When it comes to IoT data, one thing is certain: Real-time analytics in manufacturing and industrial operations continues to accelerate as companies get better results by including sensor-based/IoT device data in their decision-making process — and there is no slowing down in sight. Insights gleaned from IoT data are changing business models by automating processes, improving efficiency and driving revenue, and no other sector has been impacted by the IoT transformation more than manufacturing.
IoT has been a core component of industrial transformation efforts across the globe, with Industry 4.0 and the industrial internet (with the Industrial Internet Consortium). Smart manufacturing uses IoT data to reduce downtime, increase productivity and create efficiencies in manufacturing operations. The greatest value being experienced from IoT is stemming from monitoring industrial operations, solving complex logistics and predicting the downtime of equipment. However, the challenge of data integration still remains.
Operations teams work with diverse technologies, supporting a wide range of data formats and communications protocols. It is challenging to gain a holistic real-time view of information contained in critical industrial assets and across disparate systems, often leading to decisions being made on intuition rather than data. This lack of real-time insight contributes to a reactive approach to managing operations, creating siloed data, system downtime and lack of agility in responding to changing business needs, all resulting in millions of dollars in lost revenue and lost business opportunity.
For operations professionals, plant managers and process engineers responsible for monitoring and maintaining critical industrial assets, the key to success is being able to easily combine and correlate data from disparate sources, such as industrial control systems (SCADA), sensors, applications, infrastructure and IT systems, and deliver valuable new analyses into asset health. These new insights are driven by real-time and predictive capabilities to help businesses rapidly identify and diagnose issues such as equipment failure, reduce downtime and optimize operations.
In the past, IoT meant retrofitting sensors to existing manufacturing equipment. Today, in a world where new manufacturing equipment comes with IoT sensors preinstalled, the potential for transformation is tremendous. Sensors provide voluminous information on every aspect of industrial assets, while simultaneously enabling people and programs to make adjustments to the operation of these assets for performance optimization.
As IoT continues to become more pervasive, organizations will invest in improving operations and driving predictability in equipment downtime and processes across manufacturing, mining, oil and gas, power generation, utilities and rail. The question they face next is: Are these smart manufacturers prepared to deal with IoT’s data challenges, such as privacy, storage management, hardware and effective data mining?
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