Big Data in Logistics: Its Applications

Leveraging Big Data is already revolutionising logistics. Large companies worldwide are increasingly investing in Big Data technology to optimise supply chain efficiency and improve their income statements.

Big Data is becoming the go-to technology to address the biggest challenges in logistics today, such as the surge in e-commerce, traffic congestion, warehouse relocation, higher energy costs, environmental restrictions in urban areas of large cities…

What Is Big Data in Logistics?

In logistics, there are many different processes that continuously generate a large volume of data in their operation. The origin of Big Data in logistics is very heterogeneous. Some examples of these data sources are:

• Data from the automation and robotisation of warehouses.
• The location coordinates of entire vehicle fleets generated by GPS technologies during their journeys.
• Data generated in logistics processes with technologies such as RFID (Radio Frequency Identification).
• Alerts on stock-outs at points of sale.
• Real-time consumption patterns.
• Traffic and weather forecast

How Is Logistics Using Big Data?

Data, once converted into information, can be harnessed to make the most of it. Big Data not only serves to improve the efficiency of current processes, but also to anticipate consumer demands and detect new future business models.

Managing Big Data properly can yield valuable strategic solutions for companies, like reducing delivery times, preventing product deterioration, reducing errors, speeding up warehouse processes, saving fuel…

 Big Data in logistics allows companies to answer their strategic questions beyond intuition—in an objective and unbiased manner. If it reaches decision-makers in a timely and clear way, Big Data enables key business decisions to be made (that will achieve the desired results):

 Forecasting future levels of demand by analysing data on consumption patterns to adjust product prices to their logistics costs.

• Speeding up delivery time and incident management to improve customer satisfaction.
• Distributing resources better to reduce both stock-outs and overstocking in warehouses. As a result, significant cost and time savings are achieved.
• Mapping more efficient transport and delivery routes based on traffic data and weather forecasts.
• For temperature-sensitive products, preventing possible interruptions in the cold chain by analysing variables such as weather forecasts or traffic.
• Foreseeing predictive maintenance actions and avoiding the added cost of possible breakdowns.

In short, Big Data is already being used in national and global logistics—improving companies’ supply chain profitability and enhancing the customer’s shopping experience, resulting in increased loyalty.