September 01, 2023

Big Data in Logistics: 10 Successful Examples

• What is the definition of Big Data?
• How can Big Data use cases in logistics improve supply chains?
• Case #1. Transportation of goods with special storage conditions
• Case #2. Demand forecasting
• Case #3. Routes Optimization
• Case #4. Logistics costs reduction
• Case #5. Warehouse Management
• Case #6. Supply Chains Visibility
• Case #7. Drivers’ Safety
• Case #8. A tailored approach
• Case #9: Freight matching
• Case#10: Delivery optimization, warehouse management, and more
• How Coreteka developed a Big Data tech solution to forecast consumer demand?
• Big Data in logistics FAQ
• Conclusion

To operate a supply chain efficiently, modern companies need access to real-time data and the ability to analyze it quickly. However, only 7% of supply chains are currently able to perform this task.

Big Data in logistics can help optimize routes, enhance factory processes, and raise performance throughout the entire supply chain. Thanks to its increasing value and necessity across sectors, the big data market is likely to surpass $68 billion by 2025.

Therefore, the impact of Big Data on logistics is hard to overestimate. Supply chain management is a complex field that continually evolves, making Big Data applications an essential tool to keep logistics businesses afloat.

In this article, we’ll talk about the benefits of big data for supply chains and the winning Big Data logistics use cases to help you harness the tool for your own business success.

What is the definition of Big Data in logistics?

Data surrounds us everywhere. Today humanity generates about 1.7 megabytes of information per second and 2.5 quintillion bytes per day. When you surf the internet, buy products, or even breathe, you generate data.

You might be interested, in where humanity uses such a large scope of information. Here the principle works: the more you know about something, the easier it is to predict what will happen. So, the systems compare a great scope of information and identify patterns.

It should be noted that not all data is “big”. To identify big data, scientists operate with 4 characteristics:

1. Size. It’s the main characteristics, as value criteria. Big Data is about petabytes to exabytes of information.
2. Type. Earlier we used simple sheets with words and numbers to process data. But today we operate different sources of information, such as images, audio, PDF files, and even time spent at a mall. When data consists of one type of information, it’s called structured. It gets harder to find relations between different types of data. Then, we deal with unstructured information. Big Data is about working on information, which can be difficult to analyze. For example, how to explain, why clients order Hawaiian pizza more often on Friday.
3. Processing speed. Big Data is generated every second. The higher the processing speed, the more significant would be the potential.
4. Variability. Big Data is unstable, it has a value that depends on time.

For a better understanding, let’s compare traditional databases and Big Data.

Characteristics	Databases	Big Data
Information scope	From gigabyte to terabyte	From petabytes to exabytes
Way of keeping	Centralized	Decentralized
Granularity	Structured	Semi-structured or unstructured
Model of keeping and processing	Vertical	Horizontal
Interrelations between data	Strong	Weak

Big Data in logistics entails the use of large and complex datasets to gain insights, make informed decisions, and optimize transportation processes. It involves collecting, processing, and analyzing massive amounts of data generated by various sources, including sensors, GPS devices, RFID tags, customer interactions, and more. The goal is to extract valuable information and patterns from this data to improve operational efficiency, reduce costs, enhance customer service, and enable strategic decisions.

The picture below illustrates which areas of the logistics business Big Data can transform.

Ultimately, Big Data in the logistics industry transforms traditional supply chain management by providing data-driven insights that enhance performance and spur innovation.

How can Big Data use cases in logistics improve supply chains?

All players in the supply chain can solve a large number of problems with Big Data Analytics. For example, logistics providers predict delivery time according to weather conditions, traffic jams, characteristics of vehicles, etc. Manufacturers respond to fluctuations in demand and retailers regulate sales levels.

Here are some ways logistics companies can use Big Data for business efficiency:

• Routes optimization in real-time.
• Preventing road accidents.
• Decreasing money loss for repairing vehicles.
• Increasing delivery reliability.
• Demand forecasting.
• Improving client service.
• Cost reduction for storage.
• Optimization of warehouse stocks.

Let’s discover 10 real Big Data logistics use cases.

Case #1. Transportation of goods with special storage conditions

Transportation of medical supplies, frozen food, and hazardous materials is a big deal for the logistics industry. A transporter has to take care of package integrity, temperature conditions, and risk absence. Moreover, a carrier has to prove the freight was properly maintained.

Big Data solves the problem. In 2012, Swiss start-up SkyCell designed containers for medical transportation. These containers collect data about vibration, humidity, and temperature levels. The solution is powered with software to track changes in conditions and quickly respond. To prove that medical supplies are carried in safe conditions, reports are created. Today the annual capacity of SkyCell containers is more than 20,000 pallets.

