Every February 2nd Americans gear up for the grand indicator of how much longer winter will be with us – by checking in with a groundhog in Pennsylvania about whether he saw his shadow. This year Punxsutawney Phil did not see his shadow when he woke on Groundhog Day so this means spring is coming early!
However, “early” is certainly relative.
While I know it’s in good fun to follow little stories like that of Punxsutawney Phil, this is an example of measuring the wrong things and using indicators that aren’t wholly directly connected to value-adding results.
It’s pretty obvious there is no clear connection between the sun shining on a groundhog’s hole on a certain day in February in a certain city in Pennsylvania so as to see his shadow and the upcoming weather patterns in the northern hemisphere over the following six weeks.
So how often do we use measures or indicators that tell us information that we think is helpful but instead fails to give us the right value-adding information?
One example – when optimizing a process for quality, reduced effort by operators, speed, and overall cost, why would we be overly concerned with the number of individual steps required to complete the process? Just because a process might require more steps in the future state doesn’t necessarily mean that it is less effective – maybe more steps ends up taking less time or less effort or creating higher first pass yields. The key measures are speed and quality – why does the number of process steps matter as a measure?
Another example is with measurement of use of the Lean tools. At one previous stop, our plant was measured at the corporate level by deployment and employee knowledge of the Lean tools – why does our 5S or standardized work score matter if our defect rate is through the roof or we are way late on our shipments?
When thinking about this issue, “correlation does not imply causation” comes to mind.
Some companies, in fear of measuring the wrong things, will measure everything. At another previous stop, we used a metrics board that had 24 different charts and graphs of business-related measures. With the management turnover the plant ended up having and the new replacements brought on board not knowing how to follow the old processes for obtaining the measured data, the board became a dinosaur that stopped being regularly updated. Each of the measures meant something to someone in the plant, yet we treated the measures like they all meant everything to everyone.
Measuring everything is overprocessing – you might catch everything that needs to be measured, but think about all the things lost in the shuffle that one must muddle through in order to find the right information!
An example of measuring too much yet not always the most meaningful things is with the NFL Combine. NFL teams watch 350+ players test for speed, strength, and agility, and also go through interview sessions to learn more about the players’ intelligence and football acumen. Teams learn some specific measures of players they are about ready to shower with spectacular NFL salaries via the NFL Draft.
However, what is tested at the NFL Combine doesn’t always tell the whole story. Take this one for example:
This guy’s NFL Combine measurables included a 40-yard sprint time of 5.23 seconds and a vertical leap of 24.5 inches, pretty unathletic numbers for a quarterback. Tom Brady was drafted in the sixth round of the 2000 NFL Draft, yet has gone on to a first-ballot NFL Hall of Fame career with the New England Patriots.
The numbers didn’t tell the whole story, nor did the interviews, that would accurately predict Tom Brady would become the world-class quarterback he is today. While it’s important for teams to draft the right guys that won’t become busts, they sure do spend a lot of time analyzing every piece of data without getting the greatest return on that investment via greater identification of busts (so it seems).
Indicators are important to help users act and react accordingly. Measure the wrong things and your operators are acting based on non-useful data. Measure too much stuff and they could get distracted.