The Impact of Digitalization on the Sustainability of the Supply Chain
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.
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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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it