Demand Forecasting with Machine Learning: How Is It Different?

Let us face it: demand trends are getting harder to predict. Customer expectations and external business factors become more complex every year, so traditional prediction methods cannot cope with the growing amount of versatile data sources. But what tool can? The answer lies in technology.

To be fair, no technology provides 100% accurate predictions. Nevertheless, machine learning (ML) demand forecasting algorithms significantly change the current state of things. Our task for today is to find out more about the role of technology in business predictions.

Below, you will find a short comparative analysis of traditional and ML-based forecasting capabilities, including required data sources and possible implementation scenarios.

In the original article, we also review benefits of intelligent predictions, pre-implementation tips, and market use cases.

To make it clearer, first it’s necessary to formulate what demand forecasting is.

Demand forecasting is the process of predicting future demand for a product or service using corporate data. There is a list of traditional methods that have been used for many years. There is also an innovative approach based on machine learning. Let us figure out the difference between them.

Traditional Forecasting: Long-Term Predictions for Products on Stable Markets

Even though we call statistical methods “traditional”, they too have recently become somewhat innovative. Now all forecasting calculations are conducted automatically with the help of specialized software.

The foundation. All traditional methods are based on data from previous years. In other words, statistics needs historical data to provide valid insights.

Reasons to use it. Traditional methods have a long history, hence, they are more popular and easily incorporated into the existing tech infrastructure. For example, one can calculate trends in Excel or an ERP system with no additional technical skills required.

Also, more advanced traditional methods can apply several forecasting instruments at once to achieve higher forecast accuracy. From whatever side you look, statistics seem a more understandable route for a business trying out forecasting for the first time.

When to use it. Statistical methods are good for:

• Middle- and long-term perspectives,
• Products with more or less stable market performance,
• Calculating market rates for a brand in general rather than individual brand products.

What’s below the surface? You should remember that traditional models work best only in stable market conditions when historical data looks similar throughout the years. But we all know it is not always true. Global crises provoked by wars and pandemics are highly damaging for almost any business. Traditional forecasting does not have appropriate tools to help you with that.

Demand Forecasting with Machine Learning: Higher Accuracy at the Cost of Higher Complexity

The world is not standing still. Computational powers are rising, and so are customer demands. Both technical advancements and market fluctuations have led to the emergence of ML-based prediction models.

The foundation. Machine learning algorithms have gone further, using myriads of data sets from all sorts of environments. Along with the already mentioned historical data, innovative tools consider customer reviews, social media markers (such as shares, likes, and the number of followers), macroeconomic factors, news, and so on.

Reasons to use it. ML models consider more factors than any traditional method. They work as indicators capturing all types of signals from various data sources. Plus, algorithms are continuously learning and adapting to new conditions on the go. Forecasting looks like a multi-layered process, which guarantees more reliable results.

When to use it. ML forecasting is the best fit for:

• Short-term demand planning,
• New products with little or no historical data,
• Unstable market conditions.

What’s under the surface? We will not surprise you by saying that advanced technologies require specific skills and knowledge. Indeed, ML produces forecasting automatically, on its own. But to make machine learning models start working, you need the help of experienced data scientists. They identify the best data sources to help algorithms deliver precise predictions and analyze the results.

As you can see, the business world is not giving up traditional methods, but ML capabilities make companies more adaptable to uncertainty.

Continue reading via the link to learn more about the technology and how market leaders implement it to increase sales and business efficiency.