The Role of Predictive Analytics in Optimizing Pharmaceutical Inventory Management

Pharmaceutical companies and healthcare providers face unique challenges in inventory management due to the critical nature of drug availability, expiration risks, and regulatory constraints. This manuscript explores how predictive analytics can transform traditional inventory management approaches by anticipating demand fluctuations, reducing waste, and ensuring timely availability of essential medications. We review relevant literature up to 2018, discuss statistical models used in predictive analytics, and present an empirical analysis featuring a representative table that outlines key variables influencing inventory decisions. The methodology section details a mixed-methods approach combining historical data analysis and simulation techniques, while the results underscore the tangible benefits of applying predictive models. Our findings suggest that by leveraging advanced analytics, pharmaceutical firms can achieve a higher service level, reduce costs, and enhance patient outcomes. Finally, the conclusion highlights implications for practice and areas for future research.