Data Quality to Drive Successful Trucking Operations

In today’s reporting-driven world, quality data has become increasingly important to running a successful business. In the trucking industry, having quality data at your fingertips can help to anticipate and address problems before they happen. According to a recent article by FreightWaves, data can point out advanced maintenance notices, such as tire or sensor issues.

Vishnu Rajamanickam, staff writer for FreighWaves, writes:

Every trucking fleet exists to make money, and sustaining itself in the market requires managers to keep freight hauling competitive and to seek methods to lower operational and maintenance costs.

Over the years, managers of successful fleets have figured this out by giving driver benefits to keep churn rates low and by sending trucks to the maintenance garage anticipating a potential breakdown. However, with the proliferation of technology, fleets are now gravitating towards data analytics and machine learning that can help predict their maintenance needs, equipment failure, and even refine driver behavior to improve truck safety.

FreightWaves discussed these issues with Rebecca Grollman, data scientist at Bsquare, to understand how data can be leveraged – irrespective of the size of the data set. “Before we start out, it is important to see if the collected data is actually of high quality. If the quality is not good, there is not much that you can do, even if you have a lot of it. Quality of data is more important than quantity,” said Grollman.

It helps fleet managers to have a clear idea of the questions they want to answer before data collection begins. This is critical because truck fleets generate several data streams from everyday operations – be it from the trucks or the back office. The importance of figuring out the issues that matter and devising means to collect data specific to that cannot be overstated.

For instance, a trucking company might have thousands of data points on the exact colors and paint jobs of all the trucks in its fleet. However, all that will be worth nothing if the company ultimately wants to predict when its trucks will need to schedule a maintenance visit to the garage.

Grollman explained that with relevant historical data, company management can look at predictive analytics and root-cause analysis – helping them pinpoint where their equipment failures originate and follow it up with measures that will stem such future scenarios.

For companies that are just a few months into their operations, data analytics might be a hard sell, as they lack historical data to drive meaningful insights. However, Grollman insisted that such companies can look towards anomaly detection, as its prerequisite does not include substantial data sets.

“Even if you have only been collecting data for a few months, it should be enough to gain insights on normal operating parameters. It helps with understanding what to expect with the data that you’re collecting on a daily or monthly basis,” said Grollman. “You may be able to see some trends and seasonality using anomaly detection. You can start to pick out different anomalies in your data and even make correlations to things that those anomalies indicate.”

For instance, data can point out a spike in tire pressure. This could be because there is a problem with the tire, or perhaps one of the sensors on the truck is malfunctioning. These are anomalies and figuring out a way to work on them will help weed out operational issues. Over time, with a considerable amount of historical data, machine learning algorithms can be used to push decisions. If the insights are not well-defined at the start, it will help to keep iterating on the data until there is definitive meaning.

“Apart from collecting quality data, it is important to have domain expertise to make sense of the data. Companies should discuss the possibilities with a subject matter expert and understand the filters to use on the data, how data streams relate to each other, and what can be expected from them,” said Grollman.

“For example, there might be a number that comes up which indicates median tire pressure, but if I don’t have an idea on the reasonable number, it would be of no use. For small companies, being able to have this collaboration and understanding the data that they are collecting would actually make a big difference,” she said.

To read the article on FreightWaves, click here.

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Location Technology Puts Driver Safety First

There are many location intelligence platforms on the market today that all aim to address the same issue: driver safety. According to a recent article published in Transport Topics, location data can help companies and drivers improve overall safety compliance.

 

Information excess

With a vast increase in the data generated and available for use in fleet management applications, information overload is likely to be a significant industry challenge in the next few years.

In fact, the T&L industry risks data paralysis, where so much information is available that organizations have no idea how to utilize it fully. Against this backdrop, how can they leverage and optimize data for their own benefit and that of their customers?

Location intelligence can play a critical role in unlocking the value of this data. For example, in the T&L industry it can improve driver safety. It does this by adding context to big data and creating actionable insights that can help differentiate a business from the rest of the market.

Safety first

As driver shortages across the transportation industry are expected to remain a challenge for several years, retention of safe and experienced drivers will continue to be of paramount importance.

As such, fleet managers that want to remain competitive will need to focus closely on delivering high levels of driver safety and satisfaction.

In addition, those organizations keen to limit potential liabilities and cost of operations are likely to consider driver behavior monitoring, coaching and measurement of road use regulation compliance as critical to their futures. As transportation companies are liable for their trucks on the road, fleet managers are highly motivated to take steps that help reduce the possibility of accidents from occurring.

Smart data

Location intelligence plays two critical driver safety roles: it powers applications that monitor truck driver behavior; and it enables the analysis needed to efficiently manage and coach drivers about road safety.

Location intelligence can help improve understanding of – and compliance with – posted speed limit data; it can also help to understand what the appropriate driver action is when taking elements such as traffic, weather conditions and other road legal and physical restrictions into account.

Rich, real-time location data can mean the difference between a costly and life-threatening accident and a driver empowered with vital knowledge who can maintain a brand’s trustworthiness through safe and efficient driving.

By enabling fleet applications to create driver safety and analysis features, location intelligence plays a critical role in helping to analyze driver behavior in real time. It can also help fleet managers identify drivers who exceed performance requirements and who can then be incentivized to stay with the company.

Big data, big future

Big data may have been around for almost 30 years, but it’s only now we are starting to see how the wealth of information held by T&L companies can be transformed into a vital asset through the application of location intelligence.

 

HERE, the Open Location Platform company, enables people, enterprises and cities to harness the power of location. By making sense of the world through the lens of location, we empower our customers to achieve better outcomes – from helping a city manage its infrastructure or an enterprise optimize its assets to guiding drivers to their destination safely. To learn more about HERE, including our new generation of cloud-based location platform services, visit http://360.here.com and www.here.com

To read the article on Transport Topics, click here.

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