A multiple performance analysis of market-capacity integration policies
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
A model that uses simulation augmented with Design of Experiments (DOE) is presented to analyse the performance of a Make-to-Order (MTO) reconfigurable manufacturing system with scalable capacity. Unlike the classical capacity scaling policies, the proposed hybrid capacity scaling policy is determined using multiple performance measures that reflect cost, internal stability and responsiveness. The impact of both tactical capacity and marketing policies and their interaction on the overall performance was analysed using DOE techniques and real case data. In addition to the different insights about the trade-offs involved in capacity planning decisions, the presented results challenged the conventional capacity planning wisdoms in MTO about the negative role of the capacity scalability delay time. Finally the analysis demonstrated the importance of inter-functional integration between capacity and marketing policies. [Received 3 February 2010; Revised 13 August 2010; Accepted 6 September 2010]
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it