Changes in manufacturing facility-, network-, and strategy-types at the Michelin North America Company from 1950 to 2014
Bibliographic record
Abstract
Large manufacturing firms operate networks of facilities which they design to achieve particular manufacturing strategies. Facilities, networks and strategies are of several distinct types. The facility-, network- and strategy-types used by a firm depend on the competitive environment in which a firm operates. This paper examines the facility-, network- and strategy-types used by the Michelin North America Company during the period from 1950 to 2014. The examination shows how three changes in the competitive environment (changes in tariffs and government industrial policy, the 1964 Canada – United States Automotive Trade Agreement, and the 1996 North American Free Trade Agreement) triggered significant changes in these types. The examination produces insights into categorisations of facility-, network- and strategy-types that are useful for understanding how large firms operate, how we can predict what changes large firms will make to their facilities, network and strategy and how stakeholders such as employees, suppliers and governments can manage the risks of working with large firms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".