MétaCan
Menu
Back to cohort
Record W3011507498 · doi:10.19255/jmpm02212

Improving business process management for product-centric service organization: the case of aerospace maintenance project

2020· article· en· W3011507498 on OpenAlexaff
Adrianne Moreira, Darli Rodrigues Vieira, Alencar Bravo, Christophe Bredillet

Bibliographic record

VenueResearch Open (London South Bank University) · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Process Modeling and Analysis
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsProcess managementBusiness process managementFlexibility (engineering)Business processAerospaceProcess (computing)BusinessContext (archaeology)Service (business)EngineeringComputer scienceWork in processMarketingManagement

Abstract

fetched live from OpenAlex

This article provides discussion and analysis of the successful deployment by a university-based project management office (PMO) of the balanced scorecard as a performance measurement tool. The research study builds on a supporting literature review on the balanced scorecard along with background material on collaborative research projects. This is followed by a case study investigation of the design and implementation of the scorecard for a university PMO over a 4-year period. Various managerial insights have been generated that have value to project management professionals engaged in the roll-out of a performance measurement system to support the management of research projects. There is a need to carefully adapt scorecard metrics to the academic requirements in regard to the management of a portfolio of research projects. Furthermore, although data collection is necessary for the sustained use of the scorecard to support team operations, it is also important to consider the people or social dimensions when utilizing the scorecard approach. The article also includes specific details on how scorecard’s key performance indicators have been derived through distilling strategic objectives into operational requirements.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.451
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.016
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.002
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 head0.061
GPT teacher head0.285
Teacher spread0.224 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueResearch Open (London South Bank University)Same topicBusiness Process Modeling and AnalysisFrench-language works237,207