Enabling Technologies as a Support to Achieve Resilience in Supply Chain Operations
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
In response to the dynamic and ever-changing landscape of supply chains, which are continually challenged by internal and external factors, there is a critical need for continuous adaptation, learning, and improvement. Historically, scholars have argued that traditional information systems lack the capacity to effectively support resilience strategies within supply chains. However, advancements in Industry 4.0 technologies may have shifted this paradigm. This article explores how enabling technologies (ET) can support the development of resilient operations at the supply chain level. To that end, a systematic literature review is combined with a multiple case study to understand how these technologies can support the development of elements of resilience. Three distinct sectors from different geographical locations were chosen for this study: an agri-food company in Brazil, a manufacturing firm in the food industry in Canada, and a logistics service provider in Italy. Integrating both theoretical insights and empirical findings leads to the formulation of a research framework, the primary contribution of this study, which serves as a resource for scholars and practitioners aiming to leverage ET to increase supply chain resilience. The article concludes with key findings and suggests avenues for future research.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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