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In the early 2000s, the Oakland Athletics had a problem.
Their best players kept leaving.
After the team’s successful 2001 season, star slugger Jason Giambi left the A’s for the New York Yankees. Outfielder Johnny Damon left for the Boston Red Sox. Pitcher Jason Isringhausen joined the St. Louis Cardinals. These three players were among the best in professional baseball, and the small market Oakland A’s couldn’t throw New York (or even St. Louis) size money at any free agent.
In those years, the Oakland Athletics were the baseball equivalent of a small or mid-size Midwestern plant trying to compete with an international manufacturer that has billions of dollars and plants on several continents.
If the A’s were going to compete, they needed to get creative.
They turned to data.
General manager Billy Beane began using sabermetrics, an advanced system of statistical and data analysis invented by baseball writer and guru Bill James. Using sabermetrics, Beane and his team learned how to extract the maximum amount of value from their existing roster and the relatively low-cost free agents they could afford. Little-known (and much cheaper) Scott Hatteberg replaced Giambi, the 2000 season’s Most Valuable Player (MVP). The Athletics new first baseman hit far fewer home runs than Giambi, but he was often just as good (or better) at getting on base.
In 2002, without their previous star core, the Athletics improved upon their prior season and won 102 games, pulled off a record-setting 20-game win streak, and advanced to the playoffs—all with a payroll a fraction of the size of the New York Yankees. The team’s data-based approach would inspire the book Moneyball: The Art of Winning an Unfair Game, which was later turned into a film with Brad Pitt playing Beane.
Eventually the A’s lost their edge when the rest of the league—including large market teams like the Yankees and the Red Sox—realized Oakland’s secret weapon: Ddata visibility.
The A’s succeeded beyond everyone’s expectations by getting better access to data that was previously ignored or unavailable. Today’s small and mid-sized discrete manufacturers face the same challenges.
If they are going to compete, they need to get creative. They need their data. They need visibility.
Installing Alora by JobBOSS is the first step discrete manufacturers can take to “Moneyball” their shop floor. Alora provides manufacturing intelligence to operators and supervisors that drive results by turning data into action.
For Billy Beane, turning data into action involved translating Scott Hatteberg’s cheap price tag and high on-base percentage into more runs and more wins. For your plant, turning data into action can mean turning throughput increases of 15% per machine into an additional $20,000 on your bottom-line.
(Now, multiply that number over several machines and it becomes clear how Alora pays for itself.)
Increased visibility in your plant creates better decision-making, better aligns management and operators, increases operator engagement, focuses attention on the problem at hand, facilitates more accurate (and profitable) job bids, and transforms your plant into a team that can hold its own against much larger competitors—just like the Oakland Athletics did 20 years ago.
Working smarter and not harder is not just a cliché. It is also a foundational requirement for small-market baseball teams—and discrete manufacturers.
Improved visibility, the ability to unlock important data from your machines, and an increased capacity to turn that data into action is the best and most cost-effective way for discrete manufacturers to strengthen their bottom-line.
With Alora by M1, your plant can get the visibility your team needs to get a better handle on important data and start competing against the “New York Yankees” in your market.
With better manufacturing intelligence, operators and supervisors can team up to drive the results manufacturers need.
The only difference? Don’t expect Brad Pitt to play you in the movie version of your team’s story.
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About the Author