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Machine learning-based technique for predicting vendor incoterm (contract) in global omnichannel pharmaceutical supply chain
journal contribution
posted on 2023-06-07, 08:01 authored by Pankaj Kumar Detwal, Gunjan Soni, Suresh Kumar Jakhar, Deepak Kumar Shrivastava, Jitendar Madaan, Yasanur KayikciThe importance of supply chain management to business operations and social growth cannot be overstated. Modern supply chains are considerably dissimilar from those of only a few years ago and are still evolving in a vastly competitive environment. Technology dealing with the rising complexity of dynamic supply chain processes is required. Robotics, machine learning, and rapid information dispensation can be supply chain transformation enablers. Quite a few functional supply chain applications based on Machine Learning (ML) have appeared in recent years; however, there has been minimal research on applications of data-driven techniques in pharmaceutical supply chains. This paper proposes a machine learning-based vendor incoterm (contract) selection model for direct drop-shipping in a global omnichannel pharmaceutical supply chain. The study also highlights the critical factors influencing the decision to select a vendor incoterm during the shipment of pharmaceutical goods. The findings of this study show that the proposed model can accurately predict a vendor incoterm (contract) for given values of input parameters. This comprehensive model will enable researchers and business administrators to undertake innovation initiatives better and redirect the resources regarding the direct drop shipping of pharmaceutical products.
History
Publication status
- Published
File Version
- Accepted version
Journal
Journal of Business ResearchISSN
0148-2963Publisher
ElsevierExternal DOI
Volume
158Page range
113688Department affiliated with
- SPRU - Science Policy Research Unit Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2023-01-31First Open Access (FOA) Date
2023-02-09First Compliant Deposit (FCD) Date
2023-01-27Usage metrics
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