If you’re building an industrial IoT solution, you’ve probably already encountered the moment where your technology works perfectly in isolation, then fails to communicate with the equipment already running on a customer’s factory floor. A sensor that reads flawlessly in your test environment suddenly cannot talk to a legacy PLC. Your platform connects cleanly to one robot manufacturer’s API but throws errors with another. Your data pipeline works beautifully until it hits a control system running a proprietary protocol from two decades ago.

This isn’t a niche edge case. It’s one of the most common and costly reasons industrial IoT projects stall before they reach commercialization, and it’s the challenge that separates solutions that look impressive in demos from solutions that actually get deployed at scale.

Why Interoperability Is So Difficult in Manufacturing Environments

Manufacturing environments aren’t clean slates. Most facilities run a mix of equipment from different vendors, purchased across different decades, running different communication protocols, and managed by different teams. On any given factory floor, you might find PLCs from the 1990s sitting alongside newer OPC-UA-enabled controllers, RFID readers using their own proprietary data formats, and cloud-connected monitoring systems expecting REST APIs. Getting all of these to share data reliably is not a configuration problem. It’s an architecture problem.

One of the main obstacles to fully adopting Industry 4.0 and achieving effective digital transformation is interoperability. The challenge is compounded by the fact that groups of devices from various vendors often make data sharing impossible without additional configuration, resulting in only fractional use of industrial Internet of Things (IIoT) potential and preventing enterprises from reaching the benefits this technology can offer (Source: N-iX).

The global IIoT market reached $514.39 billion in 2025, yet only 46% of manufacturers have deployed IIoT solutions at the facility level, which means more than half the industry is still catching up (Source: Manufacturing Lead Generation). For Canadian tech startups building industrial IoT solutions, that gap represents a massive commercial opportunity, but only for companies that can prove their technology integrates cleanly with the systems their customers already have.

The Protocol Landscape: What You’re Dealing With

Understanding why interoperability is hard starts with understanding the fragmented protocol landscape that industrial IoT solutions must navigate.

OPC-UA has emerged as the closest thing the industry has to a universal standard for machine-to-machine communication. OPC-UA was developed to address the challenges of interoperability, scalability, and security, making it a reliable choice for data exchange in IoT applications. One outstanding feature of OPC-UA is its ability to provide a standardized and platform-independent way for devices to communicate, allowing different devices, regardless of their manufacturer or underlying technology, to seamlessly exchange data (Source: Hilscher). For startup teams building solutions that need to connect to modern industrial equipment, OPC-UA compliance is often a baseline expectation from enterprise buyers.

MQTT takes a different approach. MQTT, known for its lightweight and efficient messaging, is well-suited to the needs of IIoT, while OPC-UA is a reliable and secure protocol widely used in legacy automation systems (Source: IEEE Xplore). Rather than treating these protocols as competitors, most mature deployments use them together. OPC-UA is used for local factory communication, then bridged to MQTT for cloud connectivity. An edge gateway subscribes to OPC-UA servers on local equipment, then publishes selected data to cloud platforms using MQTT, performing protocol translation, data filtering, buffering during network outages, and security boundary enforcement (Source: Einnosys).

Beyond OPC-UA and MQTT, industrial environments may also run Modbus, PROFIBUS, PROFINET, MTConnect, AMQP, and a range of proprietary protocols depending on the age and origin of the equipment. Research comparing MQTT, AMQP, Modbus, PROFIBUS, OPC-UA, HTTP/REST, and MTConnect found that the process of selecting a communication protocol should consider specific industrial constraints like latency, security, and scalability, and that no single protocol is optimal across all use cases (Source: Linköping University).

For a startup building a single product that needs to work across all of these environments, the implication is clear: your solution cannot assume a clean protocol stack. It has to prove interoperability across the messy reality of what manufacturers actually have installed.

What Interoperability Testing Actually Involves

Many startups treat interoperability as a checkbox. They confirm that their solution supports OPC-UA, list it in their technical documentation, and move on. Enterprise buyers are not satisfied with that answer anymore.

