April is, without question, my favorite time of the year. Why? The end of winter? The blossoming of new life? Blah, blah, blah….April is great because baseball is back, and after missing the joy of baseball in April a year ago, I’m even more excited about this season.
One of the things I most love about baseball is its geometric perfection. The bases are 90 feet apart. If the distances between bases were 80 feet or 100 feet, it would be a completely different game. The processes of pitching, fielding and running would be altered. The precise design of a baseball diamond is a strange and wonderful thing.
The other things I love about baseball are the numbers. There are few things as enthralling to me as a box score. If you know how to interpret the statistics, you can learn in greater detail why your team won or lost that day. Sometimes it’s not the events at the end of the game that truly determine the outcome. There can be small things earlier in the game that affect how you use your players or how you change your strategy.
And since I’m also a fantasy baseball owner, the evaluation of player statistics is an all-consuming passion at this time of the year. When you’re trying to assemble the best team, it’s not just the skills of one or two key players that determine success. If you can find the right combination of 23 players to fill out your team, you can be a success.
These are the same skills we talk about all of the time when it comes to designing and optimizing manufacturing plants. Building a successful production operation is exactly like building a successful baseball team. You have to identify a plan at the start, execute on the plan, constantly evaluate data and performance, adapt to changing conditions and prepare for disruption when it does occur.
Data is key to analyzing performance. In both baseball and in manufacturing, the emergence of new and exciting data points have helped us understand not just why we won or lost today, but what trends could extend those streaks in one direction or the other. In baseball, a player with a .300 batting average is considered a high performer. But looking at that average today doesn’t tell you whether he’s been consistent, or whether he’s on a hot or a cold streak.
We have to do the same with plant data. We have to let the data speak to us, not just speak for itself, and we have to respond to what those numbers are telling us. It’s that deeper dive into the data that uncovers great performance—or shows our impending troubles.
If you love numbers and are willing to learn how to interpret them, you can be a better baseball fan or build a better manufacturing operation. The parameters are set: the geometry of a baseball diamond or the laws of physics. It’s time to start.