From drones to the Internet of Things (IoT), technology is pushing the boundaries of what is possible in the world of business. It is no different for transportation entities, especially shippers and carriers who are embracing technology while remaining mindful of ever-slimming margins.
Steve Sashihara, CEO of Princeton Consultants, recently spoke on a conference call hosted by Stifel Nicolaus about these topics and more in an event titled, the Digital Disruption in Freight Transportation: 2017 and Beyond.
Technology has clearly altered the industry, Sashihara said, but the remarkable part of it is how quickly the advances have come. The biggest driver behind most of these technological achievements is data. Today, there is more data than ever before and it comes with fancy names such as “Big Data” and the “Internet of Things.”
What is the Internet of Things (IoT)? What is Big Data? Many have heard the terms, but just as many are as confused by what they actually mean. Sashihara says IoT simply refers to “sensors everywhere.” Think trailers and trucks, pallets, individual items on pallets, and even people. Gartner research has found that IoT will produce $2 trillion of economic benefit globally. To Sashihara, though, IoT dovetails perfectly with Big Data – the “massive unstructured external data to drive decision making.”
“We’re all pretty good at collecting our own internal data, but Big Data also has a flavor of data that’s coming from outside of our organization,” he said.
To illustrate his point, Sashihara referenced Eric Schmidt, executive chairman of Alphabet, Inc. “[Seven years ago] Eric Schmidt was talking about how there are now 5 exabytes of information created – there were 5 exabytes of information created between the dawn of civilization to 2003; I don’t know how he figured that out but he does run Google so maybe they have some people that figured this out,” Sashihara said. “He said that much information is being created every two days and that was seven years ago. Now we no longer talk about exabytes when we’re trying to impress people with giant amounts of data, they talk about zettabytes which is 1,000 exabytes… [The point is] there’s a lot of data and it’s getting bigger and faster.”
Calling IoT and Big Data a fad is misguided, Sashihara said.
“We acknowledge that Internet of Things and Big Data are very trendy, but we think that underneath this trendy labeling is a real bona fide revolution in manufacturing distribution and therefore transportation,” he said. “If it’s going to change the manufacturing distribution, it’s going to change transportation. We agree with McKinsey who calls this the next frontier for innovation competition and productivity, and I think that’s notable because McKinsey … is not a technology consulting firm. They are a general management consulting firm and so they’re saying this is where the action is going to happen for innovation competition and productivity.”
There are 9 primary areas where this technology revolution is likely to impact freight transportation. They are:
- Workforce planning and schedulin
- Forecasting (better estimating of customer demand)
- Inventory management and picking/packing in distribution centers
- Last-mile deliveries
- Streamlining/elimination of paperwork such as bills of lading or proof of delivery
- Exception reporting (better estimates of ETA/late notification)
- Optimization through real-time routing of personnel/equipment
- Visibility (tracking and monitoring of shipments in transit)
“We believe digital disruption is creating new winners – the disruptors – and losers who are disrupted, and so we urge our transportation executives to ride this wave and change their strategy, their sales, operations and IT,” Sashihara summed up.
Data is driving the market
There is $139 trillion worth of goods shipped inside trucks, trains, planes and ships, he said. “The direct spend is pretty remarkable, but it’s also what’s in our vehicles and to the extent that could be more efficient, there’s a lot of room for good investments there potentially.”
Of note, though, in a Princeton Consultants’ survey, 56% of people agreed with this summary, “but they said it’s not clear how [their] company can need or harness these new market forces,” he said. “We see it coming; I have a belief this is coming, but it’s not clear what we should do about it.”
Truck manufacturers have been ramping up efforts to use data to proactively manage vehicle uptime. Fleets are now tapping data to track shipments in real time and notify customers of delivery times or delays. Vehicles themselves are utilizing data from hundreds of sensors on board to manage operational tasks as mundane as when to change the oil to monitoring the roadway for objects and braking the vehicle. And that doesn’t even get into the data being generated from onboard telematics systems that track hours of service and even drowsiness levels.
The end result is that fleets have a wealth of data to pore over – too much in many cases. So how do they handle it?
Bill Combs, director of connected fleet for Penske Truck Leasing, recently spoke to Fleet Owner magazine on the subject. His advice: avoid “analysis paralysis” and focus on one task at a time.
To read Combs interview, visit: How fleets can benefit from Big Data
How can truck fleets can advantage?
A 2015 whitepaper from software firm Omnitracs lays out the impact Big Data is having, and will likely have, on the freight market.
Vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), mobile technologies and location-based applications are among the technologies made possible by Big Data and IoT.
“Everything we do leaves a digital trace (or at least it will in the very near future) – and that’s data we can capture, analyze, and leverage to inform our decision-making,” the whitepaper’s authors wrote.
Typically, the Omnitracs paper noted, fleet software providers offer management tools such as idle time, miles per gallon, asset utilization and speed reporting. Additional, the vast wealth of data offers up additional management possibilities – some of which fleets already take advantage of, and others that are rarely used.
These include planned vs. actual miles for routes. The whitepaper used a Roadnet customer example of how Big Data can solve unsolvable problems.
“A Roadnet customer’s costs went up 20% and he didn’t know why, but he was able to build a revealing story with a data-mining tool,” the paper explained. “He looked at both his planned transportation costs versus actual costs, reviewing the components that fed into each—time and miles. As it turned out, his fleet’s actual and planned miles were right on target, so that wasn’t the culprit. He then looked at drive time versus service time. While his fleet’s service time looked solid, actual drive time versus planned was off quite significantly. Further investigation revealed that changes in road infrastructure were creating additional congestion and inflating his costs.”
Another area that fleets can exploit is predictive analytics. By following trends and incorporating real-time and historical data, a more reliable view of the future is possible. This can be used for everything from routing and driver retention (looking at why drivers are leaving) to vehicle maintenance – proactively replacing components that tend to have higher fault rates on the road during times when a vehicle is in a shop for routine maintenance – thereby saving road repair and downtime costs.
Omnitracs notes that fleets can combine their own data – payroll, trips, fuel, miles, training records, prior employment data to name a few – with external data such as weather, congestion, Census data, employment statistics, population to unlock potential business efficiencies unseen without Big Data.
Take driver retention and risky driving, for example. “If you’ve got at least three years’ worth of data on 500 drivers or more, you most likely have the rich information you need to identify risk signatures. And when you can determine what caused events to occur in the past, you can make predictions for the future—and incredibly accurate predictions, at that,” Omnitracs noted.
The paper noted how one carrier with 1,400 drivers continued to see high driver turnover. “By using a custom, big data-powered predictive model, they were able to prevent 290 truck drivers from quitting—reducing driver turnover by half and saving the company $1.2M.”
Omnitracs Analytics is using Big Data to predict accidents. “They took driver logs and turned each one into about a thousand distinct data points—from the amount of time the truck driver drove each hour of the day, to how many hours of sleep that driver got and when those hours occurred, to how many times that driver drove through sunrise. Then, they took 27,000 severe accidents from their customers’ data sets and reverse engineered a severity model, which allowed them to identify predictive data points in a particular driver logbook that indicated the potential for a bad wreck. The model is so good they can take any driver’s log book and predict the likelihood of a bad accident each hour of his or her day,” the authors wrote.
Much as Sashihara repeated throughout his presentation and Omnitracs whitepaper illustrates, the growing use and reliance on Big Data and IoT is opening doors to business efficiencies and cost-control techniques at a rate that has perhaps never been seen in the industry. And those fleets and shippers who are slow to adapt are going to be left behind with higher rates and costs.