Lean Value Creation in the Product Development Process With the Principle of Set Based Concurrent Engineering
Bibliographic record
Abstract
Lean value creation requires a value-adding network of lean activities across the whole Product Development Process (PDP). Management needs to allocate resources and properly control the process to create the value that stakeholders desire. Leading companies in industry have successfully applied Set-Based Concurrent Engineering (SBCE) for lean PDP. In SBCE, designers propose several feasible solutions and develop them relatively independently and in parallel, and then gradually narrow the sets of solutions based on updated project feedback at each stage-gate design review. As an important lean concept with many advantages, SBCE has constraints that can jeopardize lean value creation. For instance, it is unclear how resources are allocated to each stage, different functional teams, and different value creation activities related to different kinds of value, which can cause waste of talent, time, and money. This paper focuses on how resources can be allocated to SBCE by viewing product development activities as value creation cells. Under management control, lean value creation activities use knowledge and other resources to produce valuable design solutions. A mathematical feedback control model is proposed to illustrate how management can invest resources for the value creation process. This model can be used to explore resource allocation to functional teams and processes according to a holistic value creation project development strategy and the optimal creation of lean value.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".