Whose Job Is it?
By Bob Williamson, CMRP
CPMM, MIAM, Editor
Machine-downtime tracking is a fundamental, foundational, most-basic component of any reliability-improvement effort. Yet in many plants, I have noticed that any form of machine-downtime tracking is the responsibility of the Maintenance Department. Contrary
to that logic, Maintenance should NOT be the collector nor the repository of machine-downtime data.
Here’s the rub when Maintenance collects and reports downtime: All machine downtime becomes associated with maintenance. In short, it’s the “Maintenance Downtime Report.” This is especially true with an organization's executive and
senior management. Maintenance management is then often seen as making excuses or pointing fingers when reports show “operational” (non-maintenance) downtime.
Some businesses use work orders for tracking maintenance downtime. What downtime is recorded in work orders? It's the type of downtime that requires a maintenance intervention. Again, the Maintenance Department becomes associated with
CENTER OF ALL MACHINE-DOWNTIME DATA
So, where should this downtime tracking begin? The center of all machine downtime is the MACHINE. Downtime tracking associated with the machine can then be captured in reason categories such as operational (production), product
materials, product quality, setup/changeover, utilities, maintenance, and the like. Downtime categories then capture the reason, date, time, duration, and eventually the cause and corrective action at a minimum.
A REAL-WORLD CASE EXAMPLE
We were challenged by the Plant Manager and browbeaten Maintenance Manager to determine and correct the critical bottleneck machine-downtime dilemma that has been nagging them for months. The downtime was logged on a tracking sheet by the operators. This raw data was compiled and then reported by the Maintenance Manager during weekly staff meetings. So, he was the one who got the verbal smacks.
Production management calculated “downtime” by subtracting running time from total shift hours minus scheduled breaks and meals. They determined there was apparently too much downtime of an undetermined reason. Again, it became a
Our solution was to install a machine data logging device on the main motor drive to detect the motor's start, run, and shutdown times and durations. This data logger ran on all three shifts for a week, as did the typical operator
downtime tacking sheets.
The data-logger report was downloaded after a week of production. The results were revealing. On some shifts, the machine was running 15 minutes after the start of shift, ran up to scheduled breaks, restarted, and ran until 15 minutes
before the end of shift. That was perfect.
But the data-logger report also showed the culprits: Second and Third Shifts (with Third being the worst). In many cases, the machine was started an hour (or more) AFTER the start of shift, shut down BEFORE scheduled breaks and extended
past the end of breaks. The end of Third shift was an exception in that the machine was running up to the end of shift.
Third Shift was staffed differently. Since this was a bottleneck machine, it was one of very few running on Third. There was only one supervisor for the whole plant on Third Shift. The operator was taking advantage of the lack of
supervision and others in the plant. But why did the machine run so well at the end of the shift? Day Shift supervision showed up 30 minutes early to get their shift started.
The downtime-data-logging report had the granularity that the other tracking reports failed to communicate. Previously ALL downtime data was aggregated by the week’s total, not by day or
It's our job, as RAM professionals, to see that ALL the machine downtime on critical assets is captured with sufficient granularity to solve the problems regardless of the reason and
MORE FRESH FOOD FOR THOUGHT
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LEST YOU MISSED IT
Here's an interesting piece that's been running on Manufacturing.net: ""The Engineer Who Triggered The Debate Over Humans' Role In Climate Change" discusses a Brit named Guy Callendar. In 1938, Callendar, who was also an amateur meteorologist, apparently was the first scientist to connect human activities to global warming. Known as "The Callendar Effect," his
theory set off quite the debate.
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