A New Design for Production (DFP) Methodology with Two Case Studies
Bibliographic record
Abstract
Concurrent engineering (CE) design demands the consideration of product life cycle issues in the early product design stage. Among various life cycle issues, this work concentrates on production and how to optimize a product design to minimize its production costs. This paper proposes the use of cost as a measure of the productivity and defines Design for Production (DFP) as methods that lead to a product design with minimum production costs while satisfying all the functional requirements. Based on this definition, this work proposes a DFP methodology. The novelty of this methodology lies on three aspects (1) the use of the Operation-Based Costing (OBC) method to measure productivity, (2) the identification of relations and boundaries between product design and production activities, and (3) the integration of product design, production cost estimation, and metamodeling-based optimization to search for the optimal product design. The proposed DFP methodology has been applied to the optimal design of two industry products, an industrial silencer and a linear air diffuser. The results from these studies demonstrate the effectiveness of the proposed method, whose assumptions and limitations are also elaborated.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".