ROI linked to later stages of Industrial IoT

Understanding how businesses are progressing through Industrial IoT (IIoT) maturity.

industrial iot
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When most people think about the adoption of the IoT, they think about smart cities, autonomous vehicles, or the latest consumer tech and wearables. However, some of the most amazing technology applications are taking place within industrial verticals such as manufacturing, oil and gas (O&G), and transportation.

Unfortunately, when asked about the state of IoT adoption within these markets, we’re often left relying on basic information about connected endpoints, instead of truly understanding how businesses are progressing through IoT maturity within the industrial field. 

 [ Related: What is the IoT? How the internet of things works  ]

To help answer these questions (and get a bit more in the weeds on the topic) my company, Bsquare, recently conducted its first Annual Industrial IoT (IIoT) Maturity Study. We polled 300 respondents at companies with annual revenues in excess of $250 million. Participants were evenly divided among three industry groups (manufacturing, transportation and O&G) and titles covered a wide spectrum of senior-level personnel with operational responsibilities, most of whom had spent an average of six years in their organizations.

Before jumping into the study data, it will be helpful to quickly review what I like to call the IoT Maturity Index. I think about it in five progressive stages, each one typically building on the previous one, allowing organizations to drive maximum value as they move forward.

  1. Device connectivity: The process of collecting data from sensors and connected equipment and transmitting it to cloud databases for analysis. This step lays the foundation for the IoT solution.
  2. Data monitoring: The introduction of dashboard and visualization tools to gain awareness of equipment status. This allows organizations to do simple alerting and other basic visualization.
  3. Data analytics: Machine learning and complex analytics drive the development of device models and provide additional insight. This enables companies to make progress towards several use cases: predictive failure for increased asset uptime and elimination of false negative and positive reports, condition-based maintenance and more.
  4. Automation: Development and execution of logic rules that automate business activities and integrate
  5. into processes and workflows. This allows organizations to achieve the full benefit of several use cases, including asset optimization.
  6. Edge computing: Distribution of analytics and orchestration to the device level. On-board intelligence brings the IoT maturity model full circle, allowing industrial organizations to gain maximum ROI and business benefit from predictive failure, data-driven diagnostics, and device optimization. Further, true IoT device management becomes a reality as on-board intelligence monitors for conditions in order to identify events and then automates actions directly on the equipment for better predictive accuracy and more rapid response time.

What did we learn?

At a high level, the study confirmed that while IoT enthusiasm runs deep across the industrial verticals, few organizations are experiencing the ROI associated with the later stages of the maturity index. As a matter of fact, 86 percent of industrial organizations have adopted IoT, but fewer than half are using advanced analytics and only a quarter have taken steps to automate the application of insights. Despite this fact, the majority (84 percent) believe their solutions are very or extremely effective. And all respondents believe that the technology provides a significant or tremendous global impact on their industry.

We also learned that the majority of business managers have the same goals for their deployments. Most want to gain better visibility into and control over business-critical equipment. Many were also driven by device health-related goals, including real-time device information, better device management, and device optimization. And a small portion (less than a quarter) wanted to focus on operating cost reduction, increased production volume and better compliance.

To achieve these goals, the majority of IIoT investments are currently focused on the least mature stages, connectivity (78 percent) and data visualization (83 percent). Only half are doing advanced analytics on that data (or stage three) and only a small number (28 percent) are automating the application of insights derived from analytics (stage four).

The dramatic drop between the number of people who have achieved basic connectivity and those who are using advanced capabilities such as automation and enhanced on-board intelligence represents a significant opportunity for organizations to derive greater ROI and business value from their IIoT investments.

One of strongest takeaways from the survey was the correlation between the positive impact IIoT is having on a business and future investment. Nearly 100 percent of respondents expect to invest the same or more over the next 12 months, with nearly three-fourths projecting an increase during that time frame.  

However, the majority of companies plan to stay the course on their existing IIoT use cases over the next 12 months, which means a continued focus on data analytics and real-time monitoring. It is worth noting that there was a pronounced increase in advanced uses, such as automating simple single-step actions and increasing real-time monitoring capabilities.  

If you’d like to dive into the complete data by market, it’s available for download from my company, Bsquare. But in summary, IIoT is clearly serving industrial businesses well. Setting clear business goals prior to deployment, such as reducing operating costs, better managing devices, or increasing production, is a key driver of deployment maturity. What are you seeing regarding industrial IoT maturity? Reach out and let me know.

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