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Operational time variability is one of the key parameters determining the average cycle time of lots. Many different sources of variability can be identified such as machine breakdowns, setup, and operator availability. However, an appropriate measure to quantify variability is missing. Measures such as overall equipment effectiveness (OEE) used in the semiconductor industry are entirely based on mean value analysis and do not include variances. The main contribution of this paper is the development of a new algorithm that enables estimation of the mean effective process time [t.sub.e] and the coefficient of variation [c.sup.2.sub.e] of a multiple machine workstation from real fab data. The algorithm formalizes the effective process time definitions as known in the literature. The algorithm quantifies the claims of machine capacity by lots, which include time losses due to down time, setup time, and other irregularities. The estimated [t.sub.e] and [c.sup.2.sub.e] values can be interpreted in accordance with the well-known G/G/m queueing relations. Some test examples as well as an elaborate case from the semiconductor industry show the potential of the new effective process time algorithm for cycle time reduction programs. Index Terms--Capacity and cycle time losses, data extraction, equipment modeling, factory dynamics, manufacturing line performance.