Demand Forecasting and Inventory Management: why they work better together

TLDR: Demand forecasting models the factors that drive demand for your products or services over time, including external factors like weather and seasonal events that tend to matter more than most businesses expect. For inventory-driven businesses, it connects directly to inventory management, which works at the individual SKU level to keep stock in the Goldilocks zone: enough to meet demand without carrying unnecessary warehouse cost. Together, the two models let businesses plan up to 18 months ahead. The KAI orchestration layer then sits across both, along with any other models in play, so non-technical users can query what's actually driving performance without manually reconciling model outputs.

Demand forecasting and inventory management are built independently, but we would always advocate that a client integrates both as part of a complete analytics strategy. If you have physical inventory that needs optimizing, a demand forecasting model is what you connect it to. The two tend to be delivered together.

Demand forecasting looks at the factors that influence demand for a given product or service over time. It's a dynamic model: it adjusts as new data comes in, and it handles external effects like seasonality and major retail events. What surprises most people, though, is how heavily external factors feature in the model's outputs. We have an event log built in, so our models understand the difference between a day of the week, a weekend, a public holiday, Black Friday, Valentine's Day, and also things like weather patterns. It always amazes me how highly external factors rank among the things that actually drive demand.

We're currently working with a travel company as an AI data partner, and it's a good illustration. They have no physical inventory. Their product is their tours, their packages, their coach trips. What they want to understand is the demand drivers that influence the take-up of each individual tour over time, so they can optimize demand going forward and smooth capacity. There's no point in having 20% of your tours overbooked and 80% sitting with spare capacity, because that cost falls on the business. So for a non-inventory business, demand forecasting is about smoothing demand and understanding at a granular level what's influencing it in the first place, and then using that understanding to drive better messaging, better customer service, and optimized capacity.

On that project, we've seen bookings for the sunny tours increase when weather patterns in the UK are awful, which they can be. And the reverse: bookings are down for those tours when we get one of those strange 30-degree hot spells. External factors always appear in a demand model, and to a greater extent than you might otherwise assume.

For businesses that do carry physical inventory, the model gets more granular. Our inventory management solution works at the individual SKU level: down to which color of a product, which supplier is providing it, whether there are peaks and troughs in availability. It will track supplier holidays, when purchase orders have been generated, when they've been signed, when they remain unsigned. Rather than optimizing media, what we're optimizing here is the availability of stock. The goal is what I'd call the Goldilocks zone: enough availability but not so much that you're carrying inventory in warehouses at ongoing cost.

When you connect that with demand forecasting, clients can plan ahead up to 18 months. You can look at likely demand and the stock levels needed to maintain it, without holding a cost-heavy buffer that isn't justified. That puts them in a better position to manage suppliers, and if they're manufacturing directly, it has significant implications for supply chain and production planning.

The forward-looking element of inventory management is a fairly recent development. You need a sophisticated understanding of individual products, their statistical attractiveness, how that relates to demand, and how it varies by customer segment. It becomes even more complex when you look across multiple markets, because demand varies by geography. You end up with a number of factors that need to be considered in unison, and really it's machine learning that allows you to do that rather than relying on a static conventional statistical model.

That's where the KAI Analytics orchestration layer comes in. If a client has demand forecasting, price elasticity, inventory management, and segmentation all running, the orchestration layer is there to monitor the interrelationships between them. What is actually driving sales? To what extent is price influencing demand, and for which segment? Those questions don't have clean answers when you're looking at each model independently. The orchestration layer, with KAI Assistant on top, is what allows a non-technical user, a CEO, a commercial director, to ask those questions in plain English and get a coherent answer back.

See the inventory management model in action here.