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.

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 integrates into processes and workflows. This allows organizations to achieve the full benefit of several use cases, including asset optimization.
  5. 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.

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Article Credit: Network World

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