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Cat is working closely with owners to ensure that they will provide telematic data that can help the company develop prognostic programs.

IoT connections transform manufacturing, vehicle validation

Commercial vehicles are at the forefront of the Internet of Things (IoT) movement, leveraging the connectivity that many have deployed for years. Equipment manufacturers are using IoT connections to improve their manufacturing operations, upgrade diagnostics to prognostics and improve maintenance operations.

Fleet managers have been benefiting from telematic links for years, monitoring trucks and off-highway vehicles to improve efficiency. Before long, most new equipment buyers will be able to benefit from connectivity.

“We’ve had IoT connections for more than three years,” said Nahel Gandhi, Global IT Director IoT at CNH Industrial. “Now we’re making a big move so everything coming out from CNH is equipped with telematics from 2020 on.”

This transition will help owners and operators, but it will provide sizable benefits to the equipment suppliers who build in connectivity, giving them copious amounts of information about vehicles’ activities in the field. Companies at the recent Internet of Manufacturing Business Conference detailed ways they’re ramping up their ability to collect and understand large volumes of data so they can improve operations.

“The transition from collecting data to turning that data into insights that transform the company take a lot of work,” said Gyasi Dapaa, Data Science Director at Navistar. “Big data is like economic yeast, without it businesses can’t rise.”

IoT’s advantages aren’t limited to connected vehicles. Internally, manufacturing equipment is rapidly being connected in what’s often called the Industrial Internet of Things (IIoT). Suppliers are using these connections to make major strides in efficiency, learning from plants around the globe. Collecting operating information brings major advances.

“We’re doing a lot in shop floor operations improvement,” said Alexander Nazarov, Engineering Functional Excellence Manager at Cummins Inc. “IoT contributes to eliminating down time. It improves equipment effectiveness and minimizes equipment failures. We’re also getting non-conformance report analytics that let us predict future non-conformance.”

Global connectivity can also help companies compete for talent, specifically the skilled operators who are in high demand at highly automated facilities. IoT lets companies get the most from these knowledge workers, regardless of where they’re located.

“We have to leverage the knowledge of our experts; we need to address the shortage of people,” said Terri Lewis, Digital & Technology Manager, Energy & Transportation, at Caterpillar Inc. “One way is to let experts live where they want to live and let them control operations remotely.”

Data collection spurs prognostics

Presenters at the conference highlighted many advances related to data gathered from vehicles. Some OEMs have hit critical mass, selling enough vehicles with telematics to collect useful data.

“More than 500,000 Cat products are connected,” Lewis said. “We can take the information we get from them and do interesting things to predict when vehicles may need maintenance.”

This move to proactive diagnostics, or prognostics, lets suppliers forewarn owners before breakdowns occur. When companies have real data from many vehicles, they can determine the root causes of errors and understand when failures are likely to occur.

“Telematics helps us in the early detection of issues,” Dapaa said. “When you find a problem, you can comb through the values and see if that problem occurred before. You can create a proactive repair model so you can see when a product may break down. With Big Data you can anticipate a breakdown and schedule maintenance before a problem occurs.”

OEMs can also use this data to improve their maintenance capabilities. By tracking where vehicles are being used, they can determine where repair depots should be located.

“IoT lets us save on costs,” Gandhi said. “We can see how trucks are moving around Europe. Seeing where they travel gives us information so we can build customer service stations where the vehicles travel. That reduces repair time and cost.”

Information sharing and open standards

While OEMs are beginning to mine insight from vehicle data, conference speakers agreed that they must work closely with vehicle owners who provide this information. The data belongs to the owners, so suppliers need to convince them that it’s valuable to share information. That brings ethics into the equation.

“Connected data principles are important,” Lewis said. “We need to create trust. If we can’t get customers to trust what we do with the data, they will not agree to provide information. The customers own the data.”

When vehicles become nodes in the IoT, standardization becomes more important. It’s not practical for each OEM to come up with architectures and strategies for factors that can be built on industry-proven standards. This approach also ensures that vehicles are more compatible with other equipment on the Internet.

“Open standards are the basis of interoperability,” Lewis said. “We’ll pick a standard and go with it; some studies say 30-60% of your spending is wasted if you don’t set data standards.”

She cited the MIMOSA trade association’s efforts to develop and encourage the adoption of open information standards for operations and maintenance in manufacturing, fleet, and facility environments.

Once telematic links let OEMs see parameters like fuel consumption per hours, engine hours and miles per gallon, they can use this data in many ways. Data can be fed back to engineers for use in future designs, as well as to those who set product requirements and ensure that they’re met.

“Having this data transforms the way we validate our products,” Dapaa said. “We don’t want to validate trucks to the average user, we want to validate them for the guy who beats down the truck. Additionally, once we have that data, it’s good for sales and other organizations. We’re building models that determine customers’ willingness to buy products, and we’re also building related models for setting product costs.”

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