Characterization of operational time variability using effective process times

Abstract

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 and the coefficient of variation 2 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 and 2 values can be interpreted in accordance with the well-known 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. I. INTRODUCTION E QUIPMENT in semiconductor manufacturing is subject to many sources of variability. An important source is machine down time, which occurs due to highly complex and technologically advanced semiconductor manufacturing processes [1]. Many other corrupting operational influences are also present, such as batching, hot lots, rework, setup, and operator availability. All together, they introduce a substantial amount of variability in the interarrival and operational times of the lots during their flow through the fab. Queue times are mainly influenced by variability and utilization. High utilization is necessary in the semiconductor industry in order to maximize productivity and minimize costs. In combination with large variability, high utilization leads to large cycle

Modeling and Analysis of Manufacturing Systems

“…Nevertheless, still not all sources of variability are included. This is clearly illustrated in Figure 1.7, obtained from (Jacobs, Etman, Campen, and Rooda 2003). The left graph contains actual realizations of flow times of lots leaving a real manufacturing system, whereas the right graph contains the results of a detailed deterministic simulation model and the graph in the middle contains the results of a similar model including stochasticity.…”

“…In (Sattler 1996) it was also noticed that this definition of effective process time is difficult to measure. Instead of taking the bottom-up view of Hopp and Spearman, a top-down approach can also be taken, as shown by Jacobs et al (Jacobs, Etman, Campen, and Rooda 2003), where algorithms have been introduced that enable determination of effective process time realizations from a list of events. For these algorithms, the basic idea of the effective process time to include time losses was used as a starting point.…”

“…Finally, since EPT-realizations characterize operational time variability, they can be used for performance measuring. For more on this issue, the interested reader is referred to (Jacobs, Etman, Campen, and Rooda 2003;Jacobs, Bakel, Etman, and Rooda 2005). What is most important is that EPT's can be determined from real manufacturing data and yield relatively simple discrete event models of the manufacturing system under consideration.…”

Generating Cycle Time-Throughput Curves using Effective Process Time based Aggregate Modeling

“…Alternatively, an approach similar to Jacobs et al [7] may be followed, in which t e and c 2 e are calculated directly from measured arrival and departure events.…”

“…Hopp and Spearman [3] calculate the mean and variance of the EPT from these preemptive and non-preemptive outages, and use them in queueing approximation (1). Jacobs et al [7] calculate t e and c 2 e directly from lot arrival and departure events. Kock et al [5], [6] also calculate the EPT distribution directly from arrival and departure events, but additionally label each EPT realization with the momentary workload.…”