Our client, a multi-billion-dollar specialty pharmaceutical company, has been providing high-quality products to a large portfolio for over 150 years. They observed a steady decline in production levels and were looking to their historized data to diagnose the issue. However, they quickly realized that they only had 90 days of historized data. This duration provided no meaningful insight and fell short of industry standards.
Additionally, the client utilized several disparate software systems that attempted to compile data to generate accurate results; however, they failed a data integrity audit and their reputation was now on the line. Avid helped the customers’ productivity diagnosis by implementing a historian solution engineered to prioritize data capture. This allowed data storage optimization and a single source of truth for data integrity audits.
After examining the client’s production sites, we recorded over 8000 production-related tags. In addition to these tags, Avid engineers identified other unhistorized and critical sources of data required for a complete production picture. After all tags and key data points were identified, our team implemented an OSI PI Historian to monitor and store data across the entire facility. Along with the Historian, our engineers created an HTML front end, intended for supervisors to enter additional data that was not able to be captured through the tags.
The outdated system captured data every 10 seconds, regardless of a change, creating an overwhelming amount of uninformative data. The new system was engineered to prioritize data capture by exception or in the event of a change, which exponentially reduced the total amount of data and ensured that only meaningful data was recorded.
Results!
Throughout the project, our team stayed on-budget and ahead of schedule, resulting in high client satisfaction. Our client now has a compliant data system that meets industry standards and has mitigated their data integrity concerns. Additionally, they can now begin preparing for the next step: using analytics on valuable data collected to help identify problems that limit production and prohibit them from expansion.