What procurement teams in manufacturing environments want to see is evidence that a solution has been validated against actual industrial equipment under real operating conditions. That means running your sensors, applications, or platform against multiple control systems, robots, cobots, network access methods, and software platforms simultaneously, in an environment that reflects the complexity and noise of a real factory floor.

Concrete interoperability testing for an industrial IoT solution should cover several dimensions.

Protocol compliance validation confirms that your solution correctly implements the protocols it claims to support, not just in theory but under load, with real devices. OPC-UA compliance has multiple conformance tiers, and a solution that passes basic compliance testing may still fail when communicating with certain equipment profiles or operating under high message frequency. Reference architectures like RAMI 4.0 have identified OPC-UA as the primary communication protocol for breaking the interoperability barrier in industrial settings (Source: ScienceDirect).

Multi-vendor device integration tests your solution against hardware from different manufacturers. A predictive maintenance platform that works with Siemens controllers but throws errors with Rockwell Automation hardware is not an interoperable solution. It is a partial solution with a marketing problem.

Latency and throughput under mixed protocol conditions validates that your data pipeline does not degrade when translating between protocols at scale. Protocol translation through middleware gateways introduces latency overhead, and under high message volumes that overhead can become significant enough to affect the real-time decision making your solution depends on. Research has shown that lighter protocols bring measurable advantages when data needs to be delivered fast and without strict focus on data structure, meaning protocol selection has direct performance consequences (Source: ScienceDirect).

Control system and software platform integration confirms that your solution connects cleanly to the manufacturing execution systems (MES), enterprise resource planning (ERP) systems, and IoT management platforms that enterprise customers use to run their operations. A solution that produces data nobody can access from existing business systems creates adoption friction that kills deals.

Network resilience testing validates that your solution handles the connectivity conditions of a real manufacturing environment, including interference, signal degradation in metal-dense spaces, failover between wired and wireless connections, and the latency profile of private 5G networks versus older Wi-Fi infrastructure.

Why Simulated Testing Is Not Enough

One of the most common and expensive mistakes industrial IoT startups make is relying on simulated environments to validate interoperability. Simulation is valuable for early-stage development. It is not sufficient for enterprise sales validation.

The problem is that simulated environments cannot fully replicate the electromagnetic interference, physical obstructions, equipment aging effects, and edge-case protocol behaviour that appear in real manufacturing facilities. A sensor array that reads accurately in a clean lab setting may produce erratic data when mounted near high-powered motors or in a facility with dense metal racking. An OPC-UA integration that works smoothly in a development

environment may encounter unexpected behaviour when connected to a legacy PLC running firmware that predates the current OPC-UA specification.

Industrial IoT adoption has not met expectations due to challenges such as interoperability, cybersecurity, and workforce readiness, despite the significant benefits the technology offers including increased efficiency and productivity, support for autonomous operations, and data-driven insights for reporting and compliance (Source: IIoT World). A large part of the adoption gap can be traced back to solutions that were validated in controlled conditions and then failed to perform consistently when deployed in real-world environments.

Testing in a real industrial environment with actual manufacturing equipment does not just reduce the risk of post-deployment failures. It produces validation evidence that enterprise buyers and procurement teams can trust. For a startup navigating a long sales cycle with a risk-averse manufacturing buyer, that evidence is often the difference between a pilot project and a signed contract.

The Role of 5G in Closing the Interoperability Gap

Private 5G networks are becoming an increasingly important part of the interoperability picture in advanced manufacturing, and they introduce both new capabilities and new complexity for solution developers to validate against.

The latency and throughput characteristics of private 5G networks change what is possible in real-time manufacturing applications. Time-sensitive use cases that previously required wired connections, such as closed-loop quality control, real-time robot coordination, and high-frequency sensor polling for predictive maintenance, become feasible on wireless infrastructure when operating on a dedicated private 5G network. However, solutions built for Wi-Fi or wired environments do not automatically perform the same way on 5G, and the integration of 5G with existing OT systems introduces new points of potential protocol mismatch.

