Big data in logistics: 5 real use cases

With the technology shift in the supply chain landscape, there has been abundant space for modern technologies such as Business Intelligence, the Internet of Things, and Big Data Analytics. This digital transformation in the manufacturing and supply chain industry has resulted in revenue growth as the market is estimated to reach 10.17 billion USD by the year 2025. When it comes to Big Data analytics, the process is pretty explanatory. Your supply chain business generates Big Data; Big data analytics analyzes and turns that data into actionable insights.

Big Data in Logistics

Logistics is a major element of supply chain management that requires faster shipments for less cost and more transparency. Transportation in logistics also needs big data technology due to specific challenges of traffic, weather conditions, fuel prices, and government regulations.

Big data in logistics simplifies all of this by ensuring optimized routing, streamlining factory functions, and transparency to the entire supply chain. Let us unlock the realm of possibilities big data has opened in the field of logistics and transportation.

This article guides you with real-time benefits and business use cases of Big Data analytics in logistics, or as we call it; Logistics data analytics. How about starting with all the positive impacts big data brings to the logistics industry?

Impact of Big Data Analytics in Logistics

Implementing logistic data analytics may be a complex and time-consuming affair. However, the benefits of big data can result in improved data accuracy, profound insights, reduced costs, precise forecasts, and many more. Let’s take a brief look at how Big data impacts logistics.

Increased Transparency

The big data analytics systems and software, along with tracking tools and GPS devices, allow logistics businesses to monitor the movement of shipments in real-time. This information is further combined with on-road network and fleet data so you can plan and schedule deliveries efficiently considering the road and weather conditions.

Improved Customer Experience

Big data for logistics and transportation has a profound effect on the overall supply chain performance. A responsive supply chain business meets its consumer expectations delivering quality products on time. Logistics data analytics helps you understand the monitor situation, find new sales opportunities, segment your customers, and even predict the future state that enables you to make customer-centric business decisions.

Efficient Warehousing

The biggest benefit of big data in business is gaining detailed insights into the process of loading, carrying, and unloading goods. You have the power to control and modify your inventory based on your customer preferences and expectations. Warehousing is the base of supply chain logistics that requires undivided attention. In such a case, big data allows you to monitor every single movement of goods in real-time.

Now that you are familiar with the various benefits and after-effects of implementing logistics data analytics, it’s time to discover potential big data use cases for your business.

Big Data Use Cases in Logistics

With the consistently growing popularity of data management and analytics in the supply chain, we say that the future is bright for enterprises willing to adopt logistics data analytics. The below-mentioned areas are excellent examples of how big data can be used to revolutionize the way logistics businesses operate.

Last-Mile Processes Optimization

The last mile of a delivery service is the entire journey of a product from the warehouse shelves to the delivery vehicles and finally to the customer’s door. Industry experts state that the last mile process is the key to customer satisfaction and accounts for nearly 40% of the entire supply chain cost.

Last-mile logistics optimization is carried and executed by Big data analytics. By analyzing collected data from various sources, you can change and improve your internal process and control external factors in near real-time. Big data technologies such as Geo-location network analysis and complex server processing help provide precise insights on the status of last-mile goods.

For instance- DHL is a renowned logistic company that uses Big data logistics to optimize its last-mile delivery.

Route Optimization

We did give you a rough idea of big data utilization in route optimization. However, we are going to discuss this point in detail since route optimization is the hardest nut to break in logistics. Minor planning and management mistakes can lead to significant problems directly impacting the company’s bottom line.

Big data here can rescue you by determining traffic situations, weather data, shipment data, and delivery sequences.

The big data tools also help you identify the shortest route possible for delivery, saving you money that can be channeled to fuel and other expenses. If you are willing to leverage big data in logistics to its full potential, make sure you successfully implement it by monitoring the below data points:

• The distance between pickup and delivery points
• The frequency in which customers order goods
• Number of vehicles available or used for delivery
• Areas and locations with the most and least orders etc.
• Address Verification and Standardization

It is essential to gather the address data of your customers (local and global) irrespective of your business domain. Your supply chain logistics and business correspondence operations are rendered effectively with a robust address standardization and verification process.

How is this important for logistics?

Address records are not always precise since customers frequently make a number of formattings and spelling mistakes while filling the address form. To fix this, big data standardizes and validates the address on your record. So, address standardization is the process of correcting the customer address of your records, and address verification is verifying if the updated address is authentic.

The above processes are conducted by big data-powered address validation software that uses website tools and fully documented APIs to validate accurately.

Predictive Maintenance and Strategic Network Planning

Predictive maintenance is the most extensive use case of big data in logistics and supply chain management. You can use detected information such as maintenance logs and sensor data to conduct advance maintenance in your fleet conditions. Predictive maintenance generally translates to fewer delays and breakdowns and reduced fuel consumption.

Network planning and logistic location analysis are other points that logistics businesses must focus on. Several big data-based software and tools help perform storage capacity utilization, structural optimization, and transportation planning which is directly proportional to timely deliveries and cost-efficient strategies.

Operational Capacity Planning

Supply chain and logistics capacity planning is an emerging problem in the supply chain management arena. Mainly because the capacity planners deal with precise data challenges, they need their hands-on distribution of customers, demand trends, market preferences, and other diverse data points to achieve maximum information.

With big data in logistics, certain demand sensing tools exhibit market trends in real-time. This means you can easily plan your storage and transportation capacity based on actual data rather than simulated data sets.

Are you ready for big data logistics solutions?

Big data has revolutionized the nature of logistics in a big way. When you are into the logistics business, we believe it’s essential for you to be aware of the great opportunities that big data unlocks. It’s high time to seek professional data analytics services to make strides towards data-driven business models. Grab your hands on such opportunities now!