Logistics and Big Data: Use Cases & Proven Practices

“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway”, – Geoffrey Moore, consultant and author.

According to Fortune Business Insights, the global supply chain analytics market is expected to grow at a 16.7% CAGR between 2025 and 2032. At the same time, the Global Market Insights report projects the big data in logistics market size to reach USD 24.1 billion in 2032 compared to USD 4.3 billion in 2023.

In 2025, big data in logistics and supply chain management is one of the most effective solutions for a variety of business issues. And we will tell you why!

From this article, you will learn more about big data applications in logistics and how to implement them in your business ecosystem. As a big data development company, we are also sharing our experience to give you an idea of how it works in practice when combined with artificial intelligence. 

Article Highlights:

• Uber Freight’s AI-powered big data platform can cut empty trips by 10%-15%;
• Data-driven warehousing can reduce costs by 15% and inventory by 35%;
• Before planning big data for logistics, review your data sources carefully – even minor data sets, like rejected orders or driver notes, can significantly impact the quality of data-driven insights;
• CHI Software’s case study demonstrates that ML-powered big data solutions for logistics can identify defects and errors before they become a real problem.

Top 6 Big Data Use Cases in Logistics

Logistics companies typically have to handle vast data sets. Shipment origin and destination points, weight, size, and contents are only a small portion of the processed information. Companies also consider vehicle characteristics, fuel consumption, weather reports, traffic, and many other factors. 

Let’s not forget about transparency, strict schedules, and increasing customer expectations that impact transportation businesses. It can indeed be overwhelming. But it doesn’t need to be! These examples of applications will give you an idea of how to mitigate potential complications with big data analytics for logistics.

1. Route Optimization

Route optimization in logistics involves testing multiple scenarios to determine the most cost-effective route. This process may result in minor management flaws, which can ultimately lead to significant business issues. 

To find the best route, one should consider various factors, such as weather, traffic, and road conditions – so it’s no wonder that managers sometimes make errors in their assumptions. But, big data processing techniques can help to mitigate (or at least significantly reduce) business risks by considering key aspects and helping to draw more accurate conclusions. Big data tools are like a powerful supercomputer that does the hard work of making these insights possible – your task is simply to provide big data tools with essential information.

Example: Uber Freight

Uber Freight launched its AI-powered platform in 2023 to reduce needless driver mileage. By analyzing traffic, weather, and road conditions, it can reduce empty trips by 10%-15%, thus empowering carriers with pricing that’s clear up front – and has since handled over USD 20 billion in freight for Fortune 500 clients.

2. Transportation Tracking

Big data for logistics management has put transportation tracking services on a whole new level. Companies and individual consumers can track their parcels in real time, receiving automatic updates via email or app notifications.

Data from GPS-powered devices, barcodes, and advanced RFID (Radio Frequency Identification) tags enable big data solutions to track every package, plan delivery dates, and notify company managers and recipients if something doesn’t go according to plan.

The latest IoT in logistics solutions take this even further, allowing transportation companies to closely monitor temperature, humidity, and other environmental conditions within their fleet.  Data from sensors is particularly important when transporting perishable goods.

Example: Walmart’s Perishable Goods Monitoring

Walmart embeds IoT sensors throughout its cold chain to monitor temperature and humidity. Continuous tracking reduces spoilage and ensures quality throughout transit.

3. Optimization of Last-Mile Delivery

It’s often the last mile that brings the most trouble to logistics businesses. Let’s say, for example, that a big truck cannot park in an urban area. It means a courier may have to walk some distance to deliver a package. And what if that package is heavy? In such cases, workers must spend extra time and effort in order to complete their tasks. It is only one real-life example out of many, which often get overlooked.

Implementing big data in the logistics industry can dramatically improve this and a lot of similar operational issues just by using information from GPS devices, big data software. By tracking every step of the courier and identifying delivery trends, company managers can make data-driven decisions to improve internal processes and service quality.

