How AI & Digital Twins in the Supply Chain Enable Smart Scenario Planning

November 5, 2026 • 3 minute read
Author: Phyllis Jackson, Senior Manager, US Marketing, UPS

Supply chain disruption is no longer a rare event but rather a constant operating condition. Manufacturers and logistics leaders now contend with a broader mix of risks, including severe weather, geopolitical instability, labor constraints, transport congestion and unpredictable swings in demand. When these issues aren’t identified early, or ideally anticipated, they can cascade through the network, driving up costs, slowing deliveries and weakening service performance.

That reality is forcing supply chains to make better use of the data they already generate. Visibility still matters, but it is no longer enough on its own. The real advantage comes from leveraging AI to turn data into practical insights that help teams spot trouble before it affects day-to-day supply chain operations.

Modern supply chains create information at nearly every stage of movement. Supplier activity, IoT-based shipment monitoring, manufacturing systems, warehouse platforms, and RFID-enabled final-mile tracking all produce digital signals that can be used to improve efficiency and sharpen operational visibility.

The final mile, in particular, has changed significantly. What once depended on manual scans at multiple transfer points is increasingly powered by RFID-enabled automatic sensing. The old gap between pickup and delivery, once limited in visibility, has become far more transparent. In sophisticated carrier networks, shippers can now follow a package with much greater confidence from origin through final delivery.

That level of visibility offers more than reassurance. It strengthens chain-of-custody controls and reduces the risk of loss, theft, or misrouting for high-value shipments. This is especially important when a single package contains essential components that can directly affect production schedules downstream.

Still, data alone does not create value. Knowing where a package is represents only the starting point. The real sign of a mature supply chain is the ability to use that information to improve decisions, optimize workflows, and anticipate disruptions before they occur.

UPS recently surveyed manufacturers to better understand their approach to technology adoption, particularly around AI. The findings showed that many already recognize AI’s potential: 68% said AI is improving forecasting and decision-making. That signals a meaningful change in how manufacturers view the role of advanced technology in supply chain operations. ¹

But recognizing AI’s promise and embedding it into day-to-day operations are two different things. Only 6.7% of surveyed manufacturers described their operations as predictive or autonomous.¹ In other words, while attitudes are evolving, most operating models have not yet caught up.

The gap is not primarily about resistance to AI-driven insights. More often, it comes down to limited capacity. Many manufacturers lack the internal resources needed to build, deploy, and scale AI-driven solutions across their organizations. Among the top barriers cited were:

• Budget limitations: 52% ¹
• Training and adoption challenges: 43% ¹
• Data migration issues: 36% ¹
• Legacy system integration: 34% ¹

As a result, many organizations are sitting on enormous amounts of operational data without the capabilities required to translate that data into action. Used effectively, these data sets could help supply chains move beyond reporting what is happening now and toward anticipating delays and preventing disruptions before performance suffers.

For that reason, UPS has invested in RFID sensing as part of a broader effort to extend the digital twin of its global network across all transportation modes. This digital replica spans facilities, air and ground operations, and end-to-end package flows, refreshing every 10 minutes to monitor performance continuously and support adjustments across the network at scale.

At UPS, RFID serves as the eyes and ears of the operation, while AI-driven insights function as the brain. RFID helped shift the network from manual scanning to automatic sensing, creating a richer stream of movement data. AI then interprets that data, generating forecasts and recommendations that operational teams can use to improve performance and respond faster.

¹.“Logistics Technology Today”, UPS, 2026

Individual results and options will vary. UPS makes no promises of any specific outcome in this document but instead provides only example outcomes based on certain UPS customer experiences.