What's Happening?
Sigma Healthcare has implemented machine learning models within SAP's integrated business planning (IBP) system to improve demand forecasting for medications. The company achieved a 10% increase in forecast accuracy
by using models like extreme gradient boosting and auto outlier correction. These advancements have optimized inventory management and reduced manual workload for supply planners.
Why It's Important?
The use of machine learning in demand forecasting represents a significant step forward in pharmaceutical supply chain management. By improving forecast accuracy, Sigma Healthcare can better manage inventory levels, reduce waste, and ensure medication availability. This approach highlights the potential of AI and machine learning to enhance operational efficiency and competitiveness in the healthcare industry.
What's Next?
Sigma Healthcare plans to further optimize its forecasting capabilities by integrating SAP Joule, a generative AI copilot, into its systems. This integration is expected to assist in solving complex forecasting issues and improve decision-making processes. The company aims to maintain its competitive edge by keeping pace with technological advancements and quarterly upgrades of IBP.
