Two predominant aspects of Industry 4.0 are connectivity and analytics. In the digital transformation of industry, connectivity is commonly viewed as the means to an analytical end that better informs the way industrial business is conducted.
While most everyone is generally clear on what analytic technologies for industry are designed to do (e.g. transform the data generated by our factories’ systems and devices for direct application to improve operations and decision making) that’s where the clarity ends. Beyond this point, the definition of “analytics” can vary widely—especially from technology supplier to technology supplier.
Michael Risse of Seeq, a company that provides industrial analytics software, says the term “analytics” can mean anything from data visualization, machine learning, and business intelligence to dashboards and key performance indicators. No matter how analytics is defined, Risse argues, the overriding issue is the pressure to gain insight from data.
Against this backdrop, Risse says spreadsheets—industry’s long-preferred tool for analytics—are not up to the task of performing advanced analytics on the ever-larger datasets being created by Industrial Internet of Things and Industry 4.0 projects. Insights that take too long to discover, as tends to be the case with spreadsheets, languish because they cannot easily be published and shared with others. Though they’ve been the backbone of the past 30 years of analytics efforts in manufacturing, spreadsheets will simply not suffice for the next 30 years. There is too much data, too few engineering professionals, and too many demands for insight from improvements in analytics for spreadsheets to be the primary solution.
From this viewpoint, Risse sees three trends industrial companies should be aware of as they look to move beyond spreadsheets as their primary source of data analyses.
The first of these trends is the recognition of employee empowerment through self-service analytics. Risse says the reason spreadsheets have enjoyed their run of success as the primary tool for analytics is that they are accessible to the employees who know the questions to ask. So, if you lack plant-floor expertise, you likely don’t know what questions to ask.
“Engineers are the most important group of analytics users,” says Risse. “They have the required experience, expertise, and history with the plant and processes. Self-service analytics let engineers work at an application level with productivity, empowerment, interaction, and ease-of-use benefits. In the future, however, the universe of analytics users will expand beyond engineers to operators, executives, and accountants—all of whom will also benefit.
The second trend Risse notes is the emergence of advanced analytics. “This new class of analytics speaks to the inclusion of cognitive computing technologies into the visualization and calculation offerings that have been used for years to accelerate insights for end users,” he says. “The introduction of machine learning and other analytic techniques accelerate an engineer's efforts when seeking correlations, clustering, or any other needle-in-the- haystack analysis of process data. With these features built on multi-dimensional models and enabled by assembling data from different sources, engineers gain an order-of-magnitude improvement in analytic capabilities, akin to moving from pen and paper to the spreadsheet 30 years ago.”
The movement of analytics to the cloud is the third trend Risse highlights. “Analytics workloads are particularly suited for this migration, because most use cases require the scalability, agility, time to market, and reduced costs provided by the cloud,” he says.
Though this trend of moving analytics to the cloud is still in its infancy, Risse notes that some industries are ahead of the curve in this respect. He points out that Microsoft, Amazon, and Google have specifically focused on the oil and gas sector as a starting point for their efforts in targeting industrial use of the cloud for analytics purposes.
To provide some real-world context to support his view of these developing trends, Risse offered an example of a chemical company that chose a browser-based advanced analytics application running in the cloud to connect to its on-premise data via a secure HTTPS connection and a remote connection agent. “The solution was deployed and accessible in a matter of hours, and the data stayed where it was, enabling insight in days rather than months,” he says. “Another option is to make the cloud the destination for datasets collected from remote or IIoT end points. This is a more natural and easier option than trying to reroute data from carriers and wireless systems back into IT systems and then to the cloud. In this case, end users can then access the data by either running analytics on the cloud or by running the analytics solution on premise with a remote connection to the cloud-based data.”
Another option for industrial companies is to use cloud-based analytics software to access multiple sites. This kind of application facilitates cross-plant comparisons for yield and quality analyses. It also can be used as a simple remote connection for occasional queries and comparisons may suffice, depending on the frequency and requirements of the end user, Risse adds.
In any of these scenarios, Risse points out that the monitoring data may be complemented or contextualized by connecting the analytics solutions to other data sources-historians or manufacturing execution systems to get a complete view of all data.