Digital Twin – The Key for Proactive Supply Chain Planning

Reading Time: 5 minutes

Guest Blog Post: Felipe Molino, Director of Solutions Engineering and Chris Pryer, Senior Scientist, NFI 

Disruptions have become commonplace in the supply chain industry. For years, natural disasters and ever-changing regulations have highlighted company and industry instability. Most recently, companies were proven ill-equipped to manage significant and immediate change after the global COVID-19 pandemic caused rapid supply chain volatility. Additionally, shippers face daily operational challenges through increased customer pressures, production line demands, and other business complications. While all of these situations affect supply chains in different ways, most companies don’t — or can’t — systematically develop strategies to address these cases proactively. The reality is that the industry usually takes reactionary measures to handle these challenges which ultimately lead to stockouts, service issues, and loss of profits.

One of the main reasons most companies don’t conduct these proactive exercises is its inherent complexity. Models and analyses can take several weeks to conduct, and actioning on those findings can take even longer. Doing a comprehensive analysis may also require you to engage multiple resources, some of which are actively managing the issue. 

So, how do we tackle continuous improvement initiatives without inadvertently causing a larger disruption? One of the most effective ways to navigate these challenges and make quick, calculated decisions is to leverage Digital Twin technology.

What is a Digital Twin? 

An easy way to define a Digital Twin is a virtual/digital copy of your business. Using a packaged food business as an example, its supply chain may look something like this: Thirty thousand pounds of tomatoes are purchased from local farmers (procurement). The tomatoes are shipped via truckload to one of the production plants (inbound transportation) and blended together with other ingredients (inbound transportation and procurement) to make sauce (production). The tomato sauce is then packaged (inbound transportation and procurement) and transported (outbound transportation and carrier sourcing) to one of its distribution centers (distribution), and finally, from there to customers (fulfillment). 

Each of these steps generates data. If you have the ability to collect all the data points for those individual steps in a unified environment — where data is easily accessible and in a common format — and are able to analyze your network either at an individual stage or collectively, you have effectively created a Digital Twin for the life cycle of your tomato sauce’s supply chain. When scaled, we have the ability to replicate this process for all of our products, orders, shipments, and more; thus creating a digital copy of the overall supply chain.

How does this allow companies to more proactively manage their supply chains?

Although creating a Digital Twin is one of the keys to unleashing the full power of analytic capabilities, having access to that data does not do anything on its own; the algorithms and operational expertise behind it are a requirement to successfully identify and implement calculated change. For instance, a process can be created that automatically analyzes different what-if scenarios on a custom schedule in a test environment to avoid implementing new processes without validation. Theoretically, when walking into the office on Monday morning, your dashboard can present different opportunities and threats that the models identified.

Making changes to a supply chain may require significant investments (money, resources, and time) and could potentially cause service disruptions. Having a Digital Twin allows you to test these different scenarios in parallel to reality while collecting results over time. This will help the business build the case with data to implement a scenario that may involve additional risks and would be difficult to sell internally.

For example, by revisiting the different stages in the tomato sauce company’s supply chain and its collected data points, we can analyze what would happen if one of the facilities were to shut down for a week due to external forces, such as a hurricane or a global pandemic (Figure 1). If the Digital Twin is set up properly, it means that a baseline of the network and a digital copy of the business have been created. As a result, what-if scenarios can be developed to compare network changes between the current state and the model. The model might show that other network facilities carry the same or similar SKUs and that the company can leverage its inventory to ship to customers instead of expediting the process or taking another, more expensive measure. Conversely, if there are no other facilities that carry the product, the Digital Twin can assess the loss or risk, calculate the cost of adding a new facility to the network, or analyze the impact of adding a 3rd party co-packer with ready-built recipes that can turn on capacity with short notice.

There are countless scenarios that can be tested using the Digital Twin, ranging from strategic initiatives such as adding distribution centers to the network, to more tactical models such as assessing the impact of removing an underperforming carrier. The key is having a defined process on how to implement them. 

In Figure 2, a set of lanes is identified between Vendor 1 and Customer 1 that would result in $100,000 of savings through the use of backhauls or continuous moves. These locations are close in proximity and ship once per week on the same day. There may be a two-day service agreement with the customer, meaning that contractually, the Digital Twin suggestion can’t be implemented. Depending on the relationship with Customer 1, it might be possible to renegotiate service terms. In other words, if Customer 1 allows this initiative, 10 percent of the transportation savings can be shared through a decrease in the cost of goods sold.

Additional examples can be seen in Figure 3, below:

Additional Considerations

While Digital Twins have the power to give us great insight into our supply chains, it is important to understand the level of effort and involvement required to implement them. An organization’s internal capabilities, resource availability, and infrastructure are the factors that will drive an insource or outsource decision. In an outsourcing scenario, the general recommendation is to partner with a company that has the expertise to implement a Digital Twin while you build those capabilities. To learn more about the technical requirements you can find additional detail in our 4 Must-Have Skills for Supply Chain Analytics article


Final Thoughts on the Digital Twin

A Digital Twin allows a company to make dynamic decisions and control supply chain expenses. It can be used to help create strategies that are not only cost-effective but also optimal from a risk mitigation perspective. To do this, data sources need to be consolidated into one environment in order to generate and analyze scenarios promptly. Since this process can be used to create actionable plans, it is easier to engage a cross-functional audience — including customers and vendors — for faster decision making. A Digital Twin is being adopted at a higher rate in supply chains as dynamic planning continues to expand as a key focus for most organizations. Providing a proactive solution to market disruptions, daily challenges, and fluid business dynamics will support continuous cost savings and allow supply chain networks to evolve.

Felipe Molino, Director of Engineering at NFI
Chris Pryer, Senior Supply Chain Scientist at NFI