Overcoming Perceived Risks to Gain Acceptance Among Fleets
In our last Mobility Matters post, we discussed big data’s infiltration of nearly every industry, along with the importance of breaking down massive streams of information into smaller scopes to detect patterns and discover new insights. The second part of this conversation further explores data filtering and considers strategies that may help fleet managers overcome uncertainties to recognize the benefits of a prognostic approach to data.
Filtering Vehicle Data to Achieve Total Systems Integration
When it comes to big data, the possibilities can be daunting. That’s why Dana engineers are committed to determining the right data to collect. With thousands of variables to measure, it’s important to establish which are most critical for total systems integration. Measuring every single brake application might seem like overkill, but this level of detail gives dealers and OEMs the ability to provide better operation management advice for their customers. This ability to mine data is more insightful than on-board diagnostics because it conducts previously unknown measurements and makes seemingly unrelated connections. There may be many unknown relationships between vehicle components that are profitable to identif.
One savvy approach to sift through data is called smart filtering. In most cases, data is collected and averaged over time and when that average exceeds a certain threshold, a signal is sent to collect more data in that area. For example, a common problem among fleets is that the engine coolant level can get too high. When this happens, the driver or fleet operator is alerted to fix the immediate problem, but the system doesn’t stop there. The process in which data is collected changes when an event like that occurs to collect even more data and determine why the problem occurred in the first place. It provides a fuller picture of what’s happening and recognizes the difference between a true emergency – requiring the driver to pull over immediately – and a small issue that can wait until the next scheduled service. To save on downtime and improve safety, that’s a very important difference.
Low Risk, High Return for Fleet Managers
The off-highway and commercial-vehicle markets are often slower to adapt new practices than light-vehicle manufacturers and consumers. When approached with the opportunity to incorporate a system for telematics and vehicle prognostics into their fleets, the response from fleet managers is often, “how will I get a return on my investment?” Although the risks associated with data collection are very small and the cost is relatively low, fleet managers face bigger concerns due to the size of their fleets, varying operator skill levels, and revenue on the line.
But the use of big data can offer many benefits, including the determination of leading problem indicators to help improve overall fleet performance. With a pragmatic approach to data, the threat of the unknown is eliminated and the cautious nature of fleet managers can be addressed. It’s important to remember that just because a potential data trend is identified doesn’t mean action must be taken right away. But the potential for big savings arises when problems can be pinpointed before they become failures.
Additionally, when data on competing vehicles can be analyzed side-by-side, more informed purchasing decisions can be made. Fleet managers spend a lot on new trucks each year. They want to be certain that their investment is an improvement on what they currently have. By analyzing data, Dana can help them choose the right trucks for their specific needs.
Pilot testing of telematics systems with fleet customers, both large and small, has resulted in an overall perception of value in the detailed analysis made possible by the collected data. Every fleet is different, with its own way of operating, unique style of trucks, and different types of loads. While some common threads exist, the particulars vary from fleet to fleet. When it comes to increasing efficiency, there are some obvious gains that come from basic methods such as lightweighting, direct fuel injection, and turbocharging. But to reach the next level, improvement is only achievable by paying attention to details that pertain to each specific fleet and customizing a solution to fit their needs
The misuse of brakes by inexperienced operators is a common problem with commercial vehicles. Fleet managers have to train new drivers on the proper way to operate a tractor trailer and break them of the tendency to drive commercial or off-highway vehicles like a pickup. Shifting inappropriately, accelerating too rapidly, and braking incorrectly can all lead to premature component breakdown. Proper instruction will educate operators in the correct practices. But sometimes, old habits return in the field. Big data offers a solution to help keep new drivers on the right track.
That’s why it’s so important for fleet managers to understand the benefits of using data in a prognostic way. Data can help determine how operators are actually driving on the road. Sensors that monitor brake frequency, pressure, and other factors will tell the fleet manager if the operator might need further training and save them from having to replace expensive parts sooner than usual. For a fleet manager with declining profitability due to frequent brake rebuilds, embracing the use of data is an easy choice.
At Dana, we believe in determining best practices in order to optimize not only our axles and driveshafts, but also overall performance. Through data, we can create innovative solutions by learning how operators actually use vehicles and studying how our components work in conjunction with other vehicle parts. By evaluating data from our components in the context of the vast big data stream, Dana is helping to improve the transportation industry for the future.
We’d like to hear your thoughts.
- Which commonly used metrics don’t provide particularly helpful insights?
- What other concerns might fleet managers have about the use of big data? How can we help them overcome those concerns?
- What is your perception of big data’s return on investment?
Published by Don Remboski