4 relevant Big Data case studies in Logistics

Digital technologies are disrupting a vast number of industries. The logistics industry is becoming increasingly data-driven - innovations permeate every facet of this sector. 

With the irruption of Big Data, logistics operators started to realize the true potential of data analysis to optimize their operations. Behind every movement, package, or system, there are thousands of data that we can collect, process, and transform to generate compelling insights for making better decisions. That’s why big data case studies are continually growing in this industry.

The power of Big Data in Logistics

Big Data case studies in logistics unveil how data and analytics in logistics and warehouse management can be applied to current procedures and processes to improve efficiency and accuracy in the company’s operations. By providing real-time insights for tackling current concerns before they evolve into serious issues for a business, data can help improve storage, handling, transportation, and other processes when applied correctly. 

Interactions between shippers and their stakeholders continuously take place in the digital world. Thus, logistics players can collect relevant information that is highly beneficial for the company's accountability, visibility, and customer service. Data in logistics can be used to reduce inefficiencies in last-mile delivery, provide transparency to the supply chain, optimize deliveries, protect perishable goods, and automate the entire supply chain. 

Logistics companies are aware of these possibilities and are striving to make more data-driven decisions moving forward. As we will see in a selection of successful Big Data case studies, data, and analytics are helping logistics companies to shape a new way to understand their operations and embrace continuous improvement. 

Where do Logistics extract their Big Data?

Big Data works with several data sets that can be collected from different sources, including structured data, unstructured data, and even un-stored data, for the most innovative ones. 

So the very first step for a successful Big Data project lies in identifying the sources of valuable data in where we will need to drill down. 

Traditional systems, such as a Warehouse Management System of Fleet Tracking Software. These systems provide with relevant information related to the preparation times, the operations, the deliveries, etc.

Fleet data. Many companies are incorporating sensors and geolocalization technologies in the trucks so they can generate better data regarding the times, distances, and performance of the fleet. 

Motion data. Inside the warehouse, we can also collect motion data from correlating with our WMS information and better understand how we are performing in our on-site operations.

Third-party data. Meteorological and Traffic estate agencies provide valuable data relative to weather predictions or the state of the roads.

Supply chain data. Being able to gather data from other links of the supply chain such as the online demand if we provide logistics services for an e-commerce or the stock management in the stores if we work for retailers, can help us to better plan our operations. 

4 sound Big Data case studies in Logistics

Many logistics companies had started Big Data and analytical projects, and we can find several success cases. But these following 4 big data case studies are an excellent example of how Big Data strategies can make a significant impact on how logistics companies operate. 

UPS, optimizing last-mile processes.

The last mile of a supply chain is notoriously inefficient, costing up to 28% of the overall delivery cost of a package. During the process of delivery, even after parking nearby, delivery man’s phone GPS streams data to the UPS center, giving a constant account of how long the delivery is taking. This allows logistics companies to see patterns that can be used to optimize their delivery strategies. 

 DHL, sensors for gathering more data.

This logistics player has embedded sensors in all of its delivery vehicles, with GPS enabled smartphones covering any gaps. A third party validates these sensors for accuracy, and then the reliability and timeliness data from these sensors is used when DHL is bidding for new contracts. DHL has developed Smart Truck to enhance route planning based on the data taken from GPS trackers and roads. The telematics databases allow drivers to receive quick route updates and avoid traffic jams caused by accidents, weather conditions, etc. So far, Smart Truck has decreased the total number of miles by 15%, helping reduce fuel consumption and lessen CO2 emissions. 

TIBA Spain, third party data.

Keeping perishables fresh has been a constant challenge for logistics companies. However, big data and the IoT could give delivery drivers and managers a much better idea of how they can prevent costs due to perished goods. A temperature sensor is installed inside the truck to monitor the state of the products inside, and give this data along with traffic and road-work data to a central routing computer. This computer could then alert the driver if the originally chosen route resulted in the perishable products melting and suggest alternate routes instead. 

NAEKO Logistics, route optimization. 

Supply chain visibility is this warehousing company’s priority. It provides decision-makers with a powerful and dynamic visibility tool that measures information about delays, demurrage, and detention cases as well as the performance of logistics service providers, and automatically generates reports on all this data. Likewise, route optimization plays a crucial role in the determination of a vehicle path over possible routes inside a warehouse to optimize the flow of transport in terms of cost and time. This routing intelligence enables the company to save time and cost on staff manual sequencing, reduce mileage, and minimize unsuccessful deliveries.