The Theory of Constraints and the Make‐or‐Buy Decision: An Update and Review
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
SUMMARY The make‐or‐buy decision has traditionally been made using standard cost accounting methods. In this Journal , Gardiner and Blackstone (1991) made a strong case for incorporating the bottleneck capacity into the decision. However, their method did not guarantee the best solution for the more complicated make‐or‐buy problem. Additionally, their approach in some cases allowed organizations to forego opportunities for profit improvement. Since the publication of the Gardiner and Blackstone research, spreadsheets with in‐built Linear Programming (LP) based optimizers allow for quick “what‐if” analyses that encourage efforts toward optimal solutions for complicated problems. This article is a review and update of the Gardiner and Blackstone methodology based on spreadsheet LP that provides enhanced solutions in complex environments with multiple products and bottleneck situations. Specific managerial implications are offered.
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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 | 0.026 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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