Foundations of capacity analysis and supply chain design
Bibliographic record
Abstract
The importance of capacity analysis in the supply chain of any major corporation cannot be overstated. Any systems integrator (whose core competency is assembling the final product) is reliant upon its suppliers to provide good parts on time in order to meet production schedules and satisfy customer demand. This is of particular importance with Lean manufacturing systems and ‘just in time’ delivery systems. It is also of critical importance due to the observed ‘bullwhip’ effect and supply chain disruptions that still occur during the post-COVID-19 era. It is not uncommon to find that more than 80 per cent of the piece parts required for end item fabrication come from the supply chain. In addition, production schedules often follow a steep ramp for emerging technologies. Ensuring that your supply chain has the required amount of ramped capacity is critical to the success of the overall enterprise (inclusive of prime contractor, supplier, sub-tier suppliers). Fundamental concepts will be presented and demonstrated in examples. This paper will discuss supplier capacity analysis not only from a capacity modelling and simulation standpoint, but also some of the intricacies and interactions associated with supply chain design, business case analysis, lean manufacturing principles, supermarket inventories, human capacity considerations, hidden factory effects1 and the realised manufacturing yield impact on supplier capacity.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".