Characterization of operational time variability using effective process times

Operational time variability is one of the key parameters determining theaverage cycle time of lots. Many different sources of variability can beidentified such as machine breakdowns, setup, and operator availability.However, an appropriate measure to quantify variability is missing. Measuressuch as Overall Equipment Effectiveness (OEE) used in the semiconductorindustry are entirely based on mean value analysis and do not includevariances.

The main contribution of this paper is the development of a new algorithm thatenables estimation of the mean effective process time t_e and the coefficientof variation c_e^2 of a multiple machine workstation from real fab data. Thealgorithm formalizes the effective process time definitions as known in theliterature. The algorithm quantifies the claims of machine capacity by lots,which include time losses due to down time, setup time, and otherirregularities. The estimated t_e and c_e^2 values can be interpreted inaccordance with the well-known G/G/m queueing relations. Some test examples aswell as an elaborate case from the semiconductor industry show the potential ofthe new effective process time algorithm for cycle time reduction programs.