At Automate 2026, one of the largest automation conferences in North America, conversations naturally centered around robotics, AI, connected factories, and what's next for manufacturing. During his session, OEE Meets SPC: Automating Data Collection for Real-Time Process Improvement, Minitab Product Marketing Lead & Engineer Josh Goodman encouraged attendees to take a step back and consider a more fundamental question: before investing in smarter factories, are we getting enough value from the data we're already collecting?
For many manufacturers, Overall Equipment Effectiveness (OEE) is one of the first metrics they look at. It provides a valuable snapshot of how effectively equipment is running by measuring availability, performance, and quality, making it an excellent indicator of where capacity is being lost and where improvement efforts should begin.
A declining OEE score tells you something is affecting production, but it doesn't reveal whether the root cause is process variation, recurring downtime, inconsistent measurement, or an issue that's quietly developing upstream. Too often, teams are left stitching together information from different systems after production has already moved on.
This is where Statistical Process Control (SPC) changes the conversation. Rather than measuring the outcome after the fact, SPC helps engineers and operators identify process drift while production is still running.
Combined with automated data collection, it creates an opportunity to:
All before small process changes become expensive production problems. As Josh explained during his session, OEE tells you where to investigate, while SPC helps explain why performance is changing.
That distinction becomes increasingly important as manufacturers invest in automation and AI. New technology can accelerate productivity, but it also amplifies the systems already in place. Stable processes become more efficient. Unstable processes become more expensive.
The manufacturers making the greatest progress aren't choosing between OEE and SPC. They're using both to create a clearer picture of operational performance—one that measures results while helping teams understand what's driving them.