Indian Journal of Finance

Open Access Open Access  Restricted Access Subscription Access

Efficiency Assessment of Indian Textile Units Using Data Envelopment and Regression Analysis


Affiliations
1 Assistant Professor, Operations Management and Quantitative Techniques Area, Institute of Management, Nirma University, S G Highway, Ahmedabad – 382 481, Gujarat, India
2 Assistant Professor, Operations Management and Quantitative Techniques Area, United World School of Business, Karnavati University, Gandhi Nagar – 382 422, Gujarat, India
3 Research Scholar, Institute of Management, Nirma University, S G Highway, Ahmedabad – 382 481, Gujarat

   Subscribe/Renew Journal


Firms are always in the pursuit of improvizing their performance. Optimum utilization of resources and the eradication of sources of inadequacies result in improved performance and production for manufacturing firms. Improved performance and production lead to increased efficiency for the firms. In this study, data envelopment analysis was used to evaluate the efficiency using financial data of 11 years of all the 13 S&P BSE 500 listed textile firms. Consistent with the relevant literature, three inputs (power, fuel, & water charges ; compensation to employees ; and raw materials, stores, & spares) and one output (profit before tax) were selected. Additionally, outcomes from the DEA analysis were used to perform the regression analysis. Out of the 13 units analyzed for efficiency, two textile units were found to be operating efficiently. The results of the regression analysis showed that an increase in employee compensation will lead to an increase in profit since the increase in the compensation helps to increase motivation from job satisfaction. Besides adding value to the efficiency assessment literature, the research findings of this study also provide meaningful business insights for the practitioners for improving productivity performance by finding the core action area in resource planning decisions.

Keywords

Productivity-performance, Efficiency, Benchmarking, Data-Envelopment-Analysis, Regression Analysis.

JEL Classification : C61, C67, D24, L25, L6.

Paper Submission Date : April 8, 2020 ; Paper Sent Back for Revision : March 25, 2021 ; Paper Acceptance Date : April 20, 2021 ; Paper Published Online : July 5, 2021.

User
Subscription Login to verify subscription
Notifications
Font Size

  • Abdel-Basset, M., Ding, W., Mohamed, R., & Metawa, N. (2020). An integrated plithogenic MCDM approach for financial performance evaluation of manufacturing industries. Risk Management, 22(3), 192–218. https://doi.org/10.1057/s41283-020-00061-4

  • Aggelopoulos, E., & Georgopoulos, A. (2017). Bank branch efficiency under environmental change : A bootstrap DEA on monthly profit and loss accounting statements of Greek retail branches. European Journal of Operational Research, 261(3), 1170–1188. https://doi.org/10.1016/j.ejor.2017.03.009

  • Agostini, L., & Filippini, R. (2019). Organizational and managerial challenges in the path toward Industry 4.0. European Journal of Innovation Management, 22(3), 406–421. https://doi.org/10.1108/EJIM-022018-0030

  • Al-Surmi, A., Cao, G., & Duan, Y. (2020). The impact of aligning business, IT, and marketing strategies on firm performance. Industrial Marketing Management, 84, 39–49. https://doi.org/10.1016/j.indmarman.2019.04.002

  • Banerjee, A. (2018). An empirical study to compute the efficiency of Indian banks during the pre and post periods of recession with the help of data envelopment analysis. Indian Journal of Finance, 12(4), 37–53. https://doi.org/10.17010/ijf/2018/v12i4/122794

  • Banker, R., Natarajan, R., & Zhang, D. (2019). Two-stage estimation of the impact of contextual variables in stochastic frontier production function models using data envelopment analysis : Second stage OLS versus bootstrap approaches. European Journal of Operational Research, 278(2), 368–384. https://doi.org/10.1016/j.ejor.2018.10.050

  • Barasa, L., Vermeulen, P., Knoben, J., Kinyanjui, B., & Kimuyu, P. (2019). Innovation inputs and efficiency : Manufacturing firms in Sub-Saharan Africa. European Journal of Innovation Management, 22(1), 59–83. https://doi.org/10.1108/EJIM-11-2017-0176

