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Forecasting

Forecasting. Lecture 6 Dr. Haider Shah. Learning outcomes. Understand what are the primary tools for forecasting Understand regression analysis and when and how to apply it. Producing budget data. In previous lectures we have covered budget preparation

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Forecasting

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  1. Forecasting Lecture 6 Dr. Haider Shah

  2. Learning outcomes • Understand what are the primary tools for forecasting • Understand regression analysis and when and how to apply it

  3. Producing budget data • In previous lectures we have covered budget preparation • We will now look at how we could produce data to be placed in these budgets

  4. Forecasting Methods • Qualitative Methods • Quantitative methods using historical data

  5. Historical data – Can we use it? The following checks need to be applied • Data must be examined for one off costs. We only want base data which is likely to re-occur • Has the data been affected adversely by accounting policies • Is the time period appropriate. e.g long enough to reflect seasonal changes • Be able to identify dependent and independent variables

  6. Linear relationships • Y = a + bX (equation of straight line) Y b a X

  7. Linear relationships • Y = a + bX Where: Y = the dependent variable – depends on the value of X X= the independent variable a = a constant a fixed amount b = a constant ( the number by which the value of X should be multiplied to derive the value of Y

  8. Linear relationships • If there is a linear relationship between total costs and level of activity • Y will equal Total Costs • X will equal Level of activity • a will equal fixed cost • b will equal variable cost per unit

  9. Cost Classification Variable cost - linear: Total Cost Unit Cost 30 £3 10 Output Output E.g. Direct Materials

  10. Cost Classification Semi-Variable Costs: Stepped Fixed Costs: Total Cost Total Cost Total Costs Normal Operating Range Variable Costs Fixed Costs (Relevant Range) Output Output E.g. E.g. Supervision Power Telephone

  11. Semi-Variable Costs • Also called mixed costs. Comprise fixed and variable components. • Variable and fixed costs components need to be split for estimation purpose • We need some data which plot total cost against the cost driving activity.

  12. Semi-Variable Costs • The data can be used to help us split the total cost into variable and fixed components

  13. Quantitative techniques • High-Low Method • Less sophisticated estimation method • Regression analysis: • Used for both Revenue & Costs estimates • Time series analysis • Used for Revenue estimates mostly

  14. Scattergraph Method

  15. Visual Fit 28 . . 24 . . . 20 . . . . . 16 Factory O/H (£) 12 8 Variable O/H rate 4 Fixed 4 8 12 16 20 24 Direct Labour hours

  16. High-Low Method = Difference of y Difference of x = 11 = £0.6875 16 Variable Rate Fixed O/H = Total O/H – variable rate * Dir Lab Hours Fixed O/H = 25 – 0.6875 * 23 = 9.1875 Y = a + bx Y = 9.1875 + 0.6875x

  17. Linear regression analysis • Also known as ‘least squares technique’ • Historical data is collected from previous periods and adjusted to a common price to remove inflation. • Provides information for activity levels (X) and associated costs (Y).

  18. Co-efficients

  19. Example: Costs of Transport Deptt Identify fixed and variable cost elements

  20. Solution:

  21. Example 2 The following data is available for a factory Calculate the fixed cost and the variable cost per unit What would be total costs if output was 22,000 units

  22. Solution:

  23. Sales forecasting • Complex and difficult • Need to consider various factors

  24. Sales forecasting factors

  25. Sales forecasting with regression • Sales of product A over the past 7 years were as follows: Yr Sales (‘000 units) 1 22 2 25 3 24 4 26 5 29 6 28 7 30 Noting that X becomes the years, identify the sales in Year 8 using regression analysis

  26. Solution:

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