By Christopher Surdak

In this age of disruptive change, technological innovations can either ease all your worries or bring you the greatest discomforts you’ve ever known. Christopher Surdak elaborates on the Seven Deadly Disruptors that are changing manufacturing, and will inevitably change our lives.

 

In my years of researching and learning about technology and its impact on our society I have noted that certain technologies have impacts far beyond their apparent reach. When I look at our world today and the world we anticipate tomorrow I see seven technologies that are not only impactful, they are truly disruptive. In my view, these seven technologies are:

1. Robotics
2. Analytics & Big Data
3. Internet of Things
4. Blockchain
5. Nanotechnology
6. Additive Manufacturing
7.Artificial Intelligence

These “Seven Deadly Disruptors” are permeating among us, whether we realise it or not. Their impact will be profound, even in large, capital-intensive, old-school industries such as manufacturing. Indeed, I’ve spoken with a range of manufacturing executives who tell me, “That won’t really impact us, we’re different.”  When discussing disruption I hear this “Snowflake” argument all of the time, and I find it to be both amusing and distressing. Yes, disruption is coming to manufacturing, and it’s going to be big, fast and comprehensive. Let’s look at the Seven Deadly disruptors, and see how they might profoundly affect manufacturing in the coming decade.There are other, hugely important technologies emerging throughout our society, and many might be candidates for this list. CRISPR/Cas9 gene editing comes immediately to mind as a technology that will completely transform our notions of health, natural selection and basic biology. But, despite the radical changes something like CRISPR will bring, they won’t be fundamental across all aspects of society. Truly disruptive technologies don’t change things broadly or deeply, they change things BOTH broadly AND deeply. Examples are fire, iron, petroleum, rubber, plastics and the Internet. Such technologies change how humanity works, plays and lives.

 

Figure 1: CRISPR/Cas9 allows scientist to edit individual genes in DNA

Robotics

If you’re a manufacturer, you may be surprised to see robotics on this list. After all, most manufacturers have been using robots for decades; so much so that they may seem like nothing new. But, the careful, slow, methodical improvements in robotic capabilities are about to turn the corner, with new abilities that will allow robotics to move into entirely new parts of our value chains.

 

Figure 2: Advanced robotics are mimicking our fine motor skills

With physical robots, advances in their ability to dynamically sense and respond to their environment have improved greatly through the use of machine learning, Artificial Intelligence and advanced data analytics (more on these in a moment). Rather than robots merely following the guidance of their programmers, they can now adapt their movements and actions based upon feedback from their surroundings. This difference is subtle, but profound. There is an entire class of manufacturing activities that has not yet been automated, because they require this ability to sense and respond to an ever-changing environment. As robots become both more capable and cheaper they will replace more and more jobs currently performed by humans

But, the careful, slow, methodical improvements in robotic capabilities are about to turn the corner, with new abilities that will allow robotics to move into entirely new parts of our value chains.

This is also true of software-based robots, often called Robotic Process Automation (RPA). Software robots are replacing humans in a wide range of data entry, data monitoring, and data evaluation duties.  If a human now performs such tasks on a desktop, laptop or tablet, they will soon be replaced with an RPA robot that performs the same tasks faster, better and cheaper, without taking breaks to check on their Instagram feed.With physical robots, advances in their ability to dynamically sense and respond to their environment have improved greatly through the use of machine learning, Artificial Intelligence and advanced data analytics (more on these in a moment). Rather than robots merely following the guidance of their programmers, they can now adapt their movements and actions based upon feedback from their surroundings. This difference is subtle, but profound. There is an entire class of manufacturing activities that has not yet been automated, because they require this ability to sense and respond to an ever-changing environment. As robots become both more capable and cheaper they will replace more and more jobs currently performed by humans.

 

Analytics & Big Data

So, what’s new now? Scale, scope and context.

Big data and predictive analytics are still hot topics in the business world, and this is also true in manufacturing. Interestingly, most manufacturers have been collecting, analysing and improving through data analytics for decades. So, what’s new now? Scale, scope and context. First, the volume of data that can be collected, digested and put to use is dramatically larger than in years past. As for scope, we can gather information about a vast array of environmental factors which we may have never realised were relevant to our operations. Finally, context is how we apply this information to generate new outcomes, relevant in space and time.

