When it comes to predicting demand, statistical Demand Forecasting has been tried and tested for years. But, as the need for accurate forecasting continues to grow, Machine Learning techniques are becoming more and more useful. Besides doing the heavy lifting for you, Machine Learning can also improve your Demand Forecasting strategy in a multitude of ways - increased accuracy, faster forecast generation, and more (we’ll explore this further down).
If this is your first encounter with either Demand Forecasting or Machine Learning, head here to learn more about the concept of Demand Forecasting, or here to find out more about how Machine Learning works, then return. Otherwise, feel free to read on (in fact, we encourage it).
Machine Learning and Demand Forecasting
So, you know how Machine Learning and Demand Forecasting (occasionally and, in our humble opinion, incorrectly also known as Demand Prediction work in isolation, but what happens when they join forces?
Just like traditional statistical forecasting, Machine Learning-driven methods start by looking at the past - often with historical sales data. These methods identify trends and patterns in your data but (and this is where it gets exciting) in addition to this, Machine Learning algorithms can use additional datasets, including:
Business-specific variables that change over time - price, promotions, weather, etc
Related and categorical (meta)data: think product colours, brand, location, and channel
The kinds of data you have access to, as well as the purpose of your demand forecast and the goals your business has, play a crucial role in helping you choose the best type of Demand Forecasting method (which you can read more about here.
Once you have the data, what’s next?
Before you can get started on training the models of your choosing, you’ll often need to clean your data, analysing it to identify gaps or anomalies, and ensuring it is relevant to your forecast. Then, you can get down to the business of picking the best model for you and your data. Once identified, they’ll need to be trained on historical data and evaluated for their accuracy by building a retrospective model - where the model predicts demand for a past period of time and is compared against the actual values - or using metrics such as Mean Absolute Percentage Error (MAPE).
Demand Forecasting platforms (such as our very own) allow you to test multiple Machine Learning and statistical algorithms in order to identify which are the most accurate using Ensemble Method. Once you’ve found the requisite accuracy for your predictions, you can deploy your model and get stuck into managing and utilising your forecasts.
Applying Machine Learning to Demand Forecasting can lead to more accurate and speedier forecasting compared to traditional statistical methods. In addition, Machine Learning improves the adaptability of your forecasting method, meaning that your forecasts will be quick to reflect changes in market conditions, customer behaviours, and more. This is because Machine Learning automates forecast updates as new data is collected and relies on multiple data streams, and the addition of these variables will allow the forecasts to capture more subtle signifiers in shifts of demand.
Those additional variables - think everything from web traffic and online reviews, promotions, and price changes - are plugged directly into the forecasting model. This offers great advantage over traditional methods (which tend to assume that relationships between demand and other variables are linear), allowing Machine Learning algorithms to often identify changes in demand that would otherwise be overlooked. Such an oversight would usually result in lost sales.
Unlike statistical methods, the output of Machine Learning methods comes with a confidence value, called a quantile, which helps you understand how accurate your model is. For example, if your forecast comes with a P90 quantile, that means that 90% of the time, actual demand will be less than the predicted value.
Due to the wide scope of data it can use as input, Machine Learning forecasting methods can be applied in all kinds of products, including those:
In highly volatile markets
Which have little to no historical data
With short life cycles
Whose demand is influenced by multiple factors
Machine Learning methods excel in situations where data changes less predictably, whereas statistical methods are a good fit for products with demand that follows trends or has seasonality. For example, you would expect demand for bouquets to change depending on how close you are to Valentines’ or Mothers’ Day, whereas the demand for other products may change in response to particular promotions, weather changes, or crises (such as COVID-19).
The need for Demand Forecasting is a given, and will become increasingly important for all facets of successfully running a business. When combined with Machine Learning, increased accuracy and data processing (both in terms of the speed and amount of data processed) are just two of the benefits. But, while we could go on and on about the benefits of Machine Learning-driven Demand Forecasting for every single product out there, this conversation requires a bit more nuance. For some product types, forecast purposes, and business objectives, the benefits of adopting Machine Learning methods are worth (and often outweigh) the investment both in terms of data and cost. In other cases - think products with stable demand or producing long-term forecasts - traditional statistical methods will deliver adequate results, and can sometimes outperform Machine Learning methods.