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Upgrade Your New-Product Machine

2010· article· en· W176790506 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 · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsNew product developmentProcess managementProduct managementProduct engineeringBenchmarkingProduct lifecycleProcess (computing)Product (mathematics)Product designUpgradeProduct design specificationScrumBusinessComputer scienceVoice of the customerManufacturing engineeringBlueprintMarketingCustomer advocacyEngineeringService (business)Software developmentSoftware

Abstract

fetched live from OpenAlex

OVERVIEW:Where a company's new product development process is inferior to its manufacturing process, in terms of defect rate and overall effectiveness, a process called New Product Blueprinting can help its managers to close the gap. This process is a means of upgrading the “fuzzy front end” of new-product development. It builds on the Stage-Gate process and forces an “outside-in” perspective by fully engaging customers in a set of discovery and preference interviews. Guesswork is taken out of product design by using tools such as Market Satisfaction Gap, a measure of customer eagerness for each product attribute. The entire four-phase process—market segmentation, customer interviews, competitive benchmarking and new-product planning—allows the product development team to solidify its business case before undertaking costly product development.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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.

Opus teacher head0.041
GPT teacher head0.304
Teacher spread0.263 · 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