The Impact of Digitalization on the Sustainability of the Supply Chain
Bibliographic record
Abstract
Digitalization is transforming supply chains by introducing advanced technologies that enhance sustainability. This study assesses the impact of digitalization on supply chain sustainability by identifying and analyzing key factors across environmental, social, economic, and digital dimensions. Using a hybrid methodology-PRISMA for a systematic literature review, Delphi for expert validation, and DEMATEL for analyzing interrelationships among factors-we reveal critical drivers of sustainability. In the environmental dimension, energy efficiency and resource utilization are key drivers, influencing waste management and material recycling. Social factors like safety and automation drive diversity and collaboration, while economic factors such as operational costs and product quality influence customer satisfaction and competitiveness. In the digital dimension, data privacy and real-time monitoring drive database scalability. Our findings highlight the role of IoT, blockchain, AI, and cloud computing in optimizing resource use, enhancing transparency, and improving operational efficiency. Based on these insights, we develop a comprehensive framework to guide managers in leveraging these technologies to foster more sustainable, resilient, and efficient supply chains. This research contributes new empirical evidence on the relationships among factors influencing sustainability and offers practical recommendations for aligning digital transformation with long-term sustainability goals.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".