A knowledge-based system for overall supply chain performance evaluation: a multi-criteria decision making approach
Bibliographic record
Abstract
Purpose Existing supply chain (SC) performance models are not able to cope with the potential of intensive SC digitalisation and establish a relationship between decisions and decision criteria. The purpose of this paper is to develop an integrated knowledge-based system (KBS) that creates a link between decisions and decision criteria (attributes) and evaluates the overall SC performance. Design/methodology/approach The proposed KBS is grounded on the fuzzy analytic hierarchy process (fuzzy AHP), which establishes a relationship between short-term and long-term decisions and SC performance criteria (short-term and long-term) for accurate and integrated Overall SC performance evaluation. Findings The proposed KBS evaluates the overall SC performance, establishes a relationship between decisions (long-term and short-term) and decision criteria of SC functions and provides decision makers with a view of the impact of their short-term or long-term decisions on overall SC performance. The proposed system was implemented in a case company where the authors were able to develop a SC performance monitoring dashboard for the company’s top managers and operational managers. Practical implications The proposed KBS assists organisations and decision makers in evaluating their overall SC performance and helps in identifying underperforming SC functions and their associated criteria. It may also be considered as a tool for benchmarking SC performance against competitors. It can efficiently point to improvement directions and help decision makers improve overall SC performance. Originality/value The proposed KBS provides a holistic and integrated approach, establishes a relationship between decisions and decision criteria and evaluates overall SC performance, which is one of the main limitations in existing supply chain performance measurement systems.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".