Reducing Downtime with Predictive Analytics

• 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

• Pharmaceuticals

• Maintenance

• Predictive Maintenance

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 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.  

• -83% in downtime occurrences 

• 72% savings in downtime costs

• 98% on-time delivery rate