SCOR Racetrack to Improve Supply Chain Performance
Bibliographic record
Abstract
Measuring supply chain performance is crucial for enhancing competitiveness.The Supply Chain Operations Reference (SCOR) model is a widely adopted approach for evaluating supply chain performance.To facilitate successful implementation of the SCOR model, APICS has developed a straightforward, five-stage methodology known as the SCOR Racetrack, which includes defining the scope, configuring the supply chain, optimizing the project, and preparing for implementation.This paper presents a case study examining the application of SCOR 12 using the SCOR Racetrack methodology within a leather craft small and medium enterprise (SME).The study aims to improve Asset Management Efficiency Performance through a series of steps, beginning with scope definition, supply chain configuration, project optimization, and concluding with readiness for implementation.The case study demonstrates that performance improvement of AM.1.2Return on Supply Chain Fixed Assets (ROF) can be achieved, reaching an 11.9% target through three distinct projects: developing marketing strategies, enhancing brand awareness, and implementing budgeting analysis.It is estimated that executing the marketing strategy will increase ROF by 1%.In subsequent racetrack stages, the SME can undertake the second and third projects to attain the desired 11.9% ROF target.Further exploration is recommended for applying SCOR 12 across various industries and projects to augment their competitive performance.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".