Why modern manufacturing still runs like a ’57 jalopy

profit velocity

Management accounting isn’t the sexiest field of study in the world, and probably most accountants don’t ride Harley Davidson motorcycles, they don’t go skydiving and they don’t like to do things differently. But a revolution is brewing in the world of management accounting that is making businesses take a fresh look at how they figure out how much money they’re making. The surprising fact is that they’ve been doing it wrong all these years.

Typically, accounting principles measure profitability at a very detailed “margin per unit” level. And while correct, it’s not completely correct and surprisingly, it doesn’t take into account one very important metric – how fast the margin is moving through key machines.

Michael Rothschild, CEO of Profit Velocity Solutions and author of the landmark economics book “Bionomics: Economy as Ecosystem,” talked with Techie.com to explain why traditional accounting has often produced misleading results.

The A-ha Moment
His “a-ha” moment came to him in a flash while helping to turn around a division of National Semiconductor. “They were losing a tremendous amount of money and it needed to be fixed, and fixed quickly,” he recalls. “I learned about an idea that is now well known among productions and operations people called the ‘Theory of Constraints’ (TOC). As soon as I became aware of that, I realized that the way management accounting has traditionally worked leads to misleading results, and it gives decision-makers distorted signals, causing them to over emphasize certain products they make, and under-emphasize other ones, driving lower profits out of these high mix, high asset companies.”

Like most great ideas, the solution came quickly. “There was a mathematical way that struck me as I was driving home that evening. In a flash, I could see how all that could be fixed, with a way of doing management accounting that was harmonious with TOC, and harmonious at the same time with accounting and financial principles. There’s a way to solve all that with one blow, and that approach became what we call profit-per-time or profit-per-hour, or the Profit Velocity.”

What Rothschild realized is that the traditional “margin per unit” approach was never a complete answer. “It was correct as far as it went, but it didn’t properly take into account how fast the margin is moving through the key machines, and so everybody was relying on a necessary but insufficient number in guiding thousands of day-to-day decisions. All that great conflict, consternation and confusion could be clarified and eliminated, if people had a metric that actually pointed them in the direction shareholders wanted to go. Which is, how do we make more money, faster, in a period of time on the assets and machinery we already have. It was a way of pulling together everything I knew about strategy, economics, the laws of traditional accounting, all woven together within a series of constraints and with a book I had written some years before called ‘Bionomics,’ and it all snapped together in my mind in one moment.”

An obvious solution nobody ever asked for before
The thing about paradigm shifts is that once they are made known, everybody wants on board. According to Rothschild, “The issue was on the finance side of the organization, which never asked for the number. They had never woven that number into its calculations to come up with profit-per-time. It’s outside the paradigm of their thinking.”

Rothschild understands the nature of paradigm shifts, and he has certainly created one in the area of manufacturing profit metrics. “First, nobody pays attention at all. Then if they pay attention they laugh, and in a moment’s flash, everyone assumes that everybody has always done it that way. I’m sure lots of people have thought of it before, but from a feasibility standpoint, how do you manipulate that data, incorporate the units per hour? Nobody paid any attention before. To compute all that data, it takes a lot of computing horsepower to make it happen.”

That’s the second part of why nobody ever asked for it before – the technical end of it was not possible until recently. Surprisingly though, it’s not “Big Data” in the terabyte and petabyte category, but for many manufacturers it’s in the tens to low hundreds of gigabytes. Think of it not as “Big Data,” but “Pretty Big Data.”

“Big data begins at home,” said Rothschild. The traditional big data approach – which has always been to throw everything together in a big pot and see what falls out – doesn’t apply. “A lot of people out there are trying to boil the ocean of externally generated data,” he adds. “But people have compiled, particularly since the ERPs came in over the last several years, internal repositories of plain, well-organized, ‘pretty big’ data. And they have not mined or exploited that data, which is really good, hard plain data that exists within their four walls, that they know is valid because they use it for other purposes and take good care of it.”

Accuracy of Big Data
While some manufacturers might apply data from machine sensors, Rothschild says that’s not always necessary. Once the rate and flow has been established, it tends to be approximately the same every day, and the flow, whether it’s determined by machine sensor or by a man standing at the end of the assembly line with a clipboard, can be easily determined and factored into the equation to come up with the Profit Velocity (PV).

A recent Ventana Research report showed that only 11 percent of enterprises that use Big Data say that their data is “very accurate.” And that has always been an issue with any Big Data implementation, but Rothschild overcomes that objection with a razor-sharp focus on good, proven data. First, he looked at how to restrict the application into taking only a minimum number of data elements, and ensuring that those data elements are the highest quality. “For example, one of the files we grabbed stuff from was the sales invoices, because customers call you up on the phone and scream at you if your invoices are wrong. The data inside these files tends to be nearly perfect.” Restricting the big data application to only hard, empirical data that is trackable and traceable, and then adding only the one extra element of the physical speed of product through machines, establishes results that can be astounding.

Running like a ’57 jalopy
In his book “Bionomics,” Rothschild talks about how a business organization is a lot like a virtual social organism: “They exist in an economic niche just like real organisms exist in an ecological niche.” His book presents a biological metaphor for how a market economy grows and changes over time, and operates in a decentralized, self-organized fashion, and learns from experience. From that framework, the Profit Velocity equation was born. “The goal was to make a profound impact in global economy by accelerating bionomic learning in these organizations,” he says. “I was looking for that piece of the puzzle, and it turned out to be very simple – measure at the point of constraint.”

“We came at this a different way. It was the economy in this part of the industry, which is about seven trillion dollars of the economy, making steel and chemicals and computer parts, stuff we don’t pay any attention too, and it was running like a ’57 jalopy getting two and a half miles to the gallon. It’s so wildly inefficient, and it’s all because the numbers are being crunched by conventional accounting. This entire segment of the economy runs using huge amounts of capital equipment, but puts out very little in terms of return to investors because of the way they measure what they’re doing. So this was an effort to solve a fundamental economic problem. We can see about a three-point revenue move to the bottom line, and on a $7 trillion chunk of the economy that’s about $200 billion a year of goodness that can be created by un-screwing the distortions in the measurement system we inherited from the days before there were computers.”

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