Artificial intelligence in the industrial enterprise

Analytics can deliver insight as to how things are going, but artificial intelligence (AI) doesn't become a thing until you start using machine learning and semantics for insight.

By Kevin Parker March 10, 2018

Automation can improve a process. Productivity can gain from examination of workflows and leading indicators. And analytics deliver insight as to how things are going. But it isn’t till you step over into the cognitive, with things like machine learning and semantics, that the realm of artificial intelligence (AI) is entered.

For the Industrial Internet of Things (IIoT), predictive maintenance of machinery and equipment is the first application demonstrating wide commercial acceptance. "This can be done with classic regression and predictive analytics. With artificial intelligence, however, you go beyond the structured deterministic to the fuzzier stochastic," said Jeff Kavanaugh, vice president, senior partner, Infosys. "With machine learning based on input such as audio signatures, the computer learns as a human would, by first paying attention to how a machine sounds when it’s healthy and then understanding anomalies."

Infosys recently conducted a global survey on the adoption of intelligent automation. The survey’s central point, that artificial intelligence technology is going mainstream, is a good one. A certain amount of skepticism is warranted, however, as to the specific figures.

Sample set asymmetry

A question often asked is whether companies have the data needed to enable machine learning, and whether the data is in a form suitable for such use. "People have more data than they think, but less than they hope," said Kavanaugh. "While there are a lot of data stores that don’t lend themselves to machine learning, there are instances where great amounts of data simply aren’t needed. At other times, companies can build on the power of accumulated data. Industrial manufacturers do have deep troves of simple data which can be converted to use cases, where they can go deep."

Asked to compare the potential impact of today’s emerging technologies with those of the 1980s, when PLCs, DCSs, SCADA, CAD, and ERP were all introduced, Kavanaugh said, "The introduction of new technologies of the 1980s brought significant change, but it was basically the automation of rows and columns, applied to the plant floor and out in the field. Today, incorporating experience, a multi-attribute perspective of what actually happens, is a bigger part. We’re talking about things that are inherently cognitive, in other words fuzzy. While the earlier transformation was from full analog to computerized operations, the current one is more pervasive, more connected, more intelligent—and ultimately—more profound."

AI as enterprising

Many readers of CFE Media engineering titles are looking for AI on plant floors. As a feature article in this issue by veteran technology journalist Sidney Hill Jr. suggests, it’s in the enterprise as well. In fact, with control at the edge, ERP becomes the potential aggregation point for all data, bypassing traditional automation control systems. The impact could be profound.

For example, as an in-memory database ERP system, SAP’s HANA was ahead of its time. Its latest advance is the introduction of a geographic information systems (GIS) capability, but not just as an application feature. Integration extends the capabilities into a standalone product. One database runs the business applications and the GIS. In a case example, one company already combines transactional data from SAP ERP central component with geospatial data and other data taken from turbines.