1. INTRODUCTION
Ministry of MSME is implementing Micro and Small Enterprises - Cluster Development Programme (MSE-CDP) for development of clusters and to adopt Cluster Development Approach for MSMEs (Porter, CompetItive Advantage, Creating and Sustaining Superior Performance, 1985) (Porter, The Competitive Advantage of Nations, 1990). The objective of the scheme is (i) to enhance the sustainability, competitiveness and growth of MSEs by addressing common issues such as improvement of technology, skills and quality, market access etc (UNIDO, 2020).(ii) To build capacity of MSEs and Startups for common supportive action through integration of self-help groups, consortia, district industry associations etc. (iii) To create / upgrade infrastructural facilities in the new / existing Industrial Areas / Clusters of MSEs. (iv) To set up Common Facility Centres in Industrial area (for testing, training centre, raw material depot, effluent treatment, complementing production process). (v) Promotion of green and sustainable manufacturing technology for the clusters so as to enable units switch to sustainable and green production processes and products. (India, 2022)
The funding pattern as per the new guidelines for establishing Common Facility Centre (CFC) and Infrastructure Development (ID) is as below:
Source: https://cluster.dcmsme.gov.in
The funding pattern for the projects in the aspirational districts, NER, Hill station and islands would be as follows:
Source: https://cluster.dcmsme.gov.in
2. TECHNICAL SURVEY
The approved and completed Common Facility Centre (CFC) and Infrastructure Development (ID) projects in India are given in figure 1. (India, 2022) (Bhaskaran, Technical Efficiency of Automotive Industry Cluster in Chennai, 2012).
Figure 1: CFC and ID projects in India
3. OBJECTIVES OF THE STUDY
The objective is to study
4. METHODOLOGY OF STUDY
The primary data were collected from Officials of Development Commissioner, Ministry of MSME, Government of India for various periods and secondary data from various journals. The input variables are CFC projects approved(Ca) and CFC projects completed (Cc), Total CFC projects (Ct),ID projects approved (Ia), ID Projects Completed (Ic), Total ID Projects (It), Output Variables are Budget utilized (Rs. in crore) RE (Bre), Budget utilized (Rs. in crore) BE( Bbe), Budget utilized (Rs. in crore) Exp. (Bexp) (India, 2022). The data were analysed using Compound Annual Growth Rate (CAGR), Descriptive Analysis, Correlation Analysis, Trend Analysis, Regression Analysis, and Structural Equation Modelling Business Analytics Performance of MSE-CDP like Diagnostic Analytics, Descriptive Analytics, Predictive Analytics, Inferenatial Analytics, Prescriptive Analytics and Decision Analytics are studied.
5. TECHNICAL ANALYSIS
5.1 Diagnostic Analysis
5.1.1 Physical and Financial Performance.
The Physical Performance and Financial Performance of MSE-CDP are given in figure 1 and 2.
Figure 1: Physical Performance
Figure 2: Financial Performance
The model source of funds for Paramakudi Engineering Cluster under MSE CDP is given in figure 3.
Source: Ministry of MSME, GOI (Bhaskaran, The Resource Management in Chennai Heavy Engineering Cluster, 2021)
Figure 3: Source of Funds
The Map Indicating Various Linkages and Actors that exist in a Cluster is given in figure 4. (NI-MSME, 2006).
5.2. Descriptive Analysis
The descriptive analysis is given in table 1.
Source: Computed data From the table 1 it is evident that there is increase in all variables.
5.2.1 Compound Annual Growth Rate (CAGR)
The Compound Annual Growth Rate (CAGR) is given in figure 5.
Source: Computed data
Figure 5: Compound Annual Growth Rate (CAGR)
From figure 5 it is found that the CAGR of all variables are increasing especially CFC completed, ID completed and Total CFC competed are in higher trend.
5.3 Predictive Analysis
5.3.1 Trend Analysis
The trend analysis were calculated and is given in table 2.
Source: Computed data
5.4 Prescriptive Analysis
5.4.1 Correlation Analysis
The Correlation Analysis is given in Table 3.
Source: Computed data
5.4.2 Prescriptive Analysis: Multiple Regression Analysis
The Regression Equations / Models are given in Table 4.
Source: Computed data
Charts
Source: Computed data
Figure 6: Regression Curve
5.5 Inferential Analysis
Null Hypothesis 1: There is no significant relationship between Budget estimate the Budget Expenditure on MSE-CDP scheme.
Alternate Hypothesis 1: There is significant relationship between Budget estimate the Budget Expenditure on MSE-CDP scheme.
As per table 4 null hypothesis is rejected and the alternate hypothesis is accepted. There is significant relationship between Budget estimate the Budget Expenditure on MSE-CDP scheme.
