1. A method comprising: defining data points from a model to produce raw data of operations; selecting performance indicators from said raw data; measuring said indicators over at least one time period to extract a time series of data for each of said indicators; filtering out redundant indicators to produce a reduced indicator set of time series of data; detecting correlations among said time series of data within said reduced indicator set by considering time-shifts between said time series of data so as to identify correlated indicators; determining a time order among said correlated indicators; determining a causal direction among said correlated indicators so as to identify relative leading indicators among said correlated indicators; creating a similarity matrix among said correlated indicators based on said time order and said causal direction among said correlated indicators; partitioning said correlated indicators within said similarity matrix into clusters using an agglomerative clustering process; identifying said relative leading indicators within each cluster as root leading indicators of a each of said clusters; and producing a report of said root leading indicators.