From winning chess games to predicting the outcomes of chemical reactions, Artificial Intelligence and Machine Learning (a subset of AI) seem to be good at everything. So, it shouldn’t come as a surprise that AI is also achieving great things when it comes to Demand Planning. Whether that involves creating more accurate demand forecasts and faster forecast generation, or optimizing your inventory, there are plenty of benefits to teaming up with an algorithm or two. In fact, according to a 2017 survey conducted by Institute of Business Forecasting & Planning, 70% of respondents put AI and Machine Learning in their top three choices for advancements in technology that will have the biggest impact on forecasting and Demand Planning.
And, according to our forecast, we’re confident won’t be your first encounter with Demand Planning or AI. But, if it is, let’s break it down.
What is demand planning?
We’ve touched on Demand Planning in the past, but for those who want a quick recap: Demand Planning is a sub-process of sales and operations planning (S&OP) that involves predicting future demand for products and services. With these forecasts in hand, a demand planner can then execute a strategy across the supply chain to meet demand while avoiding overstocking. To do this, the modern Demand Planner uses several tools, with the big ones involving forecasting and simulations (which you can read more about here.
If that’s not enough, you can find an in-depth explainer about what Demand Planning is here or head here to find out about how AI and Machine Learning work here.
AI meets demand planning
So, you know how AI and Demand Planning work individually, but what happens when they join forces?
When it comes to forecasting, AI algorithms start by looking at historical data (just like traditional statistical forecasting). These algorithms can identify trends and patterns (including those that would be missed by you or me). And they tend to outperform traditional statistical methods (which assume that demand and other variables have a linear relationship) by incorporating data from a variety of data sources that have an effect on demand. For example, algorithms can use product metadata - think colour, brand, and location - and business-specific variables - from prices and promotions to weather and foot or web traffic - as inputs to create more accurate forecasts. In order to produce more accurate forecasts, the algorithms also learn. This generally involves comparing the output of the forecast against some measure of truth, as well as testing whether adjustments can lead to improved accuracy.
And, instead of trying to climb a (figurative) mountain of forecasts for all of your products - especially if you’re working with daily forecasts - while trying to decide which products you need to order, Machine Learning can turn your mountain into a molehill. To manage your forecasts, you can utilise Automated Machine Learning (or AutoML for short), which works by choosing the best forecasting model for your data. To help make reordering easier, auto-replenishment combines Machine Learning and simulations to determine the optimal time and amount of a particular product to order. More often than not, these tools are available on demand planning software (such as our Demand Forecasting platform) and can help demand planners reduce the amount of time they spend on tasks such as SKU monitoring to focus on more complex tasks instead.
While traditionalists might prefer the good old Excel spreadsheet to get things done, Machine Learning algorithms (and the demand planning platforms that use them) are well-suited to dealing with the number crunching, data analytics, and cyclical nature of Demand Planning - especially important for any business at scale.
Some of the benefits of integrating AI into your Demand Planning process include:
Increased availability of goods
Less stock on-hand
Reduced logistics costs
This is because coupling AI with sales and inventory forecasting enables you to optimize your inventory. You can predict inventory and optimal safety stock levels to avoid spoilage and dead stock. Plus, knowing how much of a product you need at every location can result in reduced working capital and less space being taken up by stock that you won’t sell, culminating in reduced storage costs.
And, the benefits extend beyond your physical products. Having an understanding of the demand for your products can assist you in optimizing your labour to avoid overstaffing (and wasted money on wages) and understaffing (avoiding delayed deliveries and keeping customers happy). Plus, knowing how much product to order in advance can enable you to negotiate better rates with suppliers by avoiding unexpected, last-minute orders, especially when lead-times are on the lengthier side.
Data & Other Challenges
The benefits above are tied to the accuracy of the forecasts. A core tenet of an accurate forecast is data. Specifically, the data that you plug into your forecasting model needs to be accurate and relevant, meaning it provides more information about your products (as in the case of metadata) or has some influence on demand. Putting it simply, including a host of different data streams might feel like it’s helpful, but you can only expect a great (or accurate) forecast if your data is just as great (otherwise known as the Garbage In Garbage Out effect).
In addition to this, you’ll need to establish a baseline for demand. In order to do this, you’ll need to identify one-off events - think everything from promotions and major events to economic changes and disruptive major crises such as COVID-19 - which can skew your understanding of this underlying demand.
As the potential applications of AI and Machine Learning continue to climb, there’s plenty to be excited about when it comes to Demand Planning. Since Demand Planning forms a crucial part of successful management and optimization of businesses and supply chains, it’s no surprise that more demand planners are turning to AI and planning software to reap the benefits. Letting AI pick up some of the grunt work (especially when it comes to forecasting) can help shift the focus towards the collaborative process that helps inform your forecasts and influence demand.