Managing and Optimizing Soaring Levels of Smart Label Data
June 24, 2025
Blog
As the logistics and transportation industries continue to digitize, the adoption of smart labels is accelerating. These small, Internet of Things (IOT)-enabled devices are modernizing how goods are tracked, monitored, and managed across supply chains.
As enterprises deploy more of these smart labels, they are amassing significant volumes of data that, when properly analyzed, can reveal valuable insights to improve operational efficiency, enhance supply chain security, and enable proactive decision-making. But how can businesses manage and make sense of this growing sea of data, and what does the future hold for data sharing across the broader logistics ecosystem?
The Surge in Smart Label Data
Smart labels are a relatively recent innovation, with commercial deployment only beginning to gain traction in 2023. Despite their novelty, adoption is rising rapidly. According to ABI Research, the global cellular smart label market is expected to grow from 2 million in 2025 to more than 21 million shipped units in 2028, which equals a projected growth rate of 83% (CAGR 2025 – 2028)1. With this growth comes a tidal wave of data. A single smart label may generate between 0.5 and to1.5MB of data over its lifecycle, meaning that millions of deployed labels could easily produce terabytes of data each year.
The value of this data lies in its ability to provide real-time visibility into the location and condition of goods in transit- an increasingly important need in today’s fast-paced logistics environments. Business intelligence and analytics platforms are fundamental for interpreting this information. Without tools to aggregate and analyze smart label data, its potential is largely unrealized. When applied effectively, the data can support improved decision-making and operational performance.
For example, a U.S mobile phone retailer can use smart labels on incoming shipments of iPhones at its central distribution center to better manage the supply chain of phones to retail outlets across the country. By monitoring inventory levels in real-time, the company can anticipate stock shortages at specific stores, such as a dwindling supply of black iPhone 16s, and adjust shipping strategies accordingly. This kind of responsive supply chain management can reduce delays, prevent stockouts, and enhance customer satisfaction.
Looking ahead, AI will play a growing role in automating these insights. As smart label data is continuously analyzed by BI systems, AI can be used to trigger appropriate actions – reallocating inventory, adjusting routes, or predictive maintenance needs for transport assets – based on real-time and historical data patterns. Over time, AI systems will become more accurate and proactive, ultimately reducing human intervention and streamlining logistics operations.
Enabling Data Sharing Across the Logistics Ecosystem
Currently, many organizations deploy smart labels independently for their internal tracking needs. In a typical supply chain, a product may be handled by multiple stakeholders, including manufacturers, logistics providers, warehousing firms, and retailers, with each using separate systems and collecting siloed data. This fragmented approach limits the full potential of smart label technologies.
For the logistics industry to fully benefit from smart labelling, a more collaborative model of data sharing is needed. Instead of each stakeholder using their own smart label, a unified system could enable a "single source of truth," where one label generates shared data accessible to authorized parties throughout the supply chain. This approach would reduce duplication, improve consistency, have a single source of truth, and facilitate broader operational improvements.
Returning to the mobile phone retailer example, if transport companies, warehouse operators, and retailers could all access shared data from a single smart label, it would improve coordination and decision-making. For instance, a courier could reroute deliveries in real-time to avoid disruptions, while warehouse teams could better manage unloading schedules based on incoming shipment status.
However, implementing such ecosystem-wide visibility brings challenges. These include establishing interoperable systems, ensuring cross-border connectivity, addressing network compatibility, and protecting data privacy. Cultural barriers also remain, particularly when it comes to sharing proprietary data with third parties or competitors.
Despite these hurdles, the logistics industry is gradually moving toward greater interoperability and collaboration. As smart label technologies mature and standards emerge, data sharing will become more feasible and secure. Predictive analytics will also gain prominence, enabling enterprises to anticipate supply chain disruptions, optimize asset utilization, and enhance loss prevention strategies.
For enterprises adopting smart labels, the initial focus should be on capturing and interpreting the value of data through integrated BI platforms. From there, layering AI for automation and predictive analytics can lead to faster, smarter decisions. The final step in this evolution will be to collaborate with ecosystem partners to securely share data, creating a more sustainable and resilient supply chain model.
While these developments may still seem aspirational, the pace of innovation in IoT and smart label technologies suggests that a more connected, intelligent logistics infrastructure is not only possible—it’s well on its way.
Sharath Muddaiah is the Head of Global Business Strategy and Customer Success for IoT Solutions at Giesecke+Devrient (G+D), a global SecurityTech company headquartered in Munich, Germany. For more information, please visit www.gi-de.com/en/digital-security/connectivity-iot/transport-logistics/end-to-end-tracking/smart-label.
