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Record W4306653423 · doi:10.18280/ijsse.120414

Risk Management System at an Engineering Enterprise in Conditions of Ensuring Security

2022· article· en· W4306653423 on OpenAlexvenueno aff
Nataliia PETRYSHYN, Oleh Mykytyn, Olga Malinovská, Олена Халіна, Olha Kirichenko

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBanking, Crisis Management, COVID-19 Impact
Canadian institutionsnot available
Fundersnot available
KeywordsIDEF0Risk analysis (engineering)DynamismRisk managementBusinessProcess managementComputer scienceEngineering managementEngineeringSystems engineeringComputer-integrated manufacturing

Abstract

fetched live from OpenAlex

In modern conditions, the problem of the survival of companies, and the preservation and provision of their further development has become particularly relevant. The crisis has engulfed not only individual enterprises but entire industries. The most affected, in particular, is the engineering industry. The main purpose of the study is the formation of a risk management system at an engineering enterprise in terms of ensuring its security. To do this, we applied the IDEF0 modelling methodology with its main elements. The dynamism of the economic environment and the complexity of the links between its elements necessitate the adoption of informed management decisions in the face of risk and uncertainty of future results. Risk management is becoming an obligatory activity for engineering enterprises, implementation of projects, and operations. Based on the results of the study, a basic IDEF0 model of the risk management system at an engineering enterprise in terms of ensuring its security was formed. The study has limitations and, first of all, they relate to the specifics of the activity of engineering enterprises, other areas of activity are not taken into account. Further research requires expanding the model and taking into account not only risks but also threats and direct dangers.

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)0.000
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 score0.613
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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Same venueInternational Journal of Safety and Security EngineeringSame topicBanking, Crisis Management, COVID-19 ImpactFrench-language works237,207