In 2015, Greek Finance Minister Yanis Varoufakis made the bold claim that The Matrix (the 2000 science fiction film in which all humans were being held captive in an elaborate computer simulation) was not so much a science fiction film as a documentary about modern capitalism. We’ll leave aside for now any quibbles we have about the use of the word “documentary” in this context, but it’s worth thinking about how much the world has changed in the 18 years since the movie originally came out. Why? Because computer simulations have actually become a meaningful fact of life for many businesses across the world, particularly in the manufacturing sector.
With the rise of Industry 4.0, manufacturers and supply chain managers have increasingly been able to leverage new data streams from IoT (internet of things) devices, RFID chips, and other sources to simulate not just factory floor operations, but entire complex value chains. Sure, this isn’t a simulation that could be confused for reality of the sort we see in science fiction, but it’s a development that’s increasingly making it possible for businesses to create smarter, more agile processes that promote business growth in the form of increased planning certainty. The question is, how exactly does it do so?
The Thing About the Internet of Things
In the paragraph above, we mentioned the IoT (internet of things). This has been a major buzzword in the world of Industry 4.0 for many years, but some of the use cases talked about have been less than practical. By leveraging this technology to create a real-time simulation of your production flows, however, you can create a clear path forward for IoT adoption that will feed into a specific business goal. Thus, by equipping each element of your factory floor with a connected device that can transmit data back to a centralized control tower, you can monitor your production operations in real-time from the comfort of your connected digital workstation.
This might not seem like a particularly monumental advance, but consider the following scenario: unbeknownst to you, several of the machines on your factory floor are in need of maintenance, and if they don’t get it soon they’ll break down and cause massive delays and bottlenecks. Without any digital infrastructure, you’d be hard pressed to assess this need for maintenance before the machines actually broke down, due to the limited capacity for monitoring, meaning that you’d potentially lose considerable value when the breakdown actually arrived. By contrast, real-time monitoring of their performance would give you the chance to assess early warning signs and take action before it was too late. As soon as you saw the machines slowing down or behaving strangely (signs that might not be visible to the naked eye but which an IoT device could certainly transmit), you could proactively schedule machine maintenance for the least disrupt possible time. In this way, you stave off the breakdowns and minimize the disruption caused by the scheduled machine shutdowns.
“What If?”
So, simulated factories (also known as digital twins) can give you a god’s eye view of your operations, but how exactly do you drive growth from that position? Sure, you can ward off disruptions and gain more visibility and insight into your existing operations, but how can these digital processes inform future changes and adaptations? The answer, simply put, is “what-if” scenarios. These are simulations in which you run hypothetical scenarios on the digital twin of your factor or supply chain in order to develop forecasts that predict the likely response to any given change or event. This can be a value-additive way to test the resiliency of your supply chain in the face of disruptions (to say nothing of the effectiveness of various strategies to counter those disruptions), but it also functions as a means of testing process improvements and potential value chain changes.
Let’s say you’re planning to add a new machine to your factory floor. How do you situate it relative to the other machines in order to ensure seamless integration? How will this change to your production process affect your output? What unexpected wrinkles might this adjustment introduce into your existing production schema? In a non-digitized, non-simulated environment, it’s possible to make educated guesses about most of these questions, but arriving at an answer with any degree of certainty will be tricky. If you can simulate your options before implementing any changes, however, you can gain a much clearer picture of the likely effects. As a result, you can weigh different scenarios with more certainty and ultimately choose the one that will act as the most significant value-added proposition.
Foundations of Sustainable Growth
With “what-if” scenarios, you position yourself to take smart, proactive action when it comes to your supply chain and production streams. Instead of responding reactively to events that take you by surprise, you’ll be poised to take future changes in stride. This is how we finally get to the “growth” part of the equation. Without the benefits that you gain from this simulated version of your factory, you’d have a hard time answering questions not just about individual production line changes, but broader shifts in business realities. How would your supply chain respond to the addition of a new customer? What about the loss of an existing customer or a change to that customer’s expectations or requirements? What if you wanted to change out one of your existing suppliers or carriers?
If you, as a company, can answer those questions, then you can grow your business sustainably and work towards a long future of profitability. If not, you’ll be veering into risky territory, gambling with the fate of your firm every time you need to make a change—hardly the way that any manufacturing wants to do business. Luckily, the Industry 4.0 era and the simulated workflows that it empowers can help you to avoid this fate. How? By laying the foundations for sustainable growth, in the form of enhanced visibility, greater connectivity, and bolstered forecast accuracy.
