Hanmi Science cuts inventory costs 55.1% and out-of-stock rate 22.6% with AI demand forecasting

Hanmi Science, a Korean pharmaceutical enterprise, faced the core challenge of OTC drug demand forecasting in a sector where miscalculation carries a dual penalty: stockouts deny patients access to treatment, while overstock triggers disposal costs due to expiration. Demand for OTC drugs is shaped by interlocking variables — disease incidence rates, seasonal patterns, competitor supply disruptions, and prescription behavior shifts — none of which traditional methods could model in combination. Forecasting relied on historical sales data and planners' institutional knowledge, manually assembled in Excel. The limits of experience-based predictions also become clear when an unpredictable situation such as COVID-19 occurs.

Hanmi Science deployed Deepflow Forecast, an AI-based demand forecasting platform developed by Impactive AI and purpose-built for the pharmaceutical industry. The implementation covered more than 60 OTC drugs and ingested over 6 million data points in real time. The platform runs 224 disease-specific prediction models, linking patient-number forecasts directly to downstream drug demand — achieving 80.1% sales volume forecast accuracy and 96.5% patient-number prediction accuracy across those disease models. Machine learning algorithms identify correlations across disease occurrence patterns, seasonality, weather variables, and prescription trends — relationships too complex for conventional statistical approaches. The system incorporates explainable AI (XAI), surfacing the specific variables and their weighted influence behind each forecast so the basis of each prediction can be understood.

The headline outcome was a 55.1% reduction in monthly inventory costs, achieved by aligning stock levels to AI-generated demand signals rather than conservative manual estimates. The out-of-stock rate fell 22.6%, directly reducing missed treatment opportunities across the OTC range, while excess inventory declined 32.5%, sharpening expiration-date management and minimizing pharmaceutical disposal write-offs. On the operational side, time spent on routine forecasting and order-calculation tasks dropped 80%, replacing manual Excel workflows with automated outputs. Key metrics:

• Monthly inventory cost reduction: 55.1%
• Out-of-stock rate reduction: 22.6%
• Excess inventory rate reduction: 32.5%
• Routine forecasting task time reduction: 80%

• Scope the initial deployment to a defined, measurable product set — Hanmi Science's deployment across 60+ OTC drugs produced verifiable results, lowering organizational risk in a regulated industry.
• Disease occurrence modeling amplifies forecast accuracy: linking patient-number predictions (96.5% across 224 diseases) to drug demand produces precision that conventional statistical methods cannot approach.
• Explainable AI matters in pharma supply chain — decision-makers need variable-level attribution before acting on forecasts that affect patient access.
• Automating routine forecasting frees employees to focus on more valuable tasks rather than manual spreadsheet work.