Life sciences organizations are not short on data. Most plants already have many sensors in place to drive operational data to the control system so operators always know exactly what is happening at any given moment of production. Operations teams know that good data is essential to Good Manufacturing Practice, and, as such, they treat it with the reverence it deserves.
In her recent article in Pharmaceutical Processing World, Emerson’s Kristel Biehler shared how the most successful life sciences organizations today are taking the lessons learned from operational data and applying it to maintenance and reliability data, creating a holistic data-driven culture across the enterprise as a foundation for a boundless automation vision. Using these strategies, life sciences teams are more easily moving data seamlessly from the intelligent field, through the edge, and into the cloud, where cross-functional teams can make more informed business decisions and drive innovation.
A tale of two oversights
Traditionally, life sciences companies have followed suppliers’ recommendations for equipment maintenance. But when they do, they typically either over-maintain equipment, or let it run to failure. Neither of these strategies is efficient enough to maintain competitive advantage for long, however. Groups that over-maintain find themselves spending more money on maintenance and reliability than necessary and creating excess downtime by taking well operating equipment offline prematurely. Kristel explains,
“Performing regular preventive maintenance regardless of equipment condition means teams are often increasing costs by using more parts and materials than required, decreasing availability by taking equipment offline more than necessary, and creating labor issues by focusing key personnel on low-value-added tasks.”
On the other hand, when maintenance and reliability teams run equipment to failure, they often create more headaches and more expense than if they had just followed supplier maintenance guidelines. Not only does running to failure create unexpected downtime and ruined batches, but it also typically results in repairs that are significantly more expensive than preventive or predictive maintenance would have been.
Data is the answer
Successful organizations are combatting this maintenance and reliability conundrum with data. But they aren’t simply collecting more data, they are incorporating data into the soul of operations. Adding smart instrumentation is great, but when teams add smart instrumentation that is designed to interconnect seamlessly with machinery health tools and the control system, like Emerson’s AMS Wireless Vibration Monitor, they can easily push that data through the control system to add context. And once that data has context, it is pushed out to a comprehensive data layer, built on an industrial data platform such as AspenTech’s Inmation. Industrial data platforms clean, standardize, and contextualize data so it is more valuable for anyone accessing it. That clean data, Kristel explains, is at the heart of a data-driven culture,
“This data is then prepared for use in machine learning tools that warn teams in real time when and how their equipment will fail, while providing actionable advice to remedy aberrations well before they become unplanned downtime. Armed with data and the organizational support to act upon it, maintenance and reliability teams across the enterprise can stop performing reactive maintenance by heading off problems before they become major issues.”
Are you building a data-driven culture at your facility? You can learn ways to be more successful by reading Kristel’s article in its entirety at Pharmaceutical Processing World.