Big Data Analytics in Logistics: 10 Use Cases

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 analytics in optimizing their operations.

As logistics industry transportation and management become more complex and dynamic, more bottlenecks are created at different supply chain points, making the transportation 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 data from different logistics data sources is important in evaluating all the major key performance indicators (KPIs).

This helps companies decide on the areas of optimization through data driven insights.

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

Big Data in Logistics: Understanding the Concept

Big data is a term for large and complex data sets that cannot be managed by traditional data analysis processing software.

These data sets can either be structured or unstructured. Structured logistics 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 analytics tools. It can either be textual or non-textual.

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

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

Read more about Real-time Big Data Analytics

The Critical Role of Data Analytics in Modern Supply Chain

Before diving into specific applications, it’s essential to understand how data analytics has fundamentally transformed the supply chain ecosystem. Modern supply chain management relies heavily on data driven insights to enhance operational efficiency, reduce costs, and improve customer satisfaction.

Data analytics in logistics enables companies to extract meaningful patterns from vast amounts of logistics data, leading to more accurate demand forecasting and strategic planning. By leveraging advanced data analytics techniques, organizations can identify inefficiencies within their supply chain and implement targeted improvements.

Uses of Big Data Analytics in Logistics

Modern logistics has evolved from a simple transportation model to an intricate, data-driven ecosystem where intelligence defines competitive advantage. Data analytics has become the cornerstone of this transformation, offering unprecedented insights and capabilities across multiple operational domains in the supply chain.

1. Route Optimization: The Intelligent Navigation Revolution

Route optimization is no longer about finding the shortest path, but about creating the most efficient, cost-effective journey. By integrating complex logistics data streams including real-time weather conditions, traffic patterns, historical delivery data, and vehicle performance metrics, logistics industry companies can now calculate routes with extraordinary precision using data analytics.

Companies can now reduce fuel consumption significantly, minimize delivery times, and predict potential transportation challenges before they emerge. The ability to dynamically adjust routes in real-time transforms supply chain management from a reactive to a proactive discipline through data analytics in logistics.

But you can find a way around it, or at least reduce its impact on your business by using data analytics.

Data 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 data analytics to be successfully implemented in route optimization, logistics 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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2. Demand Forecasting Through Advanced Data Analytics

One of the most powerful applications of data analytics in logistics is demand forecasting. By analyzing historical sales data, market trends, and external factors like seasonal variations, data analytics enables companies to predict future demand with remarkable accuracy.

Accurate demand forecasting helps optimize inventory levels, reduce stockouts and overstocking, and improve overall supply chain efficiency. Advanced data analytics models can incorporate multiple variables to create nuanced demand forecasting scenarios that account for market fluctuations and consumer behavior changes.

Leading retailers and manufacturers are increasingly using data analytics for demand forecasting to gain competitive advantages in their supply chain management practices.

3. Optimization of the last-mile processes

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 data analytics. 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 through data driven insights.

DHL[3] is among the many logistics companies that use data analytics 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 data analytics in logistics technologies such as geo-correlation and complex events processing.

4. Tracking the transportation of goods

Data analytics 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.

Through GPS devices, RFID tags, and bar codes, data analytics in logistics 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 data analytics 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 by leveraging data driven insights.

5. Warehouse management: Intelligent storage system

Managing a warehouse using traditional standards like legacy and ERP systems doesn’t cut it anymore in modern supply chain management. 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 data analytics 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 data analytics in logistics. This enables them to spot bottlenecks in workflow and come up with immediate solutions based on data driven insights.

Leading companies like Amazon and Alibaba have transformed warehouse management from a cost center to a strategic competitive advantage, using advanced data analytics to predict demand, optimize storage, and reduce operational waste throughout the supply chain.

6. Enhancing Inventory Management with Data Analytics

Effective inventory management is critical for supply chain efficiency. Data analytics in logistics provides real-time visibility into inventory levels, helping companies maintain optimal stock levels while reducing carrying costs.

By analyzing data from multiple sources, logistics professionals can identify slow-moving items, anticipate stockouts, and optimize replenishment schedules. This application of data analytics helps prevent both overstocking and stockouts, leading to improved cash flow and customer satisfaction.

