Automate Now

No automation deployment goes perfectly. Even with careful planning, experienced partners, and a prepared team, things will occasionally not work the way you expected. In this episode, the Formic team talks honestly about what happens when automation misfires — and more importantly, how the manufacturers who succeed respond when it does. The difference between an automation success story and a robot graveyard isn't whether problems occurred. It's how they were handled.

The episode walks through the most common causes of early-stage automation hiccups: operator errors during the learning curve (unplugged cables, wrong recipes loaded, boxes moved mid-process), workflow friction between humans and machines that weren't fully anticipated, and the natural adjustment period any team goes through when something new is introduced. The key steps for recovery are clear — resist assumptions, map where the breakdown is actually occurring, involve your operations team, stay patient with employees, and lean on your automation partner to diagnose and resolve issues quickly. Rarely is the fix a wholesale reset. More often, it's a small, smart adjustment that restores momentum and builds knowledge for the next phase.

Key Takeaways:
  • Problems in early automation deployments are normal — what matters most is how quickly and calmly you respond when they happen
  • The most common culprits aren't equipment failures — they're human learning curve issues: wrong recipes, moved boxes, unfamiliar interfaces, and operator habits carried over from manual processes
  • Resist the urge to blame the technology first — map the breakdown systematically to determine whether it's a technical limitation, a workflow gap, or a training issue
  • Your frontline team often spots friction points first and has practical ideas for fixing them — involve them in troubleshooting, not just in operation
  • A strong automation partner shoulders the diagnostic and remediation work — if they're not doing that, you have a partner problem, not a technology problem
  • Imperfect automation beats perfect inaction — every hiccup is a learning opportunity that makes the next deployment faster, smoother, and more successful
Automate Now is written by the Formic team — Saman Farid, Danijel Lolic, Molly Garrison, Brooklyn Kiosow, and Shawn Fitzgerald — and edited by Brooklyn Kiosow. Formic helps U.S. manufacturers automate for the first time through Full Service Automation: no large upfront investment, no in-house robotics expertise required. If this episode made you think about where automation could work in your facility, start the conversation at formic.co.

0:00 Intro — Hiccups Are Normal 
0:52 Don't Make Assumptions 
2:12 If You're Not Going It Alone 
3:15 Embrace the Learning Curve 
3:55 Key Takeaways

What is Automate Now?

American manufacturing is at an inflection point. Labor shortages are accelerating, global competition is intensifying, and the pressure to produce more with less has never been greater. The answer — for manufacturers of every size — is automation. But knowing you need to automate and knowing how to do it are two very different things.

Automate Now is the practical playbook for CPG manufacturers ready to take action. Written by the Formic team — the people who have helped hundreds of U.S. factories automate for the first time — this audiobook cuts through the complexity and gives you a clear, honest roadmap: where to start, how to build internal buy-in, how to choose the right partner, and how to scale from your first win into a future-proof operation.

Automate Now — Episode 12
Production Analytics and Continuous Improvement

As we keep emphasizing, automation isn't a one-and-done effort. The most successful manufacturers treat automation as an evolving journey. It should get smarter and more efficient with time. But the key to that? Data.

Technology embedded in machines and production lines now captures an incredible range of information: temperature, speed, torque, cycle time, vibration, energy use, and more. When this data is structured, visualized, and analyzed, it becomes a powerful feedback loop and the cornerstone of continuous improvement.

Why Data Matters

Data is what transforms automation from static machinery into an adaptive, high-performance system. It provides visibility, helping you understand how your systems are operating at any given moment. But more importantly, it offers insight. With the right tools and mindset, you can use this information to make smarter decisions, resolve inefficiencies, and identify opportunities for growth and improvement.

Let's say your automated case packer is falling behind during certain shifts. Production analytics might reveal that during second shift, minor jams occur more frequently due to variations in box size, forcing operators to pause the system more often to make manual adjustments. Without data, you'd be guessing. With data, you can take precise action.

As Jim LaRocco, Director of Manufacturing and Fulfillment at Garrett Popcorn, put it: "Before Formic Production Intelligence, I never got answers. I'd ask why we were slow, and the response would be, 'We had to stay two hours late to finish the order.' But now with FPI, I can see the first case didn't hit the robot until 7 a.m. There's no hiding from the data."

