Leveraging Industry 4.0 Technologies for Sustainable Humanitarian Supply Chains: Evidence from the Extant Literature
Bibliographic record
Abstract
Prevailing and exacerbating impacts of climate change call for robust and resilient humanitarian supply chains (HSCs). To that end, intelligent technologies that brought about the Industry 4.0 (I4.0) revolution, such as the Internet of Things, blockchain, and artificial intelligence, may tremendously impact the optimal design and effective management of HSCs. In this paper, we conduct a systematic literature network analysis and identify trends in I4.0 and HSCs. We posit the need to instill into current HSC efforts the quadruple bottom-line (cultural, economic, environmental, and social) pillars of sustainability and define a Sustainable Humanitarian Supply Chain (SHSC). Based on the extant literature and ongoing practice, we highlight how I4.0 technologies can aid SHSC stages from disaster risk assessment to preparedness to response to relief. The complex nature of SHSCs requires a holistic and multidisciplinary approach and collaboration by scholars, policymakers, and industry practitioners to pool solution resources. We offer future research venues in this fledgling but life-saving scientific discipline. SHSCs can be empowered with I4.0 technologies, a much needed direction in our climate-changed world.
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How this classification was reachedexpand
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 | 0.002 | 0.004 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".