Food & Beverage Industry Case Study

Improving Production Capacity with

Automated Root Cause Analysis

About the company

The company is a privately-held manufacturer of baked goods founded in 1945. It has developed from a single product to a global manufacturer with offerings that delight consumers in more than 100 countries.

Industry: Food & Beverage 

Employees: More than 1,300

Yearly Turnover: Over €500 million

“After deploying Seebo automated root cause analysis, we ended up increasing the capacity of our production line by 4.7%. We are now expanding the use of the solution to more factories in the group.”

– Plant Manager

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The Challenge

To reduce downtime and quality issues and improve production capacity, a global food manufacturer implemented Seebo process-based AI solutions in its wafer production line.

Prior to engaging with Seebo, the company investigated a recurring problem of broken wafers related to underlying quality and downtime issues. The manual root cause analysis (RCA) concluded with a recommendation to bake wafers at a lower temperature, leading to a longer retention time, extra overtime for workers, and reduced production capacity.

The company was looking to achieve three primary goals:

  1. Return to its former production capacity (+4%)
  2. Identify the root cause of production issues quickly and accurately
  3. Reduce costs through a reduction in overtime hours

The Opportunity

The company turned to Industry 4.0 and predictive analytics for a solution that would do the following:

  • Incorporate their manufacturing expertise within data analytics and machine learning
  • Provide simple and accurate insights to the operational team
  • Deliver automated root cause analysis and predictive analytics for quality and downtime events, in a single solution
  • Not require expert data science skills

Case study results


fewer manpower hours


of the time required


more accurate results


production capacity

Automated, accurate root cause analysis with Seebo

Seebo analyzed live and historical data from the wafer production line and identified correlations between statistical deviations in temperature and statistical deviations in the speed of the cooling conveyor belt.

After only six hours of investigation using Seebo Solution, the company’s operational team was able to confirm that these deviations were the source of the broken wafers.

As a result of the Seebo Solution, the factory returned to expected production capacity and the factory team was able to pinpoint the optimal schedule of predictive maintenance.  

“My intuition told me the root cause of failure was the oven temperature; it was actually something I never would have guessed, and that would have taken me ages to find, if at all. Using Seebo…made our work a lot easier.”

– Shift Manager

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