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Record W2319245053 · doi:10.2514/6.2008-8904

Aerospace Product Cost Management at the early Concept Operations Phase

2008· article· en· W2319245053 on OpenAlexaff
Mark Gilmour, Conor McAlleenen, P. Kelly

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsBombardier (Canada)
Fundersnot available
KeywordsAerospaceProduct (mathematics)Computer sciencePhase (matter)Systems engineeringManufacturing engineeringEngineeringAerospace engineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

The key aim of the presented work is to provide a product cost management system methodology that can be used as a basis for product cost estimation for new product development fr om the very outset of the design activity, when design intent first begins to be captured. The solution was shown to deliver a truly concurrent engineering process through the capture and utilization of knowledge from the design, manufacturing and procure ment functional areas, while also being developed into an automated tool that was deployed when little specialist manufactur ing knowledge was available; thus enabling a C ompany to quickly estimate their designs based on actual company manufacturing capabil ity. As part of the functional development , a part cost estimating function and an assembly time estimating function have been developed . In particular, the Ge netic -Causal costing philosophy wa s utilized in conjunction with the Cost CENTRE -ing methodology for the actual generation of the estimating relati onships. Ultimately, it is illustrated that the minimal input of basic part attributes, such as geometry, can be used in conjunction with process characterization in or der to facilitate cost estimating . Det ailed validation case studies are presented and the system is applied to show the potential economic benefit of such a cost management capability to be deployed already at the concept stage. It will be shown that this ability can have a profound affect on product development, when cost is integrated as an analytical performance metric.

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.

How this classification was reachedexpand

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.585
Threshold uncertainty score0.560

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)0.0010.000

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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

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