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Record W4403416176 · doi:10.4050/f-0070-2014-9615

Turboshaft After-Market Positioning: A Strategy Based Approach

2014· article· en· W4403416176 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Due to its subjective nature, the concept of value is not one that is easily defined. Marketers often refer to a product's 'value proposition' as an explanation to why an operator should buy a product or use a service. This statement should convince a potential operator that one particular product or service will add more value or better solve a problem than other similar offerings. In the rotorcraft market, this value proposition is often tied to capabilities of the helicopter and is typically defined as a composite metric. This metric is then compared to the acquisition cost to get a sense of helicopter value. Helicopter manufacturer's marketing and sales departments then go to the market and sell the benefits, either in range, take-off weight, reliability, operating cost, etc… One major difference in the value stream of rotorcraft products as compared to typical consumer products is that it is standard for a second-level supplier, in this case the engine manufacturer, to offer its own services and support. This creates an environment where both the helicopter and engine manufacturer have direct contact with the end operator and can influence their perception of value. A method for an engine manufacturer to define value can aid the helicopter manufacturer, and ultimately, the operator, to make an informed decision regarding the right product for their mission objectives. In order to derive a metric for the concept of value for a helicopter engine, one must understand the important aspects from an operational perspective, as opposed to the technical focus typically associated to this type of product. This paper focuses on the derivation of such a composite metric by using terms that represent a product's reliability, maintainability, and capability as compared to its direct operating cost as the acquisition cost of the engine is transparent to the end operator. Looking at the helicopter engine competitive environment in such a way serves two purposes: one for the engine manufacturer to be able to gauge the competitiveness of their product, and one for the operators to understand an engine's value proposition and how it fits into the helicopter capabilities. The details of how the metric of engine productivity were derived will not be elaborated as they are considered intellectual property to Pratt and Whitney Canada, but the benefits and concepts of viewing helicopter engines in such a way can be discussed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.997

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)0.0040.000

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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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.

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
Published2014
Admission routes1
Has abstractyes

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