Good Data & Bad Decisions: Winging it in the Maintenance Department


In business as in life, using anything other than complete, accurate, data to make decisions is both dangerous and costly. Sadly, many maintenance departments use both incomplete AND inaccurate data to make important decisions about critical assets and people every day.

For example, take the “survey method” of data management. Here’s how that method might work for a plant with 200 pieces of equipment: The manager chooses to collect, monitor and measure data from a small sample, say 5 of the machines. Then all budgetary, manpower, PM schedules, inventory counts, vendor selections, replacement schedules and other management decisions are based on assumptions extrapolated from this sample. Of course, all the data collected on the small sample needs to be complete and accurate.

In this example, survey data management might work if all 200 machines are exactly the same, used the same, in the same environment, with the same operator, maintained on the same schedule and so on. I’ve not seen any organizations that fit this description. As a result, the survey method inevitably yields incomplete data. No one willingly selects the option to use inaccurate data to base decisions on, do we?

Actually, yes we do. Inaccurate, or near accurate, data is used all the time for decision making. We call it experience-, or intuition-based decision making. I call this method of decision making: Experitive. The nice part about Experitive-based decision making is that there is NO DATA. “Fred has been here for 30 years and he knows this place like the back of his hand”.

The primary problem with Experitive decision making is that it is not documentable, measureable, nor verifiable, and there can be no expectation of repeatability (especially when Fred retires!) For these reasons, Experitive decision making should be the exception, not the rule.

Want to make the right maintenance decisions in your organization? Use complete and accurate data. It’s the only way.