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Integrated Predictive Analytics:

Enabling the Transition to Proactive Maintenance and New Business Models

As we emerge from the pandemic, companies are realizing many of the lessons learned should become standard operating procedures going forward. Nowhere is that more apparent than with servicing and managing equipment and products in the field. Fortunately, technology that allows organizations to remotely monitor the performance of their equipment in the field in real-time can play a significant role. The benefits of having that information can be complemented by using predictive analytics on that data to spot problems in the making. That allows corrective actions and proactive maintenance to be taken, eliminating downtime or the need for an unscheduled site visit.

There is no doubt the pandemic hammered home the need for product manufacturers and companies that service products in the field to revamp normal operating procedures. Travel restrictions prohibited sending people far and wide to assess and fix problems. Even if people were local and could theoretically visit a facility, the local government or the company itself might have disallowed anyone from entering a facility

Technology elements for transformation

Such obstacles during the lockdown highlighted the need to have real-time insights into how products are operating in the field. The way to do that is to make use of several technologies together.

What's needed first is embedded sensors in the products to monitor and measure current conditions and key performance indicators (KPIs). Parameters measured might include operating speed (e.g., of a motor), temperature, or pressure. It could also include things like the status level and consumption of consumables such as ink in a printer, oil in an engine, the film in an imaging system, and more. Fortunately, the wide-scale embracement of smart sensors and the Internet of Things (IoT) provides the necessary technical underpinnings for such data access.

In the past, such data was only available on-site and only accessible in industrial control systems (ICS) such as supervisory control and data acquisition (SCADA) systems or distributed control systems (DCS). Typically, the sensors were hard-wired into such systems over a private on-premises network.

The growing use of existing wireless services such as Wi-Fi and 3/4G cellular and emerging wireless connectivity services (e.g., Wi-Fi 6 and 5G) can make that data more widely available. Such connectivity can allow other systems, besides ICS systems, to access the data. And the wireless connectivity can be used to allow remote access to the data as well. So now, an organization that supplies the equipment or manages it can centrally monitor the state of the devices in the field. Remote access to such data can be used tactically. For example, many organizations have used such capabilities to move from reactive maintenance (fix it when it breaks) to preventive maintenance.

The final piece in the technology trifecta is analytics. The IoT and sensor data that is made available remotely via wireless connectivity can then be analyzed to look for trends.

Reducing downtime, optimizing performance

Using sensors and connectivity lets product manufacturers and the companies that service products in the field transition from calendar-based maintenance to condition-based monitoring and maintenance. A company might use historical data to find that vendor A’s part typically lasts twice as long as the manufacturer’s stated mean time to failure in the company’s facility. So, rather than replace it on the manufacturer’s schedule, they could confidently get extra use out of the piece. That spreads parts replacement cost out over longer times, reducing overall annual spending for spare and replacement parts.