Learning to be lean: the influence of external information sources in lean improvements
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the role of management exposure to external information sources, such as training sessions, plant visits, and conferences, in helping manufacturing organizations achieve lean goals. Design/methodology/approach A model is proposed highlighting the relationship between various key drivers of lean, external information sources, management commitment to lean, and lean thinking. To empirically test the model, 1,000 surveys were mailed to Canadian manufacturers with 109 usable surveys returned. Analyzing the data using partial least squares, the common sources of management information on lean and their effectiveness for lean improvements are discussed. Findings The final model confirms that management exposure to external information sources and commitment to lean both influence lean thinking within organizations. However, the direct relationship between external information sources and lean thinking is not supported. Instead, an indirect relationship exists, where increased exposure to sources of lean information, increases management commitment to lean, and ultimately the extent of lean thinking in the organization. Practical implications The practical implications of this research are that it will help manufacturing managers identify both organizational and environmental factors that may facilitate or inhibit the extensive use of lean in their organization, and the impact that their own understanding of lean and commitment to lean improvements will have on the overall success of a lean program. Originality/value The paper should help improve understanding of the differences in the extent of lean thinking between plants in the same company, organizations in the same industry, and organizations across industries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".