Successful leaders of organizations in the transportation industry recognize the value of harnessing data analytics. Doing so helps logistics professionals plan shipments more efficiently and use resources more wisely.

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It’s useful to examine real-world applications and tactics to better appreciate how companies can leverage data-driven insights to optimize their operations, enhance decision-making, and ultimately improve transportation systems.

Chief Benefits of Data Analytics for Transportation Companies

Here’s an overview of the main benefits transportation companies can get when working with data analytics:

Track Vehicles in Real-Time

Using a transportation management system gives you better insight into the location of each vehicle in your fleet. A TMS system for freight brokers gives managers and dispatchers awareness of where each driver is without needing to contact them directly for an update.

Optimize Routes

Of course, transportation companies must juggle a vast number of variables to get their shipments from point A to point B. For example, a freeway might be your best bet during the spring months, but by the time summer arrives, road repairs may prompt you to optimize routes to use surface streets for a particular stretch according to the analysis you perform on current information and historical data.

Alerts of Address Disruptions

Real-time data analytics will help you identify potential delays caused by unexpected bad weather and learn as soon as possible about major accidents on the road causing disruptions, so you can more effectively redeploy assets in your fleet.

Reduce Costs

By selecting a new, more efficient route on the fly, thanks to GPS data and updated information about traffic conditions, you can reduce vehicles’ idle time for a more efficient fleet.

Remain in Compliance

Analytics is instrumental in helping you stay compliant with safety regulations, such as tracking how many hours someone has been driving and whether they have taken mandated rest periods.

Using Data Analytics in Logistics

Fleet managers and dispatchers can rely on data analytics to make better decisions. For example, as real-time data pours into your system, you can detect an emerging traffic congestion problem that would prompt you to adjust routing details.

A torrent of data from telematics and GPS tracking gives managers information about the location of each vehicle in the fleet and details about driver behavior (such as “aggressive” driving or deviations from their assigned routes). Has a vehicle been stolen, or was it involved in an accident?

What Are Different Types of Data Analytics in Logistics?

There are five main types of data analytics in logistics for transportation industry executives to keep in mind:

Descriptive — Descriptive analytics provide you with a summary of historical data to get insight into past trends and performance of vehicles and drivers.

Diagnostic — Diagnostic analytics look at data to understand the root causes of inefficiency and problematic situations, and helps you identify reasons leading to previous outcomes.

Predictive — Predictive analytics data helps you forecast trends and future events. Machine learning allows you to anticipate transportation disruptions as well as patterns of demand so you can shift resources accordingly.

Prescriptive — Prescriptive analytics can drive recommendations so you can optimize your operations. For example, you’ll receive reminders about a particular vehicle needing to undergo maintenance, which is crucial for avoiding expensive repairs or replacement costs.

Cognitive — Cognitive analytics use machine learning and AI to help you analyze immense troves of complex, typically unstructured data, enabling your system to make decisions autonomously.

Looking Ahead

Analytics are already crucial for optimizing shipping, managing supply chains and conducting cost analysis studies, which all serve to enhance customer satisfaction while boosting the bottom line.

As for what the future might hold, organizations that fully digitalize their supply chain can anticipate benefiting from a more agile decision-making process thanks to real-time data integration. You can also assume that transportation companies will increasingly use data analytics to forecast future demand and stay more competitive.

Author bio

Mike Marut is the Marketing Manager at Revenova. He joined the logistics industry in 2023 after 7 years in TV news as an anchor, reporter, and multimedia journalist working as a one-man-band pitching, writing, shooting, and editing stories for TV, social media, and web. With a background in video and passion for transparency, Mike creates the majority of Revenova content with the end-user in mind.