.9.7* Lead Time and Inventory Performance Z = 0.794* Financial Performance + 0.785* Marketing Performance 47
  • 5.2 Chi-Square Analysis Results Sl.no Variable Supply Chain Concerns Supply Chain Competence Supply Chain Practices Supply Chain Organizational Performance Performance 1 Type of Industry Not Sig Not Sig Sig Sig Not Sig 2 Number of Employees Sig Sig Not Sig Not Sig 3 Total Capital Invested Sig Not Sig Sig Sig Not Sig Sig 4 Supply Chain Position Not Sig Not Sig Not Sig Not Sig Not Sig 5 Nature of Industry Sig Not Sig Sig Sig Sig 6 Side of Supply Chain Not Sig Not Sig Not Sig Not Sig Not Sig 7 Type of Goods Produced Not Sig Not Sig Not Sig Not Sig Not Sig 8 Business Organization Not Sig Sig Sig Not Sig Sig 9 Type of Ownership Not Sig Not Sig Not Sig Not Sig Not Sig 10 Type of Listing Sig Sig Not Sig Not Sig Not Sig 11 Kind of Manufacturing Not Sig Not Sig Not Sig Not Sig Not Sig 12 Manufacturing Pattern Sig Not Sig Not Sig Not Sig Not Sig 13 Type of process Not Sig Not Sig Sig Not Sig Not Sig 14 Annual Turnover Not Sig Sig Not Sig Not Sig Sig 15 Market Coverage Sig Not Sig Sig Not Sig Sig 16 Area of Market Sig Sig Sig Not Sig Sig 17 Business years Not Sig Not Sig Not Sig Not Sig Not Sig 18 Software Usage Sig Sig Sig Not Sig Sig * Sig- Significant at 0.05 level,* Not Sig- Not Significant 48
  • 5.3 Summary of Correspondence Analysis Results 5.3.1 Supply Chain Concerns Variables Number of Employees Nature of Industry Type of Listing Market Coverage Area of Market Highly Concerns More Than 1200 Small scale Not Listed Both Southern Region Moderate Concerns 101-300 Large scale Listed in India and Abroad International Entire India Low Concerns Less than 100 Medium Scale Listed In India Domestic Within Pondicherry 5.3.2 Supply Chain Competence Variables Highly Competence Moderate Competence Low Competence 301-600 1 crores to 50 crores Public Limited Less 100 Less than 50 Lakhs Private Limited 101-300 More than 50 crores Partnership Type of Listing Listed in India and abroad Listed in India Not listed Annual Sales 1 crore to 3 crores 50 Lakhs to 1 crores 6 crores to 10 crores Area of Market Entire India Within Pondicherry Southern Region Number of Employees Total capital Invested Type of Business Organization 49
  • 5.3.3 Supply Chain practices Variables Type of Industry Number of Employees Total capital Invested Nature of Industry Business Organization Type of Process Market Coverage Area of Market Electronics 601-900 Metal 101-300 Partnership Practice Food Less than 100 1crore to 50 crores 50 lakhs to 1 crore More than 50 crores Large Scale Public Limited Medium Scale Partnership Small Scale Sole proprietor Continuous Both Southern region Batch International Entire India Job order Domestic Pondicherry Highly Practice Moderate Practice 5.3.4 Supply Chain Performance Variables Type of Industry Nature of Industry Highly Performance Moderate Performance Low Performance Building Materials Automobile Pharmaceutical Large Scale Small Scale Medium Scale 50
  • 5.4 SUMMARY OF ANOVA ANALYSIS RESULTS 51
  • 5.5 Summary of Canonical Correlation Analysis Results Constructs Supply chain concerns Supply chain competence Supply chain practices Supply chain performance Organizational performance Factors variables Profile variables Coherence Capital, Industry, Geographical proximity Organization, sales, market coverage and Competition area Quality and Services Employees, Capital, Design Effectiveness Organization, listing, Operations and sales, Mark cover and Distribution software Quality and Services Industry, Employees, Design Effectiveness capital, Industry, Operations and Organization, Process, Distribution Mark cover, Mark area and software Type of Industry and Lead Time and Inventory Nature of Industry Responsiveness Financial Performance Capital, Industry, Organization, sales, Marketing Performance sales and sales Canonical Shared correlation Variance s 0.30 18 % 0.27 0.20 0 .28 0.22 0.14 14 % 0 .40 0.25 0.17 24 % 0.08 12 % 0.07 0 .39 17 % 0.15 52
  • 4.3 Causal Analysis and Proposed Hypotheses Testing 53
  • 4.3 Sub- Confirmatory Factory Analysis(CFA) Or Measurement Model 4.3.1 CFA of Supply Chain Concerns 54
  • 4.3.2 CFA of Supply Chain Practices 55
  • 4.3.3 CFA of Supply Chain Performance 56
  • 4.3.4 CFA of Supply chain competence 57
  • 4.3.5 CFA of Organizational Performance 58
  • 4.3.6 OVERALL CONFIRMATORY FACTOR ANALYSIS (CFA) OR MEASUREMENT MODEL 59
