Case Study: Biopharmaceutical Company Uses Advanced Analytics to Improve Manufacturing Processes

Challenges

At biopharmaceutical operations, fill weight analytics are essential in ensuring ready-to-ship products. Often while tracking the consistency of fill weights during batch manufacturing, manufacturers only find out if a product is over or under fill weight at the end of a batch, resulting in a product that needs to be scrapped or inadequate manufacturing capacity. Fill weights are critical for product quality assurance, ensuring consistency over time across different products, dosages, batches, and time points. 

Multivariate analysis is important to be able to predict conditions. Analysis on complex and multivariate data is challenging to use for determining and predicting when operating conditions are occurring that could be an indicator of a ‘bad batch’, signaling that proactive intervention can occur.

Monitoring consistency of batches in near real-time, predicting batch quality, and performing root cause analytics to identify and determine the cause of batches outside of optimal operating conditions is difficult and time-consuming in applications such as spreadsheets. They also make it difficult for teams to collaborate and share insights on commonly accessible data and analytics work efforts within work teams, across different manufacturing sites, and across global operational reporting.

On top of everything, it’s additionally challenging to aggregate multiple processes and contextual data sources into a single application. 

Solutions & Benefits

Using Seeq deployed on AWS to notify operators and engineers of conditions empowers teams to predict a future bad batch. Self-service analytics enable subject matter experts (process engineers, managers, and operators) to easily access, cleanse, contextualize, and perform advanced analytics and machine learning on industrial data.
 
Seeq’s analytics tools are broadly applicable, adding value across dozens of use cases, from control system validation to golden profiling to preventative maintenance. The ease of use for process engineers and teams to gain and share insights quickly on time periods of interest within the manufacturing process leads to immediate improvements to operational performance. 

Seeq increases the value of investment in OSIsoft Pi and Pi Asset Framework. Seeq’s extensive support for OSIsoft’s solutions enable teams to develop insights on one asset, batch, or process and quickly scale that analysis to hundreds of assets leveraging their asset framework. Seeq on AWS enables organizations to easily scale up, adding new users to new manufacturing sites and new data sources (including future plans to add Amazon Redshift as well as MES system data) across their global locations. 

Seeq on AWS also ensures application accessibility for remote and distributed teams with access to near real-time data while engineers and teams are working from home or from remote locations. The application provides ease of enterprise software procurement via Seeq on AWS Marketplace.

Data Sources

  • OSIsoft PI
  • Amazon Redshift