Example: Relay (UK startup)

UK-based Relay uses machine learning to manage urban logistics on the “last 100 meters” via micro-hubs and gig drivers. Relay’s approach is interesting because it helps with more than just saving money – their model also makes deliveries in cities greener and less complicated.

4. Innovative Warehouse Management

With every innovation that comes to online shopping, customers can become more demanding – they want to know exactly what goods are available in stock so that they can receive them as soon as possible. Traditional warehouse management often can’t handle increased workloads, and errors are bound to occur due to human factors.

Data-driven warehousing can reduce costs by 15% and inventory by 35%, thanks to better demand forecasting and space optimization. Company managers always have easy access to actual data about goods in stock, their locations, expiration dates, and other information. They won’t miss a thing and, hence, can satisfy demanding buyers and help to avoid overstocking.

5. Verifying Address Information

Delivering packages to the wrong or even nonexistent addresses is another major logistics pain point. Company clients are humans, and humans naturally make mistakes when setting a delivery address. How can you solve such an issue? 

Verifying each destination point by company employees is not an option because it can eventually lead to even more errors. Instead, you should consider combining logistics and big data processing.

Address records should ideally be standardized and then validated. Standardization is the process of correcting records, while verification deals with checking whether the addresses actually exist. With well-defined big data and AI strategies, you can automate these steps and minimize the impact of the human factor on your business processes.

Example: Cainiao (Alibaba’s Logistics Arm)

The Chinese logistics leader has deployed a powerful AI-driven address solution in production, known as G2PTL – a Geography‑Graph Pre‑Trained model that intelligently processes delivery addresses using both text semantics and geographic graph relationships.

An academic study “AddrLLM: Address Rewriting via Large Language Model on Nationwide Logistics Data” reports that deploying an LLM-based address rewriting system reduced parcel re-routing by approximately 43% after live integration.

6. Predictive Maintenance

Modern big data platforms open the door to advanced analytics insights previously inaccessible to managers and decision-makers. They can monitor driver habits that directly impact fleet wear and spot damaging trends.

Company employees can also track the condition of their vehicles and plan maintenance in advance by collecting data from fleet sensors. Predictive analytics provided by big data solutions for logistics can be the key to fleet durability and more effective use of business resources.

Example: Volvo Trucks

Volvo Trucks takes full advantage of big data and smart tech to keep their trucks on the road. Thanks to real-time connectivity, each truck sends out a steady stream of data on engine health and brake conditions. Volvo’s systems analyze all this info to spot the most minor of issues early, sometimes before the driver even notices.

What Are the Main Advantages of Using Big Data in Logistics? 

Each of the aforementioned use cases can cause notable changes in the organization’s workflow and results achieved. Now, let’s explore cases of exactly what can happen.

Budget-Smart Business Processes

According to McKinsey, companies that leverage big data in supply chain operations can reduce their logistics costs by up to 15% and inventory levels by 35%. Route optimization and predictive maintenance translate into immediate savings.

Loyalty-Driven Service 

Real-time tracking, personalized shipment updates, and accurate delivery forecasts transform customer satisfaction into loyalty. For instance, Amazon’s AI-powered supply chain, leveraging data from sales trends, weather, and even social sentiment, can maintain high on-time delivery rates and dynamically optimize routes.

Always-On Oversight 

Hardly any other technology can provide reports on every step taken by couriers and every mile passed by trucks. Logistics analytics powered by big data enable tireless 24/7 monitoring to help you identify small-scale process trends and deviations.

UPS’s ORION (On-Road Integrated Optimization and Navigation) is a flagship case here. This AI digital assistant can process billions of data points on a daily basis by optimizing routes based on real-time traffic, weather, and delivery commitments. As a result, UPS saves 100 million miles and roughly USD 300 million annually while cutting ~100,000 metric tons of CO₂ emissions (source). 