 

Figure 3: Poor ventilation, another variable in the production equation?

I recall working in the early 1990s in a factory that produced avionics and digital engine controls. We were applying new Statistical Process Controls (SPC) to improve our production quality. One particular step in the process had dramatic swings in production quality.  Sometimes it was nearly perfect; other times the failure rate skyrocketed. The engineering team, the line workers and management were all completely befuddled as to what was causing this variation. Only through a lot of trial and error, troubleshooting, head scratching and guesswork did one of the line workers discover the cause of the occasional defects. An air conditioning vent was directly over the work station with the variability. Every time the factory got hot, the air conditioning would turn on and blow directly onto this work station. The problem was that no one ever bothered to change the filter on that vent, because it was over a vat of molten solder. So, every time the air conditioning was turned on it blew dust, grit and grime all over our precision-made electronics. With Big Data, engineers can examine more and more variables in their production equations, leading to ever greater control of their processes.

Analytics is also applied to supply chains, both upstream and downstream in the production process, in order to drive out waste, inefficiency and under-utilised capacity. Manufacturers have gorged themselves on supply chain management, flow control, “lean production” and so on in an effort to improve efficiency and reduce costs. Unfortunately, this has also removed all resiliency, margin and wiggle room in those same production processes. Our efforts to become more efficient has also dramatically embrittled our processes, making us susceptible to hiccups in our supply chains.

Big Data and analytics can help with this. With sufficient data and appropriate analysis, engineers can begin to predict when failures may occur, machine break downs, or raw materials getting hung up in customs. With better predictions, data scientists can help manufacturers restore some degree of resilience in their processes, reducing the occurrence and severity of production interruptions.

These factors make analytics disruptive because if your organisation is not at least keeping up with your competition in these regards, you are but one missed shipment away from losing your customers, likely forever. After all, they have been leaning out and rationalising their own supply chains, and you’re part of their chain. Your missed shipment of a thousand-dollar part may end up costing them a million dollars with their own customers. In today’s age where perfection is simply expected and competition is both global and instantaneous, you’ll likely get no second chance.

[ms-protect-content id=”9932″]

 

Internet of Things

If Big Data and Analytics are being used to vastly improve manufacturing, the Internet of Things (IoT) is the source of this ridiculous amount of new data. IoT is the use of networked sensors to collect information about everything, everywhere, and every when. This information feeds the Big Data beast, and engineers are presently coating the planet with sensing devices. According to a report by the IEEE, we will have something like fifty billion IoT devices in the world by 2020. That’s 50,000,000,000 “things” telling us about the world around us 31,536,000 seconds per year. These numbers get really big, really fast!

Figure 4: Even water bottles are becoming “smart”

IoT devices are already pervasive and are enabling a wide array of new capabilities in manufacturing.  Suddenly, the data produced by, and about your products may be more valuable to your customers than the products themselves! Modern jet engines produce enough data on a transcontinental flight to fill a laptop’s hard drive. Shipping containers constantly broadcast their position to their manufacturers, buyers and carriers, enabling the ultimate in supply chain “leanness”. Manufacturing equipment constantly monitors itself, and can self-adjust when they fall out of alignment. Customers can instantaneously change their minds, and their specifications, in the middle of a supplier’s production run as their needs change.

The impact of all of this is that unless you’re datafying your operations, you are falling dramatically behind. If your competition embraces IoT before you do, not only can they operate much more efficiently and effectively, they are also able to collect an ever-growing treasure trove of data. Once they have this data they can sell it back to their customers at substantial profits, and create a permanent competitive advantage over you. You might be able to copy their products or even their processes, but you can never recreate or estimate their data. In a hyper-competitive world, the only real variables left in the production equation are people and data. Once you have the best of both, no one can compete with you.

In a hyper-competitive world, the only real variables left in the production equation are people and data. Once you have the best of both, no one can compete with you.

