4.3: Sales Forecasting (HL Only)

What is Sales Forecasting

Sales forecasting is a quantitative technique used to predict a company's future sales levels. It's important for identifying problems and opportunities in advance, but it's also challenging due to the many variables that can affect sales.
Sales Forecasting Techniques:
Market Research: Understanding consumer buying habits is crucial for accurate forecasting.
Extrapolation: Predicting future sales based on past trends using historical data.
Time Series Analysis: Identifying underlying trends in sales data by analyzing seasonal, cyclical, and random variations.
Factors Affecting Choice of Forecasting Method:
Accuracy: The desired level of precision determines the complexity of the method.
Time Horizon: Forecasting for the near future is easier than forecasting for several years.
Cost: Data availability and cost can impact the choice of method.
Product Life Cycle Stage: Market research is more important in the early stages of a product's life cycle.

Benefits
Improved working capital and cash flow: Accurate sales forecasts help businesses anticipate seasonal fluctuations in demand, leading to better cash flow management.
Improved stock control: Prevents issues of excessive or insufficient inventory by optimizing production planning.
Improved productive efficiency: Enables better resource allocation and avoids operational problems due to lack of production planning.
Securing external finance: Realistic sales forecasts can help businesses obtain loans or investments.
Improved budgeting: Allows managers to anticipate changes and adjust budgets accordingly.
Limitations
Limited information: Sales forecasting relies on historical data and trends, which may not fully capture future developments.
External influences: Unpredictable factors like natural disasters, economic fluctuations, or unexpected events can distort forecasts.
Inaccuracy of predictions: Forecasts are based on assumptions and may not accurately reflect future reality.
Garbage in, garbage out: Using outdated, irrelevant, or biased data can lead to inaccurate forecasts.