Woodward Group understands the challenge in Logistics.

Selecciona tu idioma

Latest News

Stay informed of what is happening in the industry with clear and concise analysis of trends, technologies and services.

Using Big Data in Logistics

One of the most exciting technological innovations in these last few years, along with 5G and Artificial Intelligence, is Big Data, representing an opportunity for enhanced management and precise follow-ups along the international supply chain like nothing before it.

However, it is frequently mentioned as an abstract concept without any practical use that is studied as something that could help the industry at a future point.

This article will talk about the practical applications of this technology in logistics and straightforward definitions and concepts that most publications needlessly complicate.

We need to have in mind when reading about these innovative concepts because, although they are currently used only in rare instances, their use has been linked with improved management and cost efficiency for the international companies that have deployed them. It is only a matter of time before, just like e-commerce and ELDs, Big Data is implemented internationally and ends up innovating most companies around the world.

Definition, Principles, and Specific Applications.

Also known as microdata, Big Data, as can be inferred by its name, refers to the gathering of massive quantities of data that, because of their sheer volume, complexity, or the velocity in which they exponentially grow, are hard to capture, manage and process. They include information on the behavior of people, markets, and competition, allowing the creation of strategies that can simplify informed planning and forecasting market demand.

When implementing this aspect of “intelligent logistics,” the general objective is generating an enhanced competitivity that allows optimizing processes across the better part of the logistics process to offer a better service across the board and drive costs down. The data gathered needs to adhere to five principles that define whether it can be considered part of Big Data:

  • Volume: make the gathering of data completely automatic to continuously keep accumulating it, requiring anyone who hopes to work with these quantities to acquire computers with extremely high processing levels.
  • Variety: data must come from multiple sources and platforms, starting with but not limited to smartphones, ELDs, social networking, bank transactions, etc.
  • Veracity: managing large amounts of data from many sources will inevitably encounter incomplete information or complete falsehoods. It is necessary to question everything and differentiate between true and false data.
  • Velocity: data must be generated at a speed that makes it accessible to analysts on a real-time basis.
  • Value: we must turn raw data into informative reports that can generate immense value to any company that can exploit them.

close up manager man hand typing on keyboard laptop for working report about company's profit with response e-mail marketing from vendor or customer , multitasking job concept

With these principles in mind, all data that we gather must be divided into two categories: structured, which represents data that has already been organized so that it can actually be used, and unstructured, which means the complete opposite. In most cases, companies that can harvest this data need to have a permanent effort to regulate and adjust all the data. That fleets, warehouses, climate information, traffic analysis, the economy, user interaction online, and even product shortage notifications from selling points generate, so as actually to give some insight to planning or strategy departments.

With this in mind, we must take into consideration the importance of automatic digital tracking implementation in our process because, without it, we would have to manually record and present all this data which would be, to put it mildly, impossible to manage at the speed that Big Data requires to be worth anything.

What can Big Data do for us then? Next up is a list of the aspects in which it has proven itself useful, but bear in mind that this technology will be implemented increasingly in logistics with all kinds of purposes, which means that in very little time, this list will be obsolete or, at least, incomplete:

  • Warehousing management: when it comes to inventory, there is a diverse arrangement of software that allows the user to tap into Big Data and accumulate important information about the flow of storage items. Using it, they can streamline and optimize the way things are organized to maximize cost-effectiveness and make the inventory more accessible.
  • Personalized customer attention: thanks to the combination of Big Data and CRM registry (Customer Relationship Management), it is possible to get ahead of the necessities of each customer, taking into consideration their previous purchases or any activity with our company, and offer a better service.
  • Preventive maintenance: being so integrally connected to the digitalization of all aspects in our supply chain, Big Data allows us to gather information on, for example, the physical state of our transportation fleet through the metrics reported by ELDs, giving us a heads up on when to change certain parts or submit working units for repair.
  • Adjustment of distribution flow: the more information we gather and analyze, the more software “learns” our patterns of use and, in the instance of GPS, draw better, faster, and more straightforward routes for all deliveries.

Indeed, not every aspect of Big Data nowadays is optimal. It’s a fact that implementing systems that can effectively use it is difficult at best and requires the analysis of enormous volumes of data. That also needs full cybersecurity measures to prevent it from being infringed upon by bad characters, bringing legal consequences to any company that does not allocate a significant enough security investment to avoid such a case.

Woodward encourages all companies to keep innovating on all processes that can be improved to keep up with the increased demands of international markets while enforcing all security measures necessary to exploit Big Data correctly and safely with the best interest of customers at heart.