Manufacturing IoT connects production equipment, quality systems, environmental monitors, and supply chain sensors into an integrated operational technology (OT) environment that enables real-time production visibility, predictive maintenance, quality defect detection, and the energy optimization that reduces manufacturing cost and improves sustainability performance.
Manufacturing IoT deployments span the IT/OT convergence challenge — connecting legacy industrial equipment that was never designed for network connectivity to modern cloud analytics platforms that process sensor data at scale. RLM advises on manufacturing IoT strategy, OT network architecture, protocol integration (OPC-UA, Modbus, MQTT), and the edge computing design that processes sensor data where latency requirements demand local analysis.
A structured advisory process — from use case definition and platform evaluation to deployment architecture and ongoing optimization.
We assess your manufacturing IoT landscape — current OT connectivity, equipment sensor coverage, data historian infrastructure, and the production visibility gaps that IoT can address.
We design the IT/OT convergence architecture — OT network segmentation, DMZ design for data exchange between OT and IT networks, protocol translation (OPC-UA, Modbus, MQTT), and the security controls that protect production systems from IT network threats.
We design predictive maintenance programs — identifying the equipment failure modes detectable by vibration, temperature, current, and acoustic sensors, selecting sensor hardware, and designing the ML models that predict failures before they cause unplanned downtime.
We design edge computing infrastructure for manufacturing IoT — selecting edge platforms (AWS Greengrass, Azure IoT Edge, on-premises industrial PCs) that process time-sensitive data locally while synchronizing insights with cloud analytics platforms.
The dimensions that determine whether an IoT deployment delivers lasting operational value — and the questions RLM helps you answer before any commitment.
Manufacturing IoT that connects production systems to IT networks creates cybersecurity risk to production continuity. Evaluate OT security architecture — network segmentation, unidirectional security gateways, and the monitoring approach that detects intrusions without disrupting production.
Most manufacturing facilities have significant legacy equipment with no native digital interface. Evaluate retrofit sensor approaches — clamp-on current sensors, vibration sensors, vision systems — that add data collection without modifying production equipment.
Some manufacturing use cases require real-time response (quality defect rejection, safety interlocks); others work fine with batch analytics (maintenance scheduling, energy optimization). Evaluate processing architecture based on actual latency requirements.
Manufacturing IoT systems that rely on continuous cloud connectivity may be unavailable during network outages. Evaluate edge buffering capabilities that maintain data collection and local control during cloud connectivity interruptions.
Changes to production systems carry operational risk. Evaluate the change management and testing protocols for IoT deployments that touch production equipment — and the rollback plan for changes that affect production performance.
"RLM helped us select and deploy an IoT platform across 28 facilities in under six months. Their vendor-neutral approach saved us from a costly mistake with our initial shortlist."
"We needed smart metering and energy management across our campus portfolio. RLM mapped the vendor landscape, ran the evaluation, and we're now hitting our ESG targets ahead of schedule."
Talk to an RLM advisor who specializes in enterprise IoT deployments. Independent guidance from platform selection through operational deployment.