Performance measurement of a lean product development process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the past few years, organizations have faced pressure from stakeholders to implement lean principles in their product development processes. However, the existing methods are not capable of measuring the benefits of adopting lean initiatives in the product development process. This research aims to develop a performance measurement model that can measure the effects of implementing lean in the engineering process. Engineering effort is analyzed in order to identify hidden wastes (e.g. inventory in the form of information about product specifications or engineering errors) in the engineering process. The model has been implemented in a civil design process of an engineering consultant company to validate the general applicability of the new model. The implementation of the model provides visibility on the waste hidden in the engineering process and quantifies that waste. The most significant contribution of this research is the development of new performance metrics and a decomposition chart. Finally, performance metrics are properly linked and the model treats lean as a holistic system, quantitatively measuring performance at different organizational levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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