The Modern Mantra: “Instrument, Analyze, Tune”

by Andrew McAfee on May 17, 2013

An interesting article by James R. Hagerty in the Journal yesterday tells how lots of manufacturers, including Raytheon, GE, and Harley are installing tons of gear to monitor every element of their processes, from the speed of fans in a paint booth to the number of times a screw is turned as it’s being inserted.

These are all examples of instrumentation, which we can very loosely define as ‘adding sensors so that more aspects of a process can be measured as it’s being executed.’ An instrumented process throws of mountains of data, which can be used for three main purposes.

The first is making automatic real-time adjustments. As the WSJ article describes, “At Harley-Davidson Inc.’s newly renovated motorcycle plant in York, Pa., software keeps a constant record of the tiniest details of production, such as the speed of fans in the painting booth. When the software detects that fan speed, temperature, humidity or some other variable is drifting away from the prescribed setting, it automatically adjusts the machinery.”

The second main purpose is after-the-fact analysis of all the data to troubleshoot, find improvement opportunities, etc.. The WSJ again: “Recently, by studying the data, Harley managers determined that installation of the rear fender was taking too long. They changed a factory configuration so those fenders would flow directly to the assembly line rather than having to be put on carts and moved across an aisle.”

And finally, instrumentation is hugely valuable for control; it can go a long way toward ensuring that only the right people are involved, and that they’re using only the right parts and tools. At Raytheon, “The system is designed to prevent any operator from performing a process for which he or she isn’t certified. Before using a sealant, the operator must flick the tube under a bar-code reader so a computer can verify it is exactly the right sealant. The computer also knows exactly how much torque should be applied by any wrench or screwdriver. And operators aren’t permitted to use the wrong wrench.”

The upshot of all this, as Erik Brynjolfsson and I highlighted in our Harvard Business Review article on Big Data last fall, is that stuff that was formerly an art becomes a science, and guesswork is replaced by precision. So ‘Instrument, analyze, tune’ has become the modern manufacturing mantra.

In all ways except one, this is great news. It’s great because it increases the quality and consistency of the products we buy, and lowers their prices (rework and waste are expensive for manufacturers, and instead of eating the costs they pass them on to us). I want the things I use to be perfectly crafted. The way to accomplish this today is, paradoxically enough, not by employing more craftsmen, but instead relying ever more heavily on machines and data.

The one issue here has to do with those craftsmen; we just don’t need them as much, or need as many of them, in an instrumented world. As the article points out, “Semiconductor and other high-tech companies were early adopters” of the ’Instrument, analyze, tune’ mantra; the precision required by their processes didn’t permit any alternatives. And their factories now aren’t full of people. In fact, they look very much like something out of a science fiction movie, with a few people in weird clothes walking down hallways full of humming machinery. As I watched this video, I kept waiting for the alien to jump out, or the virus to spread.

So while I’m thrilled about Big Data coming to manufacturing (and soon to just about all other sectors of the economy), I’m concerned that it will increase and accelerate the ‘hollowing out‘ of the workforce already underway.

Do you share my mix of optimism and pessimism here? If you see things differently, why? Leave a comment, please, and let us know.

 

 

  • Gary Mintchell

    I’m glad the WSJ finally discovered instrumentation and MES software (or its equivalent). I’ve been writing about these applications for years in the B2B world.

    This sort of application in discrete (or factory) automation goes perfectly with Lean Manufacturing–at least when used, such as in the story–and Lean properly speaking does not mean “replace people.” I have just toured two factories in central western Ohio and eastern Indiana who have implemented Lean and have many hourly employees whom they do not anticipate replacing. Instead, they use the expertise of all the employees to improve manufacturing processes.

    The use of instrumentation and automation was accomplished many years ago in the process industries. Rather than just blindly replacing operators, the automation removed operators from hazardous locations.

    I’ve been thinking about the employment problem for years. What I’ve observed from the huge reduction of manufacturing workforce in the once heavily industrialized western Ohio and eastern Indiana region is that most job losses were due to management and markets.

    We were heavily dependent upon automotive manufacturing–especially parts. I’ve watched the disruption of the market by foreign competitors (although Honda is a significant employer in this area). There are other examples of market disruptions that caused manufacturing downsizing due to overcapacity.

    The offshoring fad was another significant destroyer of jobs. A few enlightened C-suite people have figured out the fallacy of simply sending jobs away without thinking through the entire value chain.

    I don’t think big data and instrumentation, in and of themselves, will cause massive layoffs. But markets and managers can and will.

  • http://www.facebook.com/KillerzTan Tan Nguyen

    great article as always, solidworks is amazing, with many simulation module,

  • http://www.facebook.com/KillerzTan Tan Nguyen

    Pls post more about what’s the future of work in America going to look like with the acceleration of automation and out-sourcing.

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