Industry 4.0 refers to the revolution and disruption that the industrial sector is experiencing because of the data explosion and automation that is resulting from the cutting-edge technologies. With the advancement of artificial intelligence and IoT, industry 4.0 is going to create an entirely new realm of opportunities for revolutionizing the manufacturing process to improve throughput and operational efficiency.
The industrial IoT aims to encompass every aspect of resource utilization and cost reduction with technology and minimum human intervention. However, the complexities involved in IIoT implementation include challenges with maintaining quality and security at various stages of production.
However, not all industrial organizations can prepare themselves to implement IIoT because of various factors. As per a survey, it was revealed that more than 50% of industrial companies lack the skills and technology required to utilize the massive amount of industrial data generated across various facilities.
Here are few commonly encountered IIoT challenges currently faced by industrial organizations.
IT and OT convergence: There is still a huge gap between modern cutting-edge technology and operational technology used for the legacy manufacturing processes. Information technology has been on rapid growth since the last century, whereas the growth in operational technology has been relatively slow. For any IIoT implementation, interoperability between IT and OT is a huge differentiator.
Security: Most industrial organizations generate voluminous data which is a vital factor for setting IIoT processes. However, it is paramount to ensure an effective security infrastructure to prevent the data and other IT assets from threats of cyber-attacks such as device hijacking, Distributed-denial-of-service attacks, and permanently decommission machinery attacks.
Connectivity: Uninterrupted and seamless connectivity is a must-have for any company aspiring to adopt IIoT. Ensuring 100% connectivity even during maintenance activities and provision for data storage and retrieval in case of unplanned outages is vital for the success of IIoT implementations.
Data Storage: Today, most organizations emphasis on a data-driven culture where data can drive decision making. To achieve that, millions of data points need to be collected and saved for future reference. This can be achieved through cloud infrastructures as well as edge technologies. It is necessary to have a robust data storage before attempting IIoT.
Data Analytics: The ROI of any IIoT solution is derived from the accuracy of actionable insights and automated actions that form the outcome of the solution. This is possible only when the implemented IIoT platform is capable of handling large-scale data. In IIoT implementations, the data analytics solution must perform data processing, data cleaning as well as representation. This creates a suitable framework for a real-time predictive analytics solution to function effectively.
For an IIoT implementation to generate value for customers, it is extremely crucial to understand the key performance indicators to have a holistic understanding of customer satisfaction as well as productivity. Effective adaptation of IIoT will result in more innovations and higher value propositions for industrial organizations in the future.