Disruptions across the global supply chain are now a persistent reality rather than an occasional exception. The range of risks has widened to include extreme weather events, geopolitical tensions, labor shortages, transportation bottlenecks, and shifting demand patterns. Without early detection, or ideally prediction, a minor disruption can have knock-on effects downstream that cause delays, increase costs and reduce service levels.
The threat of disruption means supply chains must leverage the wealth of data at their fingertips to prioritize not only visibility, but actionable insights that detect disruptions before they impact operations.
Modern supply chains are rich with data
Supply chain operations today generate data points at nearly every touchpoint. From supplier transactions and IoT-enabled freight tracking to manufacturing systems, warehouse management platforms and final-mile RFID tracking, digital footprints leave valuable data points to optimize operations and increase visibility.
Especially in the final mile, data collection has evolved from manual scans across multiple handoffs to automatic sensing, enabled by RFID. Gone are the days when the time between pickup and estimated delivery date were an unknowable black box. Within the most advanced carrier networks, shippers can reliably track package movement from pickup to final delivery.
This visibility delivers more than peace of mind. It enables secure chain of custody tracking and helps protect high-value shipments from loss, theft and misrouting. These protections are of utmost concern when individual shipments may contain mission-critical components that have a downstream effect on production timelines.
Access to unprecedented levels of data, however, is only useful if it’s leveraged effectively. Knowing a package location is the first step but leveraging that knowledge to optimize operations and predict and preempt disruptions before they happen is a mark of true supply chain maturity.
Manufacturers are prioritizing technology in name only
UPS recently conducted a survey of manufacturers to learn how they’re approaching technology and specifically AI adoption. We learned that most manufacturers understand the power of AI modeling, with 68%1 agreeing that AI is improving forecasting and decision-making. This reflects a genuine shift in how the industry thinks about technology.
Recognizing the potential of AI, however, is only part of the equation, and only 6.7% of manufacturers surveyed feel that their operations are predictive or autonomous. 1 So, while supply chain decisionmakers are shifting their thinking, they have not yet shifted their operations to match.
Ultimately, we learned that the gap between believing in AI capabilities and integrating them isn't a matter of willingness. It's a matter of bandwidth. Most manufacturers simply don't have the internal resources to build and deploy AI solutions at scale. Among their primary barriers, manufacturers reported:
· Budget constraints: 52%
· Training and adoption gaps: 43%
· Data migration: 36%
· Integration with legacy systems: 34%1
The result is that many manufacturers are sitting on a goldmine of operational data without the necessary tools to act on it. If implemented correctly, these rich data sets could move supply chain operations from simply accurately reporting on the current state to predicting and avoiding disruptions and delays before they impact performance.
The brain of the operations
That’s why UPS has invested in RFID sensing to expand a digital twin of its global network to include all modes of transportation, creating a digital replica of facilities, air and ground networks, and end-to-end package flows that updates every 10 minutes to continuously track performance so the network can adjust and self-heal at scale.
At UPS, we like to say that our RFID network is the eyes and ears of the operation, while AI is the brain. By implementing RFID, we moved from a manual scanning network to an automatic sensing network. That sensing generates valuable data from package movements, while AI transforms that data into predictions and insights that our strategists translate into operational improvements.
For manufacturers who are trying to close the gap between believing in and implementing AI capabilities, this relieves them of the pressure of building new systems themselves. Rather than investing years in developing proprietary digital twins, sensor networks and predictive AI models, they can leverage a final mile delivery network readily equipped with predictive modeling without overhauling their entire operation.
Why high-tech manufacturers can't afford to wait
Not all supply chains carry the same risk. For high-tech manufacturers shipping semiconductors, enterprise hardware and other high-value goods, delivery disruption is more than an inconvenience. It can halt a production line, delay a product launch, or break a customer relationship.
And in a quickly evolving industry, capacity constraints are making delivery windows tighter, while demand volatility makes forecasting harder. Meanwhile, with rates of theft and fraud on the rise for high-value shipments, making a clear chain of custody is more critical than ever.
The manufacturers who act now will lead
High-tech manufacturers looking to close the gap between understanding the value of AI and implementing it into secure operations can begin with delivery. An RFID sensing network acts as the eyes and the ears across our network, giving the brain the real-time data it needs to anticipate disruptions, optimize performance, and continuously adapt as conditions change. Beyond delivery, this visibility creates new opportunities to rethink operational processes, improve decision-making, and unlock efficiencies across the broader supply chain ecosystem.
Ready to build a more resilient supply chain? Connect with a UPS strategist to learn how AI-powered visibility and digital twin technology can help improve final-mile performance without the complexity of implementing and maintaining the underlying technology yourself.
1. “Logistics Technology Today”, UPS, 2026