Reducing Downtime with Predictive Analytics

	Reducing Downtime with Predictive Analytics Seebo

Applicable Industries	Other
Applicable Functions	Maintenance
Use Cases	Predictive Maintenance Log in to view content
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
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 impressive sales worldwide through its diverse portfolio of innovative products. It has 4,500 employees worldwide and a $450+ million yearly turnover. 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
Solution Type	IOT Read More Log in to view content
Solution Maturity	Mature (technology has been on the market for > 5 years) Read More Log in to view content
Contents

Applicable Industries

Other

Applicable Functions

Maintenance

Use Cases

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.

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 impressive sales worldwide through its diverse portfolio of innovative products. It has 4,500 employees worldwide and a $450+ million yearly turnover.

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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Solution Type

IOT

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Solution Maturity

Mature (technology has been on the market for > 5 years)

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Contents

Overview

Reducing Downtime with Predictive Analytics

Quantitative Benefit