The concept of DSM AND ADT INTEGRATION in the PRODUCT DESIGN PROCESS
Bibliographic record
Abstract
In manufacturing, product design is often very complex and requires the participation of a large number of services and material resources. Managing these resources can be very costly in terms of time, risk and money. The use of well-known tools (e.g. PERT, CPM and Gantt) alone cannot establish a detailed andoptimized planning in the execution of different stages and tasks of a project. To address this problem theDesign Structure Matrix (DSM) and the Axiomatic Design Theory (ADT) appear to be an interesting solution.This paper presents a DSM and ADT integrated tool as a part of the decomposition-integration problem inproduct design development process, where the latter is more concerned with mapping customer’s needs from functional requirements to design parameters, while the former is better suited to modelling the interactions and the integration of the design parameters. It also presents some algorithms related to both DSM and ADT used for manipulating projects-based DSM information, coordination and timing requirements, and provides a complementary between them. The discussion in this paper addresses the implementation of concurrent design engineering which is the greatest challenge faced by design managers.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".