Much has changed since I first described a good day in 2016.

As I arrive in the morning, my personalized digital device (attached to my wrist) provides a schedule of today’s activities. Moving toward my desk activates my computer and turns it on and a retinal scan confirms my identity. I see that one of my technicians is printing a 3-D temporary part and another is using a drone to do a roof and pipe inspection. A special 4-D printed part has been ordered that will react to temperature to grow to an engineered larger size, avoiding an expensive disassembly.

Another technician is working on a repair with a cobot that works alongside to hold parts, provide tools, and answer any questions. The cobot collects data for machine learning analytics, prescriptive maintenance, and continual PM optimization. She is using safety/training glasses that provide step-by-step visual directions. Augmented reality helps to add clarity on what to do next.

For work directly performed by the technician, an enhanced ergonomic glove is used to provide additional gripping strength to avoid carpal tunnel injuries. She is also wearing her health-monitoring vest, which includes a movement-collection device for ergonomic analysis and muscle memory training to minimize stresses.

From the Enterprise Management System (EMS), I get a quick overview of production, reliability, and maintenance. Weibull-analysis software generates a chart that shows how much can be gained by improving production efficiencies and reliability. This information is supplemented by a computer-generated verbal summary that can be used in place of or with the charts. The EMS data is integrated with a “learning system” that makes some decisions (within defined parameters) and reports on those decisions, along with the underlying reasons.

Production processes are statistically controlled. As soon as there is evidence of out-of-bound parts, they are corralled for recycling. However, it doesn’t happen often. Any small deviations from cycle time are monitored for each piece of equipment to enable timely maintenance interventions and assure throughput requirements.

Maintenance schedules are dynamically integrated with production requirements to schedule optimal maintenance times. Also, since the machinery and equipment are purchased with significant “design-in” reliability and maintainability (R&M) specifications, optimal MTTR (mean time to repair) times are optimized.

An R&M simulation calculates the potential impact of the level of Design for Maintainability (DfM) for major assets. Purchasing is responsible for life-cycle costing which is measured as asset life-cycle performance. Asset providers are selected based on best historical MTBF (mean time between failures) and reliability growth performance. Once a week, the global continuous-improvement team meets virtually (using 3-D imaging to view all participants) to make decisions using data and recommendations from the learning system.

I wonder how they used to do it. I’ve heard stories from my grandfather about large backlogs, high levels of reactive maintenance, sporadic use of predictive technologies, and excess inventory. It’s hard to believe that anyone would run a business that way. EP

Based in Knoxville, Klaus M. Blache is director of the Reliability & Maintainability Center at the Univ. of Tennessee, and a research professor in the College of Engineering. Contact him at  [email protected].