I am a pretty calm guy. Sometimes too calm. I remember one time when I was standing against a wall and a basketball coach bounced a ball off the wall about three inches above my head. I didn’t react at all. Another time, I was 100 feet below the surface of the Atlantic in a place called Shark’s Cove. A nurse shark, 50 yards away from me, swam right toward me with seemingly malicious intent. I remained perfectly calm and didn’t move an inch. It came within 10 feet of me and turned away.
I’ve been in business for a long time. Over the years, I worked with one really terrible customer who bought an EtherNet/IP software solution. I’ve had some customer Profinet development projects go bad. But none of that got my blood boiling.
But I did get excited. I did get angry when I saw the ads for these IoT companies that want to process manufacturing data. I made a list of 400 of them a few years ago and I studied as many as I could without vomiting. The message (lie?) they had was all the same. “We’re going to suck all the data out of your machine, line, factory and store it in the Cloud. For $49 a month per machine, you’ll be able to optimize that system and gain tens of thousands of dollars in efficiency and productivity. It’s that easy!”
Snake Oil Salesmen from the 1800s are Back and Now They’re Selling IoT Systems to Manufacturers
Smart manufacturing isn’t that easy. Our RTA gateways are easy, but this kind of stuff isn’t. It really upsets me for all sorts of reasons and it’s one of the reasons I decided to call my LinkedIn series “Myths, Facts and Lies of Smart Manufacturing”.
It’s not always easy to get the data you need. It’s not always simple to suck that data into the Cloud in a format that can be processed. But let’s skip all that and just focus on machine optimization for a quick minute. Let’s say you can manage to suck out all the data from all the various machine components into an AWS database. The big question is “Now what?” What are you trying to achieve? You have to have some result in mind, some goal (quality, efficiency, downtime…etc..). Knowing what you want to do, what data you’ll need and how you can organize and process the data is not only important, it’s fundamental! The IoT companies that make these statements skip over this like it’s the trivial part when in fact, it’s the most crucial.
Now this doesn’t apply to the big companies. They have an army of resources (and money) to throw at these problems. They have IoT architects, data scientists, digitalization specialists falling all over themselves to work on these problems. But the average factory is less than 200 people with a tiny part, if any, of them focused on optimization issues. It’s a lot harder for them and that’s why the message these IoT companies is so damaging to those that fall victim to it.