According to Technavio, the global logistics market is projected to grow by $368.4 billion between 2025 and 2029, with an annual growth rate of 5.3%. This dynamics shows that the logistics industry is picking up steam. But as new opportunities emerge, so do new challenges.
As an expert in logistics, you’ve probably dealt with things like missing route info, guessing delivery times, and sudden delays. All that uncertainty can cost you money and hurt your business reputation.
If you don’t apply logistics analytics, you’re already behind your competitors who use big data. While it may sound harsh, we’re not here to sugarcoat the truth. We want to help you avoid costly disruptions and show you how to boost your efficiency.
With data analytics in logistics, your supply chain will never be the same. From real-time reporting to predicting risks, here’s how you can benefit from big data. We hope you’re intrigued and ready to explore the next questions:
“What are the other use cases of logistics analytics? What benefits will I gain?”
Luckily, you’re in the right place. We’re here to show you how to apply big data in logistics to elevate your business.
What Is Data Analytics in Logistics?
Big data is a huge, complex information set that traditional tools can’t handle. In logistics, this data can be structured and unstructured.
Structured data is organized and easy to analyze. For example, this can be inventory levels or delivery time.
Unstructured data is messy and harder to work with. These are emails, GPS signals, or customer feedback.
Today, logistics companies can store and process their data thanks to cloud applications. Tools like graph databases help connect the dots and make sense of complex relationships in their supply chain.
But it’s not just about having lots of data. What matters is how you can use it. Logistics analytics turns raw data into insights that improve decisions and prevent costly mistakes.
Role of Data Analytics in Logistics Optimization
Have you ever thought about how to improve your logistics operations? We bet you have, and for good reason. You have a lot of data from delivery tracking, sales, and routes.
The good news? With logistics analytics, handling all that data doesn’t have to be hard. In fact, it can help you improve your supply chain. Here’s how:
Real-time tracking for better visibility
Imagine that you know exactly where your shipments are at any moment. With data from GPS, sensors, and RFID tags, big data makes that possible. You get real-time updates and make faster decisions. Plus, if there are any delays, you keep your customers informed.
Smarter demand forecasting and inventory management
Predicting demand can be tricky. You either end up overstocked or out of stock. Data analytics in logistics helps in this case. It considers past sales, market trends, and buying habits to forecast future needs. That means less waste, better inventory control, and products ready when your customers need them.
Optimized routes and fleet management
Fuel costs and delivery costs matter. Data analytics enables companies to analyze traffic, weather, and past deliveries to find the most efficient routes. You save money on fuel, make on-time deliveries, and use your fleet more effectively.
Prevented risks
Unexpected problems like bad weather or supply delays can disrupt your supply chain. Big data helps you spot risks early by analyzing data from many sources. That way, you can act fast and keep everything running smoothly.
We hope we have persuaded you that data analytics in logistics is a win-win big data strategy for your business. With the right approach, you’ll have everything you need to optimize your supply chain operations.
If you need advice on logistics analytics, feel free to reach out to our team, we’ll guide you on how to get the most value from big data.
What Data Matters Most in Logistics Analytics
Not all data is useful for logistics analytics, there’s just too much of it. That’s why we’ve put together a list of the most important types of data you can use to make your supply chain work better:
- Order and delivery data: This is information related to orders, deliveries, and how they’re fulfilled. It helps you find delays, adjust to busy seasons, and understand how your customers behave.
- Inventory data: Keeping track of your stock in real-time helps you avoid running out or having too much. It also assists you in planning better and keeping things balanced.
- GPS and route data: This kind of data helps you find the best delivery routes. It considers traffic, delivery times, and fuel use. So, with information, you can save money and deliver faster.
- Customer feedback: Your customers can tell you a lot. Looking at what they say helps you make deliveries more accurate, fix problems, and understand what they care about most.
- Warehouse and distribution data: This is all about how things move inside your warehouse, like how space is used, how fast items are packed, and how accurate orders are. An automated warehouse keeps everything running on time.
So, now that you know what data should be analyzed and considered, it’s high time to reveal how you can use data analytics in logistics for your business. Let’s continue.
Top 6 Applications of Data Analytics in Logistics
Today, businesses use logistics data analytics for many different purposes. The best way to understand how to apply big data in your company is to explore real use cases and see how other logistics businesses are integrating it into their operations. These insights can help you follow proven strategies or even come up with something unique.
Route optimization: smarter deliveries, less waste
Route optimization isn’t just about finding the shortest way, it’s about finding the smartest one. With data from traffic, weather, past deliveries, and vehicle performance, you can plan faster, cheaper, and more reliable routes.
Logistics analytics helps you cut fuel costs, avoid delays, and even predict problems before they happen. You can look at everything from how often customers order to how many vehicles you have, helping you stay one step ahead.
For example, UPS uses an implemented AI system called ORION to plan smarter delivery routes. It considers traffic, weather, and schedules to save time and fuel. Thanks to ORION, UPS drives fewer miles and delivers faster.
