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Record W4289319415 · doi:10.20998/2313-8890.2021.07.04

CONCEPTUAL ASPECT OF TECHNOLOGICAL REENGINEERING OF INDUSTRIAL ENTERPRISES

2022· article· en· W4289319415 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueActual problems of improving of current legislation of Ukraine · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnterprise Management and Information Systems
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsBusiness process reengineeringProcess managementProduction (economics)Technological changeProcess (computing)Computer scienceBusinessManufacturing engineeringEngineeringLean manufacturing

Abstract

fetched live from OpenAlex

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.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.049
GPT teacher head0.238
Teacher spread0.189 · 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