Product Portfolio Management—Governance for Commercial and Technical Portfolios over Life Cycle
Bibliographic record
Abstract
Companies face an important question on whether they should allow product portfolio renewal to occur without interference by merely adding new products, or should the renewal be governed based on strategic and financial targets over life cycle? New product development phase is well covered in the product portfolio management (PPM) context, but later product life cycle phases are not covered well in this context. Neither are the different product structure levels adequately taken into account, instead the current PPM only discusses the “product” in general terms. Based on analysing the portfolio related practical challenges in ten case companies, and realising the deficiencies of current PPM theory motivated this explorative multiple case study. The principal results of this study involve revealing the need for a new potential PPM governance model that enables managing commercial and technical product portfolios over life cycle phases. A governance model framework, based on horizontal and vertical portfolios managed by two centralised teams, is proposed. Based on the data, and views of industrial experts, the created new PPM governance model has potential to aid business managers in understanding PPM as an entity that has a role in managing existing product portfolios and their renewal based on commercial and technical portfolios over life cycle as collaboration between business and engineering teams in all organisational levels.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".