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Record W2012130467 · doi:10.1177/1063293x08096735

Determining the Value of Sequential and Concurrent NPD Processes

2008· article· en· W2012130467 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

VenueConcurrent Engineering · 2008
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicProduct Development and Customization
Canadian institutionsMcGill UniversityConcordia University
Fundersnot available
KeywordsReworkConcurrent engineeringStochastic gameSimultaneityInterval (graph theory)Process (computing)Value (mathematics)New product developmentSequence (biology)Computer scienceProduct (mathematics)Mathematical optimizationMathematicsStatisticsScheduling (production processes)Economics

Abstract

fetched live from OpenAlex

The value of using sequential (SE) and concurrent engineering (CE) processes for new product development (NPD) is quantitatively determined by means of the maximum expected payoff method. This method models a process into a sequence of intervals and calculates the value of the maximum benefit as a result of optimum decisions during each interval. SE and CE processes are evaluated in terms of the parameters: simultaneity and intensity of interaction. The maximum expected payoff method shows under what conditions SE or CE is best, and the effect of incomplete information and resultant rework. Results are compared to actual NPD processes with good agreement.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.197
Teacher spread0.178 · 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