Project portfolio management for product innovation
Bibliographic record
Abstract
Purpose The purpose of this paper is to create a benchmark and identify best practices for Project Portfolio Management (PPM) for both tangible product‐based and service product‐based development project portfolios. Design/methodology/approach A questionnaire was developed to gather data to compare the PPM methods used, PPM performance, PPM challenges, and resulting new product success measures in 60 Australian organisations in a diverse range of service and manufacturing industries. Findings The paper finds that PPM practices are shown to be very similar for service product development project portfolios and tangible product development project portfolios. New product success rates show strong correlation with measures of PPM performance and the use of some PPM methods is correlated with specific PPM performance outcomes. Research limitations/implications The findings in this paper are based on a survey of a diverse sample of 60 Australian organisations. The results are strengthened by comparisons with similar North American research; however, they may not be representative of all environments. Research in other regions would further qualify the findings. As each organisation's PPM process is unique, case study methods are recommended for future studies to capture more of the complexity in the environment. Practical implications The paper shows that PPM practitioners and executives who make decisions about the development of tangible products and/or service products will benefit from the findings. Originality/value This paper extends the existing understanding of PPM practices to include service development project portfolios as well as tangible product development project portfolios and strengthens the links between PPM practices and outcomes.
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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 | 0.024 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".