Now, let’s take a look at applying big data looks in practice. In the next section, we will review the development process from start to finish.

How to Implement Big Data Analytics in Logistics in 5 Steps?

There’s no one-size-fits-all formula for rolling out big data analytics in logistics. Every company has its quirks – different workflows, software landscapes, and priorities. That said, we’ve seen that the most successful projects tend to follow a core set of steps. Here’s what we recommend based on our hands-on experience.

1. Start with a Deep Dive

Before planning begins, you need to get really clear about what you want to achieve. We always recommend an up-front discovery phase, where you and your technology partner dig into your business specifics:

• What are your biggest workflow headaches?
• Where are the data gaps?
• What’s your vision for improvement in terms of costs, speed, transparency, and customer experience?

CHI Software’s tip: Come prepared with your goals, describe your current processes, and point out known bottlenecks. How your preparations make all the following steps smoother.

2. Plan with Precision

At this stage, we turn ideas into a clear roadmap. In our experience, skipping or rushing the planning stage when combining big data and logistics leads to strategic mistakes and missed deadlines.

Key questions to be addressed together:

• What data will you use? (Think GPS, CRM, IoT sensors, etc.) Effective data discovery can lay the groundwork for smarter strategies. 
• How much data do you need, and how often will you need it?
• What does success look like? (e.g., real-time dashboards, predictive analytics, full integration with your existing systems)
• Security and compliance: Who needs access? Any industry standards to meet?
• Tech choices: Cloud vs. on-premises, preferred languages, and platforms;
• Timeline and budget: Map out major milestones, key roles, and budget checkpoints.

3. Design the Architecture

Here’s where you build the foundation. We advise investing time in:

• Mapping all your data sources and how they connect, including a clear approach to data modeling;
• Designing a scalable, flexible architecture that fits your data volume, analytics needs, and future growth.
  

CHI Software’s tip: Start considering from an early stage how your data will flow – not only where to store it. These considerations will help to avoid integration headaches later on in the process.

4. Develop, Test, Iterate

Now, the build begins. Big data in logistics usually means tackling the following features:

• Real-time fleet and parcel tracking,
• Automated invoicing and reporting,
• Actionable dashboards and analytics.
  

Don’t underestimate testing. From our experience, quality assurance is crucial at every stage – it enables you to catch small issues before they become bigger ones, especially when your logistics network is complex and interconnected. Iterating quickly and holding regular check-ins with stakeholders will keep everything on track.

5. Deploy and Prepare for Growth

Finally, it’s time to go live. The deployment will help you ensure your new solution runs smoothly on your current infrastructure, integrates with your day-to-day tools, and can scale as your business and data volume grow. It’s also a great moment to evaluate the benefits of data migration, such as improved accessibility, performance, and system flexibility.

Our final advice: Think of deployment as the start of an ongoing process of improvement. Big data projects never stand still, so it’s a good idea to plan regular reviews and updates as your needs and the technology continue to constantly evolve.

These tips may seem complicated when explained in general terms. Let us review a real-life case study to see “before” and “after” more clearly.

How Does Big Data Implementation Look in Practice? Our Insights

CHI Software took part in developing an advanced VHM (Vehicle Health Management) system based on applying AI in car inspections. Our client is a well-known provider of AI-driven automotive software, headquartered in Israel. 

Project Background

The client’s representatives reached out to our team intending to reduce costs for car maintenance using a VHM system – a set of sensors located in different car parts and software receiving signals from these sensors. But there is a catch: the existing systems would notify users only after a certain issue has already occurred. Our client wanted to go further and predict beforehand if something may go wrong – a perfect scenario for applying big data analytics for logistics and transportation.

