Big Data use cases in logistics: 7 examples of how to improve operations
In the article, we’ve collected Big Data logistics use cases to show you the technology power of the industry. Just notice, according to DHL’s Logistics Trend Radar, Big Data will have a huge impact on the supply chain in 5 years. Large companies such as UPS, Amazon, Walmart, Kuehne & Nagel tend to make data-driven decisions.
Let’s learn what is Big Data, what logistics problems it solves, and how it works for the greatest market players.
- 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
What is the definition of Big Data?
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, 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, not all data is “big”. To identify big data, scientists operate with 4 characteristics:
- Size. It’s the main characteristics, as value criteria. Big Data is about petabytes to exabytes of information.
- 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 Hawayan pizza more often on Friday.
- Processing speed. Big Data is generated every second. The higher is the processing speed, the more significant would be the potential.
- 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.
From gigabyte to terabyte
From petabyte to exabytes
Way of keeping
Semi-structured or unstructured
Model of keeping and processing
Interrelations between data
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 level.
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 7 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, risk absence. Moreover, a carrier has to prove a 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.
Case #2 Demand forecasting
Retail and logistics companies have to create warehousing stocks, deliver goods to outlets just in time, 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 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, the distance from point A to B.
Even UPS faced the problem of routes 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.
Case #4. Logistics costs reduction
For years logistics companies have tried to reduce fuel, repairing expenses. And a new challenge about CO2 emission has come.
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, 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 on distribution centers and warehouses. If you haven’t seen the article, just follow the link. But here is one more inspiring example.
For years Lineage Logistics shows great results on 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 on every stage of supply chains. The solution allows companies to track cargo location and status in real-time. It monitors humidity, temperature, atmospheric pressure, illumination conditions. Then, manufacturers never lose sight of the parts.
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.
To sum up, Big Data is the technology that perfectly suits logistics business needs. As managers make mistakes, but analytics stays immaculately correct.
We hope these Big Data logistics use cases will inspire you to do data-driven decisions. And if you need a technological solution, CoreTeka’s team is here to help. Just contact us.