38 KEY INSIGHTS AW OCTOBER 2021
Using CMMS (computerized maintenance management system) and OEE (overall equipment effectiveness) software to calculate MTTR (mean time to repair) and MTBF (mean time before failure) can help you zero in on more difficult-to-determine operational insights, particularly when numbers from the different software systems don’t match up. For example, your OEE system could be pulling data directly from PLCs, whereas your CMMS is likely tied to maintenance logs. When operators or maintenance personnel log data into one of these systems, discrepancies between how those two different systems are reporting can highlight information about how well your operations and maintenance are in sync, or not. David Greenfield on MTTR and MTBF maintenance metrics. awgo.to/1253
The team built a model around a quality parameter related to the paper strength and the fiber bond. By examining that data, they were able to discover possible causes of errors in the production line. For example, they were producing scrap due to adding too much chemical to the pulp mix. Stephanie Neil on Skjern Paper’s use of GE Digital’s Proficy CSense for real-time quality control. awgo.to/1254
Fault type identification provides value by getting your machines up and working faster. When you can see the behavior, you can fix the break rather than relying on guesswork. Predictive maintenance also helps you estimate the time of failure, taking away the guessing game and giving you a solid idea of when a machine will fail so you can prepare for maintenance and shutdowns on your own time. Dan Riley of Interstates Inc. on the value of predictive maintenance. awgo.to/1255
LNS Research has identified several best practices through its research, including top-down implementations, engaging business operations, including the entire manufacturing network, IT/OT (operations technology) convergence, balancing shortterm and long-term wins, and capturing and analyzing data. Diane Sacra of LNS Research on getting real with digital transformation. awgo.to/1256
Typically, de-powdering can be carried out using manual processes, but this can be costly and time-consuming. By contrast, Solukon’s Smart Powder Recuperation system removes particle residue from 3D-printed components by using automated swiveling motions and vibration at calibrated frequencies. David Miller on automated de-powdering of 3D-printed parts. awgo.to/1257
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