Seeing aisles and aisles of groceries in the store makes it all seem so easy.
But food and beverage manufacturing is anything but simple. Countless hours from quality teams, continuous improvement specialists, and packaging engineers go into ensuring every package is perfectly sealed and shelf-ready.
Tiny variations in manufacturing procedures, or even environmental changes, can lead to major issues, driving up defect rates and cutting profits.
In this example, we look at a food and beverage manufacturer facing a rise in packaging defects, especially whether these defects followed a seasonal cadence. They turned to the Minitab Solution Center to brainstorm potential causes and used Minitab Statistical Software and AI to uncover key insights.
Curious to see how they did it? Let’s break it down.
Step 1: Team Brainstorm
The quality team gathered to review several fruit snack pouches that weren’t sealing properly, creating a safety hazard.
They used a Man, Machine, Materials, Method, and Environment fishbone diagram in the Brainstorm utility within Minitab Solution Center to identify potential causes. Here’s their initial diagram:
As the discussion slowed, they used the AI Quick Generate feature to surface additional ideas—especially under the “Environment” category.
Here is what Minitab’s AI generated. The AI generated additions are noted with the green plus sign (+) in the upper right-hand corner:
Minitab’s AI suggested potential contributors, including one that caught their attention: seasonal humidity changes. They remembered that defects often spiked in warmer months.
Step 2: Statistical Analysis
They used Minitab Statistical Software to build a time series decomposition plot to better understand the seasonal changes of the total number of defects. They knew there was a seasonal cadence that they wanted to visualize, and they also wanted to understand how many defects may occur in the upcoming year, and when:
To simplify interpretation, they generated an in-app plain language summary using Minitab’s AI:
Minitab’s AI confirmed that the time series was reasonably accurate for modeling future defects. The summary also mentioned that the most defects tended to happen in July, and the fewest in February.
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Step 3: Findings
With seasonal humidity flagged as a possible cause, the team used the Graph Builder in Minitab Statistical Software to visualize factory humidity levels alongside defect counts.
They discovered a clear trend: as humidity levels rose, so did the number of packaging defects.
Step 4: Action
Armed with these insights, the team didn’t wait. They installed smart humidity sensors in key production areas that alerted when humidity exceeded optimal thresholds.
They also worked with the maintenance team to adjust the sealing machine temperature and dwell time based on ambient conditions. They ensured seals remained airtight even on humid days.
Finally, they created a dashboard in Minitab Connect to monitor environmental conditions and defect rates in real time so they could act quickly if trends started to shift.
Within two months, the team saw a 38% drop in seal-related defects, and production downtime related to rework was cut in half.
When Data and AI Seal the Deal
Solving quality issues in food and beverage manufacturing takes more than guesswork.
It takes insight.
With the combined power of brainstorming tools, AI-assisted analysis, and real-time data monitoring, this team didn’t just identify the root cause of seasonal defects—they acted on it.
And the result? Fewer defects. Less downtime. More confidence in every package that leaves the line.
Whether you're dealing with packaging problems or process inefficiencies, Minitab empowers your team to move from problem to proactive solution, with data that sticks.
Put your ideas and data to work with a free trial of Minitab Solution Center.