Logistics practitioners have applied internet of things (IoT) technologies to their operations at increasing rates in recent years, seeking to collect new data that could help eliminate blind spots throughout their supply chains. By attaching web-connected sensors to objects, this approach can improve visibility over almost any physical “thing”—items such as inventory, pallets, scanner guns, or lift trucks—and monitor and track a wide range of variables, from location to exposure to light, vibrations, and temperature.
However, as users increasingly monitor those items, many companies face a growing problem—what to do with all the data. Depending on how many “things” are in your IoT and how frequently you collect their status reports, an organization can quickly compile a deep database of digital records. And that presents a challenge: Exactly how do you analyze all those numbers, and how do you act upon the results?
There’s no question that demand for the visibility data generated via IoT tracking is on the rise. For example, demand for one type of IoT technology—radio-frequency identification (RFID) tags—is booming, driven by increasing dependence on real-time information-based asset tracking, along with automated inventory management and supply chain visibility solutions.
That assessment comes from the research and consulting firm Allied Market Research, which says that the global RFID tags market was valued at $5.9 billion in 2022 and is projected to reach $15 billion by 2032, which equates to a compound annual growth rate (CAGR) of 9.9%.
One factor behind that surge in the market is the rising use of RFID technology across the retail, health care, logistics, and manufacturing sectors, the firm said in its report, “RFID Tags Market by Type, Frequency, Application: Global Opportunity Analysis and Industry Forecast, 2023–2032.”
According to the report, “The major growth drivers for this market include the increase in penetration of IoT, advancement of [real-time location systems], and various government mandates regarding electronic product serialization in pharmaceuticals as well as food safety. This is fueling the convergence of 5G infrastructure and RFID technologies for ultra-fast data capture [and] edge-computing capabilities, thus augmenting the RFID tags market size among data-dense domains.”
So what exactly do companies gain from all that fresh data? Experts say that wins can come easily at first. A white paper from business management and technology solutions provider The DDC Group noted that, “Today’s logistics organizations can observe their operations with a level of granularity that was previously impossible. Shipments are tracked in real time, exceptions are surfaced instantly, and operational dashboards provide continuous updates across global networks.”
However, many organizations are now realizing that simply collecting more data doesn’t necessarily improve their operational performance, DDC said in the paper, “The Visibility Paradox: Why More Data Isn’t Improving Logistics Outcomes.” According to DDC, visibility was once a differentiator between competing logistics businesses but is rapidly becoming simply a baseline capability. Sure, visibility improves the speed and quality of information flow, but it does not inherently improve how that information is interpreted and then translated into action.
The next phase of logistics transformation will not be defined by the ability to see more—many logistics organizations already have the visibility they need. Rather, the opportunity now lies in consistently translating that visibility into better operational outcomes. To bridge that gap, DDC says, it is useful to separate modern logistics operations into three parts: a “Visibility Layer” (what is happening), a “Decision Layer” (what it means), and an “Execution Layer” (what should be done).
CHOOSE YOUR TOOLS CAREFULLY
As for how companies can make the most of their IoT data, one solution could come from one of the hottest technologies of the moment—artificial intelligence (AI). According to LogiNext, a New Jersey-based specialist in logistics and field service automation, freight transportation fleet operations already generate enormous amounts of telematics and vehicle data every day, but issues such as GPS outages, temperature fluctuations in refrigerated vehicles, and unsafe driving behavior often go unnoticed until they lead to delivery failures, compliance violations, or financial losses. As a solution, the firm says its new Deviation Intelligence platform fills in the gaps by using AI to continuously monitor fleet activity and automatically detect critical exceptions in real time, enabling operations teams to act before disruptions escalate.
Another example of applying AI to the IoT data problem comes from one of the nation’s biggest retailers, Bentonville, Arkansas-based Walmart. In October 2025, the company announced plans for a large-scale deployment of “ambient IoT” technology from IoT tech firm Wiliot. San Diego-based Wiliot said that Walmart would use its tags to track Walmart pallets on an extremely broad scale, immediately integrating millions of the firm’s IoT Pixels, which are battery-free Bluetooth sensors, throughout its supply chain and setting a goal of reaching 90 million by the end of 2026. The solution had already been deployed across 500 Walmart locations by the end of 2025, with plans for national expansion this year. The rollout will ultimately cover 4,600 Walmart Supercenters and Neighborhood Markets, and over 40 distribution centers, generating high-resolution supply chain data that feeds into Walmart’s AI systems.
And that’s where Walmart’s own AI enters the picture. Walmart said its artificial intelligence will generate real-time insights into inventory management, empowering the omnichannel giant to know exactly what merchandise is owned and where it is at any moment. In turn, that could enhance supply chain efficiency, inventory accuracy, and cold chain compliance, the company said in a statement.
SET YOUR LIMITS
Another way to cut through the data confusion is to set rules and limits before collecting the visibility data itself, so that unacceptable results are instantly flagged for corrective action. That’s the approach taken by lift truck manufacturer The Raymond Corp., which says its iWarehouse Real-Time Location System (iW.RTLS) tracks and controls the movements of lift trucks throughout a logistics facility. The Raymond platform enables facility-defined operational rules to be tied to physical locations throughout the warehouse by using customizable preset zone types. By setting limits on variables like location tracking, geofencing, and zoning, the system enables proactive, rule-driven control, the company says.
Yet another option for monitoring and responding to the flood of IoT data is to work with a third-party partner or vendor. That is the approach offered by Konecranes, a Finnish maker of some of the logistics world’s largest tools: the container-handling cranes that loom over maritime ports and vessels.
Konecranes provides predictive maintenance services for its products, including mobile harbor cranes (MHCs) and, more recently, rubber-tired gantry cranes (RTGs) and rail-mounted gantry cranes (RMGs). Each of those towering machines collects IoT data through vibration sensors installed on critical rotating components, including hoist and trolley motors, gearboxes, and bearings.
To help customers avoid equipment failures that could lead to costly downtime, Konecranes says it analyzes the operating data it collects and communicates the findings to users through an online customer portal. The stakes are high, but according to Konecranes, its strategy of “exception reporting” enables maintenance teams to focus on targeted interventions, helping to reduce downtime and improve spare-parts planning. And those are critical metrics for meeting the intense pressure on ports to process imports and exports swiftly.
“The expansion of our predictive services [offering] to Konecranes RTGs and RMGs is part of a broader strategy to strengthen our digital and lifecycle services for customers worldwide,” said Nico Zamzow, Konecranes’ senior vice president, port services, in a press release. “By combining technology, data analysis, and equipment expertise, we enable terminal operators to act earlier and get even better performance from their Konecranes equipment.”
All these options show that there is no single answer to the question of how companies can make the best use of their IoT data. The best approach will vary for each company, depending on its own information technology (IT) resources, its data analytics budget, its comfort level with operating AI platforms, and its need for real-time feedback.
But the variety of strategies available proves that companies throughout the logistics sector are trying out different tools as they try to capture the potential gains of business improvements fueled by the IoT data boom.
