Predicting when, where, and how much of a product your customers are going to buy has always been crucial for success (the author may concede that it’s maybe not “always”. At least since the creation of commerce), so it shouldn’t be much of a surprise that Demand Forecasting has been around for a long time. In fact, the ability to produce accurate forecasts that account for demand variability is one of the biggest obstacles when it comes to predicting demand. In recent years, advances in computing power and Artificial Intelligence (or AI) have been applied to Demand Forecasting to produce forecasts that are more accurate, dynamic, and reflective of real-life conditions. AI-driven Demand Forecasting can achieve this by taking advantage of more data than your traditional statistical methods. As with all decision making, the more information you have at your disposal, the better informed your decision is.
But, before we get into the exciting part, let’s cover the basics.
What is Demand Forecasting?
When it comes to Demand Forecasting, you might say that we’re the experts (we’ve got a little bit of market validation here, which helps). Demand Forecasting is a type of data analysis used to predict the demand for products and services for a particular period of time. These forecasts inform all kinds of decisions - everything from deciding how much of a particular product should be ordered to choosing which location to send those products to.
For a deeper dive into how Demand Forecasting works, and what types are at your disposal, head here and here. Or, if you want to brush up on your knowledge of Machine Learning (a subset of AI), head here (don’t worry, we’ll still be here when you return).
Demand Forecasting Meets AI
When Machine Learning is combined with Demand Forecasting, what can you expect?
Machine Learning-driven forecasting methods look at historical data - typically historical sales data - and identify any trends and patterns. Then, when you start producing forecasts, Machine Learning methods take the output of a particular forecast, examine it against a measure of truth, and adjust parameters or calculations involved in generating the forecast to test whether these adjustments improve the forecast accuracy.
Make Your Data AI-Ready
According to a recent survey from McKinsey, only 33% of organizations use data effectively to realize the potential of AI. So, for the other 66% (and others) looking to use AI to its fullest, you’ll need to ensure that your data is up to scratch. As a starting point, this means you’ll need to clean your data by removing anomalies and identifying gaps in your data. For example, consistent naming in your database might seem trivial to a human analyst, but any algorithm looking to segment your database by customer, product, brand, or some other characteristic, will run into problems.
Your database should also only include information relevant to your forecasting. While some data streams are useful for almost everyone - think POS and market condition data - there will be some unique streams that will be of particular use, and others that will only make your data more noisy. For instance, supermarkets and other retailers selling perishable goods might find that demand for certain products is influenced by the weather and might include it in their forecasting model, while a retailer selling furniture won’t.
When it comes to combining AI and Demand Forecasting, you can expect a whole ton of benefits. The major advantages include:
Pattern recognition: Machine Learning algorithms learn and segment products into groups based on common features. Each group can then be assigned an appropriate forecasting model that suits the group’s shared characteristics.
Enhanced demand signals and quantified key demand drivers: Machine Learning methods achieve this by considering internal factors - think price, promotions, product life cycles, and POS data - and external factors, such as GDP, market share, customer sentiment, demographic trends, and even weather.
Expanded data streams: Since Machine Learning methods are multivariate, they can consider multiple variables that influence demand. Additional datasets that you can take advantage of include everything from product colour and brand to SEO rankings and web (or foot) traffic.
Short-term forecasting: Otherwise known as Demand Sensing, this subset of Demand Forecasting uses Machine Learning to predict demand anywhere from several hours to several weeks into the future.
Because of these advantages, Machine Learning methods can more closely model the realities of demand than more traditional methods. Plus, these methods are optimal for products that:
Have sudden fluctuations in demand
Have intermittent demand
Are new (and have little historical data available)
But, in order to reap these benefits, you’ll need a way to manage the influx of information that comes with forecasting. One way to do this is by adopting a Demand Forecasting platform (such as our very own) and letting it take on the majority of the grunt work.
How Remi Does What It Does
Choosing the optimal method for forecasting might be straightforward for most products, but there are some where the decision isn’t quite as clear-cut. Or, you might be dealing with so many products (in the realm of hundreds of thousands) that manually choosing a forecasting method for each is a Herculean task that simply can’t be completed with much nuance per product. Whatever the challenge, our platform offers Automated Machine Learning (or AutoML for those in a hurry) which can be utilised to help build more effective forecasting models that suit your needs. AutoML works by using Machine Learning to determine the best model and optimize its parameters for each and every product. It is this granularity that brings cost effectiveness to your business and is one of the main reason 3 of the Fortune 500 now use our services.
Demand Forecasting is (and will continue to be) a crucial aspect of a business’ success. Integrating AI into your Demand Forecasting process has a host of benefits, particularly in enabling you to take advantage of data you are already generating. If anything, with the amount of available data expected to double in size every 12 hours by 2025, the ability to process and utilise this data with the help of AI is bound to be of paramount importance.