Deepak Srivastava
Papers
1
Total Citations
28
H-Index
1
About
Deepak Srivastava is a leading researcher at the intersection of machine learning, supply chain management, and pharmaceutical logistics. His work focuses on developing predictive models to enhance efficiency and transparency in global omnichannel supply chains, particularly within the pharmaceutical sector. Srivastava’s most cited paper, “Machine learning-based technique for predicting vendor incoterm (contract) in global omnichannel pharmaceutical supply chain” (2023), has garnered 28 citations, showcasing its timely relevance. In this study, he introduces a novel ML framework that accurately forecasts vendor incoterms—critical contractual terms governing shipping responsibilities—thereby reducing transaction costs and mitigating risks in complex pharmaceutical networks. His contributions address pressing challenges in supply chain digitization, offering practical tools for industry stakeholders. Srivastava’s work is notable for bridging advanced computational methods with real-world operational hurdles, earning him recognition as an emerging voice in supply chain analytics. His research not only advances academic understanding but also provides actionable insights for practitioners navigating the intricacies of global pharmaceutical distribution.
Research Focus
Key Achievements
Top Papers
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