Robotic Process Automation in Pharmacy Management and Inventory Control

Abstract

Pharmacies operate under constant pressure to deliver safe, timely, and cost-effective services while maintaining strict regulatory compliance and resilient supply chains. Manual, repetitive back-office work—such as purchase order creation, invoice matching, stock reconciliation, expiry tracking, and dispensing documentation—consumes significant pharmacist time and is prone to error. Robotic Process Automation (RPA) offers a pragmatic pathway to streamline these high-volume, rule-based tasks without replacing core pharmacy information systems (PIS), hospital information systems (HIS), or enterprise resource planning (ERP) platforms. This manuscript examines the application of RPA across community, hospital, and specialty pharmacy contexts with a focus on inventory control. We synthesize current practices, propose a repeatable implementation methodology, and outline a detailed study protocol suitable for real-world evaluation.

We present illustrative pilot results from a controlled simulation comparing pre- and post-RPA performance on key indicators: dispensing turnaround time, purchase-to-pay cycle time, stockout rate, data entry errors, inventory record accuracy, and expiry-related wastage. The simulated findings indicate substantial efficiency gains (e.g., 40–80% reduction in manual errors and 30–60% faster cycle times) while improving patient safety via enhanced data quality and proactive replenishment. We discuss governance, risk, and compliance (GRC) considerations including audit trails, validation, data privacy, human-in-the-loop exception handling, and change management. The proposed study protocol and methodology are designed to be vendor-agnostic and adaptable to pharmacies of varied digital maturity. Overall, RPA emerges as a practical complement to digital pharmacy initiatives—unlocking pharmacist time for clinical services, increasing inventory visibility, and reducing operating costs—provided implementations follow rigorous process discovery, quality assurance, and continuous improvement practices.