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Record W2133072277 · doi:10.1109/picmet.2008.4599779

Technology diffusion planning for ERP in aircraft manufacturing industry

2008· article· en· W2133072277 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

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicERP Systems Implementation and Impact
Canadian institutionsSystems, Applications & Products in Data Processing (Canada)
Fundersnot available
KeywordsEnterprise resource planningResource (disambiguation)Computer scienceManufacturing resource planningManufacturing engineeringEngineering managementKnowledge managementEngineering

Abstract

fetched live from OpenAlex

The Enterprise resource planning ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ERP</i> ) and material requirement planning ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MRP</i> / <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MRP</i> - <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">II</i> ) are few of the key considerations in any complex manufacturing industry. The requirement of ERP and MRP for aircraft industry is growing at a phenomenal rate. The advancement in material and manufacturing management systems has changed the dynamics of shop-floor scene. The induction of smart materials and nano-technology, ultra-high speed machining technologies and psychometric testing of highly skilled labor in a target focused team environment has tremendously enhanced the performance expectation from Man, Machine and Resources. The resource-management and supply-chain-management is becoming extremely complex and require dedicated ERP modules for better management and effective control over the industrial and financial activities through integrated business intelligence (BI) software. The implementation of ERP in industry is a cumbersome process and takes years before it yields and reveals its effectiveness. Research in all these areas is making a phenomenal addition to the volume of knowledge in limited time. The concept of competitiveness demands that the integrated framework for ERP adoption be planned for aircraft industry prior to its deployment. So as to minimize its deployment-span in terms of time and to curtail financial overheads. A number of working principles and guidelines have already been developed in other industries and can be employed in a variety of ways in aircraft industry for optimum performance and to earn competitiveness through ERP suites. This paper provides guidelines for planning ERP and detailed mapping of all activities for ERP in aerospace-industry for effective production planning and control.

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 score0.297
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.049
GPT teacher head0.296
Teacher spread0.247 · 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

Quick stats

Citations10
Published2008
Admission routes1
Has abstractyes

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