The power of data: using analytics to get smarter

Successful leaders in transportation industry organizations recognize the value of leveraging data analytics. Doing so can help logistics professionals plan shipments more efficiently and utilize resources more wisely.

Also read: Resilinc’s latest data shows that the number of supply chain disruptions will surge by 30% in the first half of 2024

It is useful to study real-world applications and strategies to better understand how companies can use data-driven insights to optimize operations, enhance decision-making, and ultimately improve transportation systems.

Key Benefits of Data Analytics for Transportation Companies

Here is an overview of the key benefits that transportation companies can gain from leveraging data analytics:

Real-time vehicle tracking

Using a transportation management system can give you better visibility into the location of each vehicle in your fleet. TMS for freight forwarders Keep managers and dispatchers informed of each driver’s location without having to contact them directly for updates.

Optimize routes

Of course, transportation companies must weigh a large number of variables to get freight from point A to point B. For example, in the spring, the freeway may be the best option, but in the summer, road repairs may prompt you to optimize routes to use surface streets on certain segments based on analysis of current information and historical data.

Address Break Alarm

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

Reduce costs

By leveraging GPS data and up-to-date information about traffic conditions to dynamically select new, more efficient routes, you can reduce vehicle idle time and improve fleet efficiency.

Stay compliant

Analytics can help you comply with safety regulations, such as tracking how many hours someone has driven and whether they have taken required breaks.

Application of Data Analysis in Logistics

Fleet managers and dispatchers can rely on data analytics to make better decisions. For example, with real-time data pouring into your system, you can detect emerging traffic congestion, prompting you to adjust route details.

A wealth of data from telematics and GPS tracking provides managers with the location of every vehicle in the fleet, as well as details on driver behavior, such as “aggressive” driving or straying from designated routes. Has the vehicle been stolen, or has it been involved in an accident?

What are the different types of data analytics in logistics?

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

Descriptive— Descriptive analytics provide you with historical data summaries to gain insight into past trends and performance of vehicles and drivers.

diagnosis- Diagnostic analytics use data to understand the root causes of inefficiencies and problem situations and help you identify what led to previous results.

Predictive Predictive analytics data helps you predict trends and future events. Machine learning lets you predict traffic disruptions as well as demand patterns so you can shift resources accordingly.

Prescriptive— Prescriptive analytics can provide you with recommendations to help you optimize operations. For example, you could be alerted about a vehicle that needs maintenance, which is critical to avoiding costly repairs or replacements.

Cognitive – Cognitive analytics uses machine learning and artificial intelligence to help you analyze large amounts of complex, often unstructured data, enabling your systems to make decisions autonomously.

Looking ahead

Analytics have become essential for optimizing transportation, managing supply chains, and conducting cost analysis studies, all of which help improve customer satisfaction and increase profitability.

As for what the future holds, organizations that fully digitize their supply chains can expect to benefit from more agile decision-making processes enabled by real-time data integration. You can also assume that transportation companies will increasingly use data analytics to predict future demand and remain more competitive.

About the Author

Mike Marut is the Marketing Manager at Revenova. After 7 years in TV news as an anchor, reporter and multimedia journalist, he joined the logistics industry in 2023 and is solely responsible for writing, composing, filming and editing stories for TV, social media and the web. With a background in video and a love for transparency, Mike creates the majority of Revenova content with the end user in mind.