Big Data in Logistics: Real-Life Use Cases, Benefits, and Examples

Big data in logistics covers massive datasets from IoT sensors, telematics, and barcodes that are used to improve supply chain visibility and operations. Explore its core implementation steps and real-life use cases in our article.

What is big data in logistics?

Big data refers to massive amounts of both structured and unstructured data that are hard to process by traditional methods. It’s usually collected from various sources and thus comes in various formats and types. For example, you can have fleet telematics data in CSV, shipment tracking in JSON, and transport management system logs in TXT.

Several important characteristics define big data:

• Volume: the size of the datasets, often measured in gigabytes or zettabytes;
• Variety: comes in both structured and unstructured forms;
• Velocity: implies real-time information generation and processing at high speed;
• Veracity: records should be kept accurate, relevant, and trustworthy;
• Value: business impact from the insights, such as costs/delivery time reduction.

For transportation and logistics, the most common big data sources include RFID tags, GPS devices, IoT sensors, and financial transactions from customers. Basically, every moment when an item is stored, transported, or processed, a new record is created. If a business owner knows how to properly collect and analyze this information, they can enjoy the high-impact benefits that big data brings.

Key benefits of business big data for the logistics industry

The companies are progressively recognizing the importance of big data. They are actively using it to become more competitive, sustainable, and client-focused. There are more advantages than meet the eye.

Increased transparency

With the help of big data based on real-time shipment updates, business owners can always know the location and state of their assets. They immediately learn about any accidents, delays, or vehicle breakdowns, and can develop a more proactive incident response strategy. The increased visibility throughout the supply chain reduces uncertainty and improves supply chain management as well, reinforcing trust with clients and partners.

Improved efficiency

Complex logistics networks, growing consumer demand, and service quality expectations drive businesses to improve performance. Big data strategy can enhance processes like route optimization, inventory management, and risk mitigation, raising the workflow efficiency. For example, optimal distribution of warehouse space leads to reduced operational costs, as companies don’t pay for extra space or unnecessary transfer of goods between storage.

Demand forecasting

Analytics-based planning is crucial for 98% of third-party logistics companies, and demand prediction is a vital part. Comprehensive analysis of massive sets of historical, market, and dynamic data helps businesses to create accurate forecasts on customer behavior and seasonal demand. It also helps to find hidden market patterns, foresee potential disruptions, and mitigate them in advance. Big data also expands forecasting horizons; so, the more information you have at your disposal, the more precise and extensive the results you’ll get.

Route optimization

Safe, reliable, and fuel-efficient routes remain one of the biggest challenges for businesses. While some believe the shortest route is best, safety should be prioritized, especially when transporting fragile goods. The wrong choice of track can result in damaged goods, transport regulations violations, or even cargo loss. 

By using big data, you can always know the condition of both the road and the vehicle, analyze which routes are the most efficient, and plan the logistics accordingly. Finding the best logistics corridor allows you to avoid high-risk areas, reduce fuel consumption, and speed up the last-mile delivery.

Reduced costs

High logistics costs not only result in higher product prices but also limit market reach and lower business competitiveness. The rising operational expenses, including fuel, vehicle parts, utilities, and storage, encourage companies to find new ways to modernize their supply chains.

Big data and automation help companies reduce their operational costs up to 10% in several ways. First, companies can allocate assets based on demand, eliminating the need to rely on guesswork. Second, companies can better manage their inventory and avoid surplus or understock. Lastly, data analytics in logistics allows companies to better plan and organize their processes, leading to freight cost optimization.

Improved customer experience

Customer experience is often overlooked when it comes to the use of big data. A detailed research of customer behavior and feedback provides insights on what works best and why:

• What customer channels are most efficient;
• What processes impact customer satisfaction;
• Which customer segments respond best to certain approaches;
• Which marketing strategies yield higher engagement.  

Big data also helps tailor future services and products to predicted customer requests. You can use it to notify users in case of a delivery delay, offer personalized recommendations, and handle customer support. Why does it matter? Companies that excel at personalization, are more likely to exceed their revenue goals (48%) and improve customer loyalty (71%).

Risk management

77% of surveyed executives reported facing negative supply chain events during a year, while only 30% of CEOs acknowledged prioritizing supply chain resilience as a prior investment focus. This gap reveals a massive underestimation of the potential of risk assessment and disaster response activities. With automated analysis of traffic patterns, weather conditions, and telematics, big data enables organizations to identify threats early and introduce coordinated disaster planning. It shifts logistics from reactive to proactive, strengthening resilience and efficiency.

