MHI Solutions Improving Supply Chain Performance
IoT sensors and edge computing are transforming supply chains by enabling real‑time insights, predictive maintenance and faster operational decisions. As these technologies mature and integrate with AI and standardized systems, companies can achieve greater visibility, efficiency and resilience across their operations.
IN TODAY’S ULTRA‑COMPETITIVE logistics landscape, data visibility alone isn’t enough. Companies must act on insights in real time to prevent downtime, optimize operations and improve customer experiences. While supply chains have long collected data, traditional cloud‑centric architectures struggle to deliver the speed required for time‑sensitive decision‑making.
Edge computing is emerging as a pivotal solution to these challenges. This capability shifts data processing closer to where it’s generated—on the warehouse floor, in transit vehicles or at distribution hubs—rather than relying solely on centralized cloud services. While cloud platforms remain indispensable for long‑term analytics—such as creating models or generating inferences that can be executed at the edge and for cross‑enterprise reporting—they are not optimized for decision velocity. That is, the rapid interpretation and actioning of sensor data that keeps goods flowing, machines running and disruptions mitigated.
In a recent Logistics Viewpoints report, Jim Frazer, Vice President of Corporate Strategy at ARC Advisory Group, noted that logistics networks are generating ever‑larger volumes of data from Internet of Things (IoT)‑enabled devices, equipment, vehicles and facilities.
“Traditional cloud‑centric architectures, which depend on centralized processing, may not meet speed and/or reliability goals needed to support operational needs at scale,” he wrote. “Edge computing… has emerged as a method to address these challenges by reducing latency and improving resiliency.”
By enabling local data pre‑processing, analytics and decision logic, edge architectures reduce dependence on upstream networks while enabling faster action and lower bandwidth consumption. When combined with IoT sensors that stream data on location, temperature, vibration and machine behavior, edge computing forms the digital nervous system, enabling greater visibility into operations and assets. That, in turn, yields smarter, more resilient supply chains.
Underscoring the importance of edge computing, Gartner forecasts explosive growth as the technology moves from a niche to a foundational technology, with the market reaching $511 billion by 2033 across key sectors like manufacturing and retail. Its analysts anticipate a 30% increase in platform adoption by 2029. Much of this adoption will be driven by increasing integration of different forms of artificial intelligence (AI), including predictive, generative and agentic. Gartner anticipates that this shift will enable enterprises to transition away from single‑edge use cases and evolve toward integrated edge‑cloud deployments, focusing on real‑time intelligence and automation.
However, in his report, Frazer cautioned that the shift to edge processing brings its own complexities. Logistics organizations must manage large fleets of distributed devices, secure edge assets against physical and cyberthreats, optimize AI models to run efficiently on edge hardware and navigate fragmented standards across vendors—all while ensuring that real‑time insights sync reliably back to cloud systems for broader analytics.
To unpack how companies are applying IoT and edge computing technologies today—and how they’ll be leveraged in the coming years—MHI Solutions spoke with industry experts from four MHI member companies:
- Dan Barrera, head of warehouse automation at Bosch Rexroth.
- Craig Henry, global account director for Amazon at Murrelektronik and author of “Super Connected: The Future of the Industrial Nervous System.”
- Troy Herman, retail account executive at SICK Inc.
- Doug Schuchart, global material handling and intralogistics manager at Beckhoff Automation.
The following recaps their observations about how IoT sensors and edge computing are reshaping supply chain visibility, enabling predictive maintenance, overcoming integration challenges and redefining cybersecurity and data governance at the operational edge.
HOW IOT SENSORS AND EDGE COMPUTING WORK TOGETHER
Barrera said that most organizations do not have a data collection problem—they have a decision‑making and latency problem.
“Companies are already capturing large volumes of supply chain data, but that data often becomes trapped in databases, historians, manufacturing execution system (MES) platforms or cloud dashboards without driving action. The real challenge is turning raw data into meaningful insight quickly enough to matter operationally,” he explained.
