Tim Sowell, Schneider Electric (Wonderware) vp and fellow, has been writing a weekly blog that I report on for a while now. His Operations Management Systems Evolution blog is always thoughtful and informative.
Recently, I have discovered another Schneider Electric blog, this one by someone whom I do not know (I think)–Gregory Conary.
Each take a look at the Industrial Internet of Things in these posts.
Conary’s recent post discussed the “business opportunities we are seeing emerge from this megatrend.”
He cites information compiled by LNS Research, in its eBook Smart Connected Operations: Capturing the Business Value of the Industrial IoT. 47 per cent of respondents to its Manufacturing Operations Management (MOM) online survey indicated that they did not expect to invest in IoT technologies in the “foreseeable future”. A further 19 per cent indicated that they did not expect to invest in IoT technologies in the next 12 months.
Conary states, “Frankly I’m not surprised. IIoT seems to bring with it the hype of something that will take a long time to adopt. In some cases I think this can be true. And while we are unclear on what time frame is meant by the term ‘foreseeable future’ referenced above, I believe there are business opportunities that can be capitalized on now and in the medium term. IIoT is more prevalent than we imagine. There are examples and business practices that we often don’t even recognize as being enabled by IIoT – things like increasing industrial performance and augmenting operators are two of the opportunities which can make a difference to your business now.”
Increased industrial performance
“Using data to improve industrial performance by connecting things to each other – this is happening now. How is it happening? Through wireless technologies, low cost sensors and using advanced analytics. In practice, this is a decision support system for complex manufacturing operations.”
I agree with Conary. We’ve had the foundation and platform for the Industrial Internet of Things for a long time. It just continues becoming more robust. As better data analytics algorithms are developed and better ways to communicate and display information are devised, then usefulness to manufacturing operators, maintenance technicians, engineers, and managers will increase dramatically.
Tim Sowell riffed off an article in Wired. “As the Internet of Things (IoT) continues its run as one of the most popular technology buzzwords of the year, the discussion has turned from what it is, to how to drive value from it, to the tactical: how to make it work.
We need to improve the speed and accuracy of big data analysis in order for IoT to live up to its promise. If we don’t, the consequences could be disastrous and could range from the annoying – like home appliances that don’t work together as advertised – to the life-threatening – pacemakers malfunctioning or hundred car pileups.”
Sowell adds this analysis, “This follows on from my discussion 2 weeks ago around the need to avoid just gathering data, vs gaining the proportional amount of knowledge and wisdom, which brings in a term you hear a lot ‘machine learning’.”
From Wired, “The realization of IoT depends on being able to gain the insights hidden in the vast and growing seas of data available. Since current approaches don’t scale to IoT volumes, the future realization of IoT’s promise is dependent on machine learning to find the patterns, correlations and anomalies that have the potential of enabling improvements in almost every facet of our daily lives.”
Sowell concludes, “In the industrial world this more applicable than nearly all industries, and in many cases we are already applying “machine levels” at different levels. A key part in the shift from ‘Information’ to ‘knowledge’ is having the tools to drill into historians based on events and to discover learnings and patterns. Once validated and discovered these are turned into ‘self-monitoring’ conditions to understand the current state of the device, and predict / recognize conditions well before they happen. Providing the ‘insight’ to make awareness and decisions where the machines/ devices are telling you where the opportunities are. But a key part of machine learning is that this knowledge in not a once off step, it is a continuous evolution leveraging the gathering history data and developing increased amounts of knowledge.”
Both Conary and Sowell point directly to the new reality and to new challenges. We can now gather much more data than we can make sense of. As soon as we have those tools, we will provide better tools to operations and maintenance to improve plant performance.