Blockchain

Some have claimed that Blockchain is the most disruptive technology since the Internet. The truth of this remains to be seen, but blockchain is absolutely the most hyped technology of our generation. In concept, blockchain is deceptively simple: Take a chunk of data, encrypt it a couple of times to create a completely unique digital signature, then publish that signature across the Internet so that anyone can verify its existence. But wait; there’s more. Use every previous signature in the creation of every subsequent signature, like links in a chain, and have each of those signatures also verified by thousands of users across the Internet.

Figure 5: Blockchain is locks built from locks, built from locks

While something of an oversimplification, this description is not far from the sum total of what’s going on in blockchain. Indeed, the original paper that introduced the world to the concept was all of nine pages long. Issues of 51 percent attacks, the rise, collapse and resurrection of the Decentralized Autonomous Organization, proof-of-work vs. proof-of-stake, and hard or soft forks grab headlines as much as they create blank stares and quizzical looks of bewilderment. While blockchain is awash in hyperbole and jargon, it seems apparent to all that something important is going on in this cloud of technical and political obfuscation.

The big “wow” of blockchain is simply this: trust. Completely transparent, completely public, and (nearly) completely reliable trust.  When a transaction is placed on a blockchain, and it’s validated a few times, anyone, anywhere can have complete faith that the transaction is a real and honest representation of what actually happened. Presently, ensuring that things are as they are claimed to be is the occupation of millions and millions of accountants, auditors, lawyers and other so-called “knowledge workers”. These highly compensated employees are used to make absolutely, positively certain that our understanding of our businesses, and our capital wealth, is accurate.

When properly implemented blockchains will not only transform our notion of “truth”, it will also make redundant those millions and millions of people presently engaged in the hunt for truth.

With blockchain, all of this human oversight is not only unnecessary, it’s obsolete. With blockchains, organisations can publish their facts to the world, and anyone they deal with can accept those facts as true. When properly implemented blockchains will not only transform our notion of “truth”, it will also make redundant those millions and millions of people presently engaged in the hunt for truth. Customers can see their supply chain with complete and instant transparency, and manufacturers can fulfil their contractual obligations, and traceability and regulatory requirements with almost no effort whatsoever. Supply chains will further rationalise, contract and speed up, with a resulting surge in productivity; the unemployment of entire swaths of auditors and administrators notwithstanding.The big “wow” of blockchain is simply this: trust. Completely transparent, completely public, and (nearly) completely reliable trust.  When a transaction is placed on a blockchain, and it’s validated a few times, anyone, anywhere can have complete faith that the transaction is a real and honest representation of what actually happened. Presently, ensuring that things are as they are claimed to be is the occupation of millions and millions of accountants, auditors, lawyers and other so-called “knowledge workers”. These highly compensated employees are used to make absolutely, positively certain that our understanding of our businesses, and our capital wealth, is accurate.

 

Nanotechnology

Nanotechnology is the creation of particles, tools and devices on a nanometer scale. A nanometer is a billionth of a meter, which is a meaningless number to most of us. To put a nanometer into perspective, a typical sheet of paper is 100,000 nanometers thick. Hence, a nano-scale engine, pump, robot, gearbox or other device is one which is less than 1000th the thickness of a sheet of paper. This is fairly small!

People in manufacturing, particularly those in so-called “Heavy Industries” likely scoff at the notion that nanotechnology will disrupt them. How can something the size of a virus be as powerful or as valuable as a locomotive? Many engineers suffer from a “bigger is better” mentality, particularly when building buildings, car engines or power tools.

 

Figure 6: A toy race car thousands of time smaller than a grain of salt

But, with nanotechnology, there’s strength in numbers. The individual devices may be minuscule, but use them by the billions or even trillions, and suddenly you have roadways, bridges or washing machines that seem to self-assemble before your eyes. This may sound far-fetched, but it is very real. The basic technologies are advancing rapidly, and once one of these devices can be created and proven, producing them by the metric ton is simply a matter of, well, manufacturing.

Yes, nano-machines may clean the insides of our arteries, kill cancer cells one by one, or filter toxins from our water one molecule at a time.

