Combining Innovation

Are you sick of pouring over Excel spreadsheets looking for ways to better 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.

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 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
• With unpredictable demand that’s influenced by multiple factors

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 widgets in the next month, and comes with a P90 and P10 quartile of 50 sales and 5 sales, respectively. This means there is a 90% chance that you’ll sell 50 widgets 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 widgets (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 complex problems, two heads are better than one. So why not apply that to AI-powered Demand Forecasting? That’s where Auto Machine Learning (or Auto ML) 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.

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 - 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 - prime, promotions, weather, and any other variables that change over time
• Related and categorical (meta)data - from product colors and brand to location and channel, this data is generally stable and unchanging

With this information available and able to be utilized, forecasts produced with Machine Learning are up to 50% more accurate than traditional statistical methods.

However, all good things come at a price, and the same can be said for AI-Powered Demand Forecasting. The price, in question, is data that is error-free, accurate, and relevant for each product you forecast for.

If you’re part of the 66% of surveyed organizations, in a recent McKinsey study, that don’t effectively use their data in their applications of AI, there is some salvation. To get your data ready for use in AI Demand Forecasting, you need to remove any anomalies and identify gaps and erroneous values. As an example, consistent and uniform naming of your products in your database ensures that any algorithm reading and segmenting it can do so properly.

On top of that, your database should only contain information that is relevant to forecasting. While it might be tempting to include as much data from as many streams as you can, there will be some streams that actually influence demand and others that just add to the noise.

As long as we need to sell products, order stock and supplies, and launch new products, we’ll need Demand Forecasting. And, as technology continues to be developed and the amount of data produced continues to skyrocket, the integration of AI (and the tools that come with it) will continue to improve the accuracy, efficiency, and usefulness of forecasts. To make the most out of these tools, adopting software platforms that use this technology (such as our Demand Forecasting platform) gives you the ability to work with an increasing amount of data without increasing your workload.