Fix hard-to-detect process variations — improve Cpk, reduce scrap, and act before variation reaches product
Applied SmartFactory SPC is an advanced process control (APC) engine that runs statistics on manufacturing data to determine whether processes are within spec, so manufacturers can improve product yield. It is designed on a common platform with other process control elements, including Run to Run, Fault Detection, and Recipe Management. That shared platform is what allows SPC to integrate data and context together, rather than treating statistical analysis as a separate system. For engineering teams, SPC is not only a charting tool. It is how a fab sees whether a process is behaving consistently across tools, chambers, and recipes, and whether the variation it sees is worth acting on.
Applied SmartFactory SPC improves yield by detecting process variation early enough for engineers to correct it, rather than after it has reached product. Fabs that improve Cpk and variability with SPC see cost savings of more than $650K per year. Because statistical analysis traces back to equipment raw data, engineers reach root cause faster and act on real signals instead of investigating noise.
SPC monitors performance trends, control limits, and variation across production operations. It runs statistics on manufacturing data to determine whether processes are staying within spec, and flags abnormal process behavior so engineering teams can respond before a shift affects yield across more lots or tools.
SPC links every violation to the underlying equipment measurements that produced it, so engineers are not reconciling a chart alarm against separate equipment logs. That connection is what shortens root cause analysis: the investigation starts from the data that caused the signal rather than beginning again in another system.
Yes. SPC enables advanced analytics and custom rules beyond standard control chart rules, and offers AI/ML capabilities with Python block support. Engineering teams can apply their own logic and models to process monitoring rather than being limited to a fixed rule set.
SPC is associated with 5% improvements in signal detection and scrap. Stronger signal detection cuts both ways: fewer investigations triggered by statistical noise, and fewer real excursions that go unnoticed until they show up as scrap. Engineering time moves toward genuine process issues.
Cost of ownership drops because SPC removes the connectivity and integration problems that separately deployed statistical tools create. Fewer configuration dependencies also mean less to stand up and less to maintain over time, which is where the faster ROI comes from.
SPC provides a single UI for rapid analysis and simple administration of SPC charts, so engineers set up, analyze, and maintain charts in one place rather than across separate tools. Automating routine statistical analysis produces overhead cost savings compared with manually managed SPC monitoring.
SPC helps engineering teams see process variation sooner, reach root cause faster, and improve Cpk with less manual investigation.
