Assessing manufacturing plant competitiveness: an empirical field study
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
In spite of the recognition that the manufacturing function can create and sustain a competitive advantage for the firm, only a few empirical studies have examined the relationship between manufacturing practices and plant performance. In this paper, based on responses from a large number of Canadian manufacturing plants and a number of Australian manufacturing plants, we identify the manufacturing practices which distinguish the "Most Successful" (MS) plants from the "Least Successful" (LS) plants. Success was measured by asking respondents to indicate year-over-year trends for each of 22 performance measures by specifying whether there had been an increase, a decrease or no change. The differences in the manufacturing practices used by the MS plants and the LS plants reflect three general distinctions between the two groups: (i) adopting a logical portfolio of practices which relate to competitive priorities, (ii) workforce focus, and (iii) process orientation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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