In the era of the Internet of Everything, the integration of digital technology into manufacturing has become an unstoppable trend. While this transformation is gradual, the accelerating pace is already placing significant pressure on companies. HP Enterprise recently conducted a comprehensive survey at the Industry of Things World Conference, aiming to assess the progress of industrial IoT initiatives over the past 12 months. The findings revealed that only 53% of respondents felt their projects met or exceeded expectations, while 47% reported unmet goals.
It's important to note that entering the industrial IoT space isn't just about purchasing technology; it requires a full architectural framework that enables seamless connectivity across all organizational nodes. This involves establishing common standards and creating a convergence between IT and OT systems.
One of the most pressing challenges today is the lack of understanding in device connectivity. Even after connecting devices to the network, new issues often arise. For example, managing these devices becomes difficult without user-friendly tools. Data translation between systems is also problematic, as there’s often no straightforward way to convert data from one format to another. This creates a gap between PLCs and ERP systems, which is just one of many hurdles during digital transformation.
This article outlines five key challenges in device connectivity and proposes two practical solutions. First, device networking is a complex task. Many manufacturers initially overlook these challenges, but once they take action, they quickly realize how difficult it is to connect diverse devices—ranging from legacy systems to modern platforms. This complexity often leads to delays and project setbacks.
Second, workshop complexity is increasing. As technology evolves, so does the diversity of equipment and software used in factories. This mix of brands, protocols, and data formats makes integration more challenging. Companies must embrace this complexity by incorporating various components and terminals into their IoT solutions.
Third, system delays are becoming more prominent. While open platform communication (OPC) is commonly used, it introduces delays due to its polling mechanism. Data must pass through multiple layers, leading to inefficiencies and slower performance.
Fourth, data accuracy remains a concern. OPC lacks built-in mechanisms for ensuring data integrity, requiring additional work to validate results. For instance, a pharmaceutical company had to develop custom code to match data packets accurately.
Fifth, communication between traditional and modern equipment is still a challenge. Although MQTT is gaining popularity, many legacy devices don’t support it, making full integration difficult. Companies often have to rely on limited MQTT-enabled devices, leading to further complications.
Despite these challenges, companies are actively seeking solutions. One approach is to use data-centric IIoT software that maps devices directly to applications, eliminating the need for custom coding. This ensures a centralized data pipeline, giving organizations greater control over their data flow.
Another solution is to leverage native drivers rather than relying solely on APIs, OPC, or MQTT. A robust IIoT platform should include a wide range of native drivers, reducing the need for manual coding and speeding up implementation. This allows companies to integrate their devices quickly and efficiently, without lengthy development cycles.
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