By analytically comparing historical performance or industry and portfolio peers, fab leaders can quickly identify decisions to solve manufacturing problems before they balloon. Fabs that have addressed these priority levers and implemented variance control methods significantly improve their utilization, cycle time, and on-time delivery. After setting and adhering to line balance and variance targets guided by variance curves, some fabs have been able to increase on-time delivery and decrease shipment variance by more than 70 percent.

Saturation curves to optimize WIP and throughput levels

As previously discussed, we used variance curve analytics to examine an instance in which a fab’s performance deteriorated due to a large increase in starts, WIP, and product mix. We saw that one key solution to reducing variance and improving performance was to decrease starts and WIP to an optimal level. Although this optimal WIP level can be calculated and modeled using precise values of tool availability, running times, and changeover frequency and duration, these values may not always be consistent with the past, present, or future conditions on the fab floor. Here, we explain how saturation curves can empirically define WIP targets.