Demand forecasting refers to the process of predicting the quantity of goods and services that will be demanded by consumers at a future point in time. More specifically, the methods of demand forecasting entail using predictive analytics to estimate customer demand in consideration of key economic conditions. This is an important tool in optimizing business profitability through efficient supply chain management. Demand forecasting methods are divided into two major categories, qualitative and quantitative methods. Qualitative methods are based on expert opinion and information gathered from the field. This method is mostly used in situations when there is minimal data available for analysis such as when a business or product has recently been introduced to the market. Quantitative methods, however, use available data, and analytical tools in order to produce predictions. Demand forecasting may be used in resource allocation, inventory management, assessing future capacity requirements, or making decisions on whether to enter a new market.
Importance of demand forecasting for businesses
Demand forecasting plays an important role for businesses in different industries, particularly with regard to mitigating the risks associated with particular business activities. However, demand forecasting is known to be a challenging task for businesses due to the intricacies of analysis, specifically quantitative analysis. Nevertheless, understanding customer needs is an indispensable part of any industry in order for business activities to be implemented efficiently and more appropriately respond to market needs. If businesses are able to forecast demand effectively, several benefits can be accrued. These include but are not limited to, waste reduction, optimized allocation of resources, and potentially large increases in sales and revenue.
Elaborating on the above, some of the reasons why businesses require demand forecasting include:
Meeting goals - Most successful organisations will have pre-determined growth trajectories and long-term plans to ensure the business is operating at an ideal output. By having an understanding of future demand markets, businesses can be proactive in ensuring that goals will be met in the this business environment. Business decisions - In reference to meeting goals, by having a thorough understanding of future industry demand, management and key board members can make strategic business decisions that encourage higher profitability and growth. These decisions are generally associated with the concepts of capacity, market targeting, raw material acquisition and understanding vendor contract direction. Growth - By having an accurate understanding of future forecasts, companies can gauge the need for expansion within a timeframe that allows them to do so cost effectively. Human capital management - If there is a rapid demand increase in an industry but a business does not have enough employees to satisy the sales orders, consumer loyalty may be adversely affected as customers are forced to purchase from competitors. Financial planning - It is crucial to understand demand forecasts in order to efficiently budget for future operations in terms of factors such as cash flow, inventory accounting and general operational costs. The use of an accurate demand forecasting model can result in significant decreases in operational costs for businesses, since less safety stock is required to be held.
Methods for Forecasting Demand
There are various statistical and econometric analyses used to forecast demand. Forecasting demand can be broken down into seven stage process, the seven stages are described as:
Stage 1: Statement of a theory or hypothesis
The first step to forecast demand is to determine a set of objectives or information to derive different business strategies. These objectives are based on a set of hypotheses that usually come from a mixture of economic theory or previous empirical studies. For example, a manager may wish to find what the optimal price and production amount would be for a new product, based on how demand elasticity affected past company sales.