By Dr. Klaus M. Blache, Univ. of Tennessee Reliability & Maintainability Center
Arriving at work in the morning, my personalized digital device (attached to my wrist) provides a schedule of the day’s activities. Moving toward my desk activates my computer and turns it on. When I sit down, the retinal scan confirms my identity. I see that one of my technicians is printing a 3-D temporary part. Another is using a drone to conduct a roof and pipe inspection for a system-reported leak and checking construction progress.
A third technician is using a maintenance-assist robot to perform simple, repetitive maintenance tasks. (This robot can also be used in the emulate mode to copy the exact movements of the technicians for more complicated and heavy work.) The technician is using safety/training glasses that provide step-by-step visual directions. In the actual work she performs, an enhanced ergonomic glove provides additional gripping strength to avoid carpal-tunnel injuries.
From the Enterprise Management System (EMS), I get a quick overview of production, reliability, and maintenance. By applying Weibull analysis, the software provides a chart showing how much can be gained by improving production efficiencies and how much can be gained by improving 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 are integrated with a “learning system” that makes some decisions (within defined parameters) and reports on those decisions and underlying reasons/rationale. Production processes are statistically controlled.
As soon as there’s evidence of out-of-bound parts, they’re corralled for recycling. This, however, doesn’t happen often. Process capability is typically better than six sigma (3.4 defects/million). Any small deviations from cycle time are monitored for each piece of equipment to enable timely maintenance interventions and assure throughput requirements. Since the machinery and equipment are purchased with significant “design-in” reliability and maintainability specifications, optimal MTTR (mean time to repair) times are realized. Among other things, those design-in specifications include items such as color-coded and easily accessible lubrication points and known replacement parts that are engineered for quick disassembly and positioned to not affect more expensive parts at removal.
Purchasing is responsible for life-cycle costing (LCC) and then measured on life-cycle performance of machinery and equipment (M&E). Component and M&E providers are selected based on best historical MTBF (mean time between failures) and reliability growth. Every part has an RFID (radio frequency identification) tag, making it easier to find parts and perform Root-Cause Analysis (RCA). This is ongoing within the learning system. Once a week, the global continuous-improvement team meets virtually (using 3-D imaging to view all participants) to make decisions with data and recommendations from the learning system. The main purpose of this meeting is to address needed decisions that are beyond the programmed scope of the software.
Everything that happens in the plant is related. A reduction in reactive maintenance improves safety. Fewer repairs mean greater throughput and lower costs. Senior leadership wants to immediately reduce maintenance cost, but data points show that safety, throughput, and cost would all be negatively affected if that were to happen. Management reconsiders and takes another approach.
Looking back, do you ever wonder how yesterday’s plants made a profit? My grandfather used to tell me stories about large backlogs, high levels of reactive maintenance, sporadic use of predictive technologies, and excess inventory. It’s hard to believe that anyone could run a business that way. Fast forward to today. While we may not yet be ready for the scenario described in this article, don’t hesitate to think big. Great things happen when individuals want to make a difference, take some risk, and tenaciously implement. MT
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 kblache@utk.edu.
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