Build Better Demand Forecasting Models with AI

One of the most difficult tasks of a retail store is being able to forecast how many products you will need for the next major shopping season. Demand forecasting is big business, and there are now many AI methods to do it even better than before. Artificial intelligence is quickly becoming a major piece of the puzzle in building demand forecasts that work. AI in retail has been instrumental in its growth because these algorithms bring so many different benefits that you didn’t think were possible.

One of the first benefits you get with AI is the fact that you can do things more accurately than with exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) methods. This technology has transformed the playing field when it comes to figuring out the best ways of getting things right with demand forecasting. This technology is excellent because it can see things that many people didn’t even think of seeing before. It is possible to use artificial intelligence to learn about the market in ways that humans never could.

The Problem of Demand Forecasting

Demand forecasting is getting ever more difficult because of how complicated supply chains have gotten. In today’s world, products are built with parts from many countries, and then they are sold all across the world. This makes it very difficult to do demand forecasts for how each shopping season will be. The pandemic has also taught us how difficult it is for supply chains when something goes wrong. Unfortunately, these are things you can never predict ahead of time.

You also see that problem in the semiconductor shortage that caused many car manufacturers to stop manufacturing vehicles temporarily. Demand forecasting in retail isn’t just about seeing how many sales you will get, but it is also about figuring out the supply chain to make that happen.

Using AI with Demand Forecasting

AI in retail makes a lot of sense because there are many AI methods to do demand forecasting better. The fact of the matter is that AI can take data from a variety of sources and turn it into useful insights. This can be done more accurately than traditional programs, and it can also spot trends in data that no other program could see. Traditional time series methods don’t perform as well as AI in doing demand forecasts.

This reality has meant that understanding how to integrate AI into the demand forecasts you build has become more important as time has gone on. AI is also a lot more scalable, meaning you can perform with the same accuracy without worrying how big or small your business is.

Benefits of Demand Forecasting with AI

Deep learning models have gotten more useful in demand forecasting as well. These are methods that can learn by being given a variety of data. Deep learning uses neural networks, and it adds the ability for the algorithm to learn on its own. This means you have an algorithm that can spot trends without being taught anything. In testing, AI models have been shown to perform up to 42% better than traditional methods. These AI methods are revolutionizing how demand forecasts will be built in the future.