An Optimization Design Method of Express Delivery Service Based on Quantitative Kano Model and Fuzzy QFD Model
Bibliographic record
Abstract
Service quality is the soul of express enterprises forever. It is of great practical significance for winning customer satisfaction, improving the market competition, and realizing sustainable performance. Unlike tangible products, express delivery service has the characteristics of intangibility, heterogeneity, indivisibility, and instability. While customer demands are complex and changeable and unpredictable, incorporating complex customer requirements into service design has been a growing interest of researchers and practitioners. This paper proposed an optimization design framework based on the quantitative Kano (QKNO) and the fuzzy quality function deployment (FQFD) to effectively achieve the best matching of enterprise service elements under the uncertainty and imprecise judgment information. An empirical study is conducted to verify the feasibility of the proposed approach. The results show that the framework could guide the express company to prioritize the enterprise service elements to maximize customer satisfaction and provide a reasonable budget allocation scheme to set the best resources match. It has theoretical and practical meaning for express enterprises to implement customization service strategy and improve service quality under the limited budget, which could be further extended to other service industries to make optimization decisions.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".