Big Data Keeps on Truckin’
The trucking industry is the latest sector to embrace big data as fleet owners look for new ways to improve operating efficiencies and schedule maintenance.
According to market researcher Frost & Sullivan, a handful of OEMS and Tier-1 suppliers have begun incorporating data analytics to drive efficiencies. Big data tools are also being leveraged in areas like design, manufacturing and warranty management. “Employing advanced data analytics tools with early detection capabilities will avoid large warranty claims,” the researcher noted by way of example.
Frost & Sullivan noted that truck makers and Tier-1 companies must still improve collaboration with IT suppliers to build “sustainable” big data analytics platforms that account for issues like secure ways to share and monetize data. Moreover, data sharing is seen as the key to exploiting big data technology and gaining a return on investments.
Critical to a sustainable platform will be a mobile framework that can connect widely dispersed truck fleets. “The real differentiating factors for OEMs will be a big data framework, a clear connectivity strategy with the ability to handle large volumes of data and, most importantly, partners to help harness the true power of this data,” noted Sundar Shankarnarayanan, a Frost & Sullivan automotive and transportation analyst.
“Furthermore, the integration of telematics and predictive analytics with the latest generation fleet automation solutions will considerably increase fleet productivity, generate faster returns and underline the business case for big data in the trucking industry,” Shankarnarayanan said.
Another integration task may involve tying fleet analytics into ongoing traffic management systems springing up across the country. Big data specialists like Apache Hadoop distributor MapR are trumpeting the potential of data tools to make sense of the growing datasets related to transportation management. While traffic management remains the leading application, MapR stressed in a recent blog post that it is also being used to manage rail traffic, long-haul trucking and delivery, even road repair.
The good news for analytics providers is that trucking companies were one of the earliest adopters of tools like predictive modeling to select fuel-efficient trucks and find the most direct shipping routes. These models are being used to make multimillion dollar decisions about what trucks to add to a fleet as well as how to shave a mile per day per driver in shipping costs. A one-mile reduction per day can sometimes translate into fuel savings as high as $50 million a year, big data proponents estimate.
Moreover, large national trucking and transportation logistics firms like Schneider Logistics of Green Bay, Wis., were among the earliest adopters of GPS and other fleet technologies. As trucking companies like Schneider reinvent themselves as logistics and supply chain specialists, they have begun integrating analytics into their logistics management strategies.
According to a Schneider white paper on logistics management, analytics tools are incorporated into its transportation procurement lifecycle to run what-if simulations about “what could have happened” in different logistics scenarios.