Demand forecasting is a process that takes historical sales data and uses it to make estimations (or forecasts) about customer demand in the future. For enterprises, demand forecasting allows for estimating how many goods or services will sell and how much inventory needs to be ordered.
Demand forecasting lays the foundation for many other critical business assumptions such as turnover, profit margins, cash flow, capital expenditure, and capacity planning. Demand forecasting is often associated with managerial economics and supply chain management, but it applies to every company in every industry.
Demand forecasting is a pivotal business process. Many strategic and operational tactics are based on this forecast, such as budgeting, financial planning, sales and marketing plans, and capacity planning. Because so many business decisions are contingent on demand forecasts, it is crucial to get an accurate prediction. Imagine if demand is predicted to grow, and the company is liberal with its yearly budgets as a result, but demand actually shrinks.
Demand forecast calculations rely on a large amount of data, and are custom to a company’s specific situation, often making them proprietary.
Many businesses rely on machine learning models to do the demand forecast calculation. This makes the forecast more accurate and reliable while saving human time that would otherwise be spent on manual calculations.
The great thing about using machine learning for demand forecasting is that once the model is built to calculate a specific formula for future demand, it can update predictions as time passes. That way, there is always a real-time prediction available that includes any new data.