In the age of the Internet of Everything, the integration of digital technology into manufacturing has become an unstoppable trend. While this transformation is a gradual process, the accelerating pace of change is already creating significant pressure on businesses. HP Enterprise recently conducted a comprehensive study in collaboration with the Industry of Things World Conference. This survey aimed to assess how much progress companies have made in their industrial IoT initiatives over the past 12 months. The findings revealed that only 53% of respondents felt their projects met or exceeded expectations, while the remaining 47% reported that their goals had not yet been achieved.
As we know, many companies believe they can easily enter the industrial IoT space by simply purchasing the necessary technology. However, industrial IoT requires a full architecture that ensures seamless connectivity across all internal systems. This involves establishing common standards and building a new technical framework that integrates IT and OT environments effectively.
One of the most pressing challenges today is the lack of understanding around device connectivity. Even after connecting all the devices to the network, more problems tend to arise. For instance, once devices are online, there's often no user-friendly tool available for managing them. Data from one system may not be easily transferable or interpretable by another, creating a gap between data from programmable logic controllers (PLCs) and enterprise resource planning (ERP) systems. These issues are just a few of the many obstacles companies face during digital transformation.
The purpose of this article is to highlight five key challenges in device connectivity and propose two practical solutions to overcome them.
**Device Networking: A Complex Task**
When companies first start exploring industrial IoT, they often underestimate the difficulty of connecting different types of devices. Once they commit to the project, they quickly realize the complexity involved—connecting legacy equipment, modern terminals, closed-source, and open-source software. This leads to delays and complications that disrupt initial timelines.
Designing a system for a factory can feel like a nightmare, especially when integrating multiple applications. Even basic data collection tasks can cause system failures, taking weeks to resolve.
**Increasing Complexity in the Workshop**
Without a unified connection technology, achieving seamless integration becomes increasingly difficult. As workshops evolve with new technologies, the complexity within the shop floor also grows. This means a mix of various brands, support agreements, and proprietary data sets. To manage this, enterprises must accept the need for multiple mobile components and terminals in their IoT solutions. Connecting these elements is essential for realizing greater efficiency.
**System Delays Are Becoming More Prominent**
To address system complexity, many companies turn to Open Platform Communications (OPC), a standard protocol for industrial automation. OPC relies on polling to retrieve data from devices at set intervals. However, this method adds multiple steps in data transmission—from the PLC to the OPC server, then to the client, and finally to the local server or cloud. Each layer introduces additional delays, which can significantly impact performance.
**Data Accuracy Is Often Uncertain**
OPC does not guarantee data accuracy, requiring extra effort to ensure correct data matching. For example, a pharmaceutical company in Florida used OPC to poll devices, but faced challenges in verifying which data packets matched the final batch. Their engineers had to write custom code to validate data sources and receivers, adding unnecessary complexity.
**Communication Between Old and New Equipment**
Message Queuing Telemetry Transmission (MQTT) is becoming a popular choice for IoT due to its speed and efficiency. However, many legacy devices do not support MQTT, making it impractical to replace them entirely. Companies may only adopt MQTT for new sensors, leaving most of their existing equipment incompatible. This creates a need for extensive custom coding to bridge the gap between old and new systems.
Despite these challenges, companies are actively seeking solutions. Here are two effective approaches:
**Avoid Custom Code with Data-Centric IIoT Software**
Rather than relying on custom code, companies should use data-centric IIoT platforms that allow direct mapping of devices to applications. Many IIoT platforms focus on analytics, but without reliable data, their insights are meaningless. A data-centric platform connects legacy and modern devices, offering a centralized data pipeline that gives organizations full control over their data flow.
**Go Beyond APIs, OPC, and MQTT with Native Drivers**
While APIs, OPC, and MQTT are widely used, they often lack native drivers, forcing engineers to write custom code. A robust IIoT platform should provide a wide range of native drivers, allowing companies to integrate devices quickly without lengthy development cycles. This approach saves time and resources, enabling faster deployment and better scalability.
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