Demand forecasting retail entails different methods that implement the use of software to make sure accuracy is achieved. The most common is “Exponential Smoothing”, which can be done manually or by forecasting an excel spreadsheet, for example.

## What is demand forecasting retail?

It means predicting the number of goods that will be sold at a particular time to provide enough quantity for sale. For example, forecasting how many apples need to be put in stock on Monday morning before opening the shop.

### Why is it important?

The main objective of **demand forecasting retail** is to provide accurate information about quantities and types of products sold. Any retailer can benefit from demand forecasting retail by giving them an overall idea of what products to order; on the other hand, it will reduce inventory and save money.

## Methods used in demand forecasting retail

*1- Exponential Smoothing*

This method is the same as a calculator when forecasting sales. In this method, the retail sales in the current day are added to a previous forecast amount and divided by 2 or 3 depending on how much time has been elapsed from the previous one. This method needs forecasting from the beginning of the month/year when retailers make their initial order. This method is accurate and straightforward for a short-term forecast of around three days to a month.

*2- Moving average*

This method works on the same principle as the previous one, but instead of adding the current day’s sales to an amount prior, it follows equal steps from zero until it reaches the 5th day of prediction. By using this method, retailers can discover trends in their sales and order accordingly.

*3- Weighted moving average*

It is a modified version of the previous method, where instead of having equal steps, it assigns different weights to each day depending on its significance. The 1st day has 20% weight, while the second has 30%, after that every day is given a 10% weight.

*4- Regression Analysis*

This method is very accurate in forecasting sales and can be applied when a retailer has a sales history over a specific period. That’s why it requires more data. In this method, retailers have to plot their forecasts with actuals from the historical data set they have, and then they will get a curve that indicates how their future projections are expected to look.

*5- Bingo card method*

This method is the least accurate since it only uses history and doesn’t consider seasonality or trends in retail sales. It needs to create different bingo cards for each product category where retailers will write predictions on what products will be sold according to the history.

*6- Last year’s performance*

This method of demand forecasting retail is one of the simplest and most accurate, where retailers need to look at their previous sales and order accordingly. It requires plenty of historical data, which is hard since different retailers have different seasonality for each category.

**Final notes**

As much as the retail industry is growing, it requires accurate forecasting methods to ensure retailers are not wasting resources on products that will not be sold. As a company, it is advisable to learn and invest in **market intelligence software**. It helps retailers meet their objectives in making resources available for them that would otherwise remain idle or unfilled.