The Last 1%: What Can You Do With It?

For manufacturers who have achieved 99% product quality, it’s not so much what’s in the last 1% that’s valuable- it’s what’s hiding behind it.

What’s Left?

One of the best questions from a potential customer goes something like this: “If my product quality is already at 99%, what can advanced technology really do with the 1% that’s left?”

For manufacturers talking about the 1% left behind after they’ve achieved 99% product quality, the answer is pretty straightforward. That 1%? Not much. It’s overrated. 

But, for manufacturers who think they have achieved 99% efficiency just because they’ve achieved 99% quality- they’re almost always wrong. That’s because no one has ever seen a manufacturer actually running anywhere near 99% efficiency and utilization. For some, that’s obvious because state-of-the-art is often closer to 60%. If you’re doing more than 60%, you’re doing it really well in an industry 3.0 world. In fact, when you actually start looking at the data, many of the top manufacturers in the world have 50, 40, even 30% efficiency or utilization in order to get near that 99% product quality. Industry 3.0 baked in the assumption that a trade-off was necessary, that manufacturers had to relinquish massive levels of both human and machine efficiency in order to achieve acceptable product quality standards. In that pursuit, most competitive manufacturers became hyper-focused on that 99%. 

Are You Looking at the Right Thing?

Really, the first question to ask is- “Are you sure you’re looking at the right thing?” What’s the real value of that 1%? 

The answer is simple- the real value of that 1% is the untapped efficiency and utilization that was left behind in the pursuit of it, sometimes for decades. Fortunately, Industry 4.0 brings the tools and processes needed to capture all that value- value that is now easily within their reach. 

Think of an iceberg. There’s no way to know the real size of an iceberg without measuring under the surface, and there’s no way to do that without specialized instruments. Today, all modern seacraft utilize these specialized instruments because, without them, they are forced to operate much less efficiently, using outdated methods of navigation. Without these tools, they ultimately make their best guess as to where they can’t travel and navigate accordingly. For those choosing to be extra cautious, they keep even farther away, just to be safe, because they lack the specialized instruments to see safe places they could otherwise travel. 

Likewise, manufacturers who focus only on the tip, or product quality, miss a giant mass of inefficiency and utilization that sits just below the surface. Their outdated processes focus on where manufacturers can’t operate, rather than use specialized tools to tell them the many places they can. 

Humans + Machines

This is exactly what Industry 4.0 is all about- using tools now at our disposal to reset assumptions and achieve truly zero-waste, AI-predictive manufacturing. Before new technology can be adopted, however, manufacturers looking to do so need to first ask two questions:

1. What is in my data? What can I give to the machine that the machine can do really well?
2. How is my organization willing to adapt? Are we ready for a 50/50 partnership between humans and machines?

At this intersection- what the tools are ready to do well and what the humans are ready to step up and do well, lies a tremendous opportunity.

Imagine that, instead of running a factory, you run an accounting firm. Your people use calculators to crunch numbers, submit reports to customers, and make revenue for your firm. Imagine, further, that your accountants use the calculators for addition and subtraction, but not for square root functions. Why? Because your older calculators didn’t have the square root function, so your humans are still doing it by hand. Personally, I don’t think I’ve ever done the square root of 2 by hand. If you try to hire me to do it I think I’d say no. I wouldn’t want the job and, if I took it, I would probably make all kinds of errors.

Imagine that an accounting firm is actually operating this way today when it is cheap to buy modern calculators that have a square root function and can immediately calculate it, never making a mistake. They just need to invest in the calculators and teach their people how to operate the square root function.

And yet, many factories are operating in a similar way.

For example, in Factory #1, a team of seasoned veteran experts is sent through the factory for about 12 months. They pick the right new sensors and hardware to increase visibility. They perform prescriptive maintenance even earlier than before and achieve 99%, maybe even 99.5% quality, by limiting issues that might arise. They’ve further defined “red zones” to stay out of in order to achieve that 1% left. After they get to that point, though, there is really nothing more that can be done.

Those who are at this point need to reexamine and even redefine these zones. The question is not simply if the green zones are really green. Crucially, all the regimes previously thought inoperable, those red zones- are they really red?

By comparison, in Factory #2, a machine data system is installed to ingest data across all machines. It automatically calculates green and red zones based on the data across the organization, flagging only those specific areas or problems it is not already confident in. Those issues are then served to a lean team of only two experts. Some of the calculated red and green zones are the same. However, some are very different- identifying areas where life can be extended, capacity freed up, and quality maintained or improved with vast efficiency increases. As the factory continues to run, the new data further paints a canvas of green and red, eventually making it into the perfect picture that defines exactly how to operate.

There is no question which factory fares better, which is poised to move forward with a modern, adaptive workforce with tools that generate a whole new plane of vision. Embracing that mindset comes with the advantage of workforce recruitment. The majority of incoming workers are interested in jobs and careers that utilize their skills- modern skills that play very well in an Industry 4.0 world. Fewer and fewer workers are looking for jobs without them, and many are coming into the workforce with efficiency and utilization skills already in hand.

An Asset in an Industry 4.0 World

But, can this alternative approach work for manufacturers with lots of machines and processes all over the world? Make no mistake, while this was a liability in Industry 3.0, it’s actually an asset in Industry 4.0 because of the ability to build a larger data set. More machines feeding information equals a faster rate of learning for the algorithms.

So, to really understand what can be done with the remaining 1%, the answer to the question lies in another question: Are you looking at the right thing?

For those who are still trying to whittle away at the remaining 1%, it requires a process of recognizing, visualizing, and clarifying the giant mass of inefficiency sitting underneath it. Those who continue to squeeze tighter on legacy process controls built up over time just to get another fraction of a percent will find it harder and harder to remain competitive going forward.

Manufacturers who adopt new tools and capabilities and go back and reevaluate the path they’ve traveled are the companies that emerge stronger, smarter, and quicker in an Industry 4.0 world.

Those who have moved forward with the latter have already found that the question was less about what could be done with that last 1% all along and more about what was hiding behind it.