  • 4.3.7 Goodness of Fit Statistics MODEL Study model NORMED CHISQURE ( 2/DF ) PVALUE GFI AGFI CFI NFI RMESA 1.00 0.070 2.25 0.00 0.87 0.85 1.00 Less than 3 >0.05 0.8-0.9 0.8-0.9 0.8-0.9 0.8-0.9 Recommended value Goodness of fit indices 1 Absolute fit indices Normed chi-square- (<4) 2 Goodness of fit Index (GFI)(>0.9) 3 Root mean square error of Approximation (RMSEA) – (0.03-0.08) Incremental fit indices Less than 0.080 Parsimony fit indices Normed Fit Index (NFI)(>0.9) Comparative Fit Index (CFI)(>0.9) Parsimony Goodness of fit Index (PGFI)- (>0.9) Parsimony Normed fit Index (PNFI)- (>0.9) Tucker Lewis Index (TLI)(>0.9) Adjusted Normed fit Index (ANFI)- (>0.9) 60
  • 4.3.8 Results of Overall CFA (Measurement Model) 61
  • 4.3.9 STRUCTURAL MODEL OR PATH ANALYSIS 4.3.9.1 Relationships between Supply Chain Concerns, Supply Chain Performance and Organizational Performance -sub conceptual model-1 62
  • 4.3.9.2 Relationships between Supply Chain Competence, Supply Chain Performance and Organizational Performance -sub conceptual model-2 63
  • 4.3.9.3 Relationships between Supply Chain Practices, Supply Chain Performance and Organizational Performance -sub conceptual model-3 64
  • 4.3.9.4 Relationships between Supply Chain Concerns, Supply Chain Competence, Supply Chain Practices and Supply Chain Performance -sub conceptual model-4 65
  • 4.3.9.5 Relationships between Supply Chain Concerns, Supply Chain Competence, Supply Chain Practices and Organizational Performance -sub conceptual model-5 66
  • 4.3.9.6 Relationships between Supply Chain Concerns, Supply Chain Competence, Supply Chain Performance and Organizational Performance- -sub conceptual model-6 67
  • 4.3.9.7 Relationships between Supply Chain Competences, Supply Chain Practices, Supply Chain Performance and Organizational Performance- -sub conceptual model-7 68
  • 4.3.9.8 Relationships between Supply Chain Concerns, Supply Chain Practices, Supply Chain Performance and Organizational Performance - -sub conceptual model-8 69
  • 4.3.10 Cumulative Structural Equations Model STRUCTURAL EQUATIONS OF CONCEPTUAL MODEL Supply chain performance = 0.075*concerns + 0.35*competence + 0.34* practices Organizational Performance = 0.45*performance + 0.24*concerns+ 0.17* competence + 0.090*practices 70
  • 71
  • 4. 3.11 Total, Direct and Indirect Path Analysis Results of Structural Model 72
  • 4. 3.12 Result of Overall Structural Model Independent Variable Dependent Variable Covariance/ Beta Standard Error T-value P-value R2 - S C concerns S C competence S C practices S C concerns S C competence S C competence S C practices S C concerns SC Performance SC Performance 0.08 0.54 0.31 0.075 0.35 0.02 0.02 0.02 0.017 0.023 4.59 28.91 16.8 4.40 14.96 0.00 0.00 0.00 0.00 0.00 S C practices SC Performance 0.34 0.024 14.27 0.00 0.24 0.019 12.30 0.00 0.17 0.028 5.98 0.00 S C concerns Organizational Performance S C competence Organizational Performance S C practices Organizational Performance S C performance Organizational Performance 0.39 0.50 0.090 0.029 3.15 0.00 0.45 0.075 5.97 0.00 73
  • 4.3.13 Findings of Structural Equation Model 74
  • 6. CONCLUSION • Supply chain management components of supply chain concerns, supply chain competence and supply chain practices are associated with each other. • Supply chain components have positive impact on supply chain performance and organizational performance of manufacturing enterprises. • Supply chain performance exerts strong positive impact on organizational performance of manufacturing enterprises. • Supply chain performance strongly influences the organizational performance of the manufacturing firms, while the supply chain performance of the manufacturing firms is strongly influenced by supply chain competence and supply chain practices of the manufacturing firms. • Hence, manufacturing firms concentrating on improving their supply chain competence and supply chain practices can significantly improve their performance as the former impacts the latter indirectly through their impact on supply chain performance. Hence, managers should concentrate on improving the supply chain competence and supply chain practice to enhance the efficiency of their firms. 75
  • 6.1 Contributions of this Research Consistent constructs with high level of reliability and validity Confirms the relationship between SCM and organizational performance. This research provides valuable inputs to strengthen academic thoughts and arguments regarding theory and proposition, methods of approaching the research issues. Valuable inputs to management practitioners 76
  • 6.2 Limitations of the Study and Directions for Future Research The study covers only manufacturing enterprises and does not concentrate on the business firms engaged in services sector The concept of supply chain management is highly complex and exhaustive. Supply chain issues may vary among firms engaged in the manufacture of different products. The study has collected data from a single executive from each manufacturing enterprise. 77
  • THANK YOU… 78