AI’s Role in Manufacturing’s Digital Transformation

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Close up of human hands using virtual panelImagine that you’re an astronaut—you’re orbiting the Earth in your shuttle when all of a sudden an alarm on your control panel goes off. Obviously, whatever the indicator is pointing to is something you want resolved as quickly as possible, so you contact ground control and ask them what to do. They give you instructions on how to safely reset the system that’s producing the error, and tell you that that should solve the problem. You trust them, but you’re understandably cautious—so, you ask them how they arrived at that decision.

In one scenario, they tell you that an experienced operator is standing by, and based on her experience that’s the right way to resolve the issue. In another scenario, they tell you that they simulated the issue on an AI-powered digital representation of the spacecraft, tried multiple different options for resolving the issue, and relayed the one that had been shown to be most effective. Which of these two scenarios inspires more confidence?

For most of you, the answer is probably the second one, and with good reason. With high stakes, you want more than just a best guess or a theory supported by anecdotal evidence—whether you’re floating in space or simply trying to create an optimized production plan at your manufacturing outfit. This, in a nutshell, is why AI is going to be such an important part of digital transformation in the manufacturing sector.


How AI Can Power Smarter Production

The concept we described above is what’s known as a digital twin, and it’s already a practice in use by some supply chain businesses (and, as it happens, by NASA). Using information from a host of sensors, planners are able to create digital reproductions of their factory floors (or production networks, or transportation networks, or entire supply chains…), on which they can then run simulations to determine the likely outcomes of different planning scenarios. Rather than relying on guesswork or past experience, you can use artificial intelligence in the form of machine learning and advanced analytics algorithms to turn huge caches of data into powerful predictions. The result—more efficient scheduling with fewer disruptions—is just one example of how AI can be operationalized in the planning and scheduling process to improve production performance.

In addition to running hypothetical scenarios on digital twins, AI-equipped planners can use prescriptive analytics to seek out areas of waste in their production networks. They can also take in sensor data from factory floor machinery in order to schedule proactive maintenance before unplanned downtime has a chance to occur. In this way, it’s possible to improve your overall operating efficiency (OOE) by decreasing disruptions and rooting out areas of waste. As your AI deployment gets more sophisticated and widespread, you can also use it to automate things like alerts and rudimentary adjustments to production plans based on emerging conditions (e.g. adjusting production ratios based on a demand spike or a parts outage). Not only does this put you in a position to make better in-the-moment decisions, it also offers you the data-visibility to keep production planning aligned with larger corporate initiatives.


Digital Transformation Hurdles for Manufacturers

While this technology suggests the latent potential of digital supply chain data, it also points us towards the kinds of hurdles and difficulties that manufacturers have to overcome to make their digital transformations a reality. For instance, to power the kind of digital twin technology we’re describing above, you first need to implement sensors up and down your value chain, potentially including IoT devices, RFID chips, and more. You need to be able to map your production processes into your planning solution comprehensively, which means those processes need to be visible and well-documented. More than that, you need to have the visibility outside your production chain to line up your scheduling flows with other demand and supply chain realities, which most likely means being positioned with a some kind of supply chain technology integration. If there are touchpoints anywhere on the chain who are still using Excel spreadsheets, for instance, you’re going to experience slowdowns.

Ultimately, digital transformation is going to remain a moving target for most businesses. As you move processes that used to be performed or planned manually into digital environments, open questions will crop up about what digital technologies to use, how to create connectivity between newly-digitized processes, what to do with all the data that digitization produces, etc. While AI-integration represents an important milestone for digital transformations, it can also provide a valuable guidepost. As you’re identifying processes for digitization and seeking out new technologies, ask yourself: how will this further my vision of a data-driven production chain? In this way, you work to ensure that every strategic or tactical decision supports a digital future where planners are able to work more quickly, efficiently, and accurately, regardless of the situation.


Industry 4.0 and AI

Digital transformation is a spectrum, and at the far end of the spectrum you’ll find highly digitized manufacturing outfits working to adopt true Industry 4.0 deployments. AI is frequently touted as one of the major pillars of Industry 4.0—but why, exactly, should that be the case? Simply put, Industry 4.0 is about radically improving visibility and connectivity across the entire supply chain, and AI presents one of the clearest paths forward for making that a reality. If we take digital twins as an example again, we can see their potential to act as more than just a tool for simulating planning decisions. In point of fact, they can also act as a tool for collaboration (with multiple users accessing the same simulation from different computers), resulting in increased cross-functional visibility.

From there, as your models become more comprehensive—e.g. simulating an entire production network spread across multiple geographies—you can collaboratively model plans that stretch well beyond the boundaries of your particular shop floor, uncovering new efficiencies in the process. In this way, you can bring about the dream of the “global factory” that’s so central to Industry 4.0. From there, you can extend smarter planning and scheduling across the entire supply chain, resulting in more responsive operations that more efficiently meet demand and delight customers.

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