Evolution in the strategic manufacturing planning process of organizations
Bibliographic record
Abstract
Abstract This study examines how strategic manufacturing planning processes vary systematically with respect to planning characteristics, and how the planning process appears to evolve over time. Through an empirical evaluation of over 200 U.S. manufacturers, we document the existence of four strategic manufacturing planning groups. These groups vary with respect to the degrees of “rationality” and “adaptability” of planning. In addition, the strategic manufacturing planning history and level of planning maturity differs between these groups, providing evidence that the planning process changes and evolves over time from “non‐rational adaptive” mode towards a more “rational adaptive” approach. Firms between these polar extremes appear to take different paths in their movement toward a “rational adaptive” mode, with some “focusing on rationality” first and others “focusing on adaptability” first. We also show that irrespective of the firm's environment, a greater degree of “rational adaptivity” is correlated with better planning outcomes and business performance. As such, it represents a “best practice” approach to strategic manufacturing planning. Insights created by this work not only make an important contribution to the manufacturing strategy literature, but can also be used by senior manufacturing managers to facilitate their progress towards more effective planning.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".