Historically, IoT sensors acted as simple signal generators. Now, the devices themselves are increasingly equipped with analytics functions and powered by AI, Barrera observed. That allows them to provide smart digital insights, adding context to data such as vibration, temperature, current, position, barcode reads or vision events. Because the data already has meaning, it can immediately trigger action rather than requiring downstream interpretation.
“The newest IoT sensors create efficiency and then action, thanks to the simple automation embedded into them,” Barrera said.
He described edge devices as the critical bridge between operations technologies (OT) and information technologies (IT), normalizing operational data and connecting it to enterprise systems. Edge computing minimizes latency by enabling decisions to be made locally, rather than waiting for cloud processing.
“Modern edge devices also support security, data formatting, communication harmonization and AI‑driven decision‑making,” Barrera added. “That allows organizations to take real‑time action directly at the edge. In short, IoT sensors provide intelligent data, edge devices minimize latency and AI enables real‑time decisions.”
Schuchart agreed. He said that to turn data into decisions, IoT sensors and edge computing must work as a coordinated ecosystem to eliminate latency and transform raw data into actionable intelligence at the point of need. Yet many organizations struggle to act on supply chain data in real time because they lack the hardware and network to do so, he added.
“Many operations are not able to react to data in real time because they are forced to collect the data from a separate network and to a separate edge device, not utilizing the fieldbus nor the programmable logic controller (PLC),” Schuchart said. “Traditional PLCs don’t support executing the machine control, data collection and edge computing in a single device like modern PC‑based systems. And most fieldbuses do not have the bandwidth to do data collection for IoT and predictive maintenance. Attempting to do so would further bog down the network and result in throughput issues.”
As a result, many organizations deploy a separate Ethernet network alongside the equipment, running in parallel to the fieldbus, to transmit sensor data to an edge device. This edge device often functions solely as a data collector or performs limited analytics at the edge. However, to act on those insights, the analytical results must ultimately be communicated to another system or back to the PLC for execution, which adds latency.
Schuchart pointed to Ethernet for Control Automation Technology (EtherCAT)—originally developed by Beckhoff Automation and now an open, global standard managed by EtherCAT Technology Group—as a more streamlined alternative.
Unlike other Ethernet‑based fieldbuses, he explained, EtherCAT uses a single data frame that updates all devices simultaneously. This leaves significant unused bandwidth available for data collection without compromising real‑time control performance. The architecture allows high‑speed control and data acquisition to coexist on the same network.
“In order to fully take advantage of AI for predictive maintenance and other functions in real time, such as machine learning, operations must embrace personal computer (PC)‑based control technology. Today’s modern PLC is in fact an industrial PC (IPC) with a closed operating system and often other limitations not typical of PC‑based systems,” Schuchart said.
SPECIFIC TYPES OF SENSOR DATA INFORM PROACTIVE PREDICTIVE MAINTENANCE
One of the most transformative outcomes of IoT and edge computing is the ability to anticipate failures before they happen. This requires not just volume, but variety and depth of data to enable pattern recognition and decision logic.
Herman emphasized that in condition monitoring and predictive maintenance, the biggest shift is not simply faster data transmission but understanding and analyzing the data once it is collected.
“For many warehouses, accessing real‑time data in the millisecond sense is less critical than establishing a baseline for assets that have never been monitored before,” he explained. “With hundreds of motors and gearboxes operating in a facility, the immediate priority should be gaining visibility into equipment health and trends over time, rather than reacting instantaneously.”
That’s because predictive maintenance depends on moving beyond raw sensor readings to analytics‑driven insights, where historical data and pattern recognition reveal early signs of failure, Herman said.
“As organizations collect more sensor data and apply analytics models—such as remaining useful life (RUL) models—they transition from reactive maintenance to preventive and eventually predictive strategies,” he said. “This allows maintenance teams to act with confidence, scheduling repairs based on evidence rather than fixed intervals or guesswork.”