“In our case, we have information, but we’re not using it to such an extent that it’s preventive,” agreed Erik Fyrwald, CEO of Univar. “It allows us to look back historically and say, ‘Oh, now we know why that happened,’ but we’re not at the point where we can make sure something won’t happen again.”
Even companies like Federal Express, which is viewed as a forerunner in real-time data collection and analytics, are struggling to take IoT’s capabilities to the next level. The company has made great strides in forecasting accuracy since equipping drivers with handheld devices and compiling information on efficiency and customer dynamics in real-time.
“Now we’re trying to link all of that together to be more predictive in nature,” Cary Pappas of FedEx Tech-Connect told participants. “The question is, how do you take forecasting and make it a more exact science by overlapping better information, and how do you then boil that down to the things that are most crucial to your business? In other words, how do you simplify that [data] so it’s usable to the people who run our operations? We’ve got a ways to go before we get to where we want to be.”
While the sheer amount of data being generated and the possibilities that data represents can be daunting, the process can be broken down into more manageable steps, pointed out Sujeet Chand of Rockwell. “You generate a lot of data in IoT,” he acknowledged, noting that crunching the data should begin before it ever reaches the Cloud. “It needs to be processed in layers. For example, data generated by a jet engine needs to be processed locally on the jet engine first and converted to what we call information, because any information you generate directly on the jet engine while the plane is flying can be acted on instantly. The moment you move the data further away from the jet engine, the loop-closure time increases.”
After information, the next step is knowledge, which requires storing information and analyzing it using simulations or other analytical processes, explained Chand. “An example would be predictive diagnostics or predicting failure,” he noted. “Once you have the knowledge, the next goal is wisdom, which comes from analyzing multiple jet engines and then figuring out how you redesign certain components to optimize performance or pre-empt a type of failure that you see across hundreds of engines. It has to be thought of in those layers or you get overwhelmed.”
That thinking resonated with Jeff Silver, CEO of the logistics transportation company Coyote Logistics.
Five years ago, he noted, sensors on refrigerated trucks could tell you after the fact the temperature of beverages during transit. Today, the technology has advanced to the point where data is available in real-time—during transit. “When a load of orange juice is about to get ruined, you know about it immediately and have a fighting chance,” he says. “Over time, we’ll take it to the next level and be able to predict when those units will fail before they actually do.”
Ultimately, that’s one of the many roads companies that embrace IoT will travel, improving efficiency by monitoring and tracking the health of your assets. “IoT is really not as much about the technology—although advances in technology are what enables it—but about taking a step back and understanding how you want to change your business,” says Edson. “It’s a business process evolution of what you’re already doing and a way to move your business forward in the digital age.”
FOR HEAVY EQUIPMENT MANUFACTURER M.G. Bryan, operating oil and gas fracturing vehicles, each of which typically costs more than $1 million, in remote, extreme environments (think no cell phone reception) has long been a challenge. The vehicles require a good deal of TLC, including oil filter replacements every 200 to 400 hours and complete engine rebuilds after 7,000 hours of service, sometimes sooner. Mismanaged maintenance can be costly—downtime on a vehicle has a price tag of between $3,000 and $7,000 per day, not including lost product revenues.
Leveraging the power of the IoT to develop a scalable solution for remote asset management of its fracturing vehicles came as a real boon for the company, which can now access and analyze in-field service information. Using mobile technology, the cloud-based system gathers control-system data to produce reports and dashboards on the condition of an individual vehicle’s drive train and fracking performance, as well as process performance and maintenance trends related to entire fleets.
Data is broken down into incredibly small packets so that information can be sent even in areas with poor cellular coverage. Or—in cases where a connection can’t be found—stored in a gateway
and sent once a connection is regained.
In addition to improving up-time and productivity for M.G. Bryan customers, the system has the potential to collect data that will demonstrate the vehicles’ competitive performance to customers.