Whether it’s the onset of a global pandemic, a dive in the financial market, or a surge in your customer base thanks to viral advertising, the demand for any given product or service can change on a dime. And, when it comes to predicting demand, being able to incorporate this kind of information into your Demand Forecasting models can not only be the separation between success and failure, but it requires the integration of Artificial Intelligence (or AI). And, with benefits such as improved accuracy and product availability, find out how you can use AI for Demand Forecasting below.
Before we get into the good stuff though, let’s kick off with a crash course in Demand Forecasting and AI. If this isn’t your first encounter with these concepts, consider this a quick refresher (or feel free just skip right past it, you won’t hurt our feelings that much).
AI and Demand Forecasting 101
Demand Forecasting refers to a field of data analysis that’s used (as the name suggests) in the process of creating forecasts that estimate future demand for a given product or service. Also (and incorrectly) known as Demand Prediction, this technique primarily involves analyzing historical data and looking for historical trends and patterns, and can be used to make more informed decisions across your business and the broader supply chain. If you still have questions or want to know more, head here for an in-depth explainer of all things Demand Forecasting.
Artificial Intelligence (or AI) describes a computer system that can learn new information and skills, which it can adapt and apply to new environments and situations. When it comes to Demand Forecasting, we tend to talk about Machine Learning (a subset of AI) and its ability to identify patterns and trends from all sorts of data and apply that to predict future demand.
How Do You Use AI For Demand Forecasting?
To illustrate how AI can be used for Demand Forecasting, let’s go on a journey with an extended metaphor. Suppose you’re walking through the woods, and Demand Forecasting is a trusty guide helping you predict where the path is likely to go.
Without Demand Forecasting, you’re just wandering around on your own with little to no idea of what to expect. With statistical forecasting, your guide might predict that the path keeps going straight, just like it did 20 minutes ago. With AI-driven forecasting, your guide would consider historical information too, but would also look at external information, such as what they can hear or see in the wider area, any signage, or footprints that indicate what others have done, and predict that the path might turn quite sharply or that you’ve reached the edge of the woods. While these two predictions don’t differ by much, they can inform what you choose to do next with differing outcomes - whether that’s choosing to keep going, taking a break to consider your situation, or turning back around.
As trivial as this example is, the point is that AI enables your forecasting to consider more variables that influence demand and find patterns in your data that would be hidden to human eyes or statistical methods, leading to more accurate predictions that can be used to make more well-informed decisions.
In addition to this, AI can be especially useful for forecasting in the short-term. This subset of Demand Forecasting, often referred to as Demand Sensing, takes advantage of the multivariate nature of AI-driven forecasting and real-time data - think everything from weather forecasts to mobility data - to make predictions on the hourly or daily scale. If this piques your interest, you can read all about Demand Sensing here.
AI vs Traditional Methods For Demand Forecasting
Let’s start by considering statistical methods. These traditional methods are univariate and rely on simple rules to produce a forecast. This means that these methods can only consider a single variable that influences demand - usually historical sales data - and have to be modified to reflect changes in demand caused by other factors. AI forecasting, on the other hand, can be thought of as bucking this tradition, where algorithms learn rules and identify patterns in demand from the data in order to make predictions. Because of this, other factors beyond sales can be considered and learned, resulting in forecasts that are more accurate and reflective of the real world’s complexity. These data streams include:
- Pricing - think current price and future pricing promotions
- Public holidays and local events
- Foot traffic (or site traffic for all the Ecommerce retailers out there)
- Google Analytics
- Web crawlers
This improvement in accuracy has the knock-on effect of optimizing buffer-stock levels, meaning that you can reduce the risk of overstocking while maintaining product availability. And, when buffer-stock levels are reduced, this in turn results in additional reductions in working capital and space used for product storage. All in all, AI-driven forecasting can enable you to have less stock on-hand (minimizing the risk of being left with dead stock that won’t sell) and reduce logistics costs all while maintaining customer satisfaction.
The effects of AI methods can be seen across the supply chain too. With McKinsey finding that AI forecasting can reduce errors by up to 50% in supply chain networks, this can lead to host of improvements, such as:
- Improved transport planning
- Optimized labour rostering
- The improved ability to negotiate with suppliers (now that you’re making fewer last-minute orders)
So, the addition of AI algorithms and their ability to utilise external data can not only help supply chain networks gain an edge on those managed more manually, but they can make your chain more dynamic and responsive to external changes.
Although AI-driven methods tend to outperform traditional methods, they do come with some challenges of their own. For instance, because AI can consider more data streams, this comes with the potential challenge of collecting the data in the first place. While ensuring that your data is in peak condition is important for traditional forecasting, the data you need to collect and clean tends to come from the one source (your POS and sales records), making it less of a concern in comparison to AI methods.
Then, once you have your data, it’ll need to be cleaned by removing any anomalies and identifying gaps in your data. These anomalies could be something as innocuous as minor spelling errors in the naming of your products in your database, but they can trip up algorithms that rely on names to segment your database.
Additionally, the data you include in your database should be relevant to your forecasting. This means that the data streams included should provide information that can help your algorithms predict demand, and this selectiveness can reduce the noisiness of your data and make it easier for your algorithms to find useful patterns in demand.
Another potential drawback of AI-powered Demand Forecasting is cost. Since traditional methods are simpler than AI-driven methods, they are often much cheaper to implement. So it’s important to weigh the cost of your method of choice against what you want to achieve with your forecasts, as well as considering which method will suit the demand profiles of your products. To find out more about the different types of Demand Forecasting available to you, head here.
As long as things are prone to change, there’ll be a need for accurate and dynamic Demand Forecasting. By incorporating AI into your forecasting models you can reap these benefits and more for your business and supply chain. But, the value of AI can only be truly felt if the information within your forecasts can be acted upon and used to make decisions across your business.
Want to find out more about how Remi gets the job done? Explore our product page or drop us a line. Or, if you’re looking for a bit more information first, make our blog your first port of call. Once there, you can find out how Demand Forecasting can benefit start-ups and Ecommerce businesses, how AI can be utilised for Demand Planning, or catch up on the latest AI reads. To catch our platform in action, pick up a case study or two and see how our platform has helped a Fortune 500 company improve their demand planning across their retail and manufacturing arms, or how we helped an Australian homewares retailer increase stock availability. All read-up and ready to improve your Demand Forecasting strategy? Sign up here.