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ASCM Insights

Digital Twins: A Business’s Better Half

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Digital twins enable supply chain professionals to monitor and analyze business problems in a safe environment, virtually testing solutions before applying them in real life. Although digital twins traditionally are discussed in relation to physical manufacturing operations, they also can be particularly effective when applied to the more abstract concepts of supply chain network planning, operations and execution. Examples of these opportunities include improving node-to-node movements across a network, driving policies that improve the customer experience, and solving many problems that present themselves during planning and execution.

The key idea is this: When an organization’s planning platform, operations platform and the digital twin are one and the same, you can evaluate choices faster and more accurately based on the top algorithms in the market, then act on those choices in real time. This makes a digital twin a true competitive advantage.

Taking this a step further, extending digital twin problem-solving techniques to a supply chain network requires modeling the entire end-to-end supply chain to form the foundation for analysis. Because the opportunities or problems that will be exposed by this analysis could manifest over strategic, tactical or operational time frames, the foundation should be seamless across time horizons. It should offer services, algorithms and analyses that run across the network representation in real time, whether solving problems predicted to happen in six months or later this afternoon.

Digital twins and imposters

A digital twin is not a simulation; it’s more of a sandbox extension of an organization’s supply chain network. In information technology (IT), a sandbox is a secure environment in which IT professionals can test out new features or codes without affecting other programs. This means the digital twin is part of the whole playground of business toys, so to speak.

Planning software vendors often try to provide digital twin capabilities by using simulation techniques or importing operating data from the enterprise resources planning (ERP) system using some kind of rapid response. However, this method falls far short in its ability to generate high-value results. If a vendor starts talking about the right level of resolution based on differing forecast horizons, that’s a red flag that the foundation or platform cannot scale properly to solve the problem. Some vendors even talk about using approximations for longer-term time horizons, which are nothing but an average — and thus will return average results.

The real problem is that such architectures are antiquated and not really designed for digital-twin-type problem-solving. Their algorithms, if modeled in detail, would run seemingly forever because of their architectural limitations. The in-memory approach we took back in the early 1990s has been surpassed in performance, scalability and reliability by newer real-time supply chain network platforms.

The correct foundation for a digital twin is an execution platform with item-level modeling and representation that can scale in detail from execution all the way forward into longer-term sales and operations planning (S&OP).

Modern platforms certainly can scale detail from operational to tactical to strategic. Temporal changes are nothing but state changes related to something like an order. The order may start life two years out as part of S&OP and then change state to a forecast order at 12 months, then a planned order at six months, then a committed order at one month, then a shipped order at one week, then an in-transit order at four hours, then a received order at time zero and then an authorized-to-pay invoice order a day later.

On a real-time supply chain network, these are just state changes across a seamless platform in which all levels of detail are available in all time horizons. When planning, operating and executing in this type of platform environment, the entire network becomes more resilient and more responsive, and the likelihood of the plan being executed without major problems is much higher. This is key to enabling a digital twin for a supply network.

A supply chain network platform as a digital twin environment

An end-to-end, real-time supply chain network platform provides the ability to

  • test out new supply chain policies, network resilience, and the feasibility of strategic or tactical plans
  • activate alternate parts or suppliers
  • modify modes of transportation
  • add additional shifts at a plant.

In this sense, it is the platform itself that enables the digital twin.

In addition, with an interconnected platform, a separate reference model and supply chain analytics platform are not needed because the solutions are all part of the same system.

As mentioned, in general, the point of a digital twin is to analyze business questions — such as improving node-to-node movement across the network or driving policies that improve the customer experience — in order to implement strategies and tactics that deliver the highest-quality product at the lowest possible cost. A digital twin should solve all the problems that present themselves across the network, given all the variables in play. This is a key consideration.

The point of an end-to-end representation of the network is to have real-time visibility and control over all material variables across all trading partners at all tiers and echelons in the network. Hot zones are going to materialize across the network, indicating that there is an actual or potential problem in meeting targets related to demand, supply, logistics or fulfillment.

When a digital twin and its operating platform are the same system in a network solution, its real function is to move hot zones into the sandbox. This way, the digital twin can run analytics to make recommendations to solve the problem in the most optimal way and then make those choices actionable as an extension of the execution system. You will be presented with the top three or four solutions that best meet your targets, along with their economic and service impacts. You are then free to choose the best one to meet your needs at that time, fully understanding the costs and effects your choice will have across all customers and trading partners in the network.

Effective ways to use digital twins

Unlike other approaches, there is no big journey to activating a digital twin and start enjoying all the benefits and value. With a real-time network architecture, the digital twin along with all the required algorithms, agents, strategies, tactics and policies are available as part of a sandbox extension and ready for action.

But what about the data? The great part of a network architecture is that it exists as part of a dual-platform approach. Here, the network instance sits on top of all the existing legacy systems and deploys a federated master data management capability along with hundreds of pre-built application programming interfaces in order to populate the models. It also includes data from external sources that are key to populating certain algorithms and creating artificial-intelligence (AI) and machine-learning vectors as part of the prescriptive analytics.

Thus, as part of an overall control tower approach when order, inventory and logistics tools are incorporated, the model is populated with information such as transportation costs at a granular stock keeping unit (SKU) level, destinations for shipments or which SKUs are in a given shipping container. It then becomes simple to analyze landed costs, for instance, at a granular level as part of a trade-off analysis. With this dual-platform approach, it’s a simple matter of populating the model.

Digital twin telepathy

Digital twins that are integrated into supply chain networks can give organizations an almost psychic ability to prepare for future risks and challenges. In reality, the system is just communicating with the populated data to find the clues that something big is about to happen.

A digital twin can be used for strategic and tactical planning in addition to hot-spot problem resolution. When AI agents in the network report that a trend could lead to a demand or supply impact weeks or even months in the future, the company can react right now. Actions can be taken to activate alternate supply, substitute parts, inventory pull-forward, outbound postponement or even allocations.

This telepathic ability also is useful for keeping operations running smoothly amid more routine risks. As a real-world example, the U.S. Department of Defense used a digital twin to help capture internet-of-things data about marine personnel carriers and track issues with existing vehicles and components. By analyzing longer-term data, the department was able to project future issues, such as mean time to failure for various parts, as well as varying geographic and environmental situations.

That kind of information is invaluable for predicting demand for parts and servicing; planning and executing maintenance schedules; and coordinating parts, equipment and labor. It can be used to keep vehicles, factories, warehouses or entire supply networks running smoothly and reliably with minimal disruption to operations while providing real-time feedback to suppliers and original equipment manufacturers to help improve their parts and products.

Having a digital twin capability that is an extension of an operating platform enables a company to evaluate choices based on the top algorithms in the market and then make those choices actionable in real time. With this streamlined process, supply chains can be more agile and always ready to handle the challenges ahead.

Don’t miss sessions on digital supply chains at the ASCM CONNECT Annual Conference this fall. 

About the Author

Joe Bellini Chief Operating Officer , One Network Enterprises

Joe Bellini is chief operating officer at One Network Enterprises, a provider of an artificial-intelligence-enabled business network platform that allows all trading partners to manage, optimize and automate complex business processes in real time. He may be contacted at jbellini@onenetwork.com.

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