Here at the QuickTSI Blog, we have taken a couple different looks at the onslaught of Big Data and what it could mean for your fleet. Yet, as the future brings change almost by the day, new methods of utilizing data to improve fleet operations become known. That’s why we wanted to take an updated look at how trucking companies can dig deeper into big data to make your fleet work better for you.
Once your fleet has moved beyond the basics of simply looking at and analyzing data, you can unlock even more advanced ways to increase your levels of fleetwide efficiency and profitability. Many fleets are using data to merely generate reports. In this way, they can glean business-critical information. And yet, many times these reports can be difficult to compile or make sense of. Whether it be complicated graphical data, pie charts or graphs, or other cumbersome methods of gleaning valuable information, a one-time report only provides a one-time view.
When management is trying to use a canned report to realize greater efficiency, that data can only get them so far. This is where a new term comes in: Business Intelligence. Utilizing business intelligence allows a fleet to take a specific data set and break it up into different dimensions or qualifying factors. Rather than being a static block of data that a fleet manager must figure out what to do with, business intelligence provides the necessary tools to slice and dice the data into several, more easily understandable chunks.
Here is an example: What if a fleet manager could take specific revenue numbers from a tractor and divide it by dispatcher, or even truck driver. Being able to dig into usable data allows fleets to better determine where to place their assets. Rather than using a canned report to scan down endless rows of data, a fleet manager can view historical trends, analysis based on asset type, and even forecasts of what may be coming down the road – so to speak.
What is a Data Cube?
If you haven’t heard of a data cube, it’s okay, most haven’t. Generally used as a purely technical term, data cubes are essentially multi-dimensional arrays of information used to determine specific points of interest. But how does this apply to a motor carrier?
Let’s say you run a report on revenue by tractor, then you want to follow that number to the specific dispatcher. Using a traditional fleet management software solution, you may need to manipulate the data in many ways to achieve the desired result. Using data cubes, you can look at a specific number (tractor or dispatcher) and look at is in several dimensions without having to dig through layers of numbers.
When you analyze that data through a data cube, you may discover that said tractor is experiencing lower revenue because it was being operated by a different truck driver or dispatched by a different dispatcher. When you can look at the data in many ways, you may discover problems that you otherwise did not know you were even looking for.
It is referred to as a data cube because each face of the cube yields different information. You may be trying to determine revenue by tractor in relation to the truck driver only to discover an operational or mechanical issue. Mechanical issues can also be isolated and resolved utilizing this method.
If you can split downtime per tractor, or by specific issue plaguing your tractors, you can then determine if a specific model is causing you more problems than another. While the data itself doesn’t tell you what to do, it can at least point you in the right direction, thus allowing you to rectify the problem in whatever way you can.
Using Data to Break Down Common Assumptions
If there is one problem that trucking companies struggle with, it is assuming the answer to a problem. There are so many moving parts to a fleet that it can become easy to just assume you know what is wrong or what is right. Utilizing advanced data analytics can help you decipher whether the things you believe to be true are actually true, rather than baseless assumptions.
Here is another actionable example of how this could work. A particular tractor is giving a lot of warnings from the in-cab technology. As a result, the fleet manager assumes that the truck driver operating that tractor just happens to be a bad performer. Yet, when utilizing the data provided in an advanced analytical way, the fleet manager determines that the warnings are all generating from a specific curve within a half-mile of fleet headquarters.
Fleets can also glean better insight into profitability using data. Many technology companies now offer profitability management tools that harness the power of big data to give fleets greater insight into where they are making and where they are losing the most money. Fleets far too often assume that what they are doing is right and is earning the most profit.
Yet, once you dig into the numbers, lanes that you once considered a bedrock of profitability can be changed or altered. Fleet managers can dig into the data to better analyze profitability, change freight lanes, or alter operations. Even better, this type of deep analysis can provide the justification a fleet needs to request a rate change.
Many fleets believe that driving faster wastes fuel, yet for many, when they crunch the numbers, they may determine that their truck drivers can travel slightly faster operating on cruise control without expending a lot more fuel. Beyond fuel consumption, fleets have also started using deep data analysis to determine if they are even spec’ing the right equipment.
Questions arise, such as, how much engine load are we using? Are we using the right engine, or could we use a lower-hp engine with a different gearing setup? Do our trucks need better aerodynamics? Furthermore, is there more that can be done to improve truck driver performance?
Finding Opportunities in the Big Data
Sure, when you are evaluating your fleet’s voluminous levels of data, it may be easy to address the low-hanging fruit before all else. Things like fuel efficiency, maintenance, and training first come to mind. But digging in the data can reveal so much more than that.
Other questions should come to mind, such as revenue-per-mile, expenses, whether you are getting the best rates, where your most profitable customers are, and so much more. Another example of this could be your tractor utilization. Is 5 percent of your fleet sitting idle? That might not seem like much to a large fleet, but compound that over time and that could be serious money.
Fleets are also starting to turn to advanced new ways of measuring profitability. They are called velocity-distance and velocity-revenue. Velocity-distance is described as the average mph a tractor travels from the moment it picks up a load to the moment it delivers it. Velocity-revenue is referred to as the revenue per hour traced from the moment the load is picked up to the moment it is delivered. Both of these measures provide valuable insight into the following question: How much money is that tractor making for you per hour, per load?
Business Intelligence Gives You an Edge
Measuring these kinds of data markers moves your fleet beyond the simple thoughts of dollars and cents. Instead, you may determine that one lane is taking far longer to deliver, which could result in a loss of capacity or ability to take more loads. When you can parse through the data to make those kinds of discoveries, you can better fine-tune your operation to handle the routes that make the most sense from a profitability perspective.
Motor carriers that are utilizing data to dig deep have also discovered momentum. This metric tracks status changes within the system, from the moment the order is placed to the moment it is delivered, including how long each step takes. When you can map out every step of the process, from order procurement to final billing, it is easy to see where you may be losing or gaining along the way.
Finally, one of the best ways to utilize big data to the benefit of your fleet is through evaluating your gross margins, which can be divided by revenue and displayed as a percentage in whatever software you may be using. Gross margin is represented by the difference between revenue and cost of goods sold.
For many fleets, standardizing this number can be a problem, but when you are crunching it using big data analytics, figuring out your gross margin can be as easy as a click of a button. Using data this way is where business intelligence gives you an edge.
If you haven’t investigated how you can use business intelligence to realize greater outcomes in your business, then you may not be realizing cost efficiencies that are right in front of your face. Consider business intelligence and dig deep into big data to streamline your operations.