Demand forecasting refers to making estimations about future customer demand using historical data and other information. Proper demand forecasting gives businesses valuable information about their potential in their current market and other markets so that managers can make informed decisions about pricing, business growth strategies, and market potential. Without demand forecasting, businesses risk making poor decisions about their products and target markets.
Get ahead with cash management and product planning with TradeGecko's Sales & Inventory Forecast Model.
There are a number of reasons why demand forecasting is an important process for businesses:
Most demand forecasting techniques fall into one of three basic categories:
Qualitative forecasting techniques are used when there isn’t a lot of data available to work with, such as for a relatively new business or when a product is introduced to the market. In this instance, other information such as expert opinions, market research, and comparative analyses are used to form quantitative estimates about demand.
This approach is often used in areas like technology, where new products may be unprecedented, and customer interest is difficult to gauge ahead of time.
When historical data is available for a product or product line and trends are clear, businesses tend to use the time series analysis approach to demand forecasting. A time series analysis is useful for identifying seasonal fluctuations in demand, cyclical patterns, and key sales trends.
The time series analysis approach is most effectively used by well-established businesses who have several years’ worth of data to work from and relatively stable trend patterns.
The causal model is the most sophisticated and complex forecasting tool for businesses because it uses specific information about relationships between variables affecting demand in the market, such as competitors, economic forces, and other socioeconomic factors. As with time series analyses, historical data is key to creating a causal model forecast.
For example, an ice cream business could create a causal model forecast by looking at factors such as their historical sales data, marketing budget, promotional activities, any new ice cream stores in their area, their competitors’ prices, the weather, overall demand for ice cream in their area, and even their local unemployment rate.
While seasonality refers to variations in demand that occur during specific times on a periodic basis (such as the holiday season), trends can occur at any time and signal an overall shift in behavior (such as a specific product growing in popularity).
When it comes to demand forecasting, you should factor in estimates of trends and estimates of seasonality to accurately plan your inventory management strategy, marketing efforts, and operational processes.
Essentially, demand forecasting is a good way to anticipate what customers are going to want from your business in the future, so you can prepare inventory and resources to meet demand.
By forecasting demand, you’ll be able to cut down on holding costs and other operational expenses when they’re not needed while ensuring you’re equipped to handle peak periods when they happen. The result? A better customer experience, streamlined operations, and improved sales performance.
Track inventory, sales, and forecast revenue with the Sales & Inventory Forecast Tool.