Inventory Forecasting Must Account for Local Demand

Distributors can have plenty of inventory across their network and still struggle to meet demand at individual branches. Stock sitting in one warehouse does little good when customers need it elsewhere, while keeping every location stocked for its highest historical demand can tie up unnecessary capital. With thousands of product and location combinations to manage, purchasing teams cannot review every decision manually. Executives evaluating demand forecasting software need to know whether it can improve local product availability without driving up inventory costs across the business. Network-wide averages can easily hide costly gaps at individual locations.

Past sales provide a useful starting point, but irregular buying patterns can make forecasting difficult. Slow-moving products may not generate enough transactions to produce a reliable forecast on their own. Seasonal spikes can also skew averages long after demand has returned to normal. Buyers should look at whether the software can draw insights from related products and distinguish recurring demand from unusual orders. External factors such as weather may also help improve predictions, but their value needs to be demonstrated for the products concerned rather than assumed across the entire catalog.

Knowing what customers will buy is only half the equation. Buyers also need to know when to reorder. Changing supplier lead times can make it difficult to act on even an accurate demand forecast, while order frequency and minimum purchase quantities affect how much stock needs to be kept on hand. Software should account for actual purchase order history rather than rely solely on fixed lead times. Safety stock should also reflect how individual products sell at each location. Applying the same buffer everywhere can leave fast-selling products out of stock while slower-moving items pile up.

“Lantern refreshes recommendations weekly and lets buyers compare suggested stocking levels with existing settings before approving or overriding them.”

The way forecasting software works with existing ERP systems is another important consideration. If recommendations sit in a separate dashboard, buyers may still have to enter them manually into their purchasing systems. They should establish whether the software can read transactions from their existing ERP and feed approved stocking parameters or purchase orders back into it. Buyers also need to understand why the system recommends a particular change. When a business wins a new account or a branch receives an unusually large order, experienced staff should be able to override a forecast without having to work outside the system.

Forecast accuracy matters, but it does not tell the whole story. Lower inventory means little if it leads to stockouts and unhappy customers. In some cases, holding more stock may be the right decision if those products sell consistently. Buyers should look at how the software affects product availability, inventory turns and the capital tied up in excess stock. Testing its recommendations against past transactions can also show where they differ from existing purchasing practices before a wider rollout. Buyers should also account for the work involved in preparing historical data and managing exceptions once the system is in place.

Lantern builds a custom machine learning forecasting model for each distributor, using historical ERP data and business-specific purchasing practices. It combines demand forecasts with projected supplier lead times to recommend order timing and safety stock at the item-location level. Lantern refreshes recommendations weekly and lets buyers compare suggested stocking levels with existing settings before approving or overriding them. Approved purchase orders or stocking parameters can be written back into the ERP. Its models also examine relationships across products, supporting forecasts where individual sales histories are sparse. Distributors with large catalogs spread across multiple branches, especially those balancing excess inventory against missed sales, are the relevant buyers for this model.