Biotechnology Case Study

Reducing Downtime with

Predictive Analytics

About the company

The company is a leading biotechnology manufacturer based in the U.S.A. which develops and manufactures nutritional ingredients using cutting-edge, proprietary technologies. Since its establishment, the company has grown rapidly, generating impressive sales worldwide through its diverse portfolio of innovative products.

Industry: Biotechnology 

Employees: More than 4,500 worldwide

Yearly Turnover: $450+ million

It’s a relief to know that we finally solved a major recurrent problem and have a practical tool to use in the future.”

– Shift Operator


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 The Challenge

To improve production capacity and avoid downtime, a global biotechnology manufacturing company implemented Seebo Predictive Analytics.

The company’s quarterly operations review revealed a 3.6% increase in downtime during production. This downtime stemmed from an unexplained viscosity in one product in the production line.

The resulting pipeline blockages between the reactor and the centrifuge in the production line led to more frequent equipment cleaning procedures and stoppage during the batch production, high levels of waste, a decreased capacity, and lengthened time to market.

The investigative team could not identify a reason for the blockage, as all relevant production parameters were in the approved working range.

The Solution

The company decided to invest in Industry 4.0 and predictive analytics and looked for a solution with these capabilities:

  • Combine their manufacturing expertise into data analytics and machine learning
  • Provide operational teams with simple and accurate insights
  • Deliver predictions on future downtime problems

Results

-83%

in downtime occurrences

-72%

savings in downtime costs

98%

On-time delivery rate

+5.1%

production capacity

Automated, simple and accurate predictions with the Seebo Solution

Seebo analyzed historical and online data from the production line and identified the correlation of variables – specific variations in mixing duration, distillation time and reaction temperature – which were causing the blockage.

Based on these findings, the Seebo solution could provide a prediction alert to the operational team before the blockage occurred again.

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

“Having the ability to predict quality and downtime problems is completely changing the way we work – moving from preventive and reactive maintenance to predictive maintenance. We dramatically reduced the overall costs of maintenance.”

– Maintenance Manager

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