Nanotechnology will likely be massively disruptive to manufacturing, as it may soon lead to entirely new ways of producing practically anything. Yes, nano-machines may clean the insides of our arteries, kill cancer cells one by one, or filter toxins from our water one molecule at a time. The feats of nano-bots on the scale of the very small will be extraordinary.  But, don’t discount nanotechnology’s impact on large scale production. While today, we create production lines, in the future we may instead create production blobs; huge formless masses of nanomachines that create whatever we can conceive, regardless of size. And don’t forget, those trillions and trillions of bots will each create their own piles of data, in an ever-expanding torrent of information.

 

Additive Manufacturing

Additive manufacturing (AM), also known as 3 dimensional (3D) printing may be the technology that will most disrupt traditional manufacturing. When this technology first appeared it allowed engineers to rapidly build small prototypes out of plastic, using lithography technology from the computer chip industry. These early prototypes were somewhat crude and were very limited in their physical properties. However, they allowed engineers to check their design for fit and finish before they entered production.

Figure 7: AM parts are smaller, lighter and stronger than machined parts

It took only a few years for this technique to advance from plastic prototypes made with ultraviolet light to workable, production-ready products made from the precise melting of aluminum, steel and titanium powders using high-powered lasers. Not only did this drastically drop the price of making a prototype product, it allowed engineers to make entirely new products. Producibility often leads to a wide range of design compromises. With AM, engineers can create exactly what a design requires, with almost limitless control of shape, form and properties. There are examples from the aerospace industry where a component made with AM weighed half as much as its machined predecessor, and yet still had higher strength.

As revolutionary as AM is to the design process, it will be even more disruptive to the manufacturing supply chain. Manufacturers work tirelessly to maximise their throughput, and minimise their downtime. This has been production gospel for two hundred years. Throughput was so important to manufacturers that they would often produce more of their products than current demand dictated. This extra output would be stored in a warehouse, to serve as spare parts. While producing this extra inventory makes production efficient, the added cost of distributing these spares and storing them, all over the world, is extremely expensive, as anyone who had to buy a replacement part for their washing machine, oven, car or stereo can attest.

Tens of thousands of warehouses around the world can each be replaced by a small storefront, or even an automated vending machine.

AM blows this entire approach to spare parts out of the water.  Now, instead of having a warehouse full of millions of random parts for thousands of different products, a distributor need only have a laser-powered AM machine, and a few tons of a variety of different metallic powders. When a customer needs a certain part for a certain device, the distributor calls up the design for the part, loads the required powder into the machine and hits “print”. Minutes later, the part is produced and ready for use. Tens of thousands of warehouses around the world can each be replaced by a small storefront, or even an automated vending machine. Suddenly, not only is the entire logistics and supply network supporting manufacturers moot, so too is maximised throughput of a centralised factory.

 

Artificial Intelligence

Finally, we come to Artificial Intelligence (AI), according to some, the technology Pandora’s box of our current world. Predictions of AI’s potential ranges from the dystopian, nihilist nightmare of the movie “The Terminator” to the idyllic, more-human-than-human character played by Robin Williams in Bicentennial Man. Whether AI enhances us or enslaves us is yet to be seen, but the future it holds for us creates no shortage of apprehension.

Figure 8: The line between humans and machines blurs, but doesn’t disappear

But, I’d like to take a moment and strip away a bit of the hype around AI. Yes, artificial intelligence will transform all aspects of our lives, if only because our ability to create information is dramatically outpacing our ability to digest and make sense of it. The prior six disruptive technologies won’t add to our glut of information, they will each cause it to explode in turn. Taken together, we have not yet seen a billionth of a billionth of the amount of data that we are likely to produce in the middle of the 2020’s. This amount of data is hopelessly beyond not only a human’s ability to understand, it is beyond humanity’s ability to understand; unless we have AI.

The power of AI is that machines can ingest this fantastic amount of information and actually find patterns and relationships. Machines can read through billions or trillions of data elements, more quickly than any human, and they can do so without bias, prejudice or the need to take a coffee break.  In order to adequately apply the first six disruptive technologies, AI is not just nice to have, it’s imperative.

In order to adequately apply the first six disruptive technologies, AI is not just nice to have, it’s imperative.

