Paradigms of choice in manufacturing strategy
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
Purpose The paper sets out to test relationships between performance improvements and the three classical manufacturing strategy paradigms of fit, best practices, and capabilities defined by Voss. Design/methodology/approach Regression analyses are carried out on an international sample of 697 manufacturers of fabricated metal products, machinery, and equipment. Findings The results indicate that capability learning and best practices are positively related to performance improvements in quality, flexibility, and dependability, whereas internal fit appears to be negatively related to flexibility improvements. Research limitations/implications The study reinforces the need for research to explore the nature and role of the three paradigms jointly rather than in isolation. In particular, more research is needed to assess the merits of maintaining fit between operations structure and processes. Practical implications Improving performance in areas such as quality, flexibility, and delivery can be achieved through building capabilities and/or adopting best practices, but not apparently by maintaining internal fit between operations structure and processes. Originality/value The study validates two of the three classical paradigms of manufacturing strategy and makes the case for research to further specify and test the merits of maintaining internal fit between operations structure and processes.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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