Our Solution

The optimal solution for the client’s needs was an AI-powered tool with the ability to process hundreds of signals in real time. Its implementation would need to cover three main tasks:

1. Monitoring the car’s state,
2. Identifying and predicting issues,
3. Suggesting maintenance options.

What else is being done:

• CHI Software’s data scientists develop deep learning models allowing for unsupervised anomaly detection;
• She also works on automated machine learning (ML) solutions based on synthetically-created anomalous data;
• Our ML engineer receives models from data scientists and runs them on the big data solution, along with cloud-based calculations.

What Are the Results So Far?

1. Using this powerful big data tool, our client is now able to detect anomalies with the highest precision ever;
2. ML algorithms highlight errors and defects before they occur, allowing the company to literally look into the future;
3. The system identifies even the smallest of anomalies that, previously, would often slip by unnoticed by technicians.

Although the concept of a VMH system is not new, our project considered the individual needs and requirements that were critical for our client. We apply the same to any big data solutions for logistics, especially when managing complex vehicle systems and large-scale operations. We believe that to achieve the maximum effect, decision-makers and developers should maintain close collaboration, especially at the project’s inception.

Conclusion

Logistics and transportation businesses have to deal with vast data sets, and those who manage them properly can soon emerge as market leaders. Reduced costs, transparent processes, and improved customer experience are just a few of the advantages that can become a reality when your logistics data analytics capacity aligns with your business goals. 

However, you shouldn’t start implementation before determining where to direct your efforts first. Any big data project starts with thorough research, planning, and then architecture development. None of these stages will run smoothly if your company management is not involved in the process.

Feel free to contact CHI Software’s data engineering team to learn more about big data opportunities for your business. Where should you start, and what data to prepare? Our experts know all the answers.

FAQs

• Do I need to overhaul my entire IT infrastructure to implement big data in logistics?

  Not at all. In our experience, the best approach is usually phased: we identify integration points, connect with your current tools (like TMS, WMS, and CRM), and gradually add new data sources and analytics. This way, you get new value fast, without business disruption or major upfront investment. Many of our clients start seeing benefits just by making smarter use of the data they are already collecting.

• What does a typical implementation timeline look like for a logistics company like mine?

  Timelines can vary, but here’s a realistic outline:
  - Discovery and planning: 2-4 weeks;
  - Architecture & integration: 3-6 weeks;
  - Development & testing: 4-10 weeks (depending on your scope);
  - Deployment and release 1-2 weeks.
  
  For most mid-sized logistics companies, an initial big data analytics solution can be up and running in 2-3 months. If you start with a pilot or focus on a single business area (like route optimization), results can start to show even sooner.

• How soon can I expect to see a return on investment (ROI) from big data analytics for logistics and transportation?

  Most logistics companies start seeing measurable results within the first 3-6 months – sometimes even faster for targeted solutions like route optimization or predictive maintenance.
  
  Early wins often include cost savings, faster delivery times, and fewer errors. The bigger impact grows over time as your analytics platform learns from more data and helps you spot new opportunities for efficiency and growth.

• Can my business benefit from logistics big data analytics if my team isn’t experienced in this technology?

  You don’t need to hire in-house data experts to get results. Our approach is to handle the technical part for you, from setup to daily operations.
  
  CHI Software focuses on user-friendly dashboards, transparent reporting, and training sessions to enable your team to make data-driven decisions without needing to become analytics experts overnight. Most of our clients start with little to no experience with big data – the key is having a partner who can guide you through the process step by step.

• What are the key risks to watch out for when launching big data for logistics?

  Like any major initiative, a big data project may encounter a few pitfalls without careful planning. Based on our experience, here are the main risks to keep in mind:
  
  - Poor or inconsistent data quality;
  - Unclear business goals or KPIs;
  - Integration challenges with existing systems;
  - Data security and compliance risks;
  - Underestimating team training and change management.
  
  Most risks are manageable with the right approach. We recommend starting small with a focused pilot, setting clear success targets, and involving both business and IT stakeholders from the very beginning. That way, you stay in control and set your project up for real, long-term value.