From the ongoing backlog at Southern California’s ports to the national shortages totaling more than 80,000 truck drivers, the COVID-19 pandemic has created myriad supply-chain challenges. With all of the changes and increased variables influencing the supply chain today, companies need a stronger understanding of various scenarios, and how those scenarios can impact a shipment at any given time.
For organizations operating supply chains, an area of business that typically generates significant amounts of data, digital twins – virtual models designed to accurately reflect physical, real-world scenarios – are highly valuable tools for more efficient planning.
Where Can Digital Twins Be Leveraged in the Supply Chain?
In the supply chain, there are three specific areas that exist in which digital twins are particularly useful:
- Risk management: With the ability to run “What if?” scenarios to simulate potential issues – such as weather changes or border crossing challenges – as well as anticipate solutions to those problems, a digital twin can analyze the impact that a scenario can have, and allow teams to optimize their contingency planning.
- Sustainability: As many companies look to lessen their carbon footprint, a digital twin can enable shippers to simulate different routes or modes for a better understanding of the carbon footprint different paths may generate. Routes can be simulated to avoid peak traffic times in urban areas or look at options for using a less popular route that yields a smaller carbon footprint.
- Time to market: Improved route planning through digital twins can help ensure companies can get shipments to their final destinations in a timely manner, and optimize deliveries for the best time and price to market. This becomes especially crucial in times where keeping products in stock is both critical and challenging, as with the ongoing national grocery shortages. A slowdown can lead to loss of business or, for manufacturers running just-in-time models, a production shutdown. Digital twins can help explore any challenges in time to market, and help companies develop action plans to avoid potential slowdowns.
By leveraging digital twins in these three areas, organizations can improve the strategic, operational, and tactical aspects of their supply chain planning.
Challenges Organizations Face in Leveraging Digital Twins
One of the biggest challenges companies face in leveraging digital twins is data drift, which is often among the top reasons many see model accuracy degrade over time. Data drift is “a variation in the production data from the data that was used to test and validate the model before deploying it in production.”
Ensuring that the data used in building the digital twin is collected accurately and consistently – thereby allowing it to represent the true conditions of the physical twin – is a constant area to monitor. Having the best-quality data is key to deriving full value from a digital twin. The ability to do this is improving as teams gradually move more toward streaming analytics.
Properly using a digital twin isn’t only about the ability to collect and store the data. It also encompasses the ability to understand it. Without good behavioral understanding, the interpretations run the risk of being off-base, which can lead to poor decision-making. To solve the challenges that directly affect growth and profitability, teams need to ensure that they make the data accessible and digestible to key stakeholders and decision-makers so that it can be used in conjunction with their respective business expertise.
As executives push toward a modernized supply chain, they continue to learn how to build the right teams with the necessary skills and expertise specific to each particular area, such as pricing and route management.
Navigating the Supply Chain’s Most Pressing Issues
Digital twins can play a critical role in successfully navigating industry issues that won’t be going away anytime soon. A great example would be the recent congestion at the Southern California ports.
With the help of digital twins, supply chain teams can create a template for scenarios that arise during crises like these. An example would be determining the best course of action to take if a shipment ends up sitting in the containers for 90 days, rather than the expected two weeks. In this way the moment the shipment is ready to be moved, the digital twin has already established the best way to ship cargo to its final destination, taking into account all the factors you may need to consider such as weather or the carbon footprint.
Another challenge currently confronting supply chains is the rising price of gasoline, and how that can increase overall shipping costs. With fuel prices surging, drivers and shippers must ensure that they’re taking the most efficient delivery routes to conserve as much gasoline as possible. Digital twins offer supply chain managers deeper visibility into their route planning to not only optimize time to delivery, but also to mitigate the effects of high fuel costs.
Managing the Modern Supply Chain
For companies with significant access to supply chain data, a digital twin can be a valuable tool at their disposal. Of course, as valuable as a digital twin can be, it’s still only one of many tools that can be used in the supply chain. But if a company can successfully combine a digital twin with artificial intelligence, data visibility, and automation capabilities, it can unveil visibility at a deeper level, and position its supply chain to become more resilient in the face of unforeseen circumstances.