Data-driven strategies for optimizing pharmaceutical supply chains in the United States: A framework for entrepreneurial excellence

Victor Alemede 1, *, Precious Azino Usuemerai 2 and Olumide Emmanuel Ibikunle 3

1 Independent Researcher, Boston, MA, USA.
2 The University of Chicago, Chicago, IL.
3 Vanderbilt University, Nashville, TN.
 
Review Article
Magna Scientia Advanced Research and Reviews, 2022, 06(01), 086–093
Article DOI: 10.30574/msarr.2022.6.1.0067
Publication history: 
Received on 10 September 2022; revised on 20 October 2022; accepted on 23 October 2022
 
Abstract: 
The pharmaceutical supply chain in the United States is a complex network that requires strategic optimization to ensure the timely and safe delivery of medications to patients. This paper explores data-driven strategies and entrepreneurial approaches for enhancing efficiency, transparency, and resilience within these supply chains. Key technologies, such as predictive analytics, machine learning, blockchain, and the Internet of Things (IoT), are examined for their transformative potential in demand forecasting, inventory management, and logistics. The critical role of data transparency and collaboration among stakeholders is highlighted, along with the disruptive innovations introduced by startups and tech-driven companies. Additionally, the paper discusses risk management strategies, including data-driven simulations and contingency planning, as vital tools for mitigating disruptions. Recommendations are provided for pharmaceutical companies, policymakers, and entrepreneurs to foster innovation, leverage advanced technologies, and build sustainable supply chain models. These strategies aim to address current challenges and position the pharmaceutical industry for future growth, resilience, and patient-centric outcomes.
 
Keywords: 
Pharmaceutical Supply Chain; Data-Driven Strategies; Blockchain Technology; Predictive Analytics; Risk Management
 
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Copyright information: 
Copyright © 2022 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0