Temperature-controlled containers with Big Data infrastructure enable safer logistics processes and improve product integrity. Supply chain managers in pharmaceutical companies should engage service providers that leverage real-life data, aiding in analysis and higher control of risk exposure instead of trying to remedy the consequences of temperature fluctuations. Ultimately, Big Data integration will make transportation processes more transparent, cutting expenses and elevating patients’ safety.

Explore why medical supplies transportation is one of the most challenging fields in logistics.

Case #2 Demand forecasting

Retail and logistics companies have to create warehousing stocks, deliver goods to outlets just in time, and control supply and demand. It helps to increase sales levels.

Domino’s Pizza is a great example of how Big Data helps to forecast demand. The company does its best to deliver orders in 30 minutes or less. For years Domino’s Pizza has been collecting data. And now the company uses the information to forecast demand. They know what pizzas will be ordered in 5 minutes. So, the further order is put in an oven beforehand. If there is no demand, Domino’s Pizza begins to stimulate it. They change content on the site and offer discounts. The system analyzes demand in every outlet by tracking visitors’ geolocation. A cook gets a pizza from an oven in 10 seconds before placing an order.

Also, read information about our automotive hackathon.

CoreTeka’s team has developed a similar tool for our client. It’s the Big Data Tool. The solution analyzes demand among outlets by weather, social, and cellular networks. So, a manager can coordinate promo activities, and a company manages the sales levels. Read more about Big Data Tool here.

Case #3. Routes Optimization

Delivery time is a critical indicator for transportation and courier companies, as the main principle of logistics is “to be just in time”. It’s difficult for managers to build optimal routes. You should consider weather conditions, traffic jams, and the distance from point A to B.

Even UPS faced the problem of route optimization. Then, in 2012, the company deployed the ORION algorithm. When a driver begins his work, the system provides guidance about the best route. Algorithms use descriptive analytics to forecast what happened in the past, and predictive analytics for what happened in the future. ORION works with more than 60,000 routes in the USA, Canada, and Europe. Since its implementation, the system saved 100 million miles for UPS.

Orion’s overall efficiency allowed the use of its data for other consumer solutions, including UPS My Choice® for home and UPS My Choice® for business. These tools help users by providing advance delivery notifications, forecasted delivery time, and the opportunity to change delivery locations. As a testament to UPS’s ORION success, in 2016, the project received the Franz Edelman Award for Achievement in Operations Research and the Management Sciences.

Case #4. Logistics costs reduction

For years logistics companies have tried to reduce fuel and cut expenses.

In 2018, to solve the problem of logistics expenses, DHL company has developed the Smart Truck solution. The company equipped its trucks with IoT sensors, which collect data about weather conditions, traffic jams, road accidents, etc. DHL plans to add more than 10,000 trucks with IoT sensors by 2028. Smart Truck has reduced empty miles by 15%, saved millions of fuel gallons, and decreased CO2 footprint.

For small and mid-sized businesses, there is no need to invest in such complex solutions. The best way to reduce logistics expenses is to implement the Transport Management System. In a custom solution, you can add functionality that perfectly suits your needs.

CoreTeka’s team has developed TMS for our client, Phillip Morris. The solution helped the company to decrease operational costs by 2% and reduce CO2 footprint by 1,2%. Check out CoreTeka’s Transport Management System here.

Case #5. Warehouse Management

Logistics operational activity depends on warehouses. Delays may result in millions of losses. By providing Big Data Analytics, companies spare warehouse workers the need to do routine tasks, such as filling in documents, picking, and packing.

We have already talked about how Amazon uses predictive analytics to manage orders in distribution centers and warehouses. If you haven’t seen the article, follow the link. But here is one more inspiring example.

For years Lineage Logistics has shown great results in warehousing. The company stores millions of goods and delivers them to more than 3,000 shops, restaurants, and cafes. Lineage Logistics’s engineers have developed smart algorithms to forecast where one or another order will be in a warehouse. So, the staff has time to organize pallets with goods. The closer the delivery date, the nearer the container to the loading area is. The solution helped the company to improve warehouse efficiency by 20%. As a result, the profit grows, and the customers are happier.

Case #6. Supply Chains Visibility

In more complex supply chains, such as large manufacturing, visibility plays a critical role. For example, in Audi’s warehouses, every part is vested with a particular auto unit. So, the company carefully plans the assembly of models and even uses drones to deliver parts to the right place. There is the question of how to track such a great scope of materials.

SenseAware, FedEx’s project, provides new opportunities to increase visibility at every stage of supply chains. The solution allows companies to track cargo location and status in real time. It monitors humidity, temperature, atmospheric pressure, and illumination conditions. Then, manufacturers never lose sight of the parts.

Specifically, SenseAware ID uses a small sensor that sends accurate package location data every two seconds via BLE to WiFi access points within the FedEx Express network. The sensor allows tracking packages hundreds of times compared to dozens of times with conventional package scanning protocols. Ultimately, users get a new level of precision in location-tracking, which facilitates safety, security, and timely deliveries.

