Economic Technology of Enterprise Risk Management Based on Information Support for Their Activity
Bibliographic record
Abstract
The article examines the main trends in the use of approaches to risk management of activities of Russian enterprises and organizations. The conclusion was made about the shift in focus in the field of risk management from the theory of calculations to minimization of uncertainty. The range of acceptability of the risk level in terms of economic instability is proved. The results of the study of the degree of readiness of entrepreneurs representing the industrial and commercial sectors of Tyumen economy to work in unstable economic conditions are mentioned. The extent of use of global management decision-making techniques in actual practice is estimated; the reasons for the low level of information support for the risk management process and the dependence of the business risk level on the diversification of activities are revealed. Five main groups of risk management techniques are proposed that predetermine the choice of options for possible actions depending on the likelihood and magnitude of potential damage represented as a damage-probability matrix. The impact of the level of information support on the level of uncertainty and risk in the implementation of 196 economic solutions in the field of managing the enterprise economy with a specialization in groups of risk situations was estimated: substantiation of the project of development of the existing production, management of the product life cycle, scheduled and urgent definition of the enterprise place in the competitive market. A risk management approach, which consists in ranking methods in descending order of their effectiveness depending on the action of risk-contributing factors, is proposed. A technology of managing the risks of the enterprise, which allows to achieve efficient allocation of resources to risk management and the best result – the lowest possible or acceptable residual risk level.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".