F5 Performance Management

2 Section C: Budgeting Designed to give you knowledge and application of: C1. Objectives C2. Budgetary systems C3. Types of Budget C4. Quantitative analysis in Budgeting C5. Behavioural aspects of Budgeting C1. Objectives C2. Budgetary systems C3. Types of Budget C4. Quantitative analysis in Budgeting C5. Behavioural aspects of Budgeting

3 C4: Quantitative analysis in budgeting Learning outcomes  Analyse fixed and variable cost elements from total cost data using high / low and regression methods. [2]  Explain the use of forecasting techniques, including time series, simple average growth model and estimates based on judgement and experience. Predict a future value from provided time series analysis data using additive and proportional data. [2]  Estimate the learning effect and apply the learning curve to a budgetary problem, including calculations on steady states. [2]  Discuss the reservations with the learning curve. [2]  Apply expected values and explain the problems and benefits. [2]  Explain the benefits and dangers inherent in using spreadsheets in budgeting. [1]

4 Secular trend (T): From the actual time series, secular trend line plotted in order to understand the trend. The use of forecasting techniques Time series analysis & variations observed in this analysis:  Series of data points  Quantitative technique  Past data are collected to understand their behaviour & projected into the future to estimate the variable under consideration. Example Monthly sales volume over the last five years or sales during a particular season over the last ten years are examples of time series. Secular trend of consumer price index Continued…

5 Cyclical fluctuations (C): Variations show movements in cyclical fluctuations in the business cycle. Here too, the variable is analysed over a long period. Seasonal variations (S): When a curve plotted representing a time series shows a similar pattern during a certain period over successive years, this is indicative of seasonal variations. Due to X-mas Continued…

6 Irregular variations (I): There is no specific trend & the variable may move inconsistently. As in time series analysis, a trend is represented by a straight line, the following equations are used to predict the future: ∑y = nc + m∑x ∑xy = C∑x + m∑x 2 Where, x = independent variable representing time y = the variable that is dependent on time The values of y can be obtained for every value of x by identifying and replacing the values of m and c in the equation of the straight line i.e. y = mx + c. Let us see an example to understand how future outcomes can be predicted using time series analysis. Refer to Example on page 271 Continued…

7 Example From the sales figures for the last five years as shown below, calculate the moving average for three years. Predict a future value from the provided time series analysis data using additive and proportional data Moving average: For a given time series, the moving total is calculated for a fixed number of periods (e.g. three years or three quarters etc.). From the moving total, the average for that period is calculated. Years Sales (in units) Moving Total Moving Average 20X X X X X8280 When the set of data considered is even, the moving averages do not become centred. In this case, we need to calculate the centred moving average. When calculating a moving average, placing the average in the middle of the time period makes sense. If we average an even number of terms, we need to smoothen value. To do this, the centred moving average is calculated by taking the mean of the two moving averages. Continued…

8 Example In the above example, if we add one more year, say 20X9 and the corresponding sales as 300, the centred moving average would be calculated as follows: Continued… Years Sales (in units) Moving Total Moving Average Centred Moving Average 20X X X X X X9300

9 Models of time series analysis Additive model It is assumed that the factors of the series i.e. seasonal variations, secular trend, cyclical variations and irregular fluctuations are independent of each other and do not have any impact on each other. Therefore under this method, time series is predicted using the formula below: y = T + S + C + I Where, y = actual time series T = trend series S = seasonal component C = cyclical component I = irregular component Refer to Example on page 274 Continued…

10 Multiplicative model It is assumed that the components of the time series have an impact on each other which thereby multiplies the effect. The formula used under this method is – y = T x S x I x C Where, y = the actual time series T = the trend series S = the seasonal component C= the cyclical component I = the irregular component Under this model, seasonal variations can be calculated as S = y/T. Refer to Example on page 275 Continued…

11 Simple average growth model This is one of the quantitative forecasting techniques, where all the past period data are given equal weight, and the average of the past data is calculated in order to identify the representative figures. This average is used to predict the future outcome. The following formula is used for calculating the simple average: n Where, ∑ Di SA is simple average i = 1 Di is demand for the past i years SA = n n number of observations Continued… Estimates based on judgment & experience Delphi method: assumed that group experts will give more realistic forecast then individual Independent technological comparisons: defines relationship between primary trend & the events to be forecasted Subjective curve fitting: depicts the change in demand for a product throughout the life cycle of the product