What our big data experts say

Big data is fundamentally reshaping the logistics landscape, moving operations from merely reacting to issues toward a proactive and predictive footing. Nevertheless, despite the push toward automation, the human factor retains a critical influence. These advanced technologies are not designed to supersede the human workforce; instead, they serve as powerful aids, helping staff eliminate routine errors, process vast data volumes efficiently, and dedicate their expertise to the complex, strategic decision-making tasks that technology cannot (or isn’t allowed) to automate yet.

Big data isn’t some magic bullet. If you don’t manage it right and have a clear game plan, it can actually turn into a huge pain. Messy data, picking the wrong tech tools, or just sloppy data oversight can lead to bad conclusions, slow everything down, and result in wasted resources. Real success doesn’t come just from gathering data; it comes from being disciplined about turning that raw info into trustworthy, useful business smarts.

Yet, in the cutthroat world of logistics today, companies that aren’t leaning on big data are going to have a tough time keeping up. The era of basing critical logistics decisions on intuition or outdated reports is essentially over. Companies that neglect the opportunity to use data for predicting supply chain bottlenecks, optimizing costs, or ensuring precise delivery times will inevitably fall behind their competitors. Big Data is no longer a strategic advantage; it has become a prerequisite for sustained growth and survival in the market.

Senior

Machine Learning Engineer at SoftTeco

Volha Hedranovich

Real-world examples of big data in logistics

Moving from general benefits to specifics, let’s look at where the rubber meets the road – the most compelling examples of big data use cases in logistics.

Intelligent routing by UPS

United Parcel Service (UPS), one of the biggest supply chain companies in the world, has invested a significant amount of time and resources into developing ORION – an On-Road Integrated Optimization and Navigation system. 

ORION is a software designed to optimize routes for delivery platforms. It uses the company’s historical data and collects a variety of information in real time, helping UPS cut down operational costs and organize multi-driver and multi-stop routes. Key implementation benefits include:

• 100 miles per year cut in distance traveled
• $300 million annual savings
• 100,000 metric tons per year reduction in CO₂ emissions

Predictive machine maintenance by DHL

The world’s leading logistics company, DHL, has 92% of its facilities equipped with digital solutions, including big data analytics. With the focus on predictive maintenance, smart facility monitoring, and asset tracking, they combined this technology with IoT, computer vision, and AI to get a 360-degree view of their equipment.

The company uses noise sensors for predictive servicing of sorting machines and vehicle monitoring. One innovation is a portable box with sensors. It is placed on a conveyor line to detect critical vibrations and potential damage. Big data turns raw IoT information, such as noise patterns, temperature, and noise levels, into strategic intelligence. The business gets a detailed, continuous picture of machine conditions, helping prevent the majority of maintenance-related issues and cutting upkeep costs.

Smart forecasting by Amazon

Another example of analytics in logistics comes from a famous online marketplace. What was a patchwork of models back in 2008 has become, in 2020, an innovative tool for automating inventory management using historical big data and advanced analytical tools. It not only predicts their inventory needs but also prevents the risk of understock or surplus. 

The forecasting model is deployed fully in the cloud and uses natural language processing and machine learning. It analyzes browsing, purchasing, and sales data to predict demand and decrease volatility. In this way, the tool solves one of the biggest challenges in the logistics industry: correct asset management and reduced under- or over-stocking.

How to implement big data in logistics: a step-by-step guide

As you can see, big data plays a significant role in the modern logistics industry. However, some companies still hesitate to adopt it. One of the main reasons behind this uncertainty is the misconception about the complexity of the implementation process. Below, we break down the process into clear steps. Still, if you need assistance with project assessment and planning, you can opt for big data consulting first.

Define key objectives

Identify a specific reason you need a transportation and logistics solution powered by big data. Depending on whether it is inventory optimization, cost reduction, or cutting delivery times, the records you need to collect may vary. A clear understanding of the exact issue/challenge big data will be solving ensures that its implementation aligns with your long-term business objectives. 

Based on the goals, you will plan your big data strategy and will also set KPIs for monitoring and measurement. 

