End of Service Planning Solution Brief

This solution brief outlines the End of Service Planning component of the SSC AI suite, which addresses challenges associated with end-of-life (EOL) planning. It describes how traditional methods, relying on static Last Time Buy (LTB) assumptions and manual reviews, can lead to excess inventory risk or stockout risk. The solution provides a machine learning-driven workflow that enables planners to manage materials effectively as they approach LTB and EOL. Key features include a consolidated material grid for visibility, ML-enabled forecasting for more accurate demand projections, and tools for comparing actual demand with forecasts. The brief also highlights the benefits of improved planning confidence, earlier risk detection, and reduced manual analysis, ultimately facilitating better decision-making regarding lifecycle management. By integrating various data points, the solution aims to enhance planner productivity and shift from reactive to proactive EOL management.