THE PERFORMANCE OF INDUSTRIAL CLUSTERS IN INDIA

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1 THE PERFORMANCE OF INDUSTRIAL CLUSTERS IN INDIA Dr.E. Bhaskaran.B.E, M.B.A., Ph.D., M.I.E., (D.Sc)., Deputy Director of Industries and Commerce, Government of Tamil Nadu Introduction The role of micro, small and medium enterprises (MSMEs) in the economic and social development of the country is well established. The MSME sector is a nursery of entrepreneurship, often driven by individual creativity and innovation. This sector contributes 8 per cent of the country s GDP, 45 per cent of the manufactured output and 40 per cent of its exports. The MSMEs provide employment to about 60 million persons through 26 million enterprises. The labour to capital ratio in MSMEs and the overall growth in the MSME sector is much higher than in the large industries. The geographic distribution of the MSMEs is also more even. Thus, MSMEs are important for the national objectives of growth with equity and inclusion. For inclusive growth and sustainable development most of the MSMEs has adopted the Cluster Development Approach. Literature Survey Pioneering contribution for Cluster Development Approach (CDA) were made by Porter (1990, 1998), Harrison (1992), Humphrey and Schmitz (1995), Nomisma (1996) and Tewari (1997). International experiences show that micro, small and medium enterprises are unable to face competitive pressure because of their isolation and ineffective linkages with relevant support organizations and commercial service providers. In developed countries like Italy, USA, France and Germany, clusters of SMEs bound together in strong and dynamic networks and relying upon strong linkages with relevant local / national support institutions have proved competitive in the global context. Cluster exists in developing countries too like India, Brazil, Peru & Mexico. According to UNIDO it is estimated that 400 modern SME clusters and 2000 artisan based rural cluster exists in India. MSME Clusters is talk of the day for the entrepreneurs in India. Hence, there is need for study on MSME clusters in India for the benefit of MSMEs. Objectives of the Study The objectives are 1. To Study the Technical Efficiency (θ) and Peer Weights (λi) of MSME Clusters in India. 2. To Study the Input Slacks (S-) and Output Slacks (S+) of MSME Clusters in India. 3. To Study the Variable Returns to Scale of MSME Clusters in India. Methodology The methodology adopted is collection of data from Department of Industrial Policy and Promotion, Government of India and analyzing with Data Envelopment Analysis of Input Oriented Banker Charnes Cooper (BCC) Model by taking Project Cost, No. of units and No. of Employment as inputs and Sales and exports in Crores as output. A DMU is efficient if θ = 1, S- = 0 and S+ = 0. Physical and Financial Performance of MSME Clusters The physical performance of 26 MSME Clusters in India is given in Figure 1 and Figure 2. indiastat.com March - April, socio - economic voices

2 Figure 1: Physical performance of 26 MSME Clusters in India. Source: Department of Industrial Policy and Promotion (DIPP), Government of India. The largest number of units is in Foundry/Pump/ Motor Cluster at Coimbatore, Tamil Nadu followed by Gem and Jewellery Cluster, Surat, Gujarat and Textiles Cluster, Ludhiana, Punjab. indiastat.com March - April, socio - economic voices

3 Source: Department of Industrial Policy and Promotion (DIPP), Government of India. The largest number of employment is in Textiles Cluster, Ludhiana, Punjab, followed by Coir Cluster, Alappuzha, Kerala and Machine Tools Cluster, Bangalore. indiastat.com March - April, socio - economic voices

4 Financial Performance of MSME Clusters Figure 3 gives the financial performance of 26 MSME Clusters in India. Source: Department of Industrial Policy and Promotion (DIPP), Government of India. The Gem and Jewellery Cluster, Surat, Gujarat, Auto components Cluster, Maharashtra and Textiles Cluster, Tirupur, Tamil Nadu have financially performed well. Correlation CoEfficiency Analysis The correlation coefficient Analysis is given in the table 1. Variables Project Cost Table 1: Correlation Coefficient Analysis No of Employment in Units persons Annual Sales (Rs crore) Exports (Rs crore) Project Cost 1.00 (0.07) (0.05) No of Units (0.07) Employment in (0.05) persons Annual Sales (Rs crore) Exports (Rs crore) Source: Computed Data indiastat.com March - April, socio - economic voices

