The COVID-19 pandemic and ensuing economic fallout highlighted the fragility of global supply chains. In the aftermath, companies have redrawn their supply chains to incorporate the pandemic lessons learned, but a lingering fear remains: how do we prepare for things we can’t anticipate?
Companies need to increase their resilience to predictable events, such as weather, currency fluctuations, and foreseeable transportation delays. This increased flexibility gives businesses a better chance of successfully adapting to plausible events that are less predictable, such as a global pandemic.
As corporations shorten and reinforce supply chains with alternate suppliers and partners, agility naturally improves—and benefits here can directly impact a business’s bottom line. Until recently, a full-spectrum understanding of one’s supply chain was extremely difficult, if not impossible; today, however, there’s a tool that can accomplish this: a digital twin.
What is a Digital Twin?
More formally named a “digital supply chain twin,” this concept is simply a digital representation of a supply chain. It is programmed to be dynamic and perform in real-time, as well as being able to forecast future needs and adjustments based on its inputs. This time-phased model incorporates all horizontal elements of your supply chain, from manufacturers and suppliers to transportation providers and distribution hubs. These inputs can be incredibly granular: using a factory’s physical address or typical flying routes allows a digital twin to pull in weather forecasts and notify operators about potential issues.
Digital twins also offer vertical benefits by allowing you to explore various scenarios years down the road, facilitating long-term, strategic decisions when adopted at the enterprise level. Executives can weigh tradeoffs between multiple suppliers and shipping providers to more fully understand all of the factors that tie into contract negotiations.
When one major auto manufacturer began utilizing a digital twin to economize their operations, they decreased transportation spending alone by more than 11 percent. This isn’t an outlier; companies that use digital twins routinely report average savings of 10 percent.
Digital twins are very tech-centric, and the reason they can exist today is due to the emergence of cloud-based computing, machine learning, and artificial intelligence (AI). The Internet of Things (IoT) provides sensors at critical junctures, while virtual reality (VR) and augmented reality (AR) can accurately recreate or enhance a three-dimensional image of nearly any infrastructure. Leveraging the advantages each of these technologies provides permits sophisticated analysis of an intricately complex supply chain.
How Digital Twins Improve Supply Chain Resiliency
Resiliency is the ability to recover from less than optimal circumstances quickly. In logistics terms, it’s the difference between shutting your supply line down when a single manufacturer experiences problems and being able to rapidly switch suppliers to minimize (or even eliminate) downline interruptions.
To do this effectively, you need to understand your supply chain fully. Traditionally, planning systems were based on historical data and the assumption of continuity. The latter wasn’t so much due to a naïve belief that nothing would go wrong, but rather a reaction to the sheer amount of data it would take to consider anything else. Factoring in commodity shortages, cold chain excursions, international politics, and natural disasters for every link in the chain would have taken so much computing power that it would have been impossible. Even if it could have been done, the sheer amount of human effort necessary to accomplish it would have made it cost-prohibitive. Now, for the first time, technology has created the possibility of fully mapping every aspect of a supply chain. Just having visibility into what the complete chain looks like can influence decisions; being able to evaluate and forecast potential alternatives has the potential to be revolutionary.
Once a digital twin has been created, vulnerabilities will be instantly apparent. Perhaps your company needs an alternate transportation provider in the rainy season due to monsoons near your primary manufacturer. Maybe you should use a different supplier because long-term fundamentals in currency changes make your current choice cost twice as much as an alternate in the long run. One of the cornerstones of resilience is being prepared for eventualities, and having a detailed diagram of the players and factors you’re dealing with allows that to happen.
Why a Digital Twin Improves Supply Chain Agility
“Agility” is often used interchangeably with “flexibility” and is simply the capacity to maneuver skillfully over, around, and through various obstacles. In the 1970s, Air Force Colonel John Boyd created the “OODA Loop,” a model that describes how humans react to unexpected stimuli. People must Observe what is happening, Orient themselves to the change, Decide how they should respond, then Act on it. The more quickly one can move through these stages, the shorter the OODA Loop, and the more nimbly one can react to changing circumstances.
Having a digital twin allows you to incorporate numerous information sources into a single model, then fast-forward months or years into the future and observe the cascading effects of various events. If you know the likelihood of what’s coming, you can proactively work on orienting your organization and deciding precisely how you’ll pivot if or when an event occurs. While no one has a crystal ball to see into the future, the next best thing is a playbook for any foreseeable events so you can jump straight from observation to action while your competitors get bogged down in the orientation and decision phases.
Implementing a Digital Twin
There are two primary obstacles to adopting a digital twin approach, and they’re the same two ever-present factors that affect every bit of the logistics process: time and money. The AI a digital twin utilizes depends on extensive machine learning, which means identifying all of the inputs you need and then accurately feeding that data into the computer.
It should come as no surprise, then, that adopting a digital twin means a significant upfront investment that might not be fully recouped for several years. While the long-term benefits are undeniable, the short-term costs can be intimidating. When evaluating whether it’s worth it, ask your board of directors two questions:
- What will we miss if we don’t implement a digital twin?
- What will it cost us if our competitors adopt digital twins, and we don’t?
The answers to those questions will help frame the price tag in the right context so you can make an informed decision. Keep in mind that accurately programming all of the right inputs into a system will take time, so expect several months to implement a working model, then several more to tweak each setting to produce accurate information.
Although digital twins can exist for any modular aspect of a logistics process, it’s most useful when adopted at an enterprise level. Consider the German city of Herrrenberg, which has created a digital twin of its entire municipality. City officials use the model to analyze the effects various traffic changes will have on air quality. This could be influenced slightly if only the traffic grid had a digital twin, but incorporating events calendars and weather forecasts creates an unparalleled 360-degree view. Creating a digital twin could revolutionize not only your supply chain but also your organization in numerous areas.
