Chapter 6 Budgeting Flashcards
1.1 The purposes of budgeting
A budget is a quantitative expression of a plan of action prepared in advance of the period to which it relates. They give an idea of the costs and revenues expected to be incurred or earned in future periods.
Most organisations prepare budgets for the business as a whole. But departmental budgets, functional budgets, profit or loss account and cash budgets can be prepared by organisations.
The main aims of budgeting are:
- Planning for the future
- Controlling costs
- Co-ordination of the different activities of the business by ensuring managers are working towards the same common goal
- Communication: budgets communicate the targets of the organisation to individual managers
- Motivation
- Evaluation
- Authorisation: budgets act as a form of authorisation of expenditure
- Resource allocation
2.1 Stages in budget preparation
Long term objectives of an organisation must be defined so that budgets prepared are working towards the goals of the business.
The budget committee is formed. Typically made up of the chief executive, budget officer and departmental or functional heads. The budget committee is responsible for communicating policy guidelines to the people who prepare the budgets and for setting and approving budgets.
Budget manual is produced. An organisation’s budget manual sets out instructions relating to the preparation and use of budgets. It also gives details of the responsibilities of those involved in the budgeting process, including an organisation chart and a list of budget holders.
Limiting factor is identified. The limiting factor is known as the principal budget factor. Generally, there will be one factor that will limit the activity of an organisation’s performance.
Other functional budgets are produced.
Then the initial budgets are prepared. Budget managers may sometimes try to build in an element of budget slack. The initial budgets are reviewed and integrated into the complete budget system. Then the master budget is prepared after adjustments are made to initial budgets. The master budget is then shown to top management for final approval. Budgets are reviewed regularly and comparisons between budgets and actual results are carried out and any differences arising are known as variances.
2.1 Budget preparation
These are the following steps:
- Sales budget
- Production budget
- Raw materials, labour and factory overhead
- Cost of sales budget
- Selling and distribution expense budget and general and admin expense budget
- Forms the master budget
3.1 Sales budgets
A functional budget is a budget of income and expenditure which applies to a particular function. The main functional budgets are sales budget, production budget, raw material usage budget, raw material purchases budget and labour budget.
4.1 Production budgets
Budgeted production levels can be calculated as:
Sales budget + closing inventory of finished goods – opening inventory of finished goods equals good/fault-free production required + faulty production/wastage equals total production.
4.2 Material budgets
There are two types of material budget, the usage budget and the purchases budget.
The material usage budget is the budgeted production for each product multiplied by the number of kgs required to produce one unit of the product. The material purchases budget is made up of the following elements.
Material purchases budget is the material usage budget plus the closing inventory less the opening inventory.
4.3 Labour budgets
Labour budgets are simply the number of hours multiplied by the labour rate per hour.
5.1 The master budget
This is the budget which all subsidiary budgets are consolidated. This comprises a profit or loss account, budgeted balance sheet and budgeted cash flow statement. These are drawn up after all the functional budgets have been approved.
5.2 Sensitivity analysis
This involves revising budget assumptions and examining the impact that this has on the budget. This is useful in that it can identify which assumptions have the greatest impact on the budget and therefore need to be considered most carefully when setting the budget.
6.1 Preparing forecasts
In order to prepare budgets, historic data is often used to predict future costs and revenues. The equation of a straight line is a linear function and represented by y = a + bx.
- A is the intercept, the point where the line cuts the y axis (y axis is 0)
- B is the gradient of the line (the change in y when x increases by a unit)
- X is the independent variable
- Y is the dependent variable
Cost equations are derived from historical data. Once a cost equation has been established, it can be used to estimate future costs. Cost equations have the same formula as linear functions.
- A is the fixed cost per period
- B is the variable cost per unit
- X is the activity level
- Y is the total cost = fixed cost + variable cost
6.2 Cost estimation
The two main methods for analysing semi-variable costs into their fixed and variable elements are:
- High-low method: estimates fixed and variable costs of a product or service
- Time series analysis: patterns found in past data is assumed to continue in the future
- Least squares regression establishes a cost equation
High low method: based on an analysis of historical information about costs at different activity levels. The steps are:
- Determining the highest and lowest activity levels and their associated costs
- Find the variable cost per unit: (increase in cost / increase in activity)
- Find the fixed cost (total semi-variable cost = fixed costs + (variable cost per unit x activity level)). Therefore, fixed cost = total semi-variable cost – total variable cost
- Calculate the expected cost: this is the fixed costs (step 3) + (variable cost per unit (step 2) x activity level)
The disadvantage of high-low method is it only takes account of two sets of data, which may not be wholly representative.
6.3 Time Series Analysis
This uses moving averages to create a trend line over time, established from historical data, that, when adjusted for seasonal variations, can then be used to make predictions for the future. The components are:
- The trend is the long-term general movement of the data
- Cyclical variations are economic cycles of booms and slumps
- Season variations are a regular variation around the trend over a fixed time period, usually one year
- Residual variations are irregular, random fluctuations in the data usually caused by factors specific to the time series
There are three ways to calculate the trend:
- Using the high-low method
- By linear regression
- Using moving averages
The seasonal variations can be estimated by comparing an actual time series with the trend line values calculated from the time series. For each season the variation is the difference between the trend line value and the actual historical value for the same period. A seasonal variation can be calculated for each period in the trend line, when the actual value is higher than the trend line value, the seasonal variation is positive. When the actual value is lower than the trend line value, the seasonal variation is negative.
Moving averages is a set of calculations used to smooth out variations. The first