Key challenges in the IIoT market include the lack of standardization in IoT protocols, while significant opportunities lie in the emergence of 5G technology and predictive maintenance applications (Source: MarketsandMarkets). For startups building solutions that will eventually operate on private 5G networks, validating interoperability in a 5G-enabled environment before going to market is not optional, it is a prerequisite for selling into enterprise manufacturing accounts that are already deploying or planning to deploy private 5G infrastructure.

How to Approach Interoperability Systematically

Building interoperability into a product from the ground up, rather than retrofitting it at the end of a development cycle, is the single most effective way to reduce the cost and risk of enterprise deployment. In practice, that means several things.

Adopt Open Standards Early

Open standards such as OPC-UA, MQTT Sparkplug, and others enable universal connectivity and integration across devices and systems, and are essential for reducing integration costs, speeding up deployment, and improving data quality (Source: N-iX). Building on open standards does not eliminate interoperability challenges, but it dramatically reduces the surface area of custom integration work required for each new customer deployment.

Design for the Middleware Layer

In most enterprise manufacturing environments, a middleware or edge gateway layer sits between OT equipment and IT systems. Designing your solution to integrate cleanly at this layer, rather than expecting direct device access, makes your product more compatible with the range of integration architectures enterprise customers actually use. The Unified Namespace has emerged as a promising data-centric architecture approach, with OPC-UA and MQTT serving as the primary interoperability anchors within it (Source: ScienceDirect).

Test Against Real Equipment Early and Repeatedly

Interoperability issues that are discovered during a customer pilot are expensive to fix and damaging to the sales relationship. The same issues discovered during structured testing in a real industrial environment before the sales cycle are simply engineering problems to solve.

Document your Integration Boundaries Clearly

Enterprise procurement teams will ask detailed questions about what your solution connects to, how, and under what conditions. Having clear, evidence-backed answers to those questions, supported by test results from real-world validation, shortens the sales cycle and reduces the risk of surprises at deployment.

How CENGN’s Advanced Manufacturing Living Lab Can Help

For Canadian tech startups and scaleups building industrial IoT solutions, gaining access to a real-world manufacturing environment for interoperability testing relieves one of the most significant barriers between a working product and a market-ready one. Purpose-built manufacturing facilities with the right mix of industrial equipment, network infrastructure, and technical expertise are not easy to access independently, and building or leasing that infrastructure is out of reach for most early-stage companies.

The CENGN Advanced Manufacturing Living Lab, powered by CNIMI and Ericsson, addresses that barrier directly. Located at CNIMI’s facility in Drummondville, Quebec, the lab gives Canadian startups access to a production-grade industrial environment equipped with private

5G on NCLL spectrum, multi-technology indoor positioning systems including Ultra-Wideband, Bluetooth Low Energy, and RFID, collaborative and autonomous robot arms, autonomous mobile robots, mixed reality devices, IoT management platforms, and a manufacturing technology demonstration area with real industrial equipment.

For interoperability testing specifically, the lab supports exactly the kind of multi-device, multi-protocol, multi-network validation that enterprise manufacturing buyers require as evidence before procurement. Startups can confirm protocol compliance and seamless integration by testing their sensors or applications against multiple IoT devices, robots, cobots, control systems, network access methods, and software platforms simultaneously, in the same environment.

CENGN’s team provides end-to-end advisory support across test planning, equipment and operations setup, execution and analytics, and custom testing configurations for complex network and automation requirements. That means startups are not navigating the testing process alone. They have access to CENGN, CNIMI, and Ericsson’s combined expertise at every stage.

If you’re building an industrial IoT solution and interoperability is the problem standing between your product and your next enterprise deal, the CENGN Advanced Manufacturing Living Lab is where you validate it.

About the Author

Being the Content Marketing Lead at CENGN, Richard researches and shares information on emerging technologies such as 5G, IoT, and Artificial Intelligence.

Through his experience in writing and support for technological growth, he’s always interested in sharing how new technologies are shaping the lives of fellow Canadians.

More by Richard Galazzo