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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".