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:
2. Analytics & Big Data
3. Internet of Things
6. Additive Manufacturing
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
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
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
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 underutilised 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.