Many of you are probably familiar with the concept of a supply chain digital twin. It’s a virtual supply chain replica that represents hundreds of assets, warehouses, logistics and material flows, and inventory positions—basically, an online, living version of the backbone of your company you can use to simulate your supply chain’s performance, including all the complexity that drives value loss and risks.

Digital twins play a leading role in a supply chain resilience stress test we’ve co-developed with the Massachusetts Institute of Technology (MIT). The test uses digital twin modeling, powered by advanced analytics and machine learning, to assess potential operational and financial risks and impacts created by major market disruptions, disasters, or other catastrophic events.

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The stress test was inspired in part by the current COVID-19 pandemic which, according to research from the Institute for Supply Management (ISM), disrupted 75% of supply chains around the world—and was especially damaging because ISM also found that 44% of companies didn’t have contingency plans for China supply disruptions. Digital twins can help create such plans.

But contingency planning to mute the impact of uncertainty is only one use for a digital twin. In this article, we’ll explore three other impactful applications where digital twins can be used to proactively and dramatically improve supply chain performance: network, process, and inventory optimization.

Network optimization: Balance between service and cost

Even in “normal” times, companies can encounter unexpected changes in demand or supply, and if their network’s not prepared to accommodate these, supply chain and overall business performance can suffer.

With a digital twin, you can assess how certain changes in demand and supply would affect the network—or the supply chain’s physical locations and supporting system that they leverage to deliver products and services to end customers. This can help to determine if the right facilities and transportation capabilities are in the right places to, for example, effectively handle a surge in sales of a certain product or deal with a product shortage from a key supplier.  Or, if you’re launching a new product and need to add nodes to your network, a digital twin also can help you model what nodes should be added and where to get this new offering to market. It can even help you determine how to downsize your network—e.g., figure out which facilities can be closed without negatively affecting the business—to respond to slackening demand or a need to reduce network costs.

Importantly, a digital twin also is valuable in helping you understand the impact of changes to the network on customer service—to balance cost and service levels in a way that still meets customer expectations. And, it can play a big role in helping to model how network design principles influence your carbon footprint and CO2 emissions so you can meet your sustainability targets without compromising cost and service.

Process optimization: Higher efficiency and productivity

In many companies, processes have become increasingly complex and, therefore, less efficient and more costly. A digital twin can help you take a deep look at key processes to understand where bottlenecks, time, waste, and inefficiencies are bogging down work, and model the outcome of specific targeted improvement interventions. This could include such things as eliminating certain steps on a manufacturing production line, adjusting product formulations to reduce the cost and improve the utility of a product, or redesigning pick-and-pack activities to minimize package handling. The result is greater efficiency, productivity, and capital utilization and lower operating costs.

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Inventory optimization: Right goods in the right place at the right cost

Inventory is an evergreen challenge for most companies: figuring out how much of what to keep and where—all in a way that enables you to maximize customer service at the lowest possible total cost. But juggling all the factors that go into that calculation is a complex exercise—especially for companies with hundreds of thousands of SKUs and customers spread across many locations and geographies. And the more variability you have in demand, the more inventory’s needed to meet required service levels.

A digital twin is uniquely fit to help here as well. It can enable you to address a “single-echelon” challenge (optimizing inventory in a single warehouse) as well as a “multi-echelon” challenge (optimizing inventory across the entire network), taking into account demand forecasts to improve replenishment policies and modify inventory levels according to demand to avoid stockouts while minimizing overall costs.

Accenture has worked with a number of companies to deploy digital twins in their supply chain, and the benefits we’ve seen are impressive:

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A digital twin is a powerful capability that can benefit any supply chain in any industry. It enables you to model solutions and optimizations without any risk to your actual, functioning supply chain, and to move forward with the solutions that will guarantee results.

In a future post, we explore how Accenture built its digital twin capabilities, how these work in practice, and how several companies used them to optimize their networks, processes, and inventory.

See more Supply Chain & Operations posts.

Jaime Lagunas

Managing Director – Accenture Applied Intelligence, Supply Chain & Operations, Digital Analytics

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