If you’re in the business of selling something - whether it’s cake mix or headphones - Demand Forecasting should be at the crux of your business strategies (although we’re sure you know that already). As a retailer, forecasting demand can both solve and pose some tough challenges. But, the continued development of technology has seen the rise of Machine Learning-powered Demand Forecasting, which comes with a host of benefits (especially for retailers).
If this is your first dip into the world of Demand Forecasting, we’ll be your guide. To put it simply, Demand Forecasting refers to the analytical suite of tools used to predict the likely demand for products and services during a particular period of time. There are a host of forecasting methods out there, but their success in your business is tied to your specific needs, the demand profiles for each of your products, and the kind of data you have at your disposal.
Still got questions? We’re no encyclopaedia, but we know a lot about Demand Forecasting (you might even say we’re experts), and we’ve put together an in-depth explainer of Demand Forecasting which you can read here (don’t worry, we’ll still be here when you come back). Or, if you’re up-to-date with Demand Forecasting but are more of a novice when it comes to Machine Learning, head here.
Demand Forecasting Meets Machine Learning
While traditional Demand Forecasting relies solely on historical sales data and statistical modelling, Machine Learning takes this a step further. Just like statistical forecasting, methods powered by Machine Learning start by considering the past via historical sales data and identifying trends and patterns within the data. But they don’t stop there. Machine Learning methods are multivariate, which means that the algorithms can also use additional datasets to predict demand, including:
Business-specific variables - these change over time and include everything from price and promotions to weather and foot traffic
Related and categorial (meta)data - fixed variables such as product colours and brand, location, and channel
For retailers in particular, demand is impacted by a host of variables outside of historical sales. With Machine Learning, you can harness this information for some great benefits (which we’ll get into below).
Perks of Machine Learning for Retail Demand Forecasting
Since retail is anything but stable when it comes to demand, it’s no surprise that merging Demand Forecasting with Machine Learning leads to some major improvements over traditional methods.
For instance, the ability to ingest multiple variables means that the more factors you consider in your forecast, the more data that you (or an unlucky analyst) will need to pore over. But, Machine Learning algorithms do all of this work for you and can look for patterns and trends that might be missed by the human eye. For example, Machine Learning can capture:
Recurring demand patterns - whether that’s seasonal spikes or weekly cycles
The impact of internal business patterns - think promotions, changing in-store displays (or which products are featured on the home-page for Ecommerce retailers)
Influences from external and unknown factors - whether you’re using bus timetables to predict demand for coffee or haven’t recorded the impactful variable but can still see it’s effect, Machine Learning can help identify these relationships
Additionally, the integration of Machine Learning can improve the accuracy of your forecasts. This is possible not only because of its ability to consider multiple factors that influence demand (as mentioned above), but also because Machine Learning methods take the output of a given forecast, compare it against some measure of truth, and can adjust parameters or calculations involved and test whether these adjustments lead to improved accuracy. And, with more accurate forecasts comes more accurate planning, with benefits including:
Predicting which products are needed by location and channel, which ensures high product availability while minimizing stock risks
Optimizing staff rostering to maintain customer satisfaction without wasting money on unnecessary wages
Supporting capacity management
Helping to manage the complexities that arise from long lead-times, with the potential to negotiate better prices by ordering products further in advance
While all of these benefits (and more) are up for grabs when you implement Machine Learning in your Demand Forecasting models, there are some caveats.
Challenges and Requirements
Since Machine Learning methods work best with ample data, it shouldn’t come as a surprise that data can be a huge obstacle for forecasting success. Specifically, you’ll need enough data to identify trends and patterns in demand, and it needs to be clean, error-free, and consistent to avoid the Garbage In Garbage Out effect. And, although it’s tempting to include every kind of data you can get your hands on, it’s best to only include data that is relevant to the demand for each of your products. For example, weather data can be incredibly handy for short-term forecasting for supermarkets, ice-cream shops, and other retailers selling perishable goods, but can just add to the noise for those selling furniture.
And, just like with anything else, retailers need to be able to act on forecasts. While having a good idea of how many products you’ll sell in the next two months, applying that information throughout your operation enables you to reap all of the benefits of Machine Learning-driven Demand Forecasting.
Final Thoughts
As long as products continue to be bought and sold, Demand Forecastin