Are you tired of poring over Excel spreadsheets and looking for a better way to predict demand for your products? We’ve got the answers you’re searching for. Combining Artificial Intelligence (AI) with Demand Forecasting not only improves the accuracy of your forecasts, but can make the whole chore of forecasting that much easier. To demonstrate, we’ll get into the AI powered Demand Forecasting tools that you can integrate into your own models and strategy.
Why combine AI and Demand Forecasting?
Just like other types of quantitative Demand Forecasting methods, those driven by Machine Learning (a subset of AI) rely on historical data - generally historical sales - to identify trends and patterns. The unique (and exciting) part comes from the ability to uncover patterns and trends that would have been otherwise missed by statistical methods or a human analyst.
While your current Demand Forecasting models work really well for some products, you might find that they’re falling short for others. It’s for these products that Machine Learning can have the biggest impact. For instance, products that Machine Learning is best suited for include those:
With short-life cycles
In volatile markets
That are new (and thus have little to no historical data)
With demand that’s influenced by multiple factors and unpredictable
To get the most out of this perfect match, let’s take a look at some of the tools you can take advantage of.
Confidence bounds (or quartiles) are values that come with the output of a Machine Learning forecasting model. These values help you understand how accurate your model is and can be used to determine how much stock to order and making other planning decisions.
For example, let’s say your forecast predicts that you’ll sell 25 shirts in the next month, and comes with a P90 and P10 quartile of 50 sales and 5 sales, respectively. This means that is a 90% chance that you’ll sell 50 shirts or less, and that there’s a 10% chance that you’ll sell less than 10. With this kind of information, you might decide to use the upper confidence bound (P90) as your forecast and order 50 shirts (which is what retailers selling high-value products tend to do), or you might choose to use the forecasted value of 25. Plus, the width of the gap between your upper and lower confidence values can tell you how accurate the forecast is, with wider gaps indicating that your forecasting model isn’t that confident.
While this isn’t unique to AI or Machine Learning powered Demand Forecasting, Machine Learning makes it much easier to use.
When it comes to solving curly problems, two heads are better than one. So why not apply that to AI-powered Demand Forecasting? That’s where Auto Machine Learning (or AutoML) approaches come in. AutoML works as an ensemble approach, meaning that it tests a variety of different Machine Learning models for each product, comparing the accuracy and confidence of each model, and selecting the optimal version. This is especially handy when a given forecasting method might work well for some of your products, but falls short for others.
More Data Sources
The demand for your products is influenced by a host of factors. Aside from the seasonality of your product, you might find that demand fluctuates due to changes in weather, competitor pricing, and even broader economic conditions - think everything from market crashes to natural disasters and global pandemics (who’d think that COVID-19 lockdowns would see a spike in sales for workout gear and gym equipment?). And, since Machine Learning methods are multivariate - meaning that they can consider multiple demand variables and additional datasets - forecasting driven by this technology can go beyond the information contained in sales data. Think:
Business-specific variables - think prime, promotions, weather, and any other variables that change over time
Related and categorical (meta)data - from product colours and brand to location and channel, this data is generally stable and unchanging
With this information available and able to be utilised, forecasts produced with Machine Learning are up to 50% more accurate than traditional statistical methods.