Big data has opened a realm of possibilities in many fields of business, and logistics is one of them. According to Allied Market Research[1], the value of the global logistics market was at $7,641.20 billion in 2017 and is expected to hit $12,975.64 billion by 2027. The rising figures can be attributed to logistics operators realizing the true potential of data analysis in optimizing their operations.

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As logistics transportation and management become more complex and dynamic, more bottlenecks are created at different supply chain points, making the industry a perfect use case for big data analytics. Customers, for example, are demanding faster shipments at affordable rates and with more transparency. In this case, collecting and analyzing different logistics data is important in evaluating all the major key performance indicators (KPIs). This helps companies decide on the areas of optimization.

In this article, we are going to talk about big data in logistics. Read on to find out how big data can be used to revolutionize logistics and supply chain management. But before that, let’s look at what big data is all about.

What is big data?

Big data is a term used to describe large and complex data sets that cannot be managed by traditional data processing software. These data sets can either be structured or unstructured. Structured data is pre-defined, searchable, and straightforward to analyze. It’s often numeric in nature. On the other hand, unstructured data is information that has not been structured in a pre-defined manner and cannot be processed or analyzed via conventional data tools. It can either be textual or non-textual.

big data

While big data has come a long way, its usefulness is just setting off. Cloud computing has expanded its horizons by offering elastic scalability when it comes to data storage and processing. Graph databases are also becoming important assets in big data. They are used to organize messy and complicated data points according to their relationships to boost performance, provide flexibility, and improve agility.

It’s not just the type or volume of data that is important for organizations. What they do with the data is what matters the most. If big data can’t be analyzed for insights that improve decision-making capabilities, then it’s useless. With this introduction done, we can take a look at big data in logistics.

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Uses of big data in logistics

The following areas are excellent examples of how big data can be used to revolutionize the way logistics companies operate:


Route optimization is one of the hardest nuts to break in logistics. This is because there is usually a simulation of different scenarios in the process of determining the best possible routes. And little planning and management mistakes can cause major problems that can damage the company’s bottom line in a big way.

logistics optimization

But you can find a way around it, or at least reduce its impact on your business by using big data. Big data and analytics tools in the logistics sector use weather data, shipment data, traffic situations, and delivery sequences to help you determine when it’s time to go. You can also assign the shortest route possible for delivery, saving a huge chunk of money that could have been channeled to fuel and other expenditures.

For big data to be successfully implemented in route optimization, data involved throughout the delivery process should be noted. These include:
• The frequency in which customers order goods
• Number of vehicles available for delivery
• Distance between the pick-up and delivery points
• Areas with the most and the least orders

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The last mile of a delivery service is the journey of a product from the warehouse shelves to the truck and finally to the customer’s door. It’s the key to customer satisfaction and accounts for up to 41 percent[2] of the entire supply chain costs.

Last-mile optimization is one of the areas in logistics operations that benefit hugely from big data. By analyzing data collected from various sources, logistics companies are better positioned to change and improve internal processes and control external factors in near real time. This increases transparency in delivery processes and improves customer satisfaction.

DHL[3] is among the many logistics companies that use big data to optimize last-minute delivery. It has a crowdsourcing initiative called MyWays. The idea behind this innovation is to form a sharing economy that resembles Uber. It uses a specifically designed mobile app to connect individuals who want to deliver packages along their everyday route for a small fee to delivery truck drivers. The platform uses big data technologies such as geo-correlation and complex events processing.


Big data has propelled the logistics industry beyond the typical track and trace practices to a whole new level of supply chain visibility. Customers and delivery companies can now track their packages while in transit and receive email alerts whenever the delivery vehicles make stops on the way.

logistics transportation

Through GPS devices, RFID tags, and bar codes, big data analytics technology can capture real-time traffic data, making it easier for logistics managers to schedule deliveries conveniently. These technologies can also send automated notifications to receiving facility managers when the delivery is within a mile of its destination, giving them ample time to plan ahead and avoid unpleasant surprises.

One of the latest applications of big data in logistics is the use of Internet of Things (IoT) sensors[4] within trailers to enable the monitoring and reporting of temperature, humidity, and other important factors in real-time. This allows dispatchers to handle minor issues before they explode into bigger problems.


Managing a warehouse using traditional standards like legacy and ERP systems doesn’t cut it anymore. We live in an era where customers want to know about the availability of a product before they purchase and get real-time updates on the delivery process.

Part of the glamour in using big data in warehouse management comes from the efficiency of operations. Managers can access a minute-by-minute overview of operations on mobile devices or computers by integrating big data analysis in warehouse management. This enables them to spot bottlenecks in workflow and come up with immediate solutions.


