Inclusive tool to assess lean manufacturing maturity and its relationship with the size or location of the company in Greater Montreal, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Manufacturing companies operate in aggressive environments and require high productivity for survival. This study examined the implementation status of lean manufacturing practices in a sample of Greater Montreal’s industries by assessing maturity levels and their relationship with the company profile. A survey questionnaire encompassing technical and non-technical practices was developed and distributed to a population of manufacturing companies in the primary and secondary sectors in Greater Montreal. The unit of analysis was the manufacturing system. Valid data were collected from 35 companies using the random sampling technique. Maturity indices were evaluated with the factor-weighting method, and maturity levels were subsequently assigned to each company. Based on ascending hierarchical classification and Fisher’s exact tests, the findings revealed a mitigated maturity level in technical practices (average index of 51.554%), influenced by location (p-value = 0.018) and size (p-value = 0.08). However, non-technical practices exhibited a high maturity level (average index of 74%) linked to company size (p-value = 0.005).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it