Case #7. Drivers’ Safety

Road accidents are one more logistics pain point. To prevent occasions, Big Data Analytics works the best.

The Fleet Risk Advisors company has developed software to collect data about drivers’ working hours, movement speed, destination, time in route, etc. The most important functionality is analyzing the driver’s behavior through data. Logistics companies can use the information to manage the risks of road accidents, decrease expenses, and enhance transportation reliability.

CoreTeka does its best to increase drivers’ safety on the roads. That’s how the team decided to develop DriverApp. The mobile application has helped our client to keep communication between managers and drivers. Read more about the solution in our case studies.

Case #8. A tailored approach

C.H. Robinson, a third-party logistics company, utilizes Big Data analytics to offer personalized solutions to its customers.

The company has developed a Navisphere platform, a cloud-based technology solution that integrates data from various sources across the supply chain. The platform uses data to track shipments, monitor carrier performance, and provide real-time visibility of supply chain processes. In a nutshell, the platform acts as a centralized hub for data collection and analysis, helping customers make informed decisions and improve efficiency.

Enabled by Big Data, the Navisphere platform empowers the company to optimize logistics processes and deliver value-added services to its customers. The platform’s data-driven approach reinforces decision-making, elevates performance, and helps businesses navigate the complexities of modern supply chain management.

Case #9: Freight matching

Uber Freight functions as a ridesharing service in the logistics industry. The platform connects shippers and carriers and uses Big Data to couple available truck capacity with shipping needs. Real-time data on truck locations, traffic conditions, and load capacities allow Uber Freight to optimize freight matching and delivery schedules.

The essence of the service lies in the advanced freight matching algorithm. It considers a variety of factors, including location, load size, equipment type, and current market conditions. By analyzing this data, Uber Freight efficiently connects shippers with carriers, reducing the time and effort required to find suitable partnerships.

The way Uber Freight utilizes Big Data transforms the traditional freight brokerage model by providing a technology-empowered platform that enhances efficiency, transparency, and collaboration. The data-enabled insights improve operations for all parties, optimizing processes and elevating service quality in the logistics industry.

Case#10: Delivery optimization, warehouse management, and more

Amazon is a company that heavily relies on Big Data for its logistics operations. The company is a pioneer in using this technology to transform its activities across various areas, including logistics and supply chain management.

In particular, Amazon employs Big Data analytics to optimize its supply chain operations and forecast market situation. The company gathers and analyzes data on customer preferences, purchasing behavior, and market trends to forecast demand accurately. This helps Amazon maintain the right levels of inventory at its fulfillment centers and reduce stockouts while minimizing excess inventory costs.

Furthermore, Amazon utilizes Big Data for warehouse management and order-picking processes. Data from sensors, RFID tags, and other sources help track the movement of items within warehouses, optimizing storage space and improving order fulfillment speed.

How Coreteka developed a Big Data tech solution to forecast consumer demand?

Thanks to our experience with Big Data and logistics, we develop technologically advanced solutions tailored to the requirements and needs of companies.

For example, one of our implemented projects is the Big Data Tool for Philip Morris. It was specifically designed to forecast demand and popular visiting times at retail points based on data. Accurate predictions enable effective supply chain management as well.

Big Data is not the only technological solution that can benefit your logistics company. If you are exploring other possibilities, consider:

• Blockchain. This can be applied to develop applications securing data transfer among different participants.
• AI-assisted chatbots. These solutions can enhance your customer service.
• Internet of Things (IoT). The tech is used in logistics for real-time fleet management and end-to-end visibility in the delivery process. IoT can also be utilized to improve warehouse operations, such as smart labeling, storage conditions control, and inventory level tracking.
• AR/VR. Such technologies can be used to enhance warehouse, transport, and last-mile delivery operations, such as accelerating picking and freight parcel-loading.
• Machine learning. This technology is closely related to Big Data. With machine learning, you can create solutions for demand forecasting, production planning, stock tracking, schedule optimization, etc.

If you have a technological project idea but are unsure how to bring it to life, we can help by analyzing your business and finding a solution.

Big Data in logistics FAQ

Conclusion

Embracing Big Data in logistics and supply chain management is crucial for companies aiming to retain a competitive edge. The tool delivers a higher transportation speed and transparency, together with multiple other benefits for service providers, carriers, and customers alike. Furthermore, efficient operations today become impossible without data-driven decisions.

In this article, we compiled a list of Big Data logistics case studies intending to demonstrate the breadth of areas where the tool can be applied. Once you understand the essence and benefits of big data in logistics and trucking and get inspired by the spectacular examples, you can think of implementing data-empowered solutions for your own prosperity.

Let Coreteka’s team streamline your journey. Contact us to plan your project, and we’ll bring the right technology to your service.