Determining Performance Metrics of Supply Chain Management in Make-to-Order Small-Medium Enterprise Using Supply Chain Operation Reference Model (SCOR Version 12.0)
Bibliographic record
Abstract
Performance measurement in supply chain management is essential to facilitate the company to achieve effectiveness and efficiency to meet customer satisfaction. One of the models to measure performance in the supply chain is SCOR version 12. This model presents a business process framework, performance indicators, best practices, and unique technologies to support communication and collaboration between supply chain partners to increase the effectiveness of supply chain management and the effectiveness of supply chain improvements. This research used SCOR 12.0 to identify the performance metrics within the supply chain. A make-to-order small-medium enterprise (SMEs) in Yogyakarta, Indonesia, is the object of the research. We portrayed the business scope diagram by identifying the process elements in each tier (plan, source, make, deliver, return, enable) and decomposing each Process into performance attributes, i.e., Reliability, responsiveness, agility, cost, and asset management efficiency. We obtained three performance attributes (Reliability, responsiveness, and cost) based on observation and interviews, 52 performance metrics spread into 47 process elements. The SMEs can use the performance metric framework to measure supply chain management performance in make-to-order products.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".