Prioritizing Engineering Characteristics of Product-Service System Using Analytic Network Process and Data Envelopment Analysis
Bibliographic record
Abstract
Product-service system (PSS) approach has emerged as a competitive strategy to impel manufacturers to offer a set of products and services as a whole. A new three-domain PSS conceptual design framework supporting engineering design methodology is proposed in this research. Identification of the critical parameters in these domains plays an important role. Engineering characteristics (ECs) in the functional domain, which include product-related ECs (P-ECs) and service-related ECs (S-ECs), are identified by translating customer requirements (CRs) in the customer domain. Quality function deployment (QFD) is used to implement this translation process. Prioritizing ECs is a crucial issue in achieving the optimal PSS planning. First, to consider complex dependency relationships between and within CRs, P-ECs and S-ECs, the analytic network process (ANP) approach is integrated in QFD to determine the initial importance weights of ECs. Second, the data envelopment analysis (DEA) approach is applied to adjust the initial weights of ECs considering requirements of the manufacturers. In order to deal with the vagueness, uncertainty and diversity in decision-making, the fuzzy set theory and group decision-making technique are used in the supermatrix approach of ANP in the first phase. A case study is carried out to demonstrate the effectiveness of the developed prioritizing approach for ECs in PSS conceptual design.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".