Lean Value Creation in the Product Development Process With the Principle of Set Based Concurrent Engineering
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it