Bibliographic record
Abstract
In the twenty-first century, the global agricultural economy has undergone profound structural transformation shaped by digitalization, climate challenges, geopolitical turbulence, and market integration. Within this evolving context, logistics management has moved beyond its traditional operational role to become a strategic factor of competitiveness, profitability, and sustainability in agricultural enterprises. For grain producers, logistics serves as the central mechanism that connects production, storage, transportation, and marketing into a coherent and value-generating chain. Efficient logistics systems reduce costs, improve product quality, and enhance resilience to external shocks, thereby influencing national food security and export capacity. The purpose of this monograph is to develop a scientifically grounded and practically applicable framework for improving logistics management in agricultural grain enterprises through the integration of systemic, digital, and sustainability principles. The research seeks to demonstrate how logistics can evolve from a supporting process into a strategic management function that enhances competitiveness and environmental responsibility. The methodological foundation of the study combines systemic and structural-functional analysis, comparative and benchmarking approaches, and risk-based and process-oriented methods. It also employs case studies of global leaders—Kernel (Ukraine), Viterra (Canada), Bunge (Brazil), InVivo (France), Greenports Holland (Netherlands), and Senwes (South Africa)—to identify best practices in digital transformation, sustainability, and logistics optimization. The main results reveal that digitalization, intelligent automation, and multimodal infrastructure development are decisive for modernizing Ukraine’s logistics system and aligning it with European and global standards. The study emphasizes the strategic importance of data-driven logistics, risk management, and cross-border cooperation for enhancing supply chain flexibility and resilience. Future research should focus on quantitative modeling of logistics performance and the creation of integrated digital platforms that connect producers, transport operators, and regulators. The findings contribute to both theory and practice, offering policymakers and enterprises a roadmap for building adaptive, innovative, and sustainable logistics ecosystems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".