  • Bellandi, M., & Santini, E. (2019). Territorial servitization and new local productive configurations : The case of the textile industrial district of Prato. Regional Studies, 53(3), 356–365. https://doi.org/10.1080/00343404.2018.1474193

  • Bhullar, A., & Singh, P. (2017). Economic efficiency and growth sustainability of Indian small-scale industries : A preand post-liberalization based comparative analysis. Arthshastra Indian Journal of Economics & Research, 6(4), 7–22. https://doi.org/10.17010/aijer/2017/v6i4/118154

  • Biener, C., Eling, M., & Wirfs, J. H. (2016). The determinants of efficiency and productivity in the Swiss insurance industry. European Journal of Operational Research, 248(2), 703–714. https://doi.org/10.1016/j.ejor.2015.07.055

  • Bockholt, M. T., Kristensen, J. H., Colli, M., Jensen, P. M., & Wæhrens, B. V. (2020). Exploring factors affecting the financial performance of end-of-life take-back program in a discrete manufacturing context. Journal of Cleaner Production, 258, 1–9. https://doi.org/10.1016/j.jclepro.2020.120916

  • Bosman, L., Hartman, N., & Sutherland, J. (2020). How manufacturing firm characteristics can influence decision making for investing in Industry 4.0 technologies. Journal of Manufacturing Technology Management, 31(5), 1117–1141. https://doi.org/10.1108/JMTM-09-2018-0283

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429–444. https://doi.org/10.1016/0377-2217(78)90138-8

  • Chen, P. - H., Ong, C. - F., & Hsu, S. - C. (2016). Understanding the relationships between environmental management practices and financial performances of multinational construction firms. Journal of Cleaner Production, 139, 750–760. https://doi.org/10.1016/j.jclepro.2016.08.109

  • Davtalab-Olyaie, M. (2019). A secondary goal in DEA cross-efficiency evaluation: A “one home run is much better than two doubles” criterion. Journal of the Operational Research Society, 70(5), 807–816. https://doi.org/10.1080/01605682.2018.1457482

  • Dobos, I., & Vörösmarty, G. (2019). Inventory-related costs in green supplier selection problems with Data Envelopment Analysis (DEA). International Journal of Production Economics, 209, 374–380. https://doi.org/10.1016/j.ijpe.2018.03.022

  • FICCI. (2016). Knowledge paper on global shifts in textile industry & India’s position. TAG 2016, Mumbai. http://ficci.in/spdocument/20817/3-FICCI-TAG-2016-Whitepaper.pdf

  • Gambhir, D., & Sharma, S. (2015a). Are exporting firms more productive in the Indian textile industry ? Measuring Business Excellence, 19(4), 72–84. https://doi.org/10.1108/MBE-07-2014-0021

  • Gambhir, D., & Sharma, S. (2015b). Productivity in Indian manufacturing : Evidence from the textile industry. Journal of Economic and Administrative Sciences, 31(2), 71–85. https://doi.org/10.1108/JEAS-092014-0021

  • Gupta, G. K., & Khan, M. A. (2017). Exports competitiveness of the Indian textile industry during and after ATC. Journal of Textile Science & Engineering, 7(5), 1–6. https://doi.org/10.4172/2165-8064.1000313

  • Gupta, S., & Kumar, S. (2018). Influence of demographic determinants on reverse mortgage willingness : An empirical study from India. Arthshastra Indian Journal of Economics & Research, 7(2), 7–20. https://doi.org/10.17010/aijer/2018/v7i2/124537

  • Halkos, G., & Petrou, K. N. (2019). Treating undesirable outputs in DEA : A critical review. Economic Analysis and Policy, 62, 97–104. https://doi.org/10.1016/j.eap.2019.01.005

