Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 278

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Learning on lean: a review of thinking and research

Moyano‐Fuentes, José, Sacristán‐Díaz, Macarena·2012·International Journal of Operations & Production Management
DOI 10.1108/01443571211226498
Open this paper in CoChat — free account

Abstract

Purpose: The purpose of this paper is to present an analysis of research on lean production (LP) since the concept was developed at the end of the 1980s with the aim of developing a model that permits an extended and comprehensive understanding of LP. Design/methodology/approach: A literature survey of peer reviewed journal articles and paradigmatic books with managerial impact is employed as the research methodology. Findings: The findings derived from the evaluation of the publications analysed have led to the creation of an extended model of LP. Specifically, two new groups of factors to be taken into account in order to achieve a comprehensive understanding of LP are presented. Apart from internal aspects at the shop floor level and value chain elements, the model provided includes work organisation and the impact that the geographical context has on LP. In addition, the critical assessment of publications has allowed a number of specific aspects to be identified for which there is no empirical evidence. Originality/value: This paper puts forward a new classification of literature identifying key aspects that should be included for LP development and management. It might represent new opportunities for rigorous and relevant research that would contribute to more transparent knowledge of LP being gained.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1108/01443571211226498

Cites

7 of 7 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Cites

Show all 7 references

Cited by

Metadata

Authors
Moyano‐Fuentes, J., & Sacristán‐Díaz, M.
Venue
International Journal of Operations & Production Management
Publisher
Emerald
Published
2012-04-20
Volume
32
Issue
5
Pages
551-582
ISSN
0144-3577 (ISSN-L)
DOI
10.1108/01443571211226498
OpenAlex
W2022045384
Cited by
278
References
7
FWCI
40.31
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

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.