Smart Operations Through Predictive Asset Analytics Enabled By The IIOT

Industries thriving on asset performance always strive for optimization of resources. While KPIs change rapidly for modern enterprises, asset longevity, limited downtime, and minimized risks are the main parameters for enhancing operational efficiency.

With transformation on the anvil, disruptive technologies like big data, advanced analytics, and cloud computing and their convergence has completely broken the traditional silos.

As a matter of fact, we are looking beyond the horizon and exploring advanced technologies like IIoT for creating smarter connected operations.

How Analytics Can Enhance Operational Technology?

A new class of applications relies on the effectiveness of Big Data and predictive asset analytics. Analytics is the most pervasive method for exploring hidden values in the data. A study conducted by ARC Advisory Group revealed that only 18% assets face a failure due to age or usage. Hence, an intensive research is required to identify the causes of failure in the remaining 82% assets.

Predictive analytics of the assets helps in extracting positive outcomes by increasing focus on these unidentifiable issues. It uses historical data for comparison with real-time operational data for detecting the changes in equipment performances. It reflects the shift of paradigm from ‘reactive to predictive’ maintenance in the industries.

Role Of IIoT In Enhancing Operational Efficiency

In the simplest terms, IIoT or Industrial Internet of Things is the connectivity of physical assets with web-enabled applications and services. This advanced internet architecture is built on existing and new technologies much of which are part of the legacy automation technology. It encompasses proprietary protocols of communication along with new challenges and use cases for IIoT. When IIoT is combined with predictive asset analytics, it helps in reducing the ad hoc downtimes.

This term has gained traction across the industries mainly due to the integration of technologies like machine-to-machine communications, cloud computing, big data analytics, machine learning, and virtualization. The best part is that IoT can become functional for industrial applications even without any substantial technological advancement.

Adoption Of IIoT With Predictive Analytics

The growing influence of cloud computing has largely resolved the issues of expensive resources management in IT infrastructures. It reduces project risks and costs. The costs are elevated only when the business moves up the ladder of success. Here, IIoT brings invasive access to the operational database. Analytics of this data helps in improving the applications in an industry.

The adoption of IIoT across the industry verticals is the testimony of its effectiveness. More than 40% organizations are looking to leverage this technology for business improvement.

For example, Duke Energy, a leading electric power holding company in the USA adopted lean digital approach powered by IIoT and predictive analytics. The system enabled the company to use information for predictive analytics and reduce equipment failures.

Impact of IIoT On Assets And Operations

The adoption of IoT has been severely crippled by the lack of understanding in the industries. Many enterprises have still not gauged the effectiveness of this system in increasing the visibility and control over their machinery and equipment. Adoption of IIoT facilitates the integration of an advanced framework that categorizes technological capabilities for developing smart connected devices and connected operations. The leading steel manufacturer based in South Korea, POSCO applied analytics and IIoT in its manufacturing units and realized 45.5% reduction in impromptu corrective maintenance costs.

These systems rely on features like seamless connectivity, cloud capabilities, application development, and Big Data analytics. They can bring about a positive change in traditional architecture of industries by converging it on IIoT-enabled platforms. Also, the next-gen solutions created by these technologies ensure mashup applications and shop floor to top floor integration. It reduces the limitations of legacy systems and allows a steady flow of data.

Conclusion

As a result, IIoT-enabled predictive asset analytics can use manufacturing data effectively by creating a proactive collaboration between OT and IT. It also helps in improving the standards of manufacturing technologies.

The smart connected assets are enabled by converged sensors, controls, and instrumentation. At the same time, smart connected operations create an autonomous production system for improved overall performances.

It can be safely added that IT and OT have always been there. But, their implementation and convergence are changing at a faster pace now. This has been necessitated by the IIoT-enabled smart connected assets and connected operations.

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