If there’s one constant since the beginning of organized production, it’s the urgency to maximize uptime and churn out the highest-quality products at the fastest possible speed. No matter how sophisticated, targeted or unique the business strategy, manufacturers are at a competitive disadvantage if their production lines can’t deliver.
While all manufacturing leaders—especially operations and plant managers—understand the crucial role maintenance plays in making things, many may be unaware that their approach to maintenance is fast becoming obsolete. New methods, powered by the industrial Internet of Things (IoT), are already beginning to shatter maintenance norms and benchmarks.
Most maintenance professionals know that the legacy approaches of reactive or scheduled (aka preventative) maintenance can’t keep up with the demands of a 21st century manufacturing marketplace. Many, however, might not realize that even predictive maintenance, can be enhanced to another level: prescriptive maintenance.
Now just beginning to be implemented, prescriptive maintenance solutions, offer the ability to resolve issues automatically by prescribing and deploying efficient solutions. Prescriptive maintenance uses artificial intelligence (AI), and its subset machine learning in combination with sensors to diagnose the root cause of problems, indicate appropriate remedial actions and manage the entire maintenance process.
With MindSphere, the cloud-based, open IoT operating system from Siemens, and data from manufacturers’ computerized maintenance management system, prescriptive approaches essentially determine and carry out all aspects of maintenance. The system “decides” that a specific machine should be overhauled or have another maintenance operation performed, initiates an alert, creates and sends a work order to field technicians, orders parts (and afterward restocks them), and assesses and records the outcome of remedial actions. It performs all of these steps autonomously in a fraction of the time required by any previous maintenance scheme.
Over time, as the system accumulates data and conducts analyses of equipment characteristics and behavior, failure modes and many other events that occur during operation, the system’s accuracy improves.
Prescriptive maintenance has already demonstrated its ability to reduce downtime and increase equipment reliability and operating lifetimes. In the coming years, prescriptive maintenance solutions will further exploit the capabilities of AI, machine learning and edge analytics to deliver insights that have never been possible before.
Manufacturers that want to ensure that their hard-won competitive advantage isn’t lost to more savvy and nimble competitors that raise their plant efficiency to unheard of levels should take note.