Artificial Intelligence-enabled Decision Support Systems for Supply Chain Management in Pharmaceutical Industry

Abstract

Artificial intelligence (AI) has become a potent instrument that utilizes personal knowledge and offers quicker fixes for difficult problems. Promising developments in artificial intelligence and machine learning offer a game-changing prospect for drug discovery, formulation, and dosage form testing. Through the application of AI algorithms that examine vast amounts of biological data, such as proteomics and genomics, scientists are able to pinpoint targets linked to disease and anticipate how those targets may interact with possible therapeutic candidates. This makes it possible to approach drug discovery in a more effective and focused manner, which raises the possibility of successful drug approvals. Additionally, by streamlining research and development procedures, AI can help lower development costs. Pharmacokinetics and toxicity of potential drugs can be predicted using machine learning algorithms, which also help with experimental design. This capacity lessens the need for extensive and expensive animal research by enabling the prioritization and optimization of lead compounds. Artificial intelligence (AI) algorithms that evaluate real-world patient data can support personalized medicine strategies, improving patient adherence and treatment outcomes. The current study examines the implications of artificial intelligence (AI), supply chain dynamism and unpredictability, and supply chain resilience (SCP), both directly and indirectly. As a result, we have based our conceptualization of AI's use in supply chains on the theory of organizational information processing (OIPT). The framework that was established was assessed using the application of structural equation modeling (SEM). Survey information was gathered from 150 businesses of all sizes, operating in different nations and industries