C3 Sales Forecasting Flashcards
Extrapolation
Using past experience or past business data to forecast future sales.
Quantitative forecasting methods
Time series analysis
Use of market research data
Qualitative forecasting methods.
Delphi technique
Brainstorming
Intuition
Expert opinion
When are quantitative forecasting methods used?
What do they rely on?
When there is historical data available
They rely heavily on data and are objective.
Time series analysis
Uses evidence from past sales records to predict future sale patterns
Methods of time series analysis
Seasonal analysis
Trend analysis
Cycle analysis
Random factor analysis
Seasonal Analysis
Sales are measured on a monthly or weekly basis to examine the seasonality of demand.
Eg the sales of ice cream will be higher in the warmer seasons and lower in the colder seasons, or according to daily weather changes.
Trend analysis
This focuses on long-term data, which has been collected over a number of years. The objective is to determine the general trend of sales - rising, falling, or stagnant.
Cycle analysis
As with trend analysis, long term figures are used but now the objective is to examine the relationship between demand and levels and economic activity.
Random factor analysis
This method of analysis attempts to explain how unusual or extreme sales figures occur.
Eg. If sales of ice creams double for a two week period could this be explained by a change in weather conditions as opposed to a successful marketing campaign?
How to work out moving averages
Take the number of adjacent figures for each month, add it together and divide by the number of months.
Result is placed in the middle month.
Months in a quarter
3 months
Benefits of time series analysis
- Helps plan ahead
- Helps financial planning
- Production planning (determine right type and quantity of supplies are ordered and that the line is running at the correct speed)
- Useful in identifying seasonal variation
- Reduces risk of surprises
- Human resource planning (ensures right number and correctly qualified staff members are available)
Limitations of time series analysis
- It is not always easy to predict the future
- Historical data is not always a good indicator
- Even complicated forecasting methods may not be accurate
- Less useful for long term forecasts
- Success is not guaranteed
Surveys of consumer intentions
Making predictions by asking people directly what they intend to do in the future. The results of these surveys allow businesses to predict sales patterns, and plan for the future in terms of staffing and production levels.