CONCEPTUAL ASPECT OF TECHNOLOGICAL REENGINEERING OF INDUSTRIAL ENTERPRISES
Bibliographic record
Abstract
The article considers the conceptual aspects of the organization of technological reengineering in industrial enterprises. It is noted that technological reengineering is a solution to a number of problems related to achieving the goals of innovative transformations of the production base of the enterprise, which depend on many factors. The content of components of technological reengineering in their interrelation is determined. It is substantiated that, at the preliminary stage of technical preparation for technological reengineering of the enterprise it is expedient to build an approximate mathematical model of the main technological process in order to identify possible options for its improvement and justify the need for technological reengineering of the entire technological system. The usefulness of the reengineering transformation model is substantiated. It is substantiated that most decisions on innovative technological re-equipment of production on a reengineering basis cannot be made out of connection with other aspects of the enterprise, including the logistics aspect. The role of logistics reengineering in these processes is shown. It is substantiated that at domestic enterprises these issues are not always considered as part of a single production process. As a result, the successful implementation of the goals of one of the types of work on the innovative transformation of production can often be to the detriment of others, which requires additional tasks. The general technological audit is aimed at solving this problem.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".