AI-Driven Predictive Analytics for Inventory Management in Community Pharmacies

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Dr. Deependra Rastogi

IILM University,

Greater Noida, Uttar Pradesh 201306, India

Abstract— Community pharmacies face a perpetual balancing act: keeping essential medicines available at the counter while avoiding capital lock-in and wastage from expiries. Traditional rule-of-thumb methods—supplier minimums, fixed reorder points, and manual visual checks—struggle against demand volatility (seasonal infections, local outbreaks, physician prescribing preferences), variable supplier lead times, and thin margins. This manuscript proposes and evaluates an AI-driven predictive analytics framework that links demand forecasting with inventory optimization tailored to community pharmacy constraints. The framework integrates point-of-sale (POS) transactions, purchase and return records, expiry metadata, promotions, and local calendar effects to train rolling forecasts at the SKU–store level. Forecasts feed a cost-aware replenishment policy that minimizes a composite objective balancing stockouts, expiry risk, and holding costs.

A prospective multi-site pre–post study protocol is detailed, followed by a pilot evaluation on anonymized data from independent pharmacies. Using gradient-boosted trees for short-term forecasting and a constrained (s, S) policy calibrated to empirical lead-time uncertainty, the pilot demonstrates reduced mean absolute percentage error (MAPE) in daily demand forecasts, fewer stockouts for fast-moving essentials, lower expired-stock loss, and improved working-capital efficiency. Beyond performance, we report deployment learnings: data quality remediation, interpretable model outputs for pharmacist trust, and governance for patient-level privacy.

Results indicate that predictive analytics can translate directly into operational and financial gains for community pharmacies without requiring large IT budgets or wholesale process overhauls. The study concludes with recommended implementation steps and a roadmap for scaling, including automated cold-start handling, integration with wholesaler availability APIs, and continuous learning to track shifting therapy protocols.

Keywords

community pharmacy; inventory optimization; demand forecasting; predictive analytics; stockout reduction; expiry management; machine learning; (s, S) policy; working capital; healthcare operations

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