MAPLE / Parameter Tracking Tool The tool answers one question for program managers: will this Key Performance Parameter reach its threshold before the program ends, and how confident can anyone be in that answer. It is a Windows desktop application that takes measured KPP data, fits seven regression families, and plots the forecast against threshold, objective and critical-path reference lines with automatic crossing detection. The MAPLE engine adds a maturity curve that keeps early-program forecasts conservative and a likelihood score reporting the probability of crossing month by month. V9 introduced a machine-learning calibration layer: trained coefficients for the likelihood formula, and a learned ensemble blend across all seven regression families. Both use classical optimization rather than neural networks, so the numbers stay defensible in governance review. It began as a demo BETA and grew across multiple major versions into a full forecasting engine.