The Development of an Integrated External Environment Monitoring Framework Aimed at the Internal Control of the Procurement Process of Fat and Oil Companies
Bibliographic record
Abstract
The article deals with the development of methodical recommendations for monitoring the external environment aimed at the internal control of the procurement process in business based on a risk-oriented approach and with the digital techniques used by fat-and-oil industry companies. For research purposes, the following were necessary: first, to develop an integrated scheme for monitoring the external environment aimed at the internal control of the procurement process, taking into account the specifics of the commercial organization’s activity; and second, to analyze the features of the integrated monitoring scheme with the use of digital techniques. The methodology for developing methodical recommendations for monitoring the external environment aimed at the internal control of the procurement process is based on a risk-oriented approach, the unforeseen circumstances theory, and the use of big data and business analytics. In the first section, the authors substantiate the relevance of the research topic. In the second section, investigations on the topic are reviewed, the theoretical foundations are summarized, and research hypotheses are formulated. The third section determines the methodology of the study. The fourth section presents the research results, their practical value, recommendations and limitations, and the developed integrated scheme for environment monitoring with regard to the internal control of the procurement process based on a risk-oriented approach taking into consideration the specifics of the fat and oil industry. This section also determines the specifics of the digital techniques used for monitoring the environment and discusses issues surrounding the external monitoring of raw material prices, different types of work, and services based on digital techniques aimed at internal control. In the fifth and final section of the article, the authors analyze the research results and substantiate the prospects for further research in this area. The research results could be used by commercial companies in processing industries which are undergoing digital transformation and developing platform solutions aimed at improving internal control. The main research result of this article is the development of methodical recommendations for monitoring the external environment aimed at the internal control of the procurement process in business based on a risk-oriented approach with the use of digital techniques and a developed integrated monitoring scheme.
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 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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".