The Industrial Internet of Things (IIoT) is attracting more attention by manufacturers, especially as the Internet of Things (IoT) takes hold in consumer and business markets. Many manufacturers are getting excited about opportunities enabled by applying advanced analytics to information generated by plant sensors and business systems. A recent LNS Research and MESA International report, Manufacturing Metrics In An IoT World: Measuring the Progress of the Industrial Internet of Things, validates this trend.

Here are some key findings from the report and infographic:

Understanding of IoT is way up – In a 2015 report, LNS and MESA found that 44% of those surveyed did not understand IoT. In 2016, that category plummeted to 19%. Also encouraging is 50%-plus of respondents reported their companies were planning IoT initiatives in the next 12 months.

Manufacturing data is moving beyond the plant – More manufacturers are looking at predictive modeling, plant analytics, and manufacturing intelligence. This indicates that they are taking a more integrated view of manufacturing operations rather than focusing on point solutions. Also, while most manufacturing operations management (MOM) and manufacturing execution system (MES) software is still deployed on-premises, movement toward cloud-based solutions is starting to take hold.

Manufacturers still care most about financial metrics – Nearly half of respondents ranked financial metrics as their top concern. The survey also reported that key financial metrics—manufacturing cost per unit, revenue per employee, and net profit margin—are improving. What’s interesting is these gains largely come from quality and operational efficiency initiatives. The question is how much more could be achieved with big data analytics?

Adoption of analytics is still limited – Surprisingly, only 14% of survey respondents have a manufacturing analytics program. The vast majority of these companies use analytics internally for process improvements. Few manufacturers are using advanced analytics that incorporate unstructured data such as climate data and images.

Our position, which is supported in the report, is that manufacturers need to dive deeper into big data analytics to adopt capabilities such as real-time machine learning and predictive maintenance. This direction will lead to more operating efficiency—and ultimately drive greater financial improvements.

We also agree with the report’s recommendations for manufacturers ready to further explore IIoT:

  1. If you don’t understand IIoT, investigate its potential impact on your operations immediately. Form a cross-functional team and plan some IIoT trials.
  2. Explore analytics that go beyond shop floor data. Vendors like Stratus can recommend solutions that provide uninterrupted access to analytics and help you understand what private and public cloud options exist.
  3. Once you’ve taken these steps, conduct a full-blown pilot with more complex analytics involving plant and business data.

There are always challenges with any major new initiative. IIoT is no exception. With more than a decade of experience supporting business-critical industrial automation, Stratus can help you work through the challenges and achieve the metrics that matter most to your operation.