Lessons learned from development and implementation of a strategic life cycle model
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).
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".