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Machine learning-based technique for predicting vendor incoterm (contract) in global omnichannel pharmaceutical supply chain

Author

Listed:
  • Kumar Detwal, Pankaj
  • Soni, Gunjan
  • Kumar Jakhar, Suresh
  • Kumar Srivastava, Deepak
  • Madaan, Jitender
  • Kayikci, Yasanur

Abstract

The 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.

Suggested Citation

  • Kumar Detwal, Pankaj & Soni, Gunjan & Kumar Jakhar, Suresh & Kumar Srivastava, Deepak & Madaan, Jitender & Kayikci, Yasanur, 2023. "Machine learning-based technique for predicting vendor incoterm (contract) in global omnichannel pharmaceutical supply chain," Journal of Business Research, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:jbrese:v:158:y:2023:i:c:s0148296323000462
    DOI: 10.1016/j.jbusres.2023.113688
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    References listed on IDEAS

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