The idea of the Internet of Things (IoT) fostering a connected manufacturing ecosystem has gone way beyond the concept stage, delivering robust solutions that improve productivity and process efficiencies. However, the transition from strategy to implementation has many practical challenges that you must consider right at step one. Else these IoT implementation hurdles will escalate into major roadblocks that will derail your entire IoT strategy.
Let’s now look at three of the most critical challenges you will face while implementing IoT applications in the process and discrete manufacturing industries.
Data Security Risks
Security has always been a point of concern with IoT implementation owing to the exponential data transactions within every manufacturing ecosystem. From remote asset monitoring to predictive maintenance, every aspect of IoT implementation has to consider security risks. Data transactions are easy targets for hackers who approach the insecure nodes and end points of the network to access sensitive information. This may, in turn, lead to a major security breaches and ransomware attacks, exposing confidential data such as intellectual property–related information, production floor data, or critical corporate intelligence.
With a smart and scalable approach, this risk can be handled effectively. Multi-layer protection, continuous monitoring, strict permission-based access, and encryption are the best solutions that overcome this threat. IIoT gateway frameworks that support DCS and industrial SCADA in connecting to the cloud within a secure and trusted manufacturing environment through strict industry-specific protocol are also ideal options that block this hurdle.
Making the Right Choice
One could easily get carried away with the innumerable IoT technologies available, leading to a wrong selection. Always ensure that you first understand your requirement clearly, the problem you are looking to solve, the nodes you need to connect, and the data you need to capture. It could be the need for real-time monitoring of critical performance parameters of your enterprise assets or remote tracking of every stage of the production process. If your implementation rushes ahead of this basic understanding, then you run the risk of a disastrous implementation. Always look for a long-term, scalable solution that takes an iterative approach so you can easily update its functionality.
The current IoT ecosystem is mostly centralized, a possible drawback in the future, risking huge investments for updates. But centralization is essential for overcoming the extensive variance in the types of devices and data requirement. In addition, complicated network architectures managing extensive data transactions across the cloud exponentially slows down the entire network. The explosive network of communicating devices, standards, and protocols complicate the entire process from data capture to analytics. In addition, most manufacturing ecosystems are an intimate mesh of legacy and futuristic assets. This makes applying a standardized implementation approach quite a challenge.
Hence, a critical balance between centralization and decentralization is a viable option worth considering. Improving the edge and IIoT platform capabilities to manage complex analytics and overcome interoperability challenges will reduce bandwidth load. Other solutions include
- choosing the best-fit middleware,
- constructing mesh networks that exclude a single point of junction or fault
- devices that enable direct data transfer with authentication without involving a third person or broker, and
- peer-to-peer communication.
Real-time, simplified, well-connected, and intelligent functioning are the key terms that determine a smooth IoT implementation. Effective data capture, smart data filtering, analytics, and insights derivation are certainly the backbone of such an implementation.
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