Strategy, uncertainty and the focused factory in international process manufacturing
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
Abstract The extant literature on the focused factory has not explored the contingencies associated with the de facto adoption and use of focused factory principles: Why are some plants focused while others are not? Is focus—or unfocus—a strategic choice, best practice or perhaps a reflection of an environmental constraint? In his pioneering work, Skinner [W. Skinner, 1974. The focused factory. Harvard Business Review 52 (3), 113–121] prescribes companies to ensure that the manufacturing task of their manufacturing units is simple and focused, for instance, by assigning a narrow product mix for each factory or concentrating on a narrow mix of production technologies. Especially in the absence of compelling empirical evidence on the effectiveness of the focused factory approach, we argue that we still do not understand why some plants may remain unfocused. We observe that in the international process industry case examined in this paper, some factories are unfocused and their manufacturing tasks are all but simple. Yet, some of them appear to be high performers. This presents an opportunity to seek empirical insight on the questions raised above. Specifically, we examine why manufacturing companies in the process industries may or may not follow the focused factory strategy. Our results suggest that in certain operating environments and with certain competitive strategies, choosing not to focus the manufacturing task should be viewed as a viable alternative manufacturing strategy, perhaps even a constraint imposed by the operating environment. We develop four contingency propositions to explain why focused manufacturing strategy may not be desirable or even possible.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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