  • Hasanuzzaman, & Bhar, C. (2017). Environmental sustainability : The emerging issues in India's textile sector. International Journal of Social Ecology and Sustainable Development (IJSESD), 8(4), 48–60. http://doi.org/10.4018/IJSESD.2017100104

  • Ibrahim, H. I., & Salau, E. S. (2016). Efficiency of village extension agents in Nigeria : Evidence from a data envelopment analysis. Journal of Agricultural Sciences, 61(1), 93–101. https://doi.org/101. 10.2298/JAS1601093I

  • India Brand Equity Foundation (IBEF). (2018). Textile industry analysis. https://www.ibef.org/industry/textiles.aspx

  • Izadikhah, M., & Farzipoor Saen, R. (2015). A new data envelopment analysis method for ranking decision making units: An application in industrial parks. Expert Systems, 32(5), 596–608. https://doi.org/10.1111/exsy.12112

  • Kang, Y. Q., Xie, B. - C., Wang, J., & Wang, Y. - N. (2018). Environmental assessment and investment strategy for China's manufacturing industry : A non-radial DEA based analysis. Journal of Cleaner Production, 175, 501–511. https://doi.org/10.1016/j.jclepro.2017.12.043

  • Kar, B., & Jena, M. K. (2019). Performance and age of companies listed on the Bombay Stock Exchange. Indian Journal of Finance, 13(5), 52–67. https://doi.org/10.17010/ijf/2019/v13i5/144185

  • Khurana, A., & Khosla, R. (2019). Economic reforms and cost efficiency in the banking sector in India. Indian Journal of Finance, 13(11), 24–35. https://doi.org/10.17010/ijf/2019/v13i11/148414

  • Kuo, R. J., & Lin, Y. J. (2012). Supplier selection using analytic network process and data envelopment analysis. International Journal of Production Research, 50(11), 2852–2863. https://doi.org/10.1080/00207543.2011.559487

  • Liu, H. H., Song, Y. Y., & Yang, G. L. (2019). Cross-efficiency evaluation in data envelopment analysis based on prospect theory. European Journal of Operational Research, 273(1), 364–375. https://doi.org/10.1016/j.ejor.2018.07.046

  • Mathiyazhagan, K., Sengupta, S., & Mathivathanan, D. (2019). Challenges for implementing green concept in sustainable manufacturing : A systematic review. OPSEARCH, 56(1), 32–72. https://doi.org/10.1007/s12597-019-00359-2

  • Mikušová, P. (2015). An application of DEA methodology in efficiency measurement of the Czech public universities. Procedia Economics and Finance, 25, 569–578. https://doi.org/10.1016/S2212-5671(15)00771-6

  • Ministry of Textiles, Government of India (MoT GoI). (2018). Department of Industrial Policy and Promotion, Union Budget 2017–18. http://texmin.nic.in/sites/default/files/AnnualReport2017-18%28English%29.pdf

  • Nguyen, T. M., Le, Q. H., Tran, T. V., & Nguyen, M. N. (2019). Ownership, technology gap and technical efficiency of small and medium manufacturing firms in Vietnam : A stochastic meta frontier approach. Decision Science Letters, 8(3), 225–232. https://doi.org/10.5267/j.dsl.2019.3.002

  • Patel, M. P., & Ranjith, V. K. (2018). Measuring efficiency and productivity change of multi-specialty private sector hospitals in India : A DEA based Malmquist Productivity Index approach. Indian Journal of Finance, 12(5), 25–42. https://doi.org/10.17010/ijf/2018/v12i5/123691

  • Piyathanavong, V., Garza-Reyes, J. A., Kumar, V., Maldonado-Guzmán, G., & Mangla, S. K. (2019). The adoption of operational environmental sustainability approaches in the Thai manufacturing sector. Journal of Cleaner Production, 220, 507–528. https://doi.org/10.1016/j.jclepro.2019.02.093

  • Ramanathan, R. (2003). An introduction to data envelopment analysis : A tool for performance measurement. Sage.

