Automation-Enabled RFI/RFP Market Intelligence Platforms: Redefining Data-Driven Business Development in Global Pharmaceutical Markets

Ezichi Adanna Anokwuru, Agama Omachi and Joy Onma Enyejo

International Journal of Scientific Research in Science and Technology, 2024, vol. 11, issue 3, 1016-1036

Abstract: This study examines the transformative role of automation-enabled RFI/RFP market intelligence platforms in enhancing data-driven business development within the global pharmaceutical sector. It explores the evolution of market intelligence, emphasizing the integration of advanced technologies such as artificial intelligence, machine learning, natural language processing, and robotic process automation to streamline data collection, processing, and analysis. The study highlights how these technologies improve operational efficiency, reduce time-to-market, and enable predictive analytics, competitive intelligence, and opportunity forecasting. By aggregating and analyzing structured and unstructured data from vendor submissions, regulatory filings, and global market reports, automated platforms facilitate strategic partnering, market expansion, and informed decision-making. The research also identifies key challenges, including data privacy and security, compliance with regulatory frameworks, integration with legacy systems, and ensuring accuracy, bias mitigation, and reliability of insights. Additionally, the study underscores the importance of cross-market intelligence and scalability, enabling firms to maintain operational efficiency while expanding globally. Recommendations for future studies and platform innovations focus on enhanced predictive capabilities, interoperable systems, interactive dashboards, and adaptive learning algorithms. Overall, the study demonstrates that automation-enabled RFI/RFP platforms redefine traditional workflows into strategic intelligence tools, driving competitive advantage and sustainable growth in the pharmaceutical industry.

Keywords: Automation-enabled; Pharmaceutical; intelligenc; machine learning; Scalability and development (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v11:y2024:i3:id:1289

DOI: 10.32628/IJSRST54310301

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