12 Y = ax b Where, Y = cumulative average time taken per unit a = time taken for the first unit x = total no. of units b = index of learning (log of learning rate/log2) log2 = Average unit & time cost Y X Learning curve Cumulative output Estimate the learning effect and apply the learning curve to a budgetary problem, including calculations on steady states Learning curve:  A mathematical expression of the phenomenon  When complex & labour intensive procedures are repeated, unit labour times tend to decrease at a constant rate.  The learning curve model expresses this reduction in unit production time mathematically. Advantages of learning curve:  Improves planning process  Sets standards  Determines labour cost for product pricing  Helps in taking many other decisions e.g. make or buy work scheduling Refer to Test Yourself 3 on page 280

13 Time required for job will decrease. Not always true. The reservations with the learning curve Reservations with learning curve:  Not applicable to machine intensive procedures  Assumes labour rate remains the same, which may not be true  Applicable only when same work or job is repeated  Applicable only to new jobs / industries / workers  Presumes learning effect only if production is doubled  Assumption that time required for a job will decrease- does not hold true in reality  Not applicable to managerial work

14 Application of expected values and explain the problems and benefits Applying expected value to sales:  Expected value is the weighted average of the alternative payoffs estimated at different states of nature (i.e. the events) with reference to the probability for occurrence of the events  When devising budgets, demand for the product / sales volume is the limiting factor  Where future outputs, costs and revenues cannot be estimated with certainty, predicting the demand for the product or the sales price etc. is quite difficult  By using the expected value technique, the weighted average of each alternative action (sales volume) can be estimated at the different states of nature. Example Futuristic Ltd’s marketing manager cannot determine the sales volume for the next year as he cannot predict the changes in the business cycle. Therefore, he has predicted three possible sales volumes at 8,000 units, 10,000 units and 15,000 units. He has also estimated their likelihood and, on that basis, assigned probabilities to the sales volumes. This will help him to calculate the expected sales volume. Continued…

15 Applying EV to sales price:  When the price that the product would fetch in the market can’t be determined the sale price can be estimated using EV technique  To weigh the alternative expected price  To identify the best possible price Example While preparing the sales budget for Petrochem Ltd, an oil company, the marketing manager predicts three possibilities. If there is an acute scarcity of oil in the international market, oil could be sold at $70 per barrel. If there is moderate scarcity, the price would be $65 and, under normal conditions, the price would be $60. As it is not possible for him to estimate the extent of the scarcity that may take place in reality, it is difficult to determine the sales price while preparing a budget. In this case, an expected value will reduce the risk involved in determining the budgeted sales price. Continued…

16 Applying EV to determine provisions / contingencies:  The EV determines the cost impact of the unmanaged risk on the success of the project.  EV = The probability of the occurrence of risk x Cost impact on the project Example The production department of Fortune Ltd is preparing the labour cost budget. It is estimated that there is an 80% chance of an increase in the labour hour rate from $5 to $8. The requirement of labour hours for the whole year is estimated at 50,000 hours. The cost impact in this case would be the difference between $8 and $5 i.e. $3. The contingency can be calculated as 50,000 hours x $3 x 0.80 which is equal to $120,000. Problems associated with the application of the expected value to the budget: By determining the expected value, the risk cannot be eliminated fully because, in the case of occurrence of an undesired event, the cost impact would be greater than the impact provided for. Example In the above example, if the labour rate increases, the cost impact would be $150,000, but the provision made is only for $120,000. Continued…

17 RECAP  Analyse fixed and variable cost elements from total cost data using high / low and regression methods. [2]  Explain the use of forecasting techniques, including time series, simple average growth model and estimates based on judgement and experience. Predict a future value from provided time series analysis data using additive and proportional data. [2]  Estimate the learning effect and apply the learning curve to a budgetary problem, including calculations on steady states. [2]  Discuss the reservations with the learning curve. [2]  Apply expected values and explain the problems and benefits. [2]  Explain the benefits and dangers inherent in using spreadsheets in budgeting. [1]