5 There is high degree of relationship exists between no. of units and employment and between annual sales and exports. Technical Efficiency of MSME Clusters Data Structure The Project Cost in Rs. crores, no. of units, employment in nos corresponding to 26 DMUs (Clusters) is taken as inputs and Annual Sales in Rs.crores and Exports in Rs.crores corresponding to 26 DMUs (Clusters) is taken as outputs. Data Envelopment Analysis Banker, Charnes and Cooper Model (BCC Model) Input Oriented BCC Model (BCC-I) Min Z 0 = θ ε 1 S + - ε 1 S - θ, λ, S +, S - Subject to Y λ - S + = Y 0 θx 0 - X λ - S - = 0 1 λ 1 λ, S +, S - 0 Model Description The Scalar variable θ appears in the primal problem, is the reduction applied to all inputs of DMUs to improve efficiency. This reduction is applied simultaneously to all inputs and results in a radial movement toward the envelopment surface. The presence of non-archimedean (Infinitesimal constant) ε in the primal objective function effectively allows the minimization over θ to preempt the optimization involving the slacks. Thus, the optimization can be computed in a two-stage process with i) maximal reduction of inputs being achieved first via θ ii) then in the second stage movement on to the efficient frontier is achieved via the positive input and output slack variables ( S -, S + ) Here, the constraint to scale (VRS). 1 λ 1 is known as convexity constraints, which will admit variable return The above discussion leads to form the following statement. A DMU is efficient if and only if a) θ = 1, indiastat.com March - April, socio - economic voices

6 b) All slacks are zero. S - = 0 and S + = 0. Computing Methodology Initially we consider Foundry Cluster, Belgaum, as the studied DMU and the LP Model is formulated as given below Min θ 0 Subject to λ λ λ Output Constraints 130 λ λ λ Output Constraints λ λ λ Output Constraints 356 θ λ λ λ 26 0 Input Constraints 45 θ 0-45 λ λ λ 26 0 Input Constraints λ 1 +λ 2 + λ 26 =1. λ 1, λ 2, λ 26 0, θ 0 is unrestricted. By solving the above and continuously changing the studied DMUs we get the values we get the value of λ i s and θ i s for each DMU. Efficiency Scores The value of θi s being the efficiency scores of the Clusters are given in the Table 2 and in the Figure 1. indiastat.com March - April, socio - economic voices

7 Table 2: Efficiency Scores of the Clusters in India No. DMU Score Rank θi s 1 Foundry Cluster,Belguam,Karnataka Machine Tools, Bangalore, Karnataka Coir, Alappuzha,Kerala Auto Components,Pitampura,M.P Auto Components, Pune, Maharashtra Textile,IchalKaranji,Maharashtra Metallurgical,Jajpur, Orissa Textiles,Ludhiana,Punjab Marble,Kishangarh,Rajasthan Auto Components,Chennai,Tamilnadu Cereals Pulses & Staples, Madurai,Tamilnadu Foundry/Pump/Motor,Coimbatore,Tamilnadu Leather,Ambur,Tamilnadu Textiles,Tirupur,Tamilnadu Leather,Kanpur,UP Chemical Multi,Haldia,WB Foundry,Howrah,WB Rubber,Howrah,WB Auto Components,Vijayawada,A.P Pharma,Hyderabad,A.P Iron & Steel,Raipur,Chattisgarh Chemical,Ahemadabad,Gujarat Chemical,Ankleswar,Gujarat Chemical, Vapi,Gujarat Gem & Jewellery,Surat,Gujarat Textile,Panipat,Haryana As per table 2 and figure 1, nearly 10 clusters have θi = 1 and rank=1 and they are highly efficient. The efficiencies and ranks of other clusters θ < 1 are also given. They have to perform well to attain 100% efficiency and rank 1. The overall efficiency of the clusters in India is given in table 3. Table 3: Overall Efficiency of the Clusters in India. No. of DMUs in Data = 26 No. of DMUs with inappropriate Data = 0 No. of evaluated DMUs = 26 Average of scores = No. of efficient DMUs = 10 No. of inefficient DMUs = 16 No. of over iteration DMUs = 0 [BCC-I] LP started at :10:22 and completed at :10:24 Elapsed time = 2 seconds Total number of simplex iterations = 221 Source: Computed data The overall technical efficiency of Clusters in India are 0.72, so the inefficient Clusters should work hard to reach 100%. indiastat.com March - April, socio - economic voices

