stable at or near the optimal production point

highly glutted with WIP, affecting cycle times and potentially choking throughput

oversupplied with WIP and underperforming and tools

undersaturated with WIP; provides an opportunity to increase WIP levels to drive additional throughput or to idle or reduce tool count (and further reduce fixed costs) to maintain the same output with fewer tools

expanded to a new performance frontier due to the addition of incremental tooling, increased performance, or capacity

This same set of events can also be triggered by increasing the mix and complexity of the devices made in the fab. As the complexity increases and more changeovers or conversions and WIP management become necessary, the position on the curve of output compared with WIP can change dramatically. Thus, improvement toward the optimal point could be due to a nonexhaustive combination of factors: rebalancing the wafer volume along the manufacturing process; optimizing the product start mix and batch sequencing (that is, increasing train size) to run larger batches, thereby minimizing setup and changeover losses based on the fab’s optimized start plan; and increasing equipment availability. Alterations in train size would likely necessitate buffer stock builds initially (or delivery date adjustments if approved by the customer) to ensure there are no lapses in shipment, but they could significantly improve output performance in high-mix fabs.

By setting target WIP levels, continuously adjusting inventory, maintaining or improving tool performance, and managing train size with capacity-backed optimized start-up sequencing, it is possible to steadily improve both inventory and cycle time while maintaining target shipment values. Some fabs were able to decrease WIP levels by 25 percent while maintaining stable monthly shipments over a 12-month transformation period because of their data-driven goals, enabled by saturation curves. Based on this success, fab leadership could consider options to decrease WIP levels to decrease the cycle time and reach optimal levels again.

Empirical equipment analytics to identify true bottleneck tools and direct actions to improve fab performance

In the prior section, we used WIP saturation analytics to set target WIP levels, which helped rebalance equipment inventories and manufacturing lines. In conjunction with adjusting WIP level, the quantity and performance of tools must also be considered. For example, if the WIP levels are remaining high and saturated but the throughput is low at a particular tool group, then this tool group needs a combination of increased availability, utilization, or tool quantity to improve line balance. In contrast, if the WIP levels remain low and the onset of saturation is never observed, then it may be possible to take a tool offline to save on operational expenditures without negatively affecting line balance. Here, we explain how empirical equipment analytics can be used to identify true bottleneck tools and improve fab performance in both cost and growth regimes.

Frequently, in long-standing operations, fab leaders rely on institutional knowledge, whether that is historical capacity models, design philosophies (for example, photo is always the bottleneck), or their experience to create lists of fab production bottlenecks. However, as processes change and mature over time or as product mix changes, this institutional knowledge can quickly become stale and outdated. Similarly, for relatively new fabs, relying only on design philosophies and capacity models may not fully capture production realities such as complex queuing schemes and the human element. Both scenarios present a risk that operations personnel focus on maintaining or maximizing performance of tools that are not truly bottlenecks.

Alternatively, fab leaders could use real-time data to identify areas or processes within the fab that act as bottlenecks and restrict the overall fab capacity to meet customer demand. This empirical approach is both data-driven and easily analyzed over multiple time periods (for example, weeks or years to determine if a tool is a transient or structural bottleneck). This approach can even pinpoint which equipment is causing delays in on-time deliveries (for example, accumulating WIP, cycle time, and variance) and enable strategic allocation of resources to enhance output based on the status of whether the tool is a bottleneck.

Exhibit 3 shows an empirical determination of bottlenecks, plotting weighted cycle time against WIP level variation. Tools located beyond the mean WIP weighted cycle time indicate some form of bottleneck, either transient (appearing at times due to maintenance events or mix changes) or structural (ever-present in gating WIP movement throughout the fab). Once identified, these bottlenecks can be appropriately categorized and managed. In growth regimes, fabs can focus on improving the availability and utilization of structural bottleneck tools and develop action plans for transient bottlenecks based on their occurrence criteria, with minimal or no capital investment to drive potentially significant increases in capacity and throughput.