A combined approach integrating gap analysis, QFD and AHP for improving logistics service quality
Bibliographic record
Abstract
Managing logistics service quality is vital to achieving higher levels of customer satisfaction and gaining productivity. In this paper, we address the problem of logistics service quality management considering multiple stakeholders point of view namely shippers, customers, municipal administrators, city residents, traffic managers, etc. A hybrid approach based on gap analysis, QFD and AHP is proposed. The purpose of gap analysis is to identify service quality gaps based on customer expectations and perceptions. QFD is used to model technical requirements to fulfil identified service quality gaps. AHP is used to evaluate service quality improvement initiatives and recommend the best one(s) for implementation. A numerical application is provided. Sensitivity analysis is conducted to determine the influence of input parameters on stability of modelling results. The strength of the proposed approach is that it takes into account multiple stakeholders point of view in determining service quality attributes. Besides, certain criteria that have become more relevant in modern times particularly those related to sustainability such as eco-friendliness, human resources, and technological soundness are also part of our study.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".