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New Problems, New Solutions: Making Portfolio Management More Effective

2000· article· en· W1532749173 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueResearch-Technology Management · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPortfolioNew product developmentProject portfolio managementPrioritizationProcess (computing)Product (mathematics)Resource (disambiguation)BusinessProcess managementRisk analysis (engineering)Application portfolio managementPipeline (software)Operations managementComputer scienceProject managementMarketingEngineeringSystems engineeringFinance

Abstract

fetched live from OpenAlex

OVERVIEW:Most companies' development portfolios suffer from: too many projects for the limited resources available; ineffective project prioritization; Go/Kill decisions made in the absence of solid information; and too many minor projects in the portfolio. The end result is poor performance: low-impact projects; too long to get to market; and higher-than-acceptable failure rates. Solutions are proposed based on the experiences of firms in the study. The first is to implement a systematic gating or Stage-Gate new product process, complete with tough Go/Kill decision points. Next, build in resource capacity analysis—a quantitative assessment of resource supply versus demand in your new product pipeline. A third solution is to develop a product innovation and technology strategy for your business to help guide the selection of the best projects. Finally, integrate portfolio management into your gating process using one of the two approaches utilized by leading companies in the study.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.719
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.008

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.

Opus teacher head0.097
GPT teacher head0.361
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it