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Record W2799546361 · doi:10.5267/j.jpm.2018.4.003

Performance evaluation of project management system based on combination of EFQM and QFD

2018· article· en· W2799546361 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Project Management · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality Function Deployment in Product Design
Canadian institutionsnot available
Fundersnot available
KeywordsQuality function deploymentQuality managementComputer scienceBusinessEngineeringOperations managementManagement system

Abstract

fetched live from OpenAlex

Project management system (PMS) is broadly recognized as the effective management tool for several organizations. European Foundation of Quality Management (EFQM) model provides some advantages for companies to be successful in current competitive environment. This paper is based on the combination of EFQM and Quality function deployment (QFD) in order to evaluate a PMS in an aviation organization. Although, an integration of these models increases the system complexity, the implementation of EFQM-QFD helps us identify all noteworthy success factors of PMS within the organization. In addition, the current status of PMS performance is evaluated based on these factors. This study attempts to find out how organizations ought to be managed to take full advantage of PMS tools. This study uses a comprehensive questionnaire to find all critical factors influencing on the success of the organization. The method of this paper is implemented in an organization in aviation industry with several management departments.

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)0.007
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 score0.710
Threshold uncertainty score0.766

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.059
GPT teacher head0.295
Teacher spread0.236 · 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