lecture 6 dr. haider shah. understand what are the primary tools for forecasting understand...
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Lecture 6
Dr. Haider Shah
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Understand what are the primary tools for forecasting
Understand regression analysis and when and how to apply it
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In previous lectures we have covered budget preparation
We will now look at how we could produce data to be placed in these budgets
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Qualitative Methods
Quantitative methods using historical data
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The following checks need to be applied
1. Data must be examined for one off costs. We only want base data which is likely to re-occur
2. Has the data been affected adversely by accounting policies
3. Is the time period appropriate. e.g long enough to reflect seasonal changes
4. Be able to identify dependent and independent variables
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Y = a + bX (equation of straight line)
a
b
X
Y
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Y = a + bX
Where:
Y = the dependent variable – depends on the value of X
X= the independent variablea = a constant a fixed amountb = a constant ( the number by which the
value of X should be multiplied to derive the value of Y
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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
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Variable cost - linear:
TotalCost
Output
E.g. Direct Materials
10
30
UnitCost
Output
£3
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Stepped Fixed Costs:TotalCost
Output
E.g.
TotalCost
Output
E.g.
Semi-Variable Costs:
Supervision PowerTelephone
Normal Operating
Range
(Relevant Range)
Variable Costs
Fixed Costs
Total Costs
Cost ClassificationCost Classification
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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.
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The data can be used to help us split the total cost into variable
and fixed components
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Regression analysis: Used for both Revenue & Costs estimates
Time series analysis Used for Revenue estimates mostly
High-Low Method Less sophisticated estimation method
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Month Factory O/H Dir Lab Hours
January 15 9
February 20 19
March 14 11
April 16 14
May 25 23
June 20 12
July 20 12
August 23 22
September 14 7
October 22 13
November 18 15
December 18 17
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4
16
12
20
8
24
12
28
84 2016 24
FactoryO/H (£)
Direct Labour hours
.
.
..
. ..
...
Fixed
Variable O/H rate
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Total O/H (y)
Activity (hours) (x)
High £25 23
Low £14 7
Difference £11 16
= 11 = £0.6875 16
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
Variable Rate = Difference of y Difference of x
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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).
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Identify fixed and variable cost elements
Month Truck maintenance(£)
Truck usage (hrs)
Jan 13,600 2,100
Feb 15,800 2,800
Mar 14,500 2,200
Apr 16,200 3,000
May 14,900 2,600
June 15,000 2,500
Total 90,000 15,200
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X Y XY Xsq000 hrs £'000
1 2.1 13.6 28.56 4.412 2.8 15.8 44.24 7.843 2.2 14.5 31.90 4.844 3.0 16.2 48.60 9.005 2.6 14.9 38.74 6.766 2.5 15 37.50 6.25
15.2 90.0 229.54 39.10
Solution:
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n=No. of pairs = 6
b = 6(229.54) - (15.2)(90)(6(39.1) - (15.2)sq)
=(1377.24 - 1368) (234.6 - 231.04)
= 9.243.56
= £2.60
a =(sumY/n) - (bsumX / n)
a = (90/6) - (2.6(15.2)/6)
a =8.41 approx = £8,410
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Month
Output(units) 000s
Costs(£k)
Jan 20 82
Feb 16 70
Mar 24 90
Apr 22 85
May 18 73
a) Calculate the fixed cost and the variable cost per unitb) What would be total costs if output was 22,000 units
The following data is available for a factory
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X Y XY Xsq000 hrs £'000
1 20 82 1640.00 400.002 16 70 1120.00 256.003 24 90 2160.00 576.004 22 85 1870.00 484.005 18 73 1314.00 324.00
100 400 8104.00 2040.00
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Complex and difficult
Need to consider various factors
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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
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Yr X Y XY X sq1 1 22 22 12 2 25 50 43 3 24 72 94 4 26 104 165 5 29 145 256 6 28 168 367 7 30 210 49
sum 28 184 771 140
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Y = a + bX
b= ((7 x 771) -(28 x 184))((7 x 140) - (28 x 28)
b= 245 / 196 = 1.25
a = (184 ) - (1.25 x 28) = 21.37 7
Y= 21.3 + 1.25X