Navigating supply chain planning in today’s world requires not only agility but also foresight. Enter digital twins: a transformative technology that is redefining how companies visualize and optimize their supply chain networks in real time.
As consumer expectations, rising costs and global disruptions create mounting pressure to deliver faster, leaner and more resilient supply chains, the need to use digital technologies for optimizing planning and demand forecasting has never been clearer. Supply chain digital twins offer a new level of visibility and intelligence, enabling continuous optimization and strategic decision-making for supply chain professionals.
Join us as we explore digital twin technology and its impact on the industry, and hear from a veteran business professional and supply chain thought leader.
A digital twin is a virtual representation of a physical system, such as a supply chain or one of its components, that continuously mirrors real-world operations. In supply chain management, a digital twin is not just a static model, but a living, evolving replica built from real-time data streams. Digital twins integrate inputs from IoT sensors, logistics networks, ERP systems, warehouse operations and other supply chain data analytics tools to reflect actual performance conditions as they happen.
This real-time connectivity enables organizations to simulate different scenarios, forecast disruptions and test strategic decisions in a risk-free virtual environment. From planning inventory to rerouting shipments during a disruption, digital twins provide end-to-end visibility and decision-making rooted in data rather than assumptions.
Improved artificial intelligence technology has transformed the relevance and accuracy of digital twins. As digital twin technology continues to mature, it's becoming an essential asset in building agile, intelligent supply chains.
One of the most powerful aspects of supply chain digital twins lies in their ability to enhance predictive modeling and performance optimization. Because they capture and interpret real-time data from supply chain execution, such as shipment delays, supplier variability or demand spikes, they offer a continuous feedback loop. This allows businesses to not only monitor the current state of the supply chain but also simulate future outcomes, identify vulnerabilities and proactively adjust plans.
For example, performance data from equipment or facilities can trigger automatic schedule adjustments, shifting output to more efficient sites or rerouting inputs to maintain service levels. Similarly, if a shipment is delayed due to weather or port congestion, the system can reroute logistics dynamically to minimize disruption.
Demand planning is the process of forecasting consumer demand to ensure that products can be delivered efficiently, cost-effectively and on time. It’s a critical function that relies on accurate predictions of future sales, market trends and buying behaviors to inform procurement and production decisions.
Digital twin technology is transforming this process by introducing real-time, data-rich forecasting capabilities. If demand surges in one region while another holds excess stock, a supply chain digital twin can instantly detect the imbalance and recommend inventory reallocation to avoid stockouts and reduce waste.
At the core of this capability is supply chain data analytics, which serves as the engine that powers digital twins. By aggregating and analyzing diverse data sources, organizations can generate insights that make the digital twin a predictive tool, rather than just a reactive one.
David Pyke is a Professor of Operations and Supply Chain Management at Knauss School of Business. Since joining the University of San Diego in 2008 as dean of the school, Professor Pyke has continued to shape the next generation of supply chain leaders through his teaching, research and industry engagement. With expertise spanning inventory management, global supply chain risk and strategic supply chain design, he brings deep insight to both the classroom and the field.
We sat down with Professor Pyke to gain his perspective on the evolving supply chain landscape and the role of digital twins in shaping the future of the industry.
Professor Pyke: A digital twin, as the term implies, is a digital representation of an actual supply chain. Researchers and software developers have been modeling supply chains for decades, using complex equations and computer simulations. I myself have built many of these models over the years. The challenge is that these models become dramatically more difficult to solve the more closely you try to capture reality.
Real supply chains are very complex, involving, among other things, decisions about production, inventory, transportation and supplier relationships. And they operate in dynamic environments, where prices change, weather events cause disruptions, suppliers delay shipments, quality issues emerge, customer demand changes and political events pose significant risks. Trying to capture these and other issues with traditional models is overwhelmingly difficult.
Advances in artificial intelligence and computing technology have spurred the development of dramatically more realistic models of the supply chain. These virtual models mirror real supply chains. Instead of limiting the number of companies and decisions captured in the model, digital twins enable supply chain managers to model the end-to-end supply chain.
AI built into the digital model can also use information about weather, political events, news reports, and other nonstandard data to prepare for disruptions in the supply chain, optimize inventory and improve demand forecasting. The goal is not only to more accurately describe a complex supply chain, but to predict the impact of decisions and events and to optimize decisions.
Professor Pyke: The great challenge of inventory management and production planning is to match supply with demand. Suppliers must manage capacity and inventory based on inaccurate predictions of future demand. Add to that the possibility of low probability, high impact events, such as a pandemic or a tsunami, and the challenge can be overwhelming.
Improved AI forecasting means lower inventory and better customer service, but only if the company employs excellent optimization algorithms. Traditional models will, say, optimize inventory based on certain “givens,” such as supplier lead times, costs, service level targets, and so on. But what happens if the “givens” change? What is the impact if a supplier lead time increases without warning, or if material costs rise? What is the impact of a particular geopolitical event, such as an armed conflict, that impacts the sourcing of many components? What if there is a fire at a supplier of a single component?
The use of AI and the ability to run simulations very quickly allow the company to consider a much more robust set of options as they optimize these decisions. The answers won’t be perfect, and they still require human oversight, but they can capture complexity and provide significant support to decision makers.
Professor Pyke: An emerging area of application is risk management. When thinking about inventory management, either using traditional models or enhancing them with AI and digital twins, the focus often is on what I call everyday, or common, supply chain risks. These are high probability, low impact risks and include moderate changes in demand, increases in lead times, and fluctuations in exchange rates. Existing models handle these risks very effectively.
However, when considering low-probability, high-impact events, many companies use narratives and other qualitative information to plan strategic responses. Some companies try to quantify the probability and impact of these events, which can be highly inaccurate and always very difficult. A digital twin of a supply chain, in conjunction with careful monitoring of relevant world and weather events, is allowing companies to get ahead of significant risks.
An example is a U.S. company that had a sole source in another country. Because of the possibility that their current source would be too expensive due to tariffs or unavailable due to armed conflict, supply chain managers went through the long process of finding and qualifying a supplier in a different country. This can be seen as an investment in a “real option.” The option provides the company with the ability, but not the obligation, to shift production if necessary. When the pandemic hit the original country, the company was able to shift production to a less-impacted country.
Digital twin technology is at the forefront of a global supply chain transformation, empowering organizations to build agile, responsive and sustainable supply chains through real-time data integration and advanced analytics. Professionals who understand how to harness the power of digital twins and other emerging technologies are poised to lead. With a master’s degree in supply chain management, you can gain a competitive edge in driving innovation, managing risk and delivering value across the supply chain.
The Master of Science in Supply Chain Management (MSSCM) at Knauss School of Business is designed to position professionals to succeed ethically and efficiently in supply chain management. With a part-time, hybrid format, the program builds on your existing experience and allows you to advance your career without disrupting your current job.
Students have exclusive access to the Supply Chain Management Institute (SCMI), connecting them to industry leaders and cutting-edge research, and the opportunity to choose a health care track concentration.
“A student or professional looking to specialize in digital twin technology should obtain a deep understanding of the end-to-end supply chain, including processes, information and decisions, in addition to the actual network of companies involved.”
—David Pyke, Professor of Operations and Supply Chain Management
In a world where digital transformation is no longer optional, exploring supply chain management at Knauss will equip you with the tools, knowledge and network to lead confidently into the future of supply chain management.