Bibliographic record
Abstract
Product complexity is driven by the interdependence of product functions, which in turn determines the interdependence of design tasks, and this is reflected in the complexity of the design process.Ever increasing product complexity has become an obstacle to effective product design.This paper introduces an agent-based model that was used to study the impact and mitigation of product complexity, where complexity was characterised by metrics defined from a knowledge perspective.In the model, a product was represented as a set of functions that required designer knowledge, component design and component integration.Designers were modelled as agents who learned knowledge through consultation and who applied knowledge to function design tasks.Variables that characterised different coordination mechanisms influenced the efficiency and quality of communication between designers and impacted the global behaviour of product design.The results from simulation experiments suggested that a growth in complexity increased effort and span time exponentially and that coordination mechanisms which quickly increased designer learning or which improved collaboration reduced overall effort.The implication for managers is that, for the design of complex products, attention should be paid to the effectiveness of coordination mechanisms, and how they reduce the time for designer learning.The implications with regard to complexity during product design can be applied to other activities where learning is a key performance factor.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".