AI is Changing Demand Forecasting
What's the Big Deal?
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 in a sailboat race across an ocean and Demand Forecasting as the navigation equipment aboard your vessel, helping you chart your course, predict the best route, and ultimately navigate the foreign waters.
Without Demand Forecasting, you’re staring up at the sun and the stars to guide you. With statistical forecasting and traditional methods, you’ve got a paper map and a compass. With AI-driven forecasting, your navigation system is state-of-the-art, pulling in external data like wind reports, tide information, and weather system development. It can give you insights from previous journeys, monitor other ships, and use its GPS to help provide insights to course-correct or adjust accordingly.
While all of these methods can work for fair-weather and non-competitive sailing, which one do you want when inevitable storms roll in?
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.
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:
- CRM & ERP Data
- Competitor Pricing
- Financial Markets
- Covid Cases
- Google Analytics
- Web Crawling Data
This improvement in accuracy has the knock-on effect of optimizing budgeting and scheduling, meaning that you can reduce the risk of overstocking or hefty expediting fees. And, when purchasing is dialed-in, 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 a host of improvements.
So, the addition of AI algorithms and their ability to utilize 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.
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.