Collect and integrate data 

Once you’ve defined your business’s big data goals, you can start collecting the data to create a rich dataset. You can use various sources of information in the logistics industry:

• GPS and telematics for vehicle tracking in real time;
• RFID and barcode systems for inventory tracking;
• IoT sensors for monitoring the transportation conditions;
• Warehouse Management Systems (WMS) and CRM systems;
• Historical records for trend analysis and risk management;
• External sources, such as weather forecasts or traffic reports.

Note that you need a data storage solution in place before starting the information collection process. We recommend cloud storage services such as Amazon S3 or Google Cloud Storage for their scalability and efficiency. We also recommend consulting cloud experts to help you properly set up and configure your cloud solution.

Moreover, data quality means a lot. Before using any records, metrics, or readings, they should be cleaned and standardized to deliver reliable outcomes. The process includes removing duplicates and errors, converting measurements to a common standard (miles/kilometers, gallons/liters), and merging data from various sources. After that, you can proceed with the implementation of data analytics for logistics.

Select the right analytics tools

Big data requires specialized analytics tools, so the next step is to select the right ones. Some of the most popular solutions include Apache Spark, Hadoop, and Snowflake, as well as analytical databases like Clickhouse and data build tools such as Transformation Orchestrator. Let’s review Apache Spark to understand its capabilities better.

Apache Spark is a unified engine for large-scale analytics. It works with big data in terms of data engineering, machine learning, and data science. You can manage all your data-related projects from a single platform that offers high speed and flexibility. Apache Spark supports Python, SQL, Scala, Java, and R, and provides an array of libraries and frameworks for convenient work with big data.

The key benefits of the computing system include:

• High-speed data processing;
• High-level APIs for easy development;
• Handling of structured, semi-structured, and unstructured data;
• Scalability from a single server to thousands of clustered nodes;
• Predictive and prescriptive analytics support.

Launch data-driven strategies and operations

With the implementation of big data, you will naturally have to integrate it into your current business strategy and operations. Connect analytics outputs to business software such as TMS, WMS, or fleet management systems to improve freight management, warehouse space utilization, and predictive vehicle maintenance. You can start data integration with a pilot in one warehouse or route to prove its accuracy, and then scale across the entire logistics network.

Train the team

With the introduction of new processes, you will also need to provide the training to your team. First, teach your staff how to use analytics dashboards and related systems. Ensure your team members can read, interpret, and act on data insights. For example, schedule vehicle servicing according to predictive maintenance reports. Then, encourage collaboration between IT, operations, warehouse, and transportation departments, ensuring seamless data flow and prompt action on insights.

One more thing you need to do is to encourage and promote data-driven decision-making. Before the implementation of big data, many processes in your business were most likely based on guesswork and intuition, which naturally led to poor outcomes. Hence, it is vital to change your organization’s way of thinking and gradually lead employees to rely on data first.

Ensure security and compliance

For any company working with confidential information, data security and compliance should be the top priority. Even the smallest data breach can lead to massive financial consequences and loss of trust from the customers’ side, so companies should pay extra attention to the ways they safeguard their information. 

The common security practices include: 

• Data encryption in transition and at rest;
• Role-based access control;
• Data masking and anonymization;
• Regular security audits;
• Disaster recovery plans and corresponding team training.

Companies should also make sure that the way they store and process the information complies with the applicable regulations, like GDPR and HIPAA, which are among the most well-known industry standards regarding sensitive data management and protection. If you don’t know where to start, the official websites of these regulations usually include information and even frameworks for reorganizing your processes to achieve compliance.

Monitor and optimize your strategies

Working with big data is a dynamic process, so you have to continuously monitor your workflows and optimize them, if necessary. The feedback from stakeholders and the KPI metrics will help you understand how effective your newly implemented processes are and what areas can be improved. Additionally, remember to timely adjust your algorithms and systems based on their performance and business needs so they remain accurate and effective.

Final word

Big data in logistics is a powerful asset that grants companies a significant competitive advantage and helps them understand how to make existing processes and services more efficient and customer-centric. While businesses acknowledge the advantages of data-driven decisions, they are still on the way to discovering their full potential. 

Today, AI, IoT, and computer vision produce more unstructured data than ever before, raising the need for its refinement and processing. We recommend partnering with a reliable IT vendor like SoftTeco, which has experience providing various big data services, from consulting to implementation and support. In this way, you will gain access to unparalleled expertise and knowledge, as well as an international talent pool. Contact us for more information, and we will gladly discuss your project.