Big Data in Logistics

In the last weeks, I outlined several Big Data benefits by industries. The next posts, I want to outline use-cases where Big Data are relevant in any company, as I will focus on the business functions.
This post’s focus: Logistics.
Big Data is a key driver for logistics. By logistics, companies that provide logistics solutions and companies that take advantage of logistics are meant. On the one hand, Big Data can significantly improve the supply chain of a company. For years – or even decades – companies rely on the “just in time” delivery. However, “just in time” wasn’t always “just in time”. In many cases, the time an item spent on stock was simply reduced but it still needed to be stored somewhere – either in a temporary warehouse on-site or in the delivery trucks themselves. The first approach is capital intensive, since these warehouses need to be built (and extended in case of growth). The second approach is to keep the delivery vehicles waiting – which creates expenses on the operational side – each minute a driver has to wait, costs money. With analytics, the just in time delivery can be further improved and optimized to lower costs and increase productivity.
Another key driver for Big Data and logistics is the route optimization. Routes can be improved by algorithms and make them faster. This lowers costs and on the other hand significantly saves the environment. But this is not the end of possibilities: routes can also be optimized in real-time. This includes traffic prediction and jam avoidance. Real-time algorithms will not only calculate the fastest route but also the environmental friendliest route and cheapest route. This again lowers costs and time for the company.
Header Image by  Nick Saltmarsh / CC BY