In the wake of the COVID pandemic, supply chains around the globe have been thrown into disorder. It’s not just the ‘supply chain’ in its entirety, it’s every individual link in the chain. Human labor shortages are causing skyrocketing prices on products ranging from food to computer chips, raw materials, cleaning products, books, and even the color blue.
An uptick in extreme weather events around the world is upending shipping schedules which, when combined with other factors, has led to factory shutdowns. Cargo ships transporting goods around the world are finding themselves stuck at port for days or even weeks as mass shortages of stevedores mean long waits to unload their products. Once the cargo is off the ships, it needs to be loaded onto a truck where it begins its journey to retail stores and warehouses. Unfortunately, the trucking industry in the U.S. is currently short by about 80,000 drivers. All of this adds up to empty shelves in many stores across America, and significantly increased prices on the products that are in stock. This has led to a 5.4% increase in the consumer price index, bringing inflation to the highest it has been in nearly 13 years.
“It’s fair to say that whatever you’re selling, you’ve got a problem right now. We’re having talks with clients every day where they’re just crying. For months, they haven’t been fully in stock for one 30-day period in a row.” Says Jason Boyce, CEO of Avenue7Media, a consulting firm that manages and advises top Amazon sellers, during an interview with MIT Technology Review. Boyce has multiple clients that would be turning tens of millions of dollars in profit each year if they could manage to keep a consistent stock.
So, how do we solve a problem so large it spans the entire globe, so complex it encompasses every variable and moving piece from the harvesting of natural resources, to production, to shipping and transportation? The reality is that the issue is so staggeringly complicated on such a grand scale that even the brightest minds in the world would likely struggle to make a dent. This is an area where artificial intelligence (AI) and digital twins could prove useful to solving the global supply chain problem.
Put simply, a digital twin is “a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision-making.” Essentially, a digital twin is an exact copy of a process, system, or physical object that is being studied. Take, for example, a wind turbine. The turbine could be outfitted with an array of sensors tracking its performance in a number of metrics such as energy output, system temperature, and resistance to weather conditions. The relevant data can then be downloaded and relayed through a processing system that applies it to the digital twin.
Armed with real-world data, researchers can then run various simulations using the digital twin to resolve performance issues and design potential improvements. These improvements can then be applied to the original object being studied, starting the process all over again. This creates a never-ending cycle of research, development, and improvement for products or processes that is both significantly cheaper and faster than traditional methods of R&D.
Simulations have already been widely used across industries for years, helping businesses to make better informed decisions regarding small-scale issues such as improved product design and office layout efficiency. However, the recent data explosion and subsequent availability of vast amounts of real-time data has enabled significantly more complex simulations for the first time. Companies are using this data to try and make sense of the chaos that is the global supply chain
Google recently launched their own cloud-based supply chain digital twin service, with French automobile manufacturer Renault announcing they would be an early adopter. Multi-billion dollar shipping titans such as, FedEx, and DHL have all recently leveraged digital twin technology in an attempt to improve their operations. Other AI development firms like Pathmind have sprung up that craft custom AI models and simulation tools for businesses.
A critical flaw in how most modern businesses operate is their inability to forecast unexpected events or disruptions throughout their supply chain, according to Hans Thalbauer, managing director of the supply chains and logistics team at Google. “It doesn’t matter which company you talk to,” he tells the MIT Technology Review. “Everyone in the supply-chain world will tell you they don’t have the visibility they need to make decisions.” Digital twins are the key piece of technology these companies are missing when it comes to identifying these problems before they happen and preparing appropriate responses.
In the years leading up to the COVID pandemic, just-in-time supply chains were the norm. Businesses prioritized hyper-efficiency to the extreme, ensuring their product moved quickly. Why pay money for warehouse storage space when you can synchronize your production line and supply chain so well that you never have a deficit nor surplus of inventory on hand? Our current labor shortage situation highlights why: a single disruption anywhere along the supply chain has a domino effect that could lead to your business with no products in stock and nothing to sell.
“Before the pandemic, most companies were focusing on cutting costs,” says David Simchi-Levi , director of the Data Science Lab at MIT. Now it seems the pendulum is starting to swing in the other direction with companies investing heavily in resiliency. “We’re seeing a growing number of companies starting to stress-test their supply chains using digital twins,” he says.
Through building these incredibly detailed digital twins, businesses can find the sweet spot between efficiency and resiliency. By implementing deep reinforcement learning, which allows the AI to learn and solve problems by trial-and-error, digital twins become the ideal tool for exploring endless ‘what-if’ scenarios. For example, a clothing company running a digital twin simulation could anticipate the risks of a drought crippling their cotton production in China, estimate the impacts it would have on their supply chain and bottom line, and even provide insights into how to best handle the situation.
Digital twins rely on such a huge volume of data that they can be used to simulate virtually endless possibilities. They can draw upon logistical and supply data, inventory and shipping telemetry, and consumer behavior patterns shaped by behavior analysis and financial projections. They can even include data gathered from social media on the world at large, using geopolitical and socioeconomic trends to help predict how people will behave. When fed the right data, digital twins can learn how to respond to completely unforeseen events, even those on a scale such as a global pandemic and subsequent supply chain crisis
Digital twins stand to benefit companies of all sizes, from small startups to massive international conglomerates. However, these larger companies have access to more capital and are already more insulated from any disruptions in the market. That means it’s easier, faster, and less risky for them to adopt the digital twin technology in comparison to their small business counterparts. This could potentially lead to increased disparity as small businesses are quickly left behind, although this could possibly be solved through government investment. As digital twin technology becomes more affordable and available, small businesses will surely be eager to capitalize on it.
Even after the current supply chain disruptions have been handled, digital twins are likely here to stay. If it’s not a pandemic, it could be climate issues or social unrest. In the future, there will be other events that will disrupt the global economy, and digital twins will keep us better prepared to proactively handle the situation.
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