The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Lean and agile manufacturing: external and internal drivers and performance outcomes
Abstract
Purpose: Lean and agile manufacturing are two initiatives that are used by manufacturing plant managers to improve operations capabilities. The purpose of this paper is to investigate internal and external factors that drive the choice of lean and agile operations capabilities and their respective impact on operational performance. Design/methodology/approach: Lean and agile manufacturing are each conceptualized as a second‐order factor and measured through a bundle of distinct practices. The competitive intensity of industry and the competitive strategy are modeled as potential external and internal drivers, respectively, and the impact on quality, delivery, cost, and flexibility performance is analyzed using structural equations modeling. The model is tested with data from the high performance manufacturing project comprising a total of 211 plants from three industries and seven countries. Findings: The results indicate that lean and agile manufacturing differ in terms of drivers and outcomes. The choice of a cost‐leadership strategy fully mediates the impact of the competitive intensity of industry as a driver of lean manufacturing, while agile manufacturing is directly affected by both internal and external drivers, i.e. a differentiation strategy as well as the competitive intensity of industry. Agile manufacturing is found to be negatively associated with a cost‐leadership strategy, emphasizing the difference between lean and agile manufacturing. The major differences in performance outcomes are related to cost and flexibility, such that lean manufacturing has a significant impact on cost performance (whereas agile manufacturing has not), and that agile manufacturing has a stronger relationship with volume as well as product mix flexibility than does lean manufacturing. Research limitations/implications: Cross‐sectional data from three industries and seven countries are used, and it would be interesting to test this model for more industries and countries. Practical implications: The results provide insights into the factors that influence the choice of lean or agile manufacturing for improving operations, and the results that can be obtained. Originality/value: To the authors' knowledge, this is the first large‐scale empirical survey of leanness and agility simultaneously, using data from manufacturing firms in Europe, Asia, and North America. The model incorporates a wide perspective on factors related to lean and agile manufacturing, to be able to identify similarities and differences.
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Cites
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Cited by
Lean manufacturing: literature review and research issues
Bhamu, J., & Singh Sangwan, K. (2014) · International Journal of Operations & Production Management · doi:10.1108/ijopm-08-2012-0315 · Cited by 983
Agile Business Model Innovation in Digital Entrepreneurship: Lean Startup Approaches
Ghezzi, A., & Cavallo, A. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2018.06.013 · Cited by 722
The impact of lean methods and tools on the operational performance of manufacturing organisations
Belekoukias, I., Garza-Reyes, J. A., & Kumar, V. (2014) · International Journal of Production Research · doi:10.1080/00207543.2014.903348 · Cited by 474
Fullerton, R. R., Kennedy, F. A., & Widener, S. K. (2014) · Journal of Operations Management · doi:10.1016/j.jom.2014.09.002 · Cited by 407
The effect of environmental complexity and environmental dynamism on lean practices
Azadegan, A., Patel, P. C., Zangoueinezhad, A., & Linderman, K. (2013) · Journal of Operations Management · doi:10.1016/j.jom.2013.03.002 · Cited by 342
Metadata
- Authors
- Hallgren, M., & Olhager, J.
- Venue
- International Journal of Operations & Production Management
- Publisher
- Emerald
- Published
- 2009-09-18
- Volume
- 29
- Issue
- 10
- Pages
- 976-999
- ISSN
- 0144-3577 (ISSN-L)
- OpenAlex
- W1990291679
- Cited by
- 458
- References
- 50
- FWCI
- 23.83
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.