A Novel Framework for Product/Service Systems Using Environment-Based Design Methodology
Bibliographic record
Abstract
The diversity of costumer’s needs requires manufacturers to provide a complex package of product and service. In contrast to traditional matured methods used for product design, Product/Service Systems (PSS) design still has a large room for development because of three following core research challenges: 1) development of a common shared structure to represent and understand PSS’s elements and their relations; 2) systematic modelling approaches to formulating design problems; and 3) holistic consideration of social, technological, economic and ecological elements. This paper aims to propose a novel framework for PSS design by addressing three issues above. The proposed framework is derived step-by-step from a natural language description of PSS environment using Environment-Based Design (EBD) methodology. The proposed framework attempts to accommodate the recursive scenarios in PSS design along with PSS lifecycle. The PSS environment will be firstly analyzed through a question-asking strategy. Besides, a set of graphical tools will be presented to support the development of framework, such as product-environment system, performance network, and conflict map. A case study, concerning the service design of intellectual property protection in collaborative product development, will be presented to illustrate the proposed framework.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".