8 Frequency of highly efficient cluster is given in the Table 4. Table 4: Frequency in Reference Set Peer set Frequency to other DMUs Foundry Cluster,Belguam,Karnataka 12 Auto Components, Pune, Maharashtra 1 Metallurgical,Jajpur, Orissa 4 Textiles,Ludhiana,Punjab 5 Textiles,Tirupur,Tamilnadu 5 Leather,Kanpur,UP 11 Rubber,Howrah,WB 2 Pharma,Hyderabad,A.P 1 Gem & Jewellery,Surat,Gujarat 1 Source: Computed data Foundry Cluster Belgaum, Karnataka is referred by other clusters around 15 times so it gets rank 1 highly efficiency among other 10 clusters. Next Leather Cluster, Kanpur is referred around 11 times and it has got rank 2 and textiles cluster, Ludhiana and Tirpur has got rank 3 as it is referred around 5 times by other clusters Input and Output Slacks of MSME Clusters in India The Input and Output Slacks of MSME Clusters is given in the table 5. Table 5: Input and Output Slacks of MSME Clusters in India No. DMU Score Proj. Cost No of Units Excess Excess Excess Shortage Shortage Emp.in persons Annual Sales (Rs crore) indiastat.com March - April, socio - economic voices Exports (Rs crore) S-(1) S-(2) S-(3) S+(1) S+(2) 1 Foundry Cluster,Belguam,Karnataka Machine Tools, Bangalore, Karnataka 3 Coir, Alappuzha,Kerala Auto Components, Pitampura,M.P Auto Components, Pune, Maharashtra 6 Textile,IchalKaranji,Maharashtra Metallurgical,Jajpur, Orissa Textiles,Ludhiana,Punjab Marble,Kishangarh,Rajasthan Auto Components, Chennai, Tamilnadu 11 Cereals Pulses & Staples, Madurai, Tamilnadu 12 Foundry/Pump/Motor, Coimbatore, Tamilnadu 13 Leather,Ambur,Tamilnadu Textiles,Tirupur,Tamilnadu Leather,Kanpur,UP Chemical Multi,Haldia,WB Foundry,Howrah,WB Rubber,Howrah,WB Auto Components,Vijayawada,A.P Pharma,Hyderabad,A.P Iron & Steel,Raipur,Chattisgarh

