Supermarket performance measurement using hybrid multi-criteria decision-making methods
Bibliographic record
Abstract
The supermarket sector is one of the most important components of the retail industry. Rapidly growing chain supermarkets stand out in this sector. This study introduces a hybrid method to assess the performance of stores within a supermarket chain. In the study, stores are compared over a five-year period using various criteria. These criteria include financial metrics such as rent cost, employee cost, energy cost, waste cost, supply cost, and sales revenue, as well as store size. Initially, the presence of differences between supermarkets based on these criteria was investigated. According to the criterion-based analysis, it is not easy to make a decision about the overall performance of supermarkets. Therefore, conducting the analysis using multi-criteria decision-making methods provides more meaningful results. In the proposed hybrid method, the criteria are first weighted using the entropy method to determine their importance levels. It was determined that the most important criterion is rent cost. The performance was then calculated using the VIKOR, MOORA, and ARAS methods. As a result of the analysis, the same store showed the best performance in all three methods.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".