What does Demand Forecasting means? Its Importance and Types

What is demand forecasting?

Analyzing historical data and other information to predict future customer demand is called demand forecasting. The forecasting of demand gives businesses valuable information about their potential market and other markets, enabling managers to make informed decisions about pricing and business growth strategies.

It is important for businesses to have accurate demand forecasts to avoid making poor decisions about their products and target markets, as ill-informed decisions can negatively impact inventory holding costs, customer satisfaction, supply chain management, and profitability.

What is the importance of demand forecasting?

Using sales forecasts can help businesses plan, budget, and set goals. In order to ensure your supply meets customer demand, you must have a solid understanding of what your future sales will look like.

By optimizing inventory, increasing inventory turnover rates, and reducing holding costs, businesses can maximize their profits.

By analyzing upcoming cash flow, businesses can budget more accurately to pay suppliers and other operational costs and invest in their growth.

As a result of sales forecasting, you can also identify and rectify any kinks in the sales pipeline ahead of time to ensure your business performance remains strong. Many eCommerce business owners know that too much or too little inventory can negatively affect operations when it comes to inventory management.

Keeping operations running smoothly during peak periods requires anticipating demand and increasing staff and other resources.

What are the different types of forecasting demand?

There are three basic categories of traditional demand forecasting techniques:

Qualitative forecasting

For a relatively new business or when a new product is introduced to the market, qualitative forecasting is used when there isn't much data available. For quantitative estimates of demand, other information such as expert opinions, market research, and comparison analyses is used. When new products are unheard of and customer interest is difficult to predict, this approach is often used in technology.

Analyzing time series

Time series analysis is used to forecast demand for products or product lines when historical data is available and trends are clear. In addition to identifying seasonal fluctuations, cyclical patterns, and key sales trends, a time series analysis can be useful for identifying cyclical patterns.

Most businesses that use the time series analysis approach have several years' worth of data to work with, and relatively stable trends to analyze.

Causal models

Causal models are the most sophisticated and complex forecasting tools for businesses because they use specific information about relationships between variables that impact demand, such as competitors, economic forces, and other socioeconomic factors. To create a causal model forecast, historical data is essential, just as it is with time-series analyses.

READ MORE: How Analytics Can Help Elevate Sales Planning