Performance assessment framework for supply chain partnership
Bibliographic record
Abstract
Purpose The purpose of this paper is to develop a framework for assessing the comprehensive performance of supply chain partnership (SCP). Design/methodology/approach Using the literature review approach, the paper proposes a framework to assess the performance of SCP. The framework is based on the self‐assessment dimensions and approaches of the business excellence model developed by the European Foundation for Quality Management (EFQM). The proposed framework could be implemented not only in entire supply chains, but also in a dyadic relationship. Findings Identifying strengths and opportunities for improvement begins with assessing the level of SCP. The proposed framework focuses on assessing two dimensions of SCP – efforts and results – that will offer practitioners both balanced insights and valuable information. This framework also highlights assessment dimensions that could help qualified assessors to produce consistent judgments and evaluate multiple aspects of SCP. The framework includes practical indicators to help measure outcomes, such as cost efficiency and flexibility. Originality/value This paper sheds light on the assessment dimensions based on the EFQM model. Assessors can conduct an objective and standardized assessment using these multiple dimensions. This paper expands the traditional concept of SCP performance into both tangible and intangible performance by emphasizing output and outcome.
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.
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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".