Foundations of capacity analysis and supply chain design
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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