The Influence of Information Systems on Supply Chain Resilience During Disruptive Events
Bibliographic record
Abstract
This study examines the influence of information systems on supply chain resilience during disruptive events. As global supply chains become increasingly complex and vulnerable to unforeseen disruptions, organizations are turning to information systems to enhance their resilience and ability to adapt in crisis situations. The research investigates the role of various information technologies, such as real-time tracking, predictive analytics, cloud-based collaboration tools, and emerging technologies like blockchain, IoT, and AI, in building resilient supply chains. By utilizing a qualitative approach, this study gathers insights from a sample of 38 professionals across diverse industries who shared their experiences in managing supply chains during disruptions. The findings reveal that information systems significantly improve supply chain visibility, enabling organizations to anticipate disruptions, respond more efficiently, and maintain operational continuity. Additionally, the integration of information systems enhances collaboration and communication among supply chain partners, fostering trust and facilitating joint problem-solving. Furthermore, the study highlights the importance of agility, flexibility, and data-driven decision-making in navigating disruptions. Despite these advantages, the research also identifies challenges related to system integration, cybersecurity risks, and resistance to change, which can hinder the effective implementation of information systems. The study concludes that while information systems are critical to enhancing resilience, their success depends on strategic planning, a culture of innovation, and context-specific solutions. The findings contribute to the growing body of knowledge on supply chain resilience and offer practical insights for organizations looking to strengthen their supply chain operations in the face of increasing uncertainty.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".