Case Study - Reducing Downtime with Predictive Analytics by Seebo

	Reducing Downtime with Predictive Analytics Seebo

Technology Category	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
Applicable Industries	Pharmaceuticals
Applicable Functions	Maintenance
Use Cases	Predictive Maintenance
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. Read More
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 Read More
Customer Name	Undisclosed Read More
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. Read More Log in to view content
Data Collected	Duration, Distillation time, Reaction temperature Read More Log in to view content
Contents

Technology Category

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

Applicable Industries

Pharmaceuticals

Applicable Functions

Maintenance

Use Cases

Predictive Maintenance

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.

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

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.

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Data Collected

Duration, Distillation time, Reaction temperature

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Contents

Overview

Reducing Downtime with Predictive Analytics

Quantitative Benefit