From the multiple regression in table 4, equation 1 it is evident that for one unit increase Budget estimate the budget expenditure increases by 1.15 units. For one unit increase in CFC approval the CFC completed increases and for one unit increase in ID approval the ID completed increases. For one unit increase in total approval the total completed increases.
Null Hypothesis 2: There is no significant relationship between Budget estimate, Revised Estimate and the Budget Expenditure on MSE-CDP scheme.
Alternate Hypothesis 2: There is significant relationship between Budget estimate, Revised Estimate and the Budget Expenditure on MSE-CDP scheme.
As per table 4, equation 5 null hypothesis is rejected and the alternate hypothesis is accepted. There is significant relationship between Budget estimate, Revised Estimate and the Budget Expenditure on MSE-CDP scheme.
5.6 Structural Equation Modelling
The SEM diagram is given in figure 7.
Figure 7: SEM diagram
Source: Developed by Researcher
6. FINDINGS, SUGGESTIONS AND CONCLUSION
A study was conducted to find performance of Government of India’s scheme namely Micro, Small Enterprises Cluster Development Programme (MSE-CDP) in India in coordination with concern State Government and Special Purpose Vehicle created by MSE in Tamil Nadu. As per diagnostic analysis there is improvement in physical performance like number of CFC and ID projects approved and completed. There is also improvement in Financial Performance like Budget estimate, Revised Estimate and Expenditure made. The CAGR increases and as per descriptive analytics the mean value also increases. As per correlation analysis there is strong relationships between approved and completed CFC and ID projects and also between revised estimate and expenditure made. As per predictive / trend analysis there is annual average increase in approved and completed projects and also budget estimate and expenditure. As per prescriptive analysis / regression analysis and structural equation modelling there is significant difference between revised estimate and expenditure made and also between budget estimate and expenditure made. As per inferential analysis it is inferred that for one unit increase in budget expenditure the approved and completed projects increases. To conclude, as per decision analysis it is decided by Government of India and concern State Government to implement the Micro Small Enterprises Cluster Development Programme for Micro and Small Enterprises in India in the form of creating Common Facility Centres and Infrastructure Development of Industrial Estates to reduce cost of production and increase in profit thereby to compete in international market.
ACKNOWLEDGMENT
The author acknowledges Department of Industries and Commerce, Government of Tamil Nadu for sending him for UNIDO’s Cluster Development Agent (CDA) training at EDII, Ahmedabad sponsored by UNIDO, New Delhi and acknowledges Tamil Nadu Small Industries Development Corporation (TANSIDCO) for helping in getting primary data and also acknowledges University of Madras for giving Ph.D. on Industrial Cluster Development Approach in Management Sciences.
REFERENCES
Bhaskaran, E. (2012). Lean Manufacturing Auto Cluster at Chennai.
Journal of The Institution of Engineers (India): Series C, 93(4), 383-390. doi:10.1007/s40032-012-0035-z
Bhaskaran, E. (2012). Technical Efficiency of Automotive Industry Cluster in Chennai.
Journal of The Institution of Engineers (India): Series C, 93(3), 243-249. doi:10.1007/s40032-012-0029-x
Bhaskaran, E. (2016). The Quantitative Analysis of Chennai Automotive Industry Cluster.
Journal of The Institution of Engineers (India): Series C, 97(3), 357-373. doi:10.1007/s40032-016-0255-8
Bhaskaran, E. (2021). The Resource Management in Chennai Heavy Engineering Cluster.
Productivity, 273-284. doi:10.32381/PROD.2020.61.03.3
India, G. o. (2022). DCMSME. Retrieved from Ministry of MSME: Source: https://cluster.dcmsme.gov.in/
NI-MSME. (2006). SME Cluster Development A Training Manual. Hydreabad, Telegana, India: National Institute for Micro, Small and Medium Enterprises (NI-MSME) (An Organisation of the Ministry of MSME, Government of India). Retrieved from www.nimsme.org
Porter, M. E. (1985). CompetItive Advantage, Creating and Sustaining Superior Performance. The Free Press. Retrieved March 1, 2023, from ISBN 0684 841460
Porter, M. E. (1990). The Competitive Advantage of Nations. New York, USA, USA: The Free Press,. doi:ISBN 0-684-84147-9
UNIDO. (2020). The UNIDO Approach to Cluster Development. Vienna, Austria, Vienna, Austria: Department Of Digitalization, Technology and Innovation, UNIDO. Retrieved March 12, 2023, from https://www.unido.org/sites/default/files/files/2020-09/Clusters_Brochure.pdf
Disclaimer : The opinions expressed within this interview are the personal opinions of the interviewee. The facts and opinions appearing in the answers do not reflect the views of Indiastat or the interviewer. Indiastat does not hold any responsibility or liability for the same.
.png)
.png)
