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Record W2995577075 · doi:10.19255/jmpm02005

Product Development Cost Management in Aerospace SMEs

2019· article· en· W2995577075 on OpenAlexaff
Darli Rodrigues Vieira, Érika Souza de Melo, Marcela Pereira dos Santos Silva

Bibliographic record

VenueJournal of Modern Project Management · 2019
Typearticle
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsBombardier (Canada)Université du Québec à Trois-Rivières
Fundersnot available
KeywordsAerospaceNew product developmentProduct cost managementScope (computer science)Product lifecycleContext (archaeology)Cost engineeringProcess managementTarget costingProduct (mathematics)Process (computing)BusinessProduct managementProduct designManufacturing engineeringComputer scienceCost estimateRisk analysis (engineering)Systems engineeringEngineeringMarketing

Abstract

fetched live from OpenAlex

Product development in the aerospace industry involves a complex environment that demands a high level of integration to ensure that the final product cost adheres to the business plan. However, in the initial development phases, the work scope is immature and cannot be completely defined a priori. In this context, cost management is a real challenge, particularly for small and medium-sized enterprises (SMEs) that have smaller margins for errors when defining their products’ pricing. This paper proposes a framework to manage costs during product development using a method based on the design structure matrix associated with the design to cost methodology. The analysis is based on the literature and the case study method, which is applied in an aerospace SME. The results indicate positive prospects for the application of the method, which can provide a holistic perspective of the development process while promoting better control of costs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.608

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 head0.012
GPT teacher head0.227
Teacher spread0.215 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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