Blog
Select Page

How Big Data Analytics in Logistics Improves Trucking Operations

08.02.2024

Cover photo for Big Data Analytics in Logistics Article

When it comes to logistics, every decision bears weight —and costs. The secret to triumph rests in maintaining streamlined operations, cost efficiency, and client satisfaction. That’s where big data analytics comes in.

For trucking companies, big data analytics provides precise and comprehensive insight into capacity usage, fuel efficiency, and profit opportunities. When effectively deployed, big data can fuel the most strategic business decisions and put logistics teams ahead of the game. Here are three key areas where teams can maximize big data analytics in logistics to boost profits.

Optimize Capacity: Increase Full Mileage and Revenue

Every trucking company knows the price paid for allowing trucks to gather dust while parked and, even worse, drive across countries empty. Big data is putting a stop to both.

Today, trucking businesses can monitor all of the relevant fleet KPIs, including mileage developments, such as empty versus full mileage and paid versus unpaid trips. Instead of covering the costs of the vacant truck to return to the hub, accurate data and strategic planning can help logistics teams ensure trucks are in the right place at the right time. They can schedule bookings proactively based on the location of assets.

By synchronizing the data from TMS and Telematics, trucking companies can get a full overview of their performance and identify points of improvement. They can balance metrics such as demand urgency, distance, truck availability, and load size to optimize capacity. 

Improve Fuel Efficiency: Decrease Direct Costs Of Trucking

Some of the largest fuel costs for truckers include shipment weight, vehicle configuration, and driver behavior. By precisely tracking these factors, logistics teams can accurately monitor and adjust pricing packages for clients based on the weight of their load, trucks, and driver capabilities. 

While logistics teams don’t have much say over the weight of clients’ products, vehicle configuration — the truck body arrangement and parts — and driver behavior are expenses that logistics teams can eliminate: Telematics systems installed into trucks can capture speed, location, idle time, and mileage data, helping back office teams to effectively monitor driver performance and provide the feedback if necessary.

Leveraging big data analytics in logistics tools to make sense of this data means teams can calculate excessive fuel consumption from high revs and idling with the engine on. This information provides a guide to encourage driving best practices, along with the evidence to show the cost savings that come with it.

Moreover, from smart sensors on tires to predictive maintenance, logistics teams can shave fuel costs even further. Instead of burning fuel on flat, friction-generating wheels or a dripping oil tank, logistics teams can automatically evaluate and foresee the next truck check-up.

Automate Back Office: Shrink Gross and Net Profit Gap

Operational visibility — insight into all operations in one place — gives logistics teams the power to leverage historical data on best-performing routes, distribute orders, optimally allocate trucks, and achieve a constant profit flow. Yet most shipping companies have data silos such as:

  • Financial systems obtaining transport costs
  • TMS platforms with fleet information
  • Data captured manually in MS Excel
  • Tacho information, such as speed and engine functionality
  • CRM systems receiving bookings and orders

By centralizing data sources, logistics teams can take a holistic view of their profits and costs, while leveraging AI-powered demand forecasting models, helping them gain insights to detect trends and make accurate predictions on how to reposition their assets. Logistics teams’ roles can shift from manual planners and managers to strategic analysts who maximize big data analytics in logistics for better alignment of supply and demand.

With operational visibility and demand forecasting tools, logistics professionals can make short to mid-term predictions to optimize operational planning and use long-range forecasts to guide decisions on asset purchases and business expansion. 

Centralized data capture not only aids logistics professionals in making informed business decisions to meet growing client expectations but is also essential for keeping costs low. Today’s logistics providers must continually gather data, predict future demand, and optimize capacity — with AI serving as a valuable tool in managing these tasks.