IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v158y2023ics0148296323000462.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296323000462
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jbusres.2023.113688?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alexandre Dolgui & Dmitry Ivanov & Boris Sokolov, 2018. "Ripple effect in the supply chain: an analysis and recent literature," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 414-430, January.
    2. Alvaro Almeida & Joana Vales, 2020. "The impact of primary health care reform on hospital emergency department overcrowding: Evidence from the Portuguese reform," International Journal of Health Planning and Management, Wiley Blackwell, vol. 35(1), pages 368-377, January.
    3. Sakib, Nazmus & Ibne Hossain, Niamat Ullah & Nur, Farjana & Talluri, Srinivas & Jaradat, Raed & Lawrence, Jeanne Marie, 2021. "An assessment of probabilistic disaster in the oil and gas supply chain leveraging Bayesian belief network," International Journal of Production Economics, Elsevier, vol. 235(C).
    4. Xiaodan Zhu & Anh Ninh & Hui Zhao & Zhenming Liu, 2021. "Demand Forecasting with Supply‐Chain Information and Machine Learning: Evidence in the Pharmaceutical Industry," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3231-3252, September.
    5. Stojanović, Đurđica & Ivetić, Jelena, 2020. "Possibilities of using Incoterms clauses in a country logistics performance assessment and benchmarking," Transport Policy, Elsevier, vol. 98(C), pages 217-228.
    6. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    7. Dahl, Andrew J. & Milne, George R. & Peltier, James W., 2021. "Digital health information seeking in an omni-channel environment: A shared decision-making and service-dominant logic perspective," Journal of Business Research, Elsevier, vol. 125(C), pages 840-850.
    8. Pereira, Marina Meireles & Frazzon, Enzo Morosini, 2021. "A data-driven approach to adaptive synchronization of demand and supply in omni-channel retail supply chains," International Journal of Information Management, Elsevier, vol. 57(C).
    9. Roberto Bergami, 2013. "Managing Incoterms 2010 risks: tension with trade and banking practices," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 6(3), pages 324-338.
    10. Chang, Victor & Doan, Le Minh Thao & Ariel Xu, Qianwen & Hall, Karl & Anna Wang, Yuanyuan & Mustafa Kamal, Muhammad, 2023. "Digitalization in omnichannel healthcare supply chain businesses: The role of smart wearable devices," Journal of Business Research, Elsevier, vol. 156(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ortiz-Barrios, Miguel & Arias-Fonseca, Sebastián & Ishizaka, Alessio & Barbati, Maria & Avendaño-Collante, Betty & Navarro-Jiménez, Eduardo, 2023. "Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study," Journal of Business Research, Elsevier, vol. 160(C).
    2. Abadie, Amelie & Roux, Mélanie & Chowdhury, Soumyadeb & Dey, Prasanta, 2023. "Interlinking organisational resources, AI adoption and omnichannel integration quality in Ghana’s healthcare supply chain," Journal of Business Research, Elsevier, vol. 162(C).
    3. Bag, Surajit & Dhamija, Pavitra & Singh, Rajesh Kumar & Rahman, Muhammad Sabbir & Sreedharan, V. Raja, 2023. "Big data analytics and artificial intelligence technologies based collaborative platform empowering absorptive capacity in health care supply chain: An empirical study," Journal of Business Research, Elsevier, vol. 154(C).
    4. Lai, Kee-hung & Feng, Yunting & Zhu, Qinghua, 2023. "Digital transformation for green supply chain innovation in manufacturing operations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    5. Muhammad Rahies Khan & Amir Manzoor, 2021. "Application and Impact of New Technologies in the Supply Chain Management During COVID-19 Pandemic: A Systematic Literature Review," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(2), pages 277-292.
    6. Di Zio, Simone & Bolzan, Mario & Marozzi, Marco, 2021. "Classification of Delphi outputs through robust ranking and fuzzy clustering for Delphi-based scenarios," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    7. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    8. Behl, Abhishek & Gaur, Jighyasu & Pereira, Vijay & Yadav, Rambalak & Laker, Benjamin, 2022. "Role of big data analytics capabilities to improve sustainable competitive advantage of MSME service firms during COVID-19 – A multi-theoretical approach," Journal of Business Research, Elsevier, vol. 148(C), pages 378-389.
    9. Antonio Zavala-Alcívar & María-José Verdecho & Juan-José Alfaro-Saiz, 2020. "A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain," Sustainability, MDPI, vol. 12(16), pages 1-38, August.
    10. Yi Zheng & Li Liu & Victor Shi & Wenxing Huang & Jianxiu Liao, 2022. "A Resilience Analysis of a Medical Mask Supply Chain during the COVID-19 Pandemic: A Simulation Modeling Approach," IJERPH, MDPI, vol. 19(13), pages 1-21, June.
    11. Belhadi, Amine & Venkatesh, Mani & Kamble, Sachin & Abedin, Mohammad Zoynul, 2024. "Data-driven digital transformation for supply chain carbon neutrality: Insights from cross-sector supply chain," International Journal of Production Economics, Elsevier, vol. 270(C).
    12. Mustafa Polat & Karahan Kara & Avni Zafer Acar, 2023. "Competitiveness based logistics performance index: An empirical analysis in Organisation for Economic Co-operation and Development countries," Competition and Regulation in Network Industries, , vol. 24(2-3), pages 97-119, June.
    13. Liu, Huiyuan & Perera, Sandun C. & Wang, Jian-Jun & Leonhardt, James M., 2023. "Physician engagement in online medical teams: A multilevel investigation," Journal of Business Research, Elsevier, vol. 157(C).
    14. Maureen S. Golan & Laura H. Jernegan & Igor Linkov, 2020. "Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic," Environment Systems and Decisions, Springer, vol. 40(2), pages 222-243, June.
    15. Sisi Zhou & Kuanching Li & Lijun Xiao & Jiahong Cai & Wei Liang & Arcangelo Castiglione, 2023. "A Systematic Review of Consensus Mechanisms in Blockchain," Mathematics, MDPI, vol. 11(10), pages 1-27, May.
    16. Jason R. W. Merrick & Claire A. Dorsey & Bo Wang & Martha Grabowski & John R. Harrald, 2022. "Measuring Prediction Accuracy in a Maritime Accident Warning System," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 819-827, February.
    17. Yang, Cheng-Hu & Wang, Hai-Tang & Ma, Xin & Talluri, Srinivas, 2023. "A data-driven newsvendor problem: A high-dimensional and mixed-frequency method," International Journal of Production Economics, Elsevier, vol. 266(C).
    18. Weili Yin & Wenxue Ran, 2023. "Explaining Firm Performance During the COVID-19 With fsQCA: The Role of Supply Network Complexity, Inventory Turns, and Geographic Dispersion," SAGE Open, , vol. 13(2), pages 21582440231, June.
    19. Sharma, Neeru & Fatima, Johra Kayeser, 2024. "Influence of perceived value on omnichannel usage: Mediating and moderating roles of the omnichannel shopping habit," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    20. Jaya Priyadarshini & Rajesh Kr Singh & Ruchi Mishra & Surajit Bag, 2022. "Investigating the interaction of factors for implementing additive manufacturing to build an antifragile supply chain: TISM-MICMAC approach," Operations Management Research, Springer, vol. 15(1), pages 567-588, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:158:y:2023:i:c:s0148296323000462. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.