Case Study: Leading Medical Devices Manufacturer improves demand forecasting with Mu Sigma

Leading Medical Devices Manufacturer - Customer Case Study

Mu Sigma worked with a leading medical devices manufacturer that needed to revamp its supply chain model, improve demand forecasting accuracy, and reduce inefficiencies caused by back orders and volatile demand streams. The customer had struggled with black-box analytics approaches and wanted a more transparent, scalable forecasting framework to support production planning and inventory optimization.

Mu Sigma implemented a data-driven demand forecasting solution that included data quality checks, anomaly detection, time-series modeling using 14 statistical models, ensemble forecasting, feedback incorporation from demand planners, and variance reporting for root-cause analysis. The approach also classified SKUs into 18 demand classes to better handle demand variability. As a result, Mu Sigma improved forecast accuracy from 53% to about 70%, achieved nearly 95% accuracy in stable segments, and helped reduce lead times and operational costs.