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What Technologies Are Most and Least Likely to Change Manufacturing?

Whether you’re watching “Shark Tank” or reading a crowdfunding campaign, the term revolutionary gets tossed around quite a bit. I like Peter Drucker’s definition of revolutionary; the true revolution is when technology or processes from a totally different sector overturn a market, such as genetics upending medicine or the internet altering everything from social interactions to looking up information.

There are four technologies often cited as certain to dramatically alter manufacturing: the Internet of Things, machine learning, artificial intelligence and 3D printing.

 

Internet of Things

 

The Internet of Things is simply a level up from the already high level of automation in many factories. In some regards, the IoT only extends the “factory” into the broader world, tracking items until they reach their end use and monitoring them when they return for rework.

Perhaps the greatest impact of the Internet of Things and masses of data collected as a result of it will be what we and our creations can glean from it.

 

Machine Learning and AI

 

Machine learning and Artificial Intelligence go hand in hand; machine learning is how AI will advance and how we’ll train it for particular applications. Analyzing data from consumers could lead to improvements in product design, customer service and product delivery.

Optimization through data analysis becomes faster and more accurate as we develop both better data analysis tools and faster systems to run them. Conversely, we have to have faster computers and streamlined analysis simply to keep up with the firehose of data being generated today and the flood the IoT will generate tomorrow. This is where understanding the difference between information and knowledge is essential, and that is where industrial engineers will find their niche no matter how smart the AI is. Ignore the need for human input, and you’ll have an advanced AI generating its own equivalent of the Spurious Correlations website, spotting trends that humans know are irrelevant. That’s aside from recommending massive changes that aren’t worth the effort or simply aren’t practical.

AI will increasingly become a partner in the design process. I think evolutionary design processes with human oversight has strong potential. And the ability to model processes and products will improve as we gain more empirical data on how systems and parts actually behave in various situations.

 

3D Printing

 

3D printing remains a technology for small lots and unique items. Yes, 3D printers may be integrated into CNC machines. However, they are a long way from producing as many items as an injection molded machine or extruder, much less at a comparable per-unit price. Work to create 3D printed metal parts and printed products from other materials is ongoing, but it will be decades before it will replace mass-production lines for most items.

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How to Identify Potential Continuous Improvement Projects

There’s an old business axiom that if you want to find a business idea, truly listen to the complaints of others because the solution to that problem probably has a market. And something similar can be said about continuous improvement projects, though there are additional questions you should ask before investing company resources.

To quote Dr. Jordan Peterson, before you decide what problems to solve, notice what bothers you.

First, pay attention to what bothers you. If you can’t actually name the problem, you can’t solve it.

Second, be careful of who and what you’re identifying as the problem.  You might be blaming the easiest target instead of the actual one, and there are times that we blame the official, approved target instead of the actual root cause.

And if you make the problem too broad, you can’t really solve it. “Precision may live tragedy intact but it chases away the ghouls and demons.” If you blame “management”, “the economy” or an “ism”, you’ve made the problem too big to be solved. Nor do you want to destroy everything, including what works, in the name of fixing the real, small problem. You can address problems like a product that doesn’t fit customer expectations anymore or researching new niche markets that use your product.

Third, ask “Can you fix it?” If the answer is no, ask who could solve it. There are times where the problem requires enlisting help to solve, whether you need to redesign the product or take the issue to your supplier. If the answer is “no one”, then look for something else until you find something you can fix.

Fourth, consider the return on investment for fixing the problem. It isn’t worth spending a hundred thousand dollars trying to fix a problem that wastes thousands of dollars of material. Nor should you invest hundreds of hours fixing minor annoyances unless these issues truly impact the bottom line.

The Quality Habit, a Reflection

“Quality is not an act, it is a habit.” I’ve heard this quote attributed to Aristotle, though there is some debate about this.

Another variation of this quote is “We are what we repeatedly do. Excellence, then, is not an act but a habit”. But what does this quote mean for industrial engineers?

 

Habits are learned behaviors, something you intentionally start out doing and then maintain. Introducing best practices doesn’t matter much if you don’t implement them and stick with them.

 

Habits can affect one’s overall behavior, but it is that behavior that determines culture. That’s demonstrated by the axiom “One’s actions are a better indicator of beliefs than one’s words.” Management saying quality is number one is irrelevant if day to day decisions are contrary to that mantra. Conversely, management and employees whose actions are consistently of high quality and people mindful of such can maintain quality in operations and service delivery even if the culture isn’t specifically hyping it up.

 

Quality only gets better if you get in the habit of looking for ways to improve it and then train the new and improved methods to the point they become a habit. If you don’t monitor during the learning period and “sustain the gain”, people revert to the old ways, their old habits.

Habits can be good or bad. Bad habits can ingrain poor quality, such as shortcuts and rushing through the job. Good habits ensure high quality.

Good habits can become bad habits as things change. You need to re-evaluate your habits to see what you need to stop doing in addition to how to improve on what you choose to continue doing.

IE and BI – the Intersection of Industrial Engineering and Business Intelligence

A classic Peter Drucker quote is “What gets measured, get managed.” A quote attributed to W. Edwards Deming is “You can’t manage what you don’t measure.” Big Data attempts to collect as much information as possible in the hope that the data can be used to make better decision. However, you will still fail if you don’t ask the right questions, give information to the wrong people, fail to give it to the right people at the right time or waste time trying to make sense of it all.

Many enterprises are struggling to manage the firehose of data they’re funneling in, much less mining it for useful information that might be turned into insight (wisdom). We’ve seen a rise in Business Intelligence tools to try to literally make sense of it all. This is leading to a number of changes in industrial engineering and business in general.

Business intelligence expertise is increasingly seen as essential to work in data analysis for manufacturing planning, demand planning and operational decision making.

If you’re going to work in consulting or analytics, job descriptions often ask the candidate to have experience with business intelligence or operation research tools if not both.

Engineers who work in sales or support regularly work with business intelligence experts, if they are not one themselves.

If you’re working as an analyst to improve business operations, you’re likely going to be expected to know how to design, write and publish reports through commonly used business intelligence tools like Cognos.

IT hasn’t entirely replaced time studies.  However, productivity experts regularly use business intelligence tools to study the productivity of different groups and review business operations.

Quality engineers are starting to be expected to know how to write queries of databases and business intelligence systems.

Business intelligence reports may provide leads on aspects to improve or document the changes that have taken place after process improvement projects.

In the case of CMMS and manufacturing systems where nearly everything is tracked by the Internet of Things, business intelligence tools may be how you get the information you need to do your job.

In summary, business analytics is focused on analysis while industrial engineering tends to be focused on improvements, but the distinction is starting to blur.