9 22 Chemical,Ahemadabad,Gujarat Chemical,Ankleswar,Gujarat Chemical, Vapi,Gujarat Gem & Jewellery,Surat,Gujarat Textile,Panipat,Haryana Source: Computed Data 10 Clusters, are efficient by having θ = 1, S - = 0 and S + = 0. The remaining cluster needs improvement by either decreasing the no. of enterprises or employment or to increase annual sales or exports. It is impossible to reduce the no. of enterprises or employment, so the clusters can go in for increase in sales or exports to become efficient like other 10 clusters. The S > 0 obtained for other 16 MSME clusters reveals the excess no. of units (S - ) or employment (S - ) in the MSME Clusters or shortage in annual sales or exports (S + ) as constant Variable Return to Scale of MSME Clusters For a long time DEA models were based on Constant Returns to Scale (CRS) and it has been criticized as a limiting factor for the application of DEA. Many economists viewed CRS assumption as over restrictive and preferred alternative methodologies. Banker et al (1984) for the first time introduced the VRS in DEA models through convexity constraints and thereafter remarkable change has led to make changes in CCR DEA models. Variable Return to Scale of MSME Clusters is given in table 6. Table 6: Variable Return to Scale of MSME Clusters in India No. DMU Score RTS RTS of Projected DMU 1 Foundry Cluster,Belguam,Karnataka 1.00 Increasing 2 Machine Tools, Bangalore, Karnataka 0.25 Increasing 3 Coir, Alappuzha,Kerala 0.39 Increasing 4 Auto Components,Pitampura,M.P Increasing 5 Auto Components, Pune, Maharashtra 1.00 Constant 6 Textile,IchalKaranji,Maharashtra 0.39 Increasing 7 Metallurgical,Jajpur, Orissa 1.00 Constant 8 Textiles,Ludhiana,Punjab 1.00 Increasing 9 Marble,Kishangarh,Rajasthan 0.68 Increasing 10 Auto Components,Chennai,Tamilnadu 0.68 Increasing 11 Cereals Pulses & Staples, Madurai,Tamilnadu 0.66 Increasing 12 Foundry/Pump/Motor,Coimbatore,Tamilnadu 0.39 Increasing 13 Leather,Ambur,Tamilnadu 0.57 Increasing 14 Textiles,Tirupur,Tamilnadu 1.00 Constant 15 Leather,Kanpur,UP 1.00 Increasing 16 Chemical Multi,Haldia,WB 0.79 Increasing 17 Foundry,Howrah,WB 0.57 Increasing 18 Rubber,Howrah,WB 1.00 Increasing 19 Auto Components,Vijayawada,A.P 0.78 Increasing 20 Pharma,Hyderabad,A.P 1.00 Constant 21 Iron & Steel,Raipur,Chattisgarh 0.46 Increasing 22 Chemical,Ahemadabad,Gujarat 1.00 Constant 23 Chemical,Ankleswar,Gujarat 0.40 Increasing 24 Chemical, Vapi,Gujarat 0.50 Increasing 25 Gem & Jewellery,Surat,Gujarat 1.00 Constant 26 Textile,Panipat,Haryana 0.75 Increasing Source: Computed Data indiastat.com March - April, socio - economic voices

10 Source: Computed Data From table 6 and 7 it is proud to say that the RTS of 16 Clusters is increasing RTS. For other 6 Clusters it is constant RTS and there is nil decreasing RTS. Findings and Conclusions To conclude, for inclusive growth and sustainable development, the inefficient MSME clusters should increase their sales and exports as decrease in no. of enterprises and employment is practically not possible. Moreover the MSME Clusters should strengthen infrastructure interrelationships, technology interrelationships, procurement interrelationships, production interrelationships and marketing interrelationships and should make use of the benefits announced by Government of Tamil Nadu on New Industrial Policy, 2006 and Micro, Small and Medium Industries Policy, 2008 and Government of India on MSMED Act, 2006 and National Manufacturing Competitiveness Programme. The soft and hard intervention on MSE-Cluster Development Programme, Lean Manufacturing Programme, ISO Certification Programme and other ICT programs of Government of India will help Industrial Clusters in India to increase their productivity and efficiency. References Table 7: Overall RTS of MSME Clusters RTS Efficient Projected Total No. of IRS No. of CRS No. of DRS Total [1] William W.Cooper, Lawrence M.Seiford and Kaoru Tone, Data Envelopment Analysis, Kluwer Academic Publishers, Boston / Dordrecht / London. [2] R. Ramanathan., An Introduction to Data Envelopment Analysis, Sage Publications. [3] Ranjan Ghosh and Chiranjib Neogi, Theory and Application of Productivity and Efficiency, Econometric and DEA Approach, Macmillan India Limited. [4] V.Prakash and A.Subbarayan (2009), Bulletin of Pure and Applied Sciences, Ranking the efficiency performance of a selected set of States in India by DEA Vol.28 E, No.1, pp [5] Abraham Charnes, William W.Cooper, Airs Y. Lewin, Lawrence M.Seiford, Data Envelopment Analysis: Theory, Methodology and Application, Kluwer Academic Publishers, Boston / Dordrecht / London. indiastat.com March - April, socio - economic voices