Lessons learned from development and implementation of a strategic life cycle model
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
A strategic life cycle model, coined triple "A" (adolescence, adult and aging), has been developed in 2000 at Nortel networks to drive repair outsourcing and to allocate resources freed up from outsourcing to accelerate quality growth of new products. A three-layer TOP (triple "A" - organizing people) strategy was then established to drive the implementation of the triple "A" effectively. The far-ranging impact of the triple "A" upon plant layout as well as roles and responsibilities is reviewed in this paper. For one of the pilot products, the cost avoidance due to early resolution of quality problems is estimated to be in excess of $7 million. The factors that have contributed to the success of this model are briefly discussed, and there are several key lessons learned from the implementation process. These include: a paradigm shift on the concept and value of repair, quantum improvement requires preventing "birth" defects in the design phase, synergy between various initiatives, mathematical correlation to expand the capability of the triple "A". Lastly, it is suggested that further research be done to explore the application of the model to practically all-human endeavors as a basis of continuous quality improvement. To substantiate this suggestion, the paper briefly sketches the concept of the triple "A" at several levels: vision (perspective), science (predictive), and engineering (prescriptive).
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