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All too often, downtime tracking isn’t done with root-cause analysis and problem-solving in mind. The high end of the Pareto chart catches our attention as the most penalizing downtime. But we will be further from solving the problem when tracking downtime “reasons” versus “causes.”

In my Feb. 19, 2022, newsletter column (see link below), I wrote the following about downtime tracking that looked good on paper but, in reality, was almost useless: “The new downtime tracking system did not capture the ’cause’ for the downtime. Instead, it collected the location and ‘reason,’ such as ‘filler door open,’ ‘no product,’ and ‘mechanical.'”


Click Here To Read The Referenced Newsletter Column
“Fragmented Efforts Give Fragmented Results”


A PLANT-FLOOR EXAMPLE
Let’s look at a filling/packaging operation where “bottles jamming” was one of the highest downtime reasons. To begin, though, we must first ask “WHERE” did all these bottle jams occur?” Since there were several spots on the line where that could have happened, it wasn’t possible to look at the downtime-data report and solve the problem. Furthermore, when the operators were asked about the jamming bottles, they BLAMED “the new bottles,” rather than pointing to the location of the chronic bottle jamming.

So, the next logical question for the operators was to ask how do you know it’s the new bottles? To which came the reply “Well, the old bottles didn’t jam as often as these new ones we started running a month or so ago.” Wow. Here was some meaningful information that could be verified. But an inspection on the downtime reports “before” the new bottles were supposedly introduced to the line did not show much of a difference. In fact, the rate was about the same.

Confronted with this information the operators seemed to agree that the  new bottles didn’t jam as much in the unscrambler but, rather, jammed in the filler conveyors and labeler. At last, we were getting somewhere. Still, we didn’t know the extent of the jams in the filler conveyors or labeler, when the downtime data was merely recorded as “bottles jamming.” No matter, let’s run with “bottles jamming” and see where it takes the problem-analysis process.

The question, “What’s different about the new bottles?” yielded several insights. From the operators: “They don’t feed right; they jam up.” From the procurement person who sourced bottles from a new supplier: “They are the SAME. Those operators haven’t adjusted the line properly.”

Well, to clarify here, the operators don’t adjust the line for the bottles. That’s the job of the maintenance/setup people who adjust the line for new products. Moreover, they’re using fixed gauges to set the conveyor-rail widths and adjust other pinch points. Those gauges are marked with bottle sizes such as “16-ounce,” “12-ounce” and “10-ounce.”

The maintenance/setup crew knew nothing of “new bottles.” They just knew the line was supposed to run 10-ounce bottles. And that’s what they set up the line to run.

So, it was back to the downtime reports to learn what size bottles were being run when high rates of “bottles jamming” were said to occur. Unfortunately, the reports were even more useless, since they did not have the granularity of the sizes of the bottles. That information was in the production reports.

OK, let’s recap what we knew (or didn’t know). The bottles were jamming because they supposedly were “new bottles.” That was only pointed out by the line operators. So, we went with that for a minute and asked the line operators. “What sizes of the new bottles jammed a lot?” The answer was obvious to all: “It was the 10-ounce bottles.” And that was verified by the production reports.

At that point, we grabbed several “new” 10-ounce ” bottles out of the cases and headed off to look at the setup gages used by the maintenance/setup people. They looked the “new” bottles over carefully, checked their diameters with the setup gages, and pronounced them to be “A-OK.”

So, there we were, again: still plowing along with anecdotal data to help solve one of the filling/packaging line’s highest downtime problems (and the cause of the plant’s largest production losses).

A second brief meeting with the procurement person proved a bit more enlightening, since we were told, “There is absolutely NO difference in the bottles. The new supplier’s bottles matched the previous supplier’s bottles. They were the 10-ounce, same material, same weight, same shipping cartons and quantities as before. Absolutely the same. But they were cheaper.”

Following that brief meeting, it was back to the line operators to ask how those “new bottles” jammed. The operators all pointed to several spots on the line and inside equipment where the top shoulder of SOME of the “new bottles” hit a top rail or a limit-switch bracket. That was was revealing. It was the height of the new bottles, not the diameter.

Note that the operative word used by the line operators was “SOME” (of the new bottles). That meant there must have been some degree of inconsistency in the bottles, the molding, or the annealing processes. Collected samples revealed the slight variation in shoulder height. (Yup: You get what you pay for! “They were cheaper.”)

The fix was an easy one: Move the components that hit the shoulders up a bit so the bottles would clear. But getting to this simple fix was cumbersome, due to lack of granularity on the downtime-log sheets at the  filling/packaging line, which, in turn, fed the downtime reports.

GET THE RIGHT DATA RIGHT
When analyzing problems there are at least three sources of data: 1) the reports; 2) those people closest to the problem; and 3) equipment observation. Trying to analyze and correct problems with reports alone can be a trial-and-error experiment.

In the case of the “new bottles,” the standard downtime-log sheets weren’t specific enough to use for problem-solving. They pointed to a downtime reason, not a location or a cause of the downtime. As RAM professionals we can contribute to better downtime tracking by working with production staff and people on the front line. Let’s do our part and help them get the right data right the first time, every time.TRR


ABOUT THE AUTHOR
Bob Williamson is a long-time contributor to the “people-side” of the world-class-maintenance and manufacturing body of knowledge across dozens of industry types. His vast background in maintenance, machine and tool design, and teaching has positioned his work with over 500 companies and plants, facilities, and equipment-oriented organizations. Contact him directly at 512-800-6031 or bwilliamson@theramreview.com.


Tagsreliability, availability, maintenance, RAM, asset management, machine downtime