The optimal management of manufacturing processes can be achieved through a set of optimal decisions, which must be made to choose the best methodto follow every time the process planner is in a point at which several potential manufacturing paths branch off. A dedicated method, namely the Holistic Optimization Method (HOM), has already been developed for this purpose and was validated in several studies based on artificial and real instances databases. The HOM consists of two algorithms: i) the causal identification of a manufacturing process and ii) the comparative assessment with already performed, similar manufacturing cases, recorded in an instances database. The two algorithms can be used to estimate the values of the different performance indicators of the manufacturing processes. Their application for processing cost estimation in the case of manufacturing processes of bearing components has already shown good results. In this paper, it is presented as a solution to predict the timespan needed for grinding roller bearings rings, applying the specific algorithms of the HOM, grounded on the use of a database with data collected from the industrial environment. The cause variables selected to describe the grinding process of roller bearing rings are the inner and outer diameter of the ring, its width and weight, the machined surface roughness, the grinding stone rotation speed, the feedrate and the cutting depth, while the effect variable to be used by the process planner as decision criterion is the timespan.