Advanced data analytics can also identify seasonal patterns and trends in logistics data, enabling more accurate demand forecasting for inventory planning.

7. Delivery of perishable goods

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, data analytics 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 using logistics data.

8. Improved customer service

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.

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

Amazon[5] attributes its excellent customer service to data analytics in logistics. 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 through effective supply chain management.

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.

9. Address verification and standarization

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 data analytics-powered address validation software such as SmartyStreets[6]. This software uses website tools and fully-documented APIs to validate and standardize addresses, providing valuable data driven insights for logistics operations.

10. Predictive maintenance

Advanced data analytics have transformed fleet management by ushering in a new era of predictive maintenance. By leveraging real-time vehicle sensor data, historical maintenance records, and advanced AI-driven insights into driving behaviors, logistics companies can now anticipate and mitigate mechanical failures before they happen.

This proactive approach minimizes unexpected breakdowns, reduces operational disruptions, and extends vehicle lifespans. Additionally, it optimizes maintenance schedules, significantly cutting costs and improving overall efficiency in the supply chain. What was once a reactive, costly, and inefficient process has now evolved into a data driven insights strategic advantage for fleet operators through data analytics in logistics.

11. Strategic network planning

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 using data analytics.

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 through data analytics in logistics.

12. Operational capacity planning and Demand Forecasting

Logistics capacity planning is an emerging problem in the context of supply chain management. Capacity planners grapple with logistics 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.

But the use of data analytics technology and AI in logistics has made operational capacity planning easier than ever. Demand forecasting 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 by analyzing data effectively.

The Future of Data Analytics in Logistics and Supply Chain

As technology continues to evolve, the role of data analytics in logistics will only grow more significant. The integration of artificial intelligence and machine learning with data analytics is creating new possibilities for predictive and prescriptive analytics in the supply chain.

Future supply chain management systems will leverage real-time data analytics to enable truly autonomous decision-making, further optimizing logistics operations and reducing costs. The continued development of IoT sensors and connectivity will generate even more valuable logistics data for analysis, leading to deeper data driven insights.

Organizations that invest in developing their data analytics capabilities today will be well-positioned to lead the logistics industry tomorrow through innovative supply chain management practices.

Big Data Analytics in Logistics – Final Thoughts

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!

Big Data in Logistics – FAQ

How is Big Data used in Logistics?

Big Data is used in logistics in various ways, such as route optimization, last-mile process optimization, tracking the transportation of goods, warehouse management, delivery of perishable goods, improving customer service, address verification and standardization, predictive maintenance, strategic network planning, and operational capacity planning.

What are the benefits of using Big Data in Logistics?

Using Big Data in logistics can result in improved efficiency, cost savings, enhanced customer service, and better decision making. It can also lead to innovative solutions for complex logistics challenges.

How does Big Data improve customer service in Logistics?

Big Data improves customer service in logistics by providing valuable insights into customer preferences and behaviors. This allows logistics companies to tailor their services to meet and exceed customer expectations, thereby enhancing customer satisfaction.

How does Big Data contribute to route optimization in Logistics?

Big Data contributes to route optimization in logistics by utilizing data like weather conditions, shipment data, traffic situations, and delivery sequences to determine the most efficient routes for delivery. This can result in significant cost savings and improved delivery times.

This article is an updated version of the publication from Nov 24, 2021.

References

[1] Alliedmarketresearch.com. Logistics Market. URL: https://www.alliedmarketresearch.com/logistics-market. Accessed November 18, 2021.
[2] Jungleworks.com. Cost of Last-mile Delivery. URL: https://t.ly/unHS. Accessed November 18, 2021.
[3] Weforum.com. Digital Transformation. URL:  http://reports.weforum.org/digital-transformation/dhl/. Accessed November 18, 2021.
[4] Omron.com. IoT and Sensor Technology. URL: https://bit.ly/3J0HrqJ. Accessed November 18, 2021.
[5] Supplychaindigital.com. How Amazon Uses Big Data to Transform Operations. URL: https://bit.ly/3B9R3NU. Accessed November 18, 2021.
[6] Smartystreets.com. Single Address Verification Tool. URL:  https://www.smartystreets.com/products/single-address.  Accessed November 18, 2021.