Logistics Demand Forecasting: The Benefits of AI & How to Implement It

Logistics Demand Forecasting

For many logistics companies, the road to digital transformation and AI implementation is not an easy one. In an industry that has largely been run by pen, paper, and phone for decades, the transition to using modern software and tools can seem challenging and even overwhelming. What many of these companies don’t realize, however, is that they are creating an even bigger challenge for themselves by not implementing some of this cutting-edge technology into their operations. 

Companies who don’t use logistics demand forecasting find that it makes the operational planning of assets very difficult. The multi-faceted problem requires businesses to consider how many assets they need, whether or not those assets are positioned correctly at any given moment in time, and how to best plan the technical breaks.

This is a very complicated problem to solve, as it requires a large volume of interdependent information. Luckily, logistics companies already generate a tremendous amount of data internally and have access to even more data from public sources. Nevertheless, the challenge remains that only a few tools currently exist which allow companies to synthesize all of this information and enable data-driven decision-making in conjunction with the experience and instincts of their managers.

But with the help of modern predictive optimization tools, logistics companies can shift to an anticipatory strategy based on accurate demand forecasting, and thereby achieve far greater operational efficiency. Let’s take a look at what exactly logistics demand forecasting does, how it works and its many benefits for logistics companies.