  • Rashidi, K., & Cullinane, K. (2019). Evaluating the sustainability of national logistics performance using Data Envelopment Analysis. Transport Policy, 74, 35–46. https://doi.org/10.1016/j.tranpol.2018.11.014

  • Raut, R., Gardas, B. B., & Narkhede, B. (2019). Ranking the barriers of sustainable textile and apparel supply chains : An interpretive structural modelling methodology. Benchmarking : An International Journal, 26(2), 371–394. https://doi.org/10.1108/BIJ-12-2017-0340

  • Sharma, A., & Narula, S. A. (2020). What motivates and inhibits Indian textile firms to embrace sustainability ? Asian Journal of Sustainability and Social Responsibility, 5(1), 1–23. https://doi.org/10.1186/s41180020-0032-8

  • Textile mills tie up with GEDA to reduce energy consumption. (2018, December 2). The Times of India. https://timesofindia.indiatimes.com/city/surat/textile-mills-tie-up-with-geda-to-reduce-energy-consumption/articleshow/66900593.cms

  • Tran, T. H., Mao, Y., Nathanail, P., Siebers, P. - O., & Robinson, D. (2019). Integrating slacks-based measure of efficiency and super-efficiency in data envelopment analysis. Omega, 85, 156–165. https://doi.org/10.1016/j.omega.2018.06.008

  • Vikas, V., & Bansal, R. (2019). Efficiency evaluation of Indian oil and gas sector : Data envelopment analysis. International Journal of Emerging Markets, 14(2), 362–378. https://doi.org/10.1108/IJoEM-012018-0016

  • World Trade Organization. (n.d.). Textiles. www.wto.org/english/tratop_e/texti_e/texti_e.htm


Abstract Views: 356

PDF Views: 2




  • Efficiency Assessment of Indian Textile Units Using Data Envelopment and Regression Analysis

Abstract Views: 356  |  PDF Views: 2

Authors

Chetan A. Jhaveri
Assistant Professor, Operations Management and Quantitative Techniques Area, Institute of Management, Nirma University, S G Highway, Ahmedabad – 382 481, Gujarat, India
Gunjan Sood
Assistant Professor, Operations Management and Quantitative Techniques Area, United World School of Business, Karnavati University, Gandhi Nagar – 382 422, Gujarat, India
Riya Shah
Research Scholar, Institute of Management, Nirma University, S G Highway, Ahmedabad – 382 481, Gujarat

Abstract


Firms are always in the pursuit of improvizing their performance. Optimum utilization of resources and the eradication of sources of inadequacies result in improved performance and production for manufacturing firms. Improved performance and production lead to increased efficiency for the firms. In this study, data envelopment analysis was used to evaluate the efficiency using financial data of 11 years of all the 13 S&P BSE 500 listed textile firms. Consistent with the relevant literature, three inputs (power, fuel, & water charges ; compensation to employees ; and raw materials, stores, & spares) and one output (profit before tax) were selected. Additionally, outcomes from the DEA analysis were used to perform the regression analysis. Out of the 13 units analyzed for efficiency, two textile units were found to be operating efficiently. The results of the regression analysis showed that an increase in employee compensation will lead to an increase in profit since the increase in the compensation helps to increase motivation from job satisfaction. Besides adding value to the efficiency assessment literature, the research findings of this study also provide meaningful business insights for the practitioners for improving productivity performance by finding the core action area in resource planning decisions.

Keywords


Productivity-performance, Efficiency, Benchmarking, Data-Envelopment-Analysis, Regression Analysis.

JEL Classification : C61, C67, D24, L25, L6.

Paper Submission Date : April 8, 2020 ; Paper Sent Back for Revision : March 25, 2021 ; Paper Acceptance Date : April 20, 2021 ; Paper Published Online : July 5, 2021.


References





DOI: https://doi.org/10.17010/ijf%2F2021%2Fv15i5-7%2F164490