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