The transport of goods is an essential industry that has a tangible impact on the activities of organizations around the world, particularly e-commerce companies. Indeed, by ensuring correct delivery at the allotted time, you can guarantee optimal inventory management. This also enables the deadlines for placed orders to be met. Therefore, it has become necessary for transport networks, logistics, and e-commerce to manage transport in a more optimal way to make it faster and more economical. Therefore, logistics has embarked on a profound data transformation over the past 10 years, as in almost all industries. Let’s take a look at some compelling use cases of big data in logistics.

It doesn’t really matter what aspect or sector of the logistics industry you have in mind. Almost every single one of them has been recently improved, optimized, and accelerated thanks to big data and data science. This refers to air/sea/road transportation, supply chain management, inventory management, and warehousing processes. And in this article, we are going to show you some of the most interesting examples of such applications of big data in logistics.

Big Data and Transport Management

Big data is used in companies to improve efficiency thanks to the collected information from customers and inside the company itself. According to the Parcel Shipping Index report, in 2019, an average of 3,248 parcels was sent every second in 13 major markets globally, corresponding to an average of 27 packs per person. So it is essential to analyze data to save time and achieve savings. Of course, the use of this data at the logistics level is only in its infancy, but already now, you can see the interest it brings for logistics and e-commerce. By integrating Big Data into their supply chain, companies offer high-quality customer service in deliveries and shortage prediction, which are the two main aspects that determine customer loyalty.

Big Data Optimizes Procurement and Inventory Management

Big data intervene on two levels in the supply chain, first of all, replenishment and inventory management. Until now, many companies have faced a significant problem: their predictions gave very rudimentary information because they were based only on inventory rotation management. Replenishment was assessed based on stock levels and several criteria, such as the seasonality factor.

Thanks to big data, logistics companies can analyze many variables, such as the weather, sales growth around the world in line with fashion, the selling price of the raw material, or information about the possible shortage of materials. All information is integrated and processed by machine learning, streaming analytics, and predictive algorithms. This enables the interoperability of various tools present in the company – CRM, ERP, purchasing management, inventory, or supply.

Supply Chain Management Optimization

Big data also significantly influence the optimization of deliveries. For example, data such as road traffic, weather conditions, selection of the type of loading, delivery method, route optimization, or warehouse selection enable optimization of the supply chain and rationalization of transport costs.

The model company in this area is, of course, Amazon. But big data is not reserved for big companies. Even tiny ones, SMEs also effectively implement big data strategies to improve their supply chain management. The growing popularity of the big data approach to the supply chain has made BD solutions a significant issue in terms of competitiveness. 

Improved Customer Experience

Customer satisfaction is one of the primary goals of any logistics chain. Thanks to big data technologies, companies operating in this industry can offer their clients more personalized services. Data and its skillful use enable segmentation of customers according to their profile and better targeting of services and promotional offers provided to them.

Big data strategies are often combined with other modern technologies. A great example is the combination of BD capabilities with Natural Language Processing (NLP). Thanks to NLP and chatbots to conduct dialogue with the client, it effectively meets their needs and expectations. NLP also enables the analysis of comments on forums and social networks and the improvement of complaint handling. Researching the data combined with analyzing customer feedback gives you better visibility of the steps that need to be taken to further improve quality and customer satisfaction.

Also Read: 9 Ways Any Business Can Improve Customer Relations

Data-driven Decision Making

People have to make decisions from the simplest to the most complex in all contexts, including external logistics. They are, of course, taken based on data. However, when data sets become abundant, too much information makes them useless in the raw state for decision making. In addition, all collected data may not be suitable for your use and will not be of any value for the development of your business. Moreover, useless data can be misleading and produce meaningless results, which can have very dramatic consequences.

To make effective decisions that bring companies closer to business goals, it is necessary to equip staff with technologies to facilitate this process and make the right choices and arrangements.

The Holistic Approach to Big Data in Logistics

The entire logistics industry can benefit significantly by placing big data solutions at the heart of its operations. Thanks to more efficient processes, not only processing times can be reduced, but also costs. In addition, the minimization of risk in conjunction with the optimization of procurement and inventory management allows for an overall improvement in customer service. In turn, optimized performance and greater customer satisfaction are the perfect recipes to move the logistics industry towards a proactive, predictive and personalized industry.

Of course, data collection is no magic wand. Logistics companies realize they need to stop relying solely on supply or demand to solve supply chain problems quickly. Instead, you need a holistic approach that focuses on people and their skills, processes, predictive analytics, and scalable technology based on structured and unstructured data for both supply and demand. Indeed, this is the essence of big data, mobilizing untapped resources to enable companies to achieve previously unexpected operational and financial performance. It would be a pity not to be able to take advantage of these technological possibilities.

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