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Reducing Downtime with Predictive Analytics

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.

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  • SUPPLIER
  • Seebo
    Seebo is an Industry 4.0 SaaS platform with laser-focused business solutions that ‘move the needle’ for manufacturers in 3-months or less.Founded in 2012, the company has raised over $22M from Viola Ventures, TPY Capital, Pritzker Group, and other investors. Seebo was named a Gartner Cool Vendor in the Internet of Things for 2017.You can learn more by visiting our website: seebo.com or introduce Seebo to others with the following link: seebo.com/introducing-seebo.
  • TECHNOLOGIES
  • Analytics & Modeling - Machine Learning
    Analytics & Modeling - Predictive Analytics
    Functional Applications - Remote Monitoring & Control Systems
    Platform as a Service (PaaS) - Data Management Platforms
    Sensors - Temperature Sensors
  • INDUSTRIES
  • Pharmaceuticals
  • FUNCTIONS
  • Maintenance
  • USE CASES
  • Predictive Maintenance
  • ABOUT CUSTOMER
  • 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 impressiv
  • CUSTOMER NAME
  • Undisclosed
  • CONNECTIVITY PROTOCOLS
  • 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

     

    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.  

  • DATA COLLECTED
  • Duration, Distillation time, Reaction temperature
  • OPERATIONAL IMPACT
  • QUANTITATIVE BENEFIT
  • Benefit #1

    -83% in downtime occurrences 

    Benefit #2

    72% savings in downtime costs

    Benefit #3

    98% on-time delivery rate

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