Years ago, the structural axis of the supply chain shifted from being in the goods to reside in the figures. Terms such as Big Data or Data Mining are gaining ground in the sector, leading its transformation.

Today's society generates more data in two days than in the entire history of mankind. Even so, the potential of numbers is an undiscovered terrain in a landscape in which the logistics sector has shown itself to be far from reaching its ceiling.

It is precisely in this context, when companies start to grow, that they have to face the challenges resulting from increased operational complexity and lack of visibility.

As organizations begin to invest more in the functional components of their business, such as manufacturing, warehousing, logistics or order processing, communication silos occur. The resulting departmental disconnects lead to inefficiencies.

To combat these operational challenges, many companies often rely on key performance indicators(KPIs). However, operational data alone is not always useful in its original state. Typically, organizations need to take additional steps to make the information useful and actionable.

The value of data

The application of Big Data and Data Mining in the logistics and transportation business can improve the efficiency of many companies. According to recent studies, more than 90% of large companies worldwide are applying and increasing their investments in this type of technology for their operations.

Its potential is not only capable of improving current processes. It also makes it possible to anticipate future consumer demands and detect new business models, among other things.

In this sense, both concepts have been essential to materialize the definitive entry of logistics and the supply chain into the digital era.

Are we ready for Big Data in logistics?

Logistics and transportation are among the sectors that, in their activity, generate the greatest amount of data. These, in turn, can be exploited thanks to Big Data and Machine Learning.

Precisely for this reason, studies aimed at determining which industries are most prepared to interpret and analyze information place transportation and logistics in an intermediate position with respect to the rest of the fields, with a data literacy index of 75.5.

However, the concepts that bring logistics closer to Industry 4.0 are still confusing for many of its stakeholders, both in terms of definition and application.

Data Mining and Big Data: what are they?

Two of the concepts that generate the most confusion in the industry are directly related to information processing. We are talking about Data Mining, also known as data mining, and Big Data, also called macro data.

Both concepts relate to how to process large amounts of information to leverage it to optimize business operations and services.

In its definition, Big Data refers to a large amount of data. With volume, velocity, value and validity as its main characteristics, the term encompasses the technological developments that make it possible:

  • Storage of large volumes of information
  • Processing and analysis of structured and unstructured data in the shortest possible time for its efficient use.

Its importance, therefore, is not centered on the large amount of data, but on the benefit that can be obtained from it.

Data Mining is the process of discovering new correlations, patterns and significant trends by selecting from a large amount of data stored in repositories. To do so, it uses pattern recognition technologies, as well as statistical and mathematical techniques.

This analysis makes it possible to find unsuspected relationships and summarize the data in novel ways that are understandable and useful to the data owner.

What advantages do they offer to the supply chain?

Big Data and Data Mining establish a relationship in which the former represents the procedure and the latter the tool with which the analytical process is carried out.

In this way, both complement each other to offer multiple advantages to the sector.

In the case of the application of Big Data to logistics and transportation, its potential is applicable to procurement, slotting and picking processes, route planning and monitoring, and the study of future user behavior, among others.

Data Mining is already present in many common tools for the industry, from the SGACRMs and ERP logistics software. With these techniques, solutions become more efficient, allowing operations to respond more quickly to anticipated changes through data analysis.

Relying on the potential of data

In short, although different, both data processing techniques are complementary for the logistics sector. Their application undoubtedly allows for more efficient process management.

Companies such as Walmar, Rolls-Royce or John Deere, have been among the pioneers in using data in their operations, managing up to 2.5 petabytes of data per hour.

If these new technologies continue to be incorporated into the work dynamics, the supply chain will be more profitable and will offer a better experience to all the actors involved.