Know the score on the plant floor
The goal of better maintenance data is to understand the health of the equipment now and use the acquired data to better predict the future.
We're on the cusp of another baseball season, and I am once again preparing for my annual fantasy baseball draft. This will be the 33rd season that my team, the Bobbysox, has competed in this league, making it one of the longest continually-operating fantasy leagues anywhere. This means I've been a numbers geek for more than half my life.
In the beginning, the weekly league statistics were prepared manually, with a rudimentary spreadsheet on a TS-80 computer and a printed copy of USA Today. Since our fantasy teams consist of a mix of players from all major league teams, it wasn't as simple as just totaling up numbers. It was a painstaking process that took the better part of a day. When the printouts were finally mailed to each team member, we'd have to analyze our team's stats relative to all the other teams and make decisions based on our best guesses as to what might happen next. Those decisions would impact player movement, potential trades and help determine where we might finish in the standings. It was, to say the least, an inexact science.
I suspect some of the people who helped develop the maintenance software used in manufacturing must have been fantasy baseball players. The dynamics aren't very different: the goal is to help assess the strengths of your individual assets (players) in the context of the overall health of the enterprise (your team). The measurement is against other similar operations within your organization (league). The teams with the best metrics get financial rewards and recognition (in our fantasy league, that includes a trophy).
The evolution of fantasy sports data and maintenance data is pretty similar, actually. Thanks to the emergence of better software analytics and data mining tools, the days of manual input of statistics—plant or baseball—should be far behind us.
For my fantasy league, those numbers that were updated weekly and input by hand are now available on a phone app, updated in real time as the games are played. I can also get a historical record of what any of my players have done, both in the last seven days and throughout their career, and check on their injury status.
Isn't that what you'd like to do with your equipment? The goal of better maintenance data is to understand the health of the equipment now, understand its past operational data, see what past maintenance has been performed on the equipment and use all of that to better predict the future. It's not too dissimilar to baseball, when you get down to the data. In manufacturing, we call it the repair shop. In baseball, we call it the disabled list. The goal in both cases is to keep your prime assets productive, and replace them when needed so you can remain profitable.
That's called predictive maintenance, one of the areas we studied in the 2017 Plant Engineering Maintenance Survey, co-sponsored by Advanced Technology Services (ATS). Only 47% of all plants utilize predictive maintenance as part of their overall maintenance strategy. It ranked behind preventive maintenance, which is employed in 78% of manufacturing plants, run to failure (the opposite of predictive maintenance) which is still utilized in 61% of plants, and computerized maintenance management systems (CMMS), which is part of the strategy just 59% of the time.
While a multitude of maintenance strategies is employed in any given plant, perhaps one reason predictive maintenance is so far down the list is this other data point from our study: clipboards and other paper records are still utilized 39% of the time to manage maintenance data. While there is recognition of the value of automated data capture (53% of respondents use CMMS for at least part of the plant's maintenance), there still is far too much manual data gathered in plants today. This is an unnecessary waste in an era where managing waste is one of the prime drivers of productivity.
In manufacturing, the Industrial Internet of Things era allows us to get at that data quickly, analyze it more precisely and act on it immediately. Knowing the score about a piece of equipment allows you to make a smart decision before it goes down, and before it costs you the game.
Bob Vavra, content manager, Plant Engineering, CFE Media, firstname.lastname@example.org.
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Annual Salary Survey
Before the calendar turned, 2016 already had the makings of a pivotal year for manufacturing, and for the world.
There were the big events for the year, including the United States as Partner Country at Hannover Messe in April and the 2016 International Manufacturing Technology Show in Chicago in September. There's also the matter of the U.S. presidential elections in November, which promise to shape policy in manufacturing for years to come.
But the year started with global economic turmoil, as a slowdown in Chinese manufacturing triggered a worldwide stock hiccup that sent values plummeting. The continued plunge in world oil prices has resulted in a slowdown in exploration and, by extension, the manufacture of exploration equipment.
Read more: 2015 Salary Survey