6/27/2023 0 Comments Relationship between wip and wipqIn the chart above, the 3X cycle-time-constrained capacity (or 3X capacity) for the low variability system is approximately 80% of the maximum theoretical capacity of the system. So, the shorthand term for three times raw process time is 3X. The multiple of raw process time is usually called an X-factor. This is defined as the maximum capacity at which the factory can achieve a given average cycle time, expressed as a multiple of raw process time. We have worked on projects in which the factory performance measure used was cycle-time-constrained capacity. This keeps factories out of the steep portion of the curve shown above. That is, they typically plan for about 15% idle time on all of their tool groups. In the real world, factory planners account for this by including “catch-up capacity” in their plans. In fact, the limiting case for systems with any variability is that as the factory loading approaches 100%, the cycle time approaches infinity. Even the low variability system cannot be run at 100% of the maximum throughput, because cycle time increases rapidly to unacceptable levels. This is sometimes called the "hockey stick effect," as illustrated below.Ĭycle time limits the effective capacity of a wafer fab. In most fabs, once the system is loaded above approximately 85%, cycle time starts to increase rapidly. Exactly how much the cycle time increases will depend on the amount of variability in the system. In a real fab, with variability, cycle time tends to increase with start rate (or throughput rate - the two measures are directly related by line yield). At that point, if start rate is increased further, cycle time increases linearly. In the (imaginary) no-variability case, cycle time remains constant as start rate is increased, up to the maximum system capacity. Similarly, if we have 1500 wafers in the factory at a time, the average cycle time will be three weeks, etc. On average, each wafer will spend two weeks in the factory (one week waiting for the backlog of other wafers to be processed, the next week being processed). We can only process 500 of the wafers in a given week. Each week we get 500 more in, so that the total WIP is 1000. However, suppose that we start out with a backlog of 500 wafers in the fab. Under these assumptions, if we start 500 wafers or less in each week, the cycle time for each will be one week (because we have enough capacity to process them all during the week). Although this is a highly unrealistic assumption, we will relax it later in the tutorial. Little’s Law can be illustrated with a simple example: assume a factory with a capacity of 500 wafers per week and no variability. Over the long term, it will hold true for an entire factory or for a single workstation (as long as the units used for each term are consistent with one another). It is important to understand that this is a known mathematical relationship. If throughput is held constant, it is impossible to reduce average WIP without reducing average cycle time, and vice versa. ![]() In other words, for a factory with constant throughput, WIP and cycle time are proportional. ![]() WIP includes units being processed on equipment, as well as units in transit, or awaiting processing at an equipment group. The average number of units of product in the factory (or at a workstation). The throughput of a factory is equal to the factory start rate multiplied by average line yield. The average output rate of a factory or workstation. An illustration is available as part of this tutorial. Little’s Law states that at a given WIP level, the ratio of WIP to cycle time equals throughput. Little concerning cycle time, work-in-process and throughput. For example, the 3X cycle-time-constrained capacity is the throughput rate at which the weighted average cycle time for the factory is no more than three times the weighted average raw processing time.Ī fundamental relationship derived by J. The throughput rate for a factory at which the average cycle time is equal to some target amount, usually expressed as a multiple of the total weighted average raw process time of the factory. Cycle times by operation are also sometimes reported, and include the time from arrival at the operation until completion of processing. Cycle time includes time actually spent processing, as well as transport time and time spent waiting in queue. The total time required to produce a product, from entering the factory to leaving the factory. For a factory, the capacity is the throughput rate that drives the idle time on the bottleneck to zero. The maximum throughput of a factory or workstation. ![]() However, in common use, bottleneck usually refers to the most highly loaded machine group. Some authors define bottleneck as having a loading of 100%. The machine group in a factory that has the highest loading for a given product mix.
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