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disruptions that are within a normal but wide range. Most of the batches (about 90 percent) will reach their destination on or ahead of plan—less than or equal to 40 days. For example, some of the batches will experience far fewer than normal disruptions and these could be completed in, say, just 10 days, a time that is much shorter than the planned production lead time. Similarly, there will be a small number of batches that will experience much more than their fair share of disruptions. In the absence of corrective action, these batches will finish well past their due dates based on a 40-day planned lead time—the batches will be late. The distribution curve for the sample of 100 batches is illustrated in Fig. 8-9.

FIGURE 8-9 Graph showing the number of batches with actual lead times ranging from 10 days to 45 days where the planning lead time was 40 days with review at 35 days.

If the only point where we can identify that a batch is experiencing large disruptions is at the end of the product flow, we will have no opportunity for corrective action. We need to know that a batch in production is in trouble while there is still enough time to do something about it. What is the minimum time that will leave us enough time for corrective actions that, in the majority of cases, can help bring the batch back on track? To better understand this, let us consider a batch with a planned production lead time of 40 days that is released today. (Today is Day 1 and Due Date is Day 40.) If we simply let the normal shop floor mechanisms take place without any intervention or without even any monitoring, we expect this order to reach completion sometime between Day 10 (no major problems encountered) and Day 45 (many major problems encountered). Suppose we choose to monitor this order after 35 days have elapsed. From the statistical distribution curve shown in Fig. 8-9, approximately 70 percent of the time the order will already have been completed and the monitoring is a non-issue. However, in the remaining 30 percent of the time the monitoring will reveal the extent of the disruptions suffered; hence, the urgency of taking corrective action. In many of these cases (approximately 20 percent), the batch will be almost near completion and no action is necessary. In a small number of cases (10 percent), the batch is far behind in its progression through the shop and corrective action will be necessary. We can then initiate these corrective actions and bring this batch back on track.

The general rule that emerges from this example is the following. In trying to determine whether intervention is required, we are comparing two time periods. The first time period, time available, is the amount of time that is actually available to finish the batch on time. This is the time from Today/Now to the Date/Time when the batch is due. The second time period, planning or standard production lead time, is the amount of time that is required to complete the batch. As the ratio of available time to planning production lead time becomes smaller (this will happen naturally with the passage of time), the degree of certainty that the batch will finish on time will diminish. We refer to the ratio of available time to standard production lead time, expressed as a percentage, as the buffer status of the batch.

Buffer Status (%)= (Available Time)/(Standard Production Lead Time) × 100%

Figure 8-10 shows the buffer status in the form most frequently used, which is by assigning each work order a color based on the buffer status. If the time remaining for an open work order is less than one-third of its standard production lead time, then the buffer status is less than 33 percent. (If the batch was released on time, then we have less than one-third of the standard lead time available to complete the batch on time.) Such a batch should be flagged. Production personnel will have to investigate where the batch is currently located and determine whether corrective action is necessary. The rule that every batch whose buffer status is smaller than 33 percent should