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Policy Brief 4.7

Logistics Optimization Adoption in Data Science Teams: Scaling And Replication — Post-Pandemic Lessons

10
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IEEE
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~ 10–15 mins
Reading Time
AI IoT Governance
Abstract

This policy brief investigates “Logistics Optimization Adoption in Data Science Teams: Scaling And Replication — Post-Pandemic Lessons” using a mixed-methods case study. Through a economic lens, the analysis integrates multi-source data to derive an actionable roadmap for researchers and practitioners.

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Logistics Optimization Adoption in Data Science Teams: Scaling And Replication — Post-Pandemic Lessons

ABSTRACT
Logistics Optimization Adoption in Data Science Teams: Scaling And Replication — Post-Pandemic Lessons is unpacked across themes: scalability, usability, interoperability, and risks. Limitations and future research paths are noted.
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