The DOI matches its Crossref record.
Classification scheme for lean manufacturing tools
Abstract
For the past few years almost every manufacturing industry has been trying to get 'lean'. A headlong rush to become lean also resulted in many misapplications of existing lean manufacturing tools often due to inadequate understanding of the purpose of tools. While tool descriptions abound, there is no way systematically to link a manufacturing organization to its problems and to the possible tools to eliminate these problems. The main purpose of this paper is to propose a classification scheme to serve as a link between manufacturing waste problems and lean manufacturing tools. A manufacturing organization can then match its manufacturing wastes with the appropriate lean manufacturing tools. The classification of existing knowledge is often the first step in moving from a practice to a science. This classification scheme systematically organizes lean manufacturing tools and metrics according to their level of abstraction, appropriate location of application of the tool in the organization, whether it addresses management waste or activity waste, the type of resource waste it addresses, and whether it identifies waste, measures waste, eliminates waste, or a combination of the three. We have organized 101 lean manufacturing tools and metrics using this classification scheme. We have also described some common manufacturing problems using this classification scheme and shown the problem-tool connection through examples. The classification scheme is not intended as a decision-making tool, i.e. it does not decide if something is a waste. However, the proposed scheme does an excellent job of classifying all well-known lean manufacturing tools and metrics and suggests lean manufacturing tools and metrics that will help to address manufacturing problems. This classification scheme will assist companies trying to become lean and can serve as a foundation for research into the science of lean.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
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
Four decades of lean: a systematic literature review
Stone, K. B. (2012) · International Journal of Lean Six Sigma · doi:10.1108/20401461211243702 · Cited by 393
Implementing 5S within a Japanese context: an integrated management system
Gapp, R., Fisher, R., & Kobayashi, K. (2008) · Management Decision · doi:10.1108/00251740810865067 · Cited by 300
In pursuit of implementation patterns: the context of Lean and Six Sigma
Shah, R., Chandrasekaran, A., & Linderman, K. (2008) · International Journal of Production Research · doi:10.1080/00207540802230504 · Cited by 296
Metadata
- Authors
- Pavnaskar, S. J., Gershenson, J. K., & Jambekar, A. B.
- Venue
- International Journal of Production Research
- Publisher
- Informa UK Limited
- Published
- 2003-01
- Volume
- 41
- Issue
- 13
- Pages
- 3075-3090
- ISSN
- 0020-7543 (ISSN-L)
- 1366-588X
- OpenAlex
- W1998109490
- Cited by
- 466
- References
- 0
- FWCI
- 2.51
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Classification scheme for lean manufacturing tools. https://app.cochat.ai/papers/10.1080/0020754021000049817
- The paper
- Pavnaskar, S. J., Gershenson, J. K., & Jambekar, A. B. (2003). Classification scheme for lean manufacturing tools. International Journal of Production Research. https://doi.org/10.1080/0020754021000049817
Data sources
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.