Methodical Approach to Evaluation of Efficiency of Transformation of Business Processes on Engineering Enterprises in the Context of Ensuring Security
Bibliographic record
Abstract
The main purpose of the study is the formation the methodological approach to assessing the options for transforming of the main business process in the context of ensuring security. The study is based on the assessment results of several of leading engineering enterprises. The methodology of estimation of efficiency of carrying out transformation of the basic business processes of the enterprises, based on the theory of fuzzy sets, is a convenient means of carrying out multicriteria evaluation, comparative analysis from the point of view of integrated criterion and obtaining orderly priority of business processes on their efficiency. The study of the process of transformation of a business process for the production and sale of product enterprises was conducted and a methodology for assessing its effectiveness based on the use of the mathematical apparatus of the theory of fuzzy sets was formed and allows us to formalize and evaluate the effectiveness of the implementation of each of the options for the business process. Methodology for assessing the effectiveness of the transformation of the main business processes at the enterprises can be used in enterprises of various profiles after the necessary analysis and optimization. The study conducted a comprehensive assessment of the effectiveness of the transformation of the main business processes at the enterprises using as example engineering enterprises in the context of ensuring security.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".