Quantitative sales forecasting

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  • Created by: noe
  • Created on: 18-09-20 09:22
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  • Quantitative sales forecasting
    • Time-series analysis: method that allows a business to predict future levels from past figures
      • Main components a business wants to identify in time-series analysis: trend and seasonal, cyclical and random fluctuations.
    • Accurate sales forecasts helps businesses to make a number of key decisions. E.g. how much stock to hold or how many people to employ.
    • Identifying the trend: analysis of figures will tell a business whether there is an upward, downward or constant trend.
      • Moving average: a succession of averages derived from successive segments of a series of values
        • When plotted onto a graph, line is 'smoother' removing any fluctiations in sales each year + gives a more obvious picture of the trend.
        • Three-year moving average
          • Add up sales for the first 3 years and divide by 3 Then, the first year's sales dropped out and the next year's sales added to calculate average, and so on.
      • Centring: method used to calculate a moving average, where the average is plotted or calculated in relation to the central figure.
        • Four-year moving average: Add up first four years, drop out the first year and add the fifth, add those up and add the answer to the first four years' average and divide by 8
    • Extrapolation: use of past sales data to forecast future sales.
    • Sales figures can be predicted by drawing a line of best fit through the trend figures and extending it making sure it matches the general slopes of all points in the trend.
      • However, prediction is made believing everything will stay the same but, if other factors changed, it would be inaccurate.
    • Seasonal variations. E.g. a three-year period divided in quarters.
    • Variations from the trend
      • Actual sales - trend. If the answer is positive it is said to be favourable but if it's negative it would be adverse.
        • Average of variations calculated by adding the variations of a certain years and diving it into the number of variations.
        • Then, this would be added to the prediction value based on the trend to make it more accurate

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