Stock and storage control: more efficient inventory
Tracking your inventory means that you always know where your items are in the warehouse. By analyzing data from your systems and sensors, you can see when stock is running high and when shelves are empty. Logistics analytics also helps you predict future demand, so you’re ready for busy and slow seasons.
For example, Amazon uses robots and AI in their warehouses to manage inventory. Robots pick and move items, while AI tracks stock in real time and predicts demand. This helps them keep shelves stocked and deliver fast.
Warehouse slotting: smarter storage, faster picking
Running a warehouse isn’t just about stacking boxes, it’s about putting the right items in the right places. With data analytics in logistics, you can track how fast items sell and how often they’re picked. This helps you store popular products closer to the packing area, speeding up order fulfillment.
And it’s not a one-time thing. As your sales change, the system updates your layout suggestions. That means faster packaging, better space use, and lower costs. Less time searching leads to more orders handled.
Big brands like Nike are already doing this. They use advanced slotting algorithms to decide where each item should go based on demand and picking speed. By keeping fast-moving items easy to reach, they process more orders per hour, and so can you.
Demand forecasting: smarter planning
When you can predict your demand, you can plan it. So, knowing what your customers will need next week or next month can save you a lot of money.
With the help of logistics analytics, companies can look at past sales, seasonal trends, and market changes to predict future demand. This means they won’t have too much stock sitting around or run out when people need it most.
FedEx is a great example. They use predictive analytics to forecast demand by analyzing shipment history, seasonal patterns, and market shifts. This helps them plan ahead, making sure they have the right number of vehicles and staff and deliver on time without wasting resources.
Risk management: stay ready, avoid disruptions
Things don’t always go as planned in logistics, from bad weather to supplier issues. That’s where logistics analytics works perfectly.
By looking at logistics data, you can spot potential problems early and make a plan B before things go wrong. For example, if a storm is coming that could delay a shipment, data can help you reroute it in advance. This kind of foresight keeps your supply chain running smoothly and helps you avoid costly delays.
DHL’s Resilience360 platform is a great example of managing risks in real time. It gives companies a clear view of their supply chains, helping them track and spot risks as they happen. By looking at things like weather and political issues, Resilience360 can suggest new routes or backup plans to avoid disruptions.
Fewer delivery failures with customer data
Understanding how your customers behave can help cut down on failed deliveries. These failures cost money and hurt your reputation.
By looking at past delivery data and using smart tools, companies can plan better delivery times and improve success on the first try.
There are now many logistics analytics tools that help your team collect and understand data. Here are some easy-to-use ones:
- Tableau and Power BI: Turn data into simple charts and graphs everyone can understand.
- SAP SCM and Oracle SCM: Help track everything across the supply chain.
- Detrack: Finds the best delivery routes to save time and money.
Using these tools, companies can manage their data better and make smarter logistics decisions every day.
If you’re struggling to manage your data or unsure how to use it for growth, contact the Forbytes team, we know how to help.
How Forbytes Helps Implement Logistics Analytics
At our company, we have a story about handling data and turning it into valuable logistics insights. So see, how we did it.
The NoWaste story
A leading logistics company with dozens of warehouses across Europe was growing fast, but so was its data problems. Every part of their operations, from warehouse management to invoicing and HR, ran on different systems.
As the company expanded, the data became messy, inconsistent, and hard to use. Reports took forever to compile. Billing mistakes were costly. And decision-making was slowing down.
They knew they had to change. That’s when they teamed up with Forbytes to build NoWaste, a powerful business intelligence platform designed to make sense of the chaos.
Data chaos
With over 17 years of experience and a strong foothold in 3PL services, the company has always invested in automation. But their existing tools just couldn’t keep up.
Their data was scattered across multiple systems. Errors were frequent in invoicing and resulted in missed revenue. Finally, our client didn’t have real-time insights for decision-makers
They needed a centralized system that could clean, organize, and visualize all their data accurately and fast.
The solution: BI platform
Forbytes built a modern BI platform on top of a lakehouse architecture using Azure and Databricks. Here’s how it worked:
- Bronze Layer: Collected raw data from all source systems
- Silver Layer: Cleaned and unified the data for an enterprise-wide view
- Gold Layer: Delivered ready-to-use, reliable data for reports and analytics
To make things even better, they added automated data pipelines, materialized views, and Power BI dashboards that could deliver insights in seconds, not days.
The result: automated free-error reports
After implementing the NoWaste business intelligence platform, the logistics company experienced significant improvements. Financial reporting accuracy reached 99%. Billing errors were eliminated. And automation saved 5-10 hours weekly.
Additionally, all warehouse data was consolidated into a single, unified system. The clarity and professionalism of the new reports impressed clients, leading them to request custom dashboards tailored to their specific needs.
Revolutionize Your Logistics Operations with Powerful Data Analytics Solutions
Big data has changed how logistics companies work, and for the better. More and more businesses in this field admit the value of data and move toward smarter, data-driven decisions.
At Forbytes, we help logistics companies make the most of their data. This helps them uncover useful insights, improve operations, and boost efficiency. The examples in this article show just a few ways big data can make a real difference.
If you’re in the logistics industry and want to get more value from your data, check out our big data consulting services.
