Intelligent edge technology simplifies supply chain logistics
Commercial vehicles are an essential link in the global supply chain that keeps product flowing from manufacturer to consumer. Keeping a fleet running efficiently is a massive logistical task and one that is an ideal use case for digital connectivity and intelligent data analysis.
Introducing fleet management systems that give centralized oversight into performance has resulted in a 10-15% increase in productivity, 20-25% reduction in fuel costs, and 20-30% reduction in vehicle idle time, according to industry studies by Frost and Sullivan. This explains why, despite an overall decline in the commercial vehicle market during the pandemic, connected truck telemetric services is a growth sector predicted to reach $17.1 billion by 2025.
“This isn’t technology for technology’s sake, these connected trucks are coming onto the marketplace because it can provide tremendous value to the business,” stated Michael Ger (pictured), managing director of manufacturing and automotive at enterprise data cloud company Cloudera Inc.
Ger spoke with Dave Vellante, host of theCUBE, SiliconANGLE Media’s livestreaming studio, during the Cloudera “Transform Innovative Ideas Into Data-Driven Insights” event. In the first of two interviews, Ger and Vellante discussed the importance of connectivity to commercial fleet operations and how Cloudera is bringing together an ecosystem of partners to make the future of trucking both interconnected and intelligent. (* Disclosure below.)
Connectivity comes before intelligence
The first step toward creating an interconnected and intelligent fleet of commercial trucks is to install software that can collect data and transmit it back to a central platform for analysis. This identifies issues such as inefficient scheduling, circuitous routes, and excessive driver downtime. Streamlining operations based on this information results in the immediate savings found in the Frost and Sullivan research.
“That monitoring piece is really, really important,” emphasized Ger, who cautions companies not to head straight into intelligent analytics without getting the basics in place first. “Let’s not minimize the value of good old-fashioned monitoring to give you that kind of visibility first, then moving to smarter use cases as you go forward.”
Truck telemetrics solutions have come a long way from bumper stickers asking, “How am I driving?” Now, the earlier hardwired, extremely customized systems are giving way to flexible and intelligent systems provide real-time analysis at the edge.
“We see that the vehicle itself is getting smarter,” Ger said. “Now you’ve got incredible compute both at the edge in the vehicle and on the cloud.”
From “dumb” gateways that pushed data up and down and provided a security layer, the newer generation of in-vehicle technology incorporates what are known as service-orientated gateways that provide intelligent analysis on the edge, as well as connecting up to the cloud via 5G. This technological optimization fuels a machine-learning lifecycle with real-time data collection, analysis and action, and is where the Cloudera Data Platform comes into play.
“This end-to-end ability to ingest data, store it, put a query lay over it, create machine learning models, and then run those machine learning models in real-time, that’s what we do as a business,” Ger stated.
At the end of the day, Cloudera’s goal is to provide an end-to-end data management platform, enabling customers to achieve higher efficiency through connected analytics and machine learning.
“Our job as a software maker is to make that easier and connect those dots so customers don’t have to do it all on their own,” Ger said.
Navistar success provides a compelling use case
The benefit of layering intelligent analytics through machine learning models and onto connected vehicle monitoring is shown by vehicle manufacturer — and early adopter of connected truck analytics — Navistar International Corp. The company’s Intelligent Fleet Care connected vehicle solutions package comes standard on all of the on-highway commercial trucks it manufactures, and Cloudera provides the technology that enables Navistar to integrate data from multiple vendor products and provide a single-source overview of vehicle and driver performance. Intelligent analysis of incoming data can predict problems such as potential equipment failure.
“They were centralizing data from their trucks’ telematics service providers … and what they started to do was to build out machine learning models aimed at predictive maintenance,” Ger said. “So rather than waiting for a truck to break and then fixing it, they would predict when that truck needs service, condition-based monitoring, and service it before it broke down.”
This brought the maintenance costs of Navistar vehicles down significantly, saving owners an average of three cents per mile over the industry-standard expense of 15 cents per mile, according to Ger.
Other truck manufacturers are now working with Cloudera to build similar intelligent monitoring systems into their vehicles, and the company provides more detail and other use cases on its website.
Ecosystem collaboration is essential
Recognizing that there was an established group of vendors already successfully competing in the connected vehicle space, Cloudera started the Fusion Project to provide compatibility between the various products used in a connected vehicle.
“We joined forces with them to build an end-to-end demonstration and reference architecture to enable the complete data management life cycle,” Ger said.
Demonstrating how the technology integrates, Ger explained how ecosystem member NXP Semiconductors NV provides the service-oriented gateways, while Wind River Systems Inc. provides a Linux-powered in-car operating system. Cloudera then runs Apache MiNiFi data collection as part of Cloudera data flow on the operating system within the vehicle. The data is sent into the cloud for data analytics and machine learning, where Cloudera builds out specialized models. Over-the-air-update specialist Airbiquity Inc. is then able to use those models and provide rapid updates to the vehicle. Chip manufacturer Nvidia Corp. is also involved, providing hardware that speeds the machine learning process.
“I always say about these types of use cases, it does take a village,” Ger stated. “And what we’ve tried to do is build out an ecosystem that provides that village so that we can speed that analytics and machine learning lifecycle just as fast as it can be.”
Watch the complete video interview here (registration required), and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the Cloudera “Transform Innovative Ideas Into Data-Driven Insights” event. (* Disclosure: TheCUBE is a paid media partner for the Cloudera “Transform Innovative Ideas Into Data-Driven Insights” event. Neither Cloudera Inc., the sponsor for theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
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