Quantifying operational time variability: the missing parameter for cycle time reduction

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 equipment breakdowns, set-up, and operator availability. However, an appropriate measure to quantify variability is missing. Measures such as the overall equipment efficiency (OEE) 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_e and the coefficient of variation c_e² of a multiple machine equipment family from real fab data. The algorithm formalizes the effective process time definitions as given by Hopp and Spearman (2000), and Sattler (1996). The algorithm quantifies the claims of machine capacity by lots, which includes time losses due to down time, set-up time, or other irregularities. The estimated t_e and c_e ² values can be interpreted in accordance with the well-known G/G/m queueing relations. A test example as well as an elaborate case from the semiconductor industry show the potential of the new effective process time (EPT) algorithm for cycle time reduction programs