Evaluation of the allocation performance in a fashion retail chain using data envelopment analysis
Bibliographic record
Abstract
Efficiency is one of the most important criteria of performance evaluation in any supply chain management, especially in the fashion retail industry. In the fashion industry, products are characterized by short life cycles and demand uncertainty. Most fast-fashion companies have employed the allocation practice that includes initial allocation and multireplenishment to capture the latest market information. Previous studies focus more on optimizing allocation policies, but overlook the efficiency issue, and the models always tend to be complex and are difficult to understand or apply. In this study, we model the allocation process as a multi-stage system with multiple inputs and outputs. A time-based dynamic network Data Envelopment Analysis (DEA) model, called multi-stage efficiency model (MEM), is developed to evaluate the allocation performance. The MEM considers undesirable outputs, dual-role factors (inventory as an output at the end of previous stage and the input at the beginning of the next stage) and the inconsistent attributes of the dual-role factors among the multi-stages. Meanwhile, the traditional DEA model is introduced to demonstrate the MEM is essential to capture the allocation performance. Based on the MEM results, the more appropriate initial allocation strategy is identified and the factors affecting allocation performance are discussed. The model is applied to a major Canadian fast-fashion company to validate the effectiveness. This article not only provides valuable managerial insights of supply chain management for fashion companies, but also makes contributions to the industrial application of theoretical OR (operation research) models.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".