Lean manufacturing performance in Indian manufacturing plants
Abstract
Abstract Purpose – The purpose of this paper is to examine the current state of lean adoption in Indian manufacturing plants and its impact on operational performance. Design/methodology/approach – A survey questionnaire was developed and adapted based on work done in US industries. The survey questionnaire was sent to 400 firms in four geographic regions in India. In total, 79 usable responses were received for the study. Findings – Since lean manufacturing is a multi‐dimensional construct, the results demonstrate that approximately 80 percent of the respondents have implemented many dimensions of lean – focus on customer needs, pull system, setup time reduction, total productive maintenance, supplier performance, statistical process control, and cross‐departmental problem solving. The operational metrics have improved on all accounts: high productivity, reduced lead time, improved first‐pass correct output, reduced inventory and space requirement. Interestingly, respondents have indicated that first‐pass correct output, reduced manufacturing lead time, and increased productivity are the three main drivers of lean implementation. Research limitations/implications – Since the sample size is not very large, the results need to be considered with caution. Originality/value – Lean manufacturing is a very popular concept in the developed world and in some countries in the developing world as well. Yet, little is known about its current status in India, except for a few case studies. The study investigates the degree of lean production implementation in Indian manufacturing plants and its impact on operational metrics. The study also indicates the relationship between lean dimensions/practices and operational outcomes.
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Cites
Singh, B., & Sharma, S. K. (2009) · Measuring Business Excellence · doi:10.1108/13683040910984338 · Cited by 133 · see citing works
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Belekoukias, I., Garza-Reyes, J. A., & Kumar, V. (2014) · International Journal of Production Research · doi:10.1080/00207543.2014.903348 · Cited by 474 · see citing works
Lean supply chain management: Empirical research on practices, contexts and performance
Tortorella, G. L., Miorando, R., & Marodin, G. (2017) · International Journal of Production Economics · doi:10.1016/j.ijpe.2017.07.006 · Cited by 166 · see citing works
Lean implementation in Indian process industries – some empirical evidence
Panwar, A., Jain, R., & Rathore, A. P. S. (2015) · Journal of Manufacturing Technology Management · doi:10.1108/jmtm-05-2013-0049 · Cited by 101 · see citing works
Maware, C., Okwu, M. O., & Adetunji, O. (2022) · International Journal of Lean Six Sigma · doi:10.1108/ijlss-12-2020-0223 · Cited by 88 · see citing works
An empirical study for implementation of lean principles in Indian manufacturing industry
Jasti, N. V. K., & Kodali, R. (2016) · Benchmarking: An International Journal · doi:10.1108/bij-11-2013-0101 · Cited by 84 · see citing works
Development of lean manufacturing implementation drivers for Indian ceramic industry
Singh Sangwan, K., Bhamu, J., & Mehta, D. (2014) · International Journal of Productivity and Performance Management · doi:10.1108/ijppm-06-2013-0105 · Cited by 70 · see citing works
An empirical investigation on lean production system framework in the Indian manufacturing industry
Jasti, N. V. K., & Kodali, R. (2019) · Benchmarking: An International Journal · doi:10.1108/bij-10-2017-0284 · Cited by 33 · see citing works
El-Khalil, R. (2022) · International Journal of Lean Six Sigma · doi:10.1108/ijlss-10-2017-0118 · Cited by 29 · see citing works
- Authors
- Ghosh, M.
- Affiliations
- Institute of Management Technology (IN)
- Corresponding author
- Ghosh, Manimay
- Venue
- Journal of Manufacturing Technology Management
- Publisher
- Emerald
- Published
- 2012-12-14
- Volume
- 24
- Issue
- 1
- Pages
- 113-122
- ISSN
- 1741-038X (ISSN-L)
- OpenAlex
- W1996336753
- Cited by
- 201
- References
- 1
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 14 · 2023: 15 · 2022: 13 · 2021: 27 · 2020: 25 · 2019: 20 · 2018: 15 · 2017: 18 · 2016: 15 · 2015: 15 · 2014: 12 · 2013: 3
- FWCI
- 19.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Manufacturing Technology Management (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Management Information Systems › Quality and Supply Management
- Social Sciences › Business, Management and Accounting › Strategy and Management › Management and Optimization Techniques
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- lean manufacturing, lean adoption, operational performance, pull system, setup time reduction, total productive maintenance, supplier performance, statistical process control, production lead time, productivity, inventory reduction
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