We’ve talked for years—maybe even decades—about closing the loop in process manufacturing control and business practices. Automating processes by using data to drive end-to-end seamless functionality improves safety while increasing efficiency and profitability.
However, as Erik Lindhjem shares in a recent episode of the Processing Pros podcast over at Processing magazine, many organizations are still missing a key element of capturing their best results: closing the loop on reliability. Modern automation technology makes it easy for organizations to turn data into actionable information and drive those benefits across the enterprise. By closing the loop through a boundless automation vision for improved reliability practices, teams take information from the field, process it effortlessly, and tie it into enterprise maintenance management—ultimately turning raw data into information that drives work.
Challenges drive new needs
The days of easy reliability wins in the plant are over. In most individual plants, the easy problems have already been solved, and reliability teams are turning their attention to more complex enterprise-wide problems. However, as teams focus on problems existing across many different facilities, trying to bring everything together and optimizing at a higher level, data management becomes complex. Erik explains,
“It requires a massive amount of data that’s becoming overwhelming. How do you stitch that together? How do you organize it? And how do you utilize it to solve for those problems, both in terms of creating the information that’s required and the knowledge to really solve those problems?”
Teams not only need massive amounts of contextualized data to be efficient across the enterprise, but they also need fast and effective ways to work to track, trend, and apply workflows no matter what the geography is. Many of today’s small reliability teams are supporting plants across multiple world areas.
Enterprise-level automation is the solution
Fortunately, reliability teams have options to help them manage data across the enterprise and close the loop on reliability.
“Enterprise software is designed and fit-for-purpose to take data, standardize it, consolidate it—bring it into a common repository, but then also being able to contextualize all that data as it’s collected, making it more useful, insightful, and easy to understand.”
Enterprise-level reliability solutions like Emerson’s AMS Optics leverage a boundless automation vision for seamless data mobility from the intelligent field, through the edge, and into the cloud, to make this task as easy as possible. With seamless connectivity via open standards and protocols like OPC UA and MQTT, AMS Optics brings all the information reliability teams need into a single, intuitive dashboard, making it easy to drive analysis and monitoring from a centralized system.
Centralized dashboards reduce the complexity of collecting raw data from the system and making it usable. Instead of navigating a complex web of VPNs, jump servers, and multiple logins, users simply connect to their enterprise software—via mobile device from anywhere—and instantly see an easy-to-read health score for any asset they have access to across the fleet.
With everything they need in one place and intuitive decision support tools to help them make the most accurate decisions about plant health, technicians can not only act decisively, but they can also improve efficiency, pushing data from dashboards and analytics tools right into computerized maintenance management systems for faster response from local teams.
Closing the loop on reliability is a key step towards driving operational excellence, and one that too often flies under the radar. By taking control of data across the enterprise, reliability teams can begin tackling the more complex problems that drive competitive advantage while simultaneously giving themselves the tools they need to do more with less. As the global marketplace continues to expand and competition becomes increasingly more intense, having such tools in place will be a critical differentiator.