For seamless management of warehouse operations, data is analyzed by use of big data systems that utilize trillions of sensors. Alibaba, Flipkart, and Amazon are among the earliest adopters of these technologies for effective maintenance of goods in the warehouses.


Grocery stores and other businesses that sell perishable goods operate on paper-thin profit margins. This means that they cannot overstock inventories or let their products go to waste. They also want to avoid any form of contamination that might tarnish their brand’s reputation.

For stakeholders in these industries, big data is the best bet they’ve got when it comes to delivering quality products to their clients. Sensor-based Internet of Things (IoT) and barcodes take center stage in tracking perishable goods from their points of origin to their respective destinations. This enables brands that sell perishable goods to monitor the quality of their merchandise throughout the supply chain.


We live in a technology-driven era where consumers are well-aware of what they want and are not willing to settle for anything less than that. This is why every business owner should put the needs of their customers first. Some of the factors that diminish customer satisfaction in logistics include limited global coverage and late deliveries.

Big data presents an opportunity for logistics companies to improve the overall experiences of their customers by optimizing operations. Data analytics tools, for example, can analyze data collected from surveys and social media, helping companies gain valuable insights into what customers want. This allows them to tailor their services to exceed the expectations of their clients.

customer service

Amazon[5] attributes its excellent customer service to big data. The company gathers data on its customers while they use the website and analyzes customer reviews to understand their sentiments. This has significantly increased its customer base.


It’s important to get the address data of your customers right regardless of whether you’re doing business locally or abroad. Without a robust address standardization and verification process in place, your logistics and business correspondence operations are rendered ineffective.

Address records are not always accurate. People make a number of formatting and spelling mistakes when they fill in address data. To fix this, you need to standardize and validate the addresses on your record. Address standardization is the process of correcting address records, while address verification is the process of verifying if the address is authentic or if it exists.

To verify and standardize your address, you need a big data-powered address validation software such as SmartyStreets[6]. This software uses website tools and fully-documented APIs to validate and standardize addresses.


Logistics managers can now use advanced data analytics to dig deeper into driving habits such as braking, speeding, and driving time, among others. By doing so, they can spot and weed out inefficient practices.

They can also use information such as maintenance logs and sensor data about fleet conditions to conduct maintenance in advance. This translates to fewer delays caused by breakdowns and reduced fuel consumption.


The logistics market environment is very dynamic. This is why companies need well-founded concepts to enable them to act flexibly and set a strategic course for the future. Logistics location analysis and network planning are the main points that companies should focus on.

As part of network planning, they can use special software tools that enable them to perform strategic location planning, transport network planning, storage capacity utilization, and structural optimization. The goal behind this software optimization is to reduce costs and achieve a strategically-oriented logistics network.


Logistics capacity planning is an emerging problem in the context of supply chain management. Capacity planners grapple with data challenges. They need to get their hands on diverse information to achieve maximum information. These include data on demand trends and the distribution of customers. If data inputs are flawed, the whole planning operation goes awry.

network planing

But the use of big data technology and AI has made operational capacity planning easier than ever. Demand sensing tools, for instance, can show market trends in real-time. This means that logistics companies can plan their transportation and storage capacity based on real data and not simulated datasets.

Final thoughts on big data in logistics

Big data has transformed the nature of logistics in a big way. Logistics companies are aware of the great opportunities that big data can help them unlock and strive to make strides towards data-driven decision-making moving forward.

Using big data combined with artificial intelligence and business intelligence software allows companies to accumulate more information that can be used to generate valuable insights. The use cases discussed in this article are just a few examples of how big data is beneficial to logistics. If you operate in the logistics sector, take a look at our big data consulting services to find out more. We’re always happy to assist you with your AI and data-related endeavors!


[1] Logistics Market. URL: Accessed November 18, 2021.

[2] Cost of Last-mile Delivery. URL: delivery/#:~:text=total%20shipping%20costs.-,The%20last%20mile%20delivery%20services%20account%20for%2041%25%20of%20the,range%20of%20%2416%20to%20%2424. Accessed November 18, 2021.

[3] Digital Transformation. URL: Accessed November 18, 2021.

[4] IoT and Sensor Technology. URL:,that%20detect%20physical%20properties. Accessed November 18, 2021.

[5] How Amazon Uses Big Data to Transform Operations. URL:,of%20the%20e%2Dcommerce%20pile. Accessed November 18, 2021.

[6] Single Address Verification Tool. URL:  Accessed November 18, 2021.

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