Data isn't just useful for problem-solving; it's essential for scaling. The more you automate, the more interdependent systems become. A strong data infrastructure helps you monitor performance across multiple systems, ensuring everything runs smoothly and in sync, and enables benchmarking across different lines so you can compare performance and identify best practices.

Key Performance Indicators

Not all data is created equal. Here are some foundational KPIs to track.

Overall Equipment Effectiveness, or OEE: a comprehensive metric that combines availability, performance, and quality to assess how effectively a system is running.

Cycle Time: the time it takes to complete one full operation or product cycle.

Downtime, both planned and unplanned: identifying when and why systems stop running and how often.

Throughput: the volume of product being processed or produced over a given time.

First Pass Yield, or FPY: the percentage of products completed correctly without requiring rework.

Mean Time Between Failures and Mean Time to Repair: essential for predictive maintenance planning.

Energy Consumption: especially useful for identifying inefficiencies in older systems or during non-peak times.

Tracking these metrics over time helps build a historical baseline, making it easier to spot anomalies, trends, or dips in performance.

Turning Data Into Action

Collecting data is only the first step. To extract value from it, manufacturers must invest in systems and people who can interpret the numbers and act on them. This often involves visualization tools — dashboards that present real-time data in a clear, actionable format — automated alerts and thresholds, root cause analysis using historical data, and continuous improvement programs that structure teams around regular reviews of data.

Predictive and Prescriptive Analytics

While some manufacturers have advanced to using predictive and prescriptive analytics, many are just starting to collect meaningful data. As systems mature, manufacturers can shift from reactive data use to predictive analytics, which uses historical patterns to forecast future events — like predicting when a motor might fail based on vibration data.

Prescriptive analytics takes this further by recommending or even automatically triggering actions, such as ordering replacement parts or rerouting product flow to prevent downtime.

A good place to start is with simple automation: don't just automate your machinery — automate your process flow and information flow. Make the operator's job easier by eliminating manual paperwork. That's the easy win that builds the foundation for more advanced analytics.

Case in Point: A Palletizing Line Got Smarter

Even the most experienced operations teams can find themselves stuck in reactive mode when production data is missing, misleading, or too hard to interpret. That was the case for one manufacturer struggling to meet daily targets on a fully automated palletizing line.

Production schedules were built around assumed case-per-minute rates for each SKU. The team expected every shift to hit within five percent of its goal. They rarely did. Despite careful planning, the line kept missing its production targets. Crews fell behind schedule day after day. No one could pinpoint exactly why. Schedules were padded. Overtime hours stacked up.

Once the facility began leveraging Formic Performance Insights — FPI — everything changed. Data collected directly from the palletizing system was translated into intuitive performance metrics.

Current CPM shows the real-time pace at which cases are being palletized. Average CPM reflects the pace sustained over the current production run. And Lifetime CPM gives the long-term average rate for each SKU.

Each SKU is benchmarked against itself, with visual status indicators to flag performance. Green means operating at 81 percent or higher of lifetime CPM. Yellow indicates operating between 41 and 80 percent. And red means operating at 40 percent or less.

The team could also monitor rate trends: an upward arrow means performance is improving, a flat arrow shows no change, and a downward arrow means performance is slipping.

The results were significant. With accurate, SKU-specific performance data, production schedules became more realistic. Staffing plans and fulfillment timelines aligned more closely with what the line could actually achieve. Overtime dropped. When performance dropped into the yellow or red zone, the team could dig into the cause immediately. And managers could prioritize where to focus efforts, making problem-solving more manageable even with limited resources.

This palletizing line didn't need more people or more hours; it needed better information. Once performance data became visible and actionable, everything shifted.

Key Takeaways

Automation is an ongoing journey that relies heavily on data to drive continuous improvement and scalability. By capturing detailed production metrics and transforming them into actionable insights, manufacturers gain real-time visibility into performance, enabling smarter decisions and faster problem-solving. Key performance indicators like OEE, cycle time, and downtime help benchmark and monitor operations, while tools like Formic Production Intelligence empower teams to respond proactively. Starting with simple data capture and maturing toward predictive and prescriptive analytics allows manufacturers to move beyond guesswork and firefighting, fostering a culture of learning and continuous optimization. Treating data analytics as a core capability turns automation into a competitive advantage that evolves and improves with every shift.