The following three analytical frameworks are core to understanding holistic performance, are often overlooked, and can be implemented rapidly. Additionally, these frameworks can be leveraged at multiple levels (for instance, at the fab, subsegment, or equipment level) to clearly identify improvement opportunities, set goals, and implement change in the chain of operations. These frameworks help simplify the complexity of operations and provide guidance for diving deeper into the fab, ultimately implementing solutions and improving fab performance either for cost or throughput. We present these frameworks in a hypothetical scenario of a fab with ongoing production issues, framed by the key questions fab management may be asking at any point along the fab’s improvement journey.
Fab leaders frequently grapple with assessing performance of a fab over time and against other fabs, either in the portfolio or industry benchmarks. Leveraging variance curves—also known as alpha or frontier curves—facilitates a seamless comparison of current performance against historical benchmarks and industry standards by charting capacity utilization versus normalized cycle time (in other words, how long an operation takes compared with the theoretical minimum). This approach generates precise insights on operations variance, which can be used to identify specific points where the fab began to deviate from peak performance, what tool groups and areas of the fab are driving the variance compared with the ideal (or previously demonstrated) state, and if the trade-offs between equipment utilization and product cycle time are justifiable. Minimizing variance is always the goal, regardless of being in a cost or throughput regime, so employing and quantifying variance curves can improve the interpretation of more-traditional performance metrics (for example, shipments, work in progress [WIP], and cycle times).