Does this mean the end of humanity? Far from it. Despite wide-eyed claims to the contrary, AI cannot actually make sense of the patterns that it finds, nor can it interpret them; at least not yet. Such abilities may be found in the near future, and much wealth and power ride upon such a discovery. But, until that time, people still need to interpret what AI finds. Someone still must teach a machine for there to be machine learning. An untaught AI is no more intelligent than a new-born baby. It’s full of potential, but that potential must be realised through an extensive amount of teaching, trial and error, and experience.

This, then, is why humans are still relevant in a world full of artificial intelligence; someone must teach these machines, and do so correctly. If you want to learn a different language, and you learn from a terrible teacher, you’re not likely to fare well when you travel to lands where that language is widely used. A person taught math incorrectly is no better off than someone who never learned math. Indeed, they may actually be worse off! The value of people in the use of AI is that of teacher, but only the best teachers need apply.  Being the best at what you do will be even more important in the future, and being anything less than the best at what you do means, inevitably, you’ll be doing something else.

As Charles Darwin stated in his book on evolution, “It’s not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is the most adaptable to change.” And so it will be for manufacturers too

This will be as true in manufacturing as it will be in any other profession, even as our definition of “the best” constantly changes due to technology disruption. As Charles Darwin stated in his book on evolution, “It’s not the strongest of the species that survives, nor the most intelligent that survives. It is the one that is the most adaptable to change.” And so it will be for manufacturers too.

 

Survival is not Mandatory

Edward Deming, the father of the quality revolution is famously quoted as saying, “It is not necessary to change. Survival is not mandatory.” He was right in the 1950s and he is even more right today. Change is all around us, but not necessarily within us. Part of our survival instinct is to reply upon what we know, and to view new stimulus with skepticism. This instinct starts out fairly weak when we are young, because we have much to learn. But, the older we get, and the more we learn, the more we hold onto those hard-earned lessons and we often find it harder and harder to let go. If we weren’t wired this way, we would not be as successful a species as we have been.

Figure 9: Possum in the road. Playing dead is one strategy.

The rub is this: sometimes, the new stimulus is right, and our learned beliefs are wrong. In a world changing as fast as ours presently is, this situation is becoming the rule, rather than the exception. Manufacturers, indeed all business people, need to embrace the idea that these changes aren’t aberrations, fads or misinterpretations; they’re the new reality.Edward Deming, the father of the quality revolution is famously quoted as saying, “It is not necessary to change. Survival is not mandatory.” He was right in the 1950s and he is even more right today. Change is all around us, but not necessarily within us. Part of our survival instinct is to reply upon what we know, and to view new stimulus with skepticism. This instinct starts out fairly weak when we are young, because we have much to learn. But, the older we get, and the more we learn, the more we hold onto those hard-earned lessons and we often find it harder and harder to let go. If we weren’t wired this way, we would not be as

successful a species as we have been.

Figure 10: Mrs. Doubtfire, “Dude looks like a lady”

Letting go of our beliefs when faced with new data is very disquieting for a reason. However, in this age of disruptive change, discomfort must become your new best friend.

In this age of disruptive change, discomfort must become your new best friend.

But, if you end up failing at manufacturing, you might be able to play the song “Dude Looks Like a Lady” with Mrs. Doubtfire in her band “Severe Tire Damage”!Otherwise, your survival will definitely not be mandatory.

[/ms-protect-content]

About the Author

Christopher Surdak is an industry-recognised expert in Mobility, Social Media and Analytics, Big Data, Information Security, Regulatory Compliance, and Cloud Computing with over 20 years of professional experience. Mr. Surdak is author of “Data Crush: How the Information Tidal Wave is Driving New Business Opportunities”, published by AMACOM Publishing, recipient of GetAbstract’s International Book of the Year Award, 2014. He is contributing author to the book “Big Data Combat”, a 2016 best-seller in China.

Surdak & Company is an international consultancy providing strategic business and technology guidance to leading organisation around the world. To learn more about us, visit www.surdakandco.com

LEAVE A REPLY

Please enter your comment!
Please enter your name here