Picture this: it’s right before the winter holidays, and you are reordering inventory. You order the exact same amount you always do when you replenish stock – but when the holidays hit, you sell out almost immediately.

Cut to the next year, around the same time. You suspect this year will be as busy as last year and definitely don’t want to stock out again.

So, you look back in your records to see how many sales you made and how many backorders there were last season, and use that data to estimate how much inventory you’ll need. You end up ordering twice the stock you normally would, and sail through the holidays without a hitch.

This is a classic example of demand forecasting. By making data-driven predictions about sales, customer interest, order volume (rather than guessing blindly), ecommerce brands set themselves up to satisfy customers, save money, and streamline their entire supply chain.

In this article, we’ll dive deep into what demand forecasting is, what factors influence demand, how to forecast accurately, and how experts like ShipBob can help.

What is demand forecasting?
Demand forecasting is the process of estimating how much demand there will be for a product in the future. Demand is typically measured in sales, so the goal of demand forecasting is to predict how many units of a particular product you will sell in a given period of time.

If you forecast demand accurately, you’ll end up with enough inventory to fulfil all the customer orders for that product without accidentally overstocking (which increases your inventory holding costs) or understocking (which can lead to costly stockouts and backorders). You will also be able to improve decision-making across your supply chain, warehousing operations, and inventory management.

To forecast demand as accurately as possible, many brands track historical sales and order data, and analyse it for patterns that can help them predict what might happen again in the future.