Editorial: Manufacturing is Moneyball | Trailer body builders

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[The following originally was published in the December 2021 print edition of Trailer/Body Builders, focused on fabrication news.]

If you’re not getting the results you expect from investing in the technology to extract your manufacturing data, chances are you are looking at the wrong numbers and have now given up on all that talk about the power of “analytics”. . “

But you shouldn’t quit the game. To help manufacturers stay competitive in the digital age, Jon Sobel wants to keep an eye on the ball.

Sobel is the CEO and Founder of Sight Machine, a factory data and analytics company. He used his niche during FABTECH’s recent FABx Tech Talks to discuss the national hobby, specifically the book by Michael Lewis and the film that followed. Silver ball.

“What I’m going to do today is try to reduce some of the noise and confusion around this digital world we’re all in,” Sobel began. “Some $ 200 billion will be spent next year on digital technologies for manufacturing, but it’s an incredibly fuzzy and unclear concept: exactly why are we doing this? And how do we start?

His Moneyball pitch was simple and engaging, especially if you’re a baseball fan or Michael Lewis. Or Brad Pitt. Basically, as this “incredible story” says, the game has been played the same way for over 100 years. Pitching and hitting stats have been plotted from the early days, as managers all played by the same strategy book, building teams and setting lineups based on numbers like batting averages, RBIs. and circuits.

But in the late ’90s, “really creative people” took a look at a century of statistics and “completely rethought” how to think baseball.

“They asked a simple question: Does the batter enter the base? he said. “There are at least 27 at bat in the game. Each batting is a unit of production that should be used as efficiently as possible.

Likewise, most manufacturers are “incredibly rigorous,” data-driven and goal-oriented, with a deep tradition of continuous improvement – “always trying to improve,” Sobel continued.

But, back to baseball, Sobel quoted great slugger Mickey Mantle, who once said, “It’s amazing how much you don’t know about the game you’ve played your whole life.

Indeed, this new look at baseball statistics (called sabermetrics) helped the 2002 Oakland A’s, with one of the smallest payrolls in professional baseball, set a league record by winning 20 straight games and by qualifying for the playoffs after winning 103 games over the season. Soon after, the Boston Red Sox won their first World Series in 86 years by applying the model, and now the new measurements are included in the rough scorecards.

“The use of data has changed baseball forever – and in almost every industry there is a story like this,” Sobel said.

Current manufacturing statistics focus on things like asset usage, material waste, production quality, and output.

“We’ve all been trying to answer these questions for a long time, but here’s what’s different with digital: you can see more; there is data everywhere, ”he said. “Of course, its use is very difficult. And this is where my community in Silicon Valley has, perhaps unwittingly, participated in some real disservice: We made it easy. “

Typical complications for manufacturers include attempting to obtain data from 50-year-old machines or a dozen software systems that have accumulated and changed over time. Sobel suggested that before investing in a new technology, manufacturers should fully understand their goals and be able to “see a straight line” between that investment and the goal.

“Companies like mine are better able to show you that it’s going to help you with your business and they are better able to explain how in one syllable. If we can’t, don’t do business with us, ”he said.

And you don’t need to invest in “giant systems”: a few sensors on critical machines will do. The key is to link this data “to productivity, not production”.

“It’s not how many things I did today, is how well I did it?” Are we getting better? “Sobel says.” We work with some of the biggest manufacturers in the world, and it’s amazing that none of them had an accurate measure of their productivity when we started working with them, they have no idea.

They had data, but they weren’t using it effectively.

“Stop paying attention to RBIs and start paying attention to base percent and stroke average – you just have to measure to know: are each of these machines producing the same output?” What is my quality really? he said. “And then select tools that bring you closer to those answers. It is, respectfully, digital transformation. Nothing more, nothing less, but it’s incredibly powerful and efficient.

So take a new look at this lineup and “Play ball! “

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