Unleash the Power of Precision: Revolutionize Your Business with AI-driven Demand Forecasting
Inventory challenges have never been more prevalent than they are today. Global supply chain issues have put a tremendous strain on companies trying to make sure that they have the right product in the right place at the right time.
Many companies are still relying on antiquated tools, spreadsheets, or just good old-fashioned know-how to make decisions on how to optimize their product distribution.
Surely, there has to be a better way, right?
We caught up with Justin Richie, our VP of Data and Growth, to talk about how companies can leverage new technologies like ML/AI and other frameworks to boost their Demand Forecasting capabilities and build better processes around inventory management.
Q: Why do companies turn to AI for improving their demand forecasting process?
A: There are so many factors to building a demand forecasting practice that AI makes a lot of sense. The ability to process millions of data points to understand customer behavior is critical to creating an accurate forecast. AI is the best tool in helping teams manage all of that complexity when building a model.
Q: What is a “demand model”?
A: At its core, a demand model predicts where and when a customer is most likely to purchase a particular product. Model development also combines historical data and other factors likely to influence buying behavior. Once we’ve done that, we can use AI to re-evaluate and optimize our models to improve them continually.
Q: You mentioned historical sales data, what other types of data go into creating a demand model?
A: It depends on the factors specific to the product you're selling. Still, these models are typical for things like customer distance to a location, seasonality, product assortment mixes, and other factors.
For example, suppose you're working with an agricultural equipment company. In that case, things like seasonality and population density play a much more critical role in a model than they would for breakfast cereal. Many of the examples above make this such a complex problem to solve; each product/business is unique and different regarding demand modeling.
Q: What’s the biggest challenge with creating a demand model?
A: Finding those key factors in your demand model can be daunting, and often it's very company-specific. That's why the value of AI in these models is so significant because we're seeing more and more capability for AI to help identify and highlight those key factors and allow us to automate more and more of this process.
What used to take an analyst weeks, if not months, combing through data and interviewing SMEs can now be done relatively quickly and easily using AI.
Q: How do I actually implement this in an organization?
A: Getting started is easier than you might think it is. The most significant barrier to entry is data. Identifying what you are trying to model from a testing perspective and creating a plan to acquire and organize that data is critical to kicking off a successful demand forecasting practice.
We spend a lot of time working with clients who want to do a multitude of different initiatives with their data, only to find out that the systems and tools they use aren't well set up for taking advantage of the capabilities that are out there in the market.
Q: What is the best strategy to use in approaching demand forecasting?
A: The best advice for someone approaching demand forecasting is to start small. Early on, the simplest models give you the most ROI. You can always add complexity and more factors over time, but starting with quickly deployable and measuring the performance is the way to go. The next step is to make minor adjustments over time to better the model rather than trying to figure out what you need to adjust in a large, more complicated one.
Want to continue this demand forecasting conversation with an expert?
Our team would love to talk more about how you can get started with demand forecasting and start to grow your internal inventory management process.
In the meantime, check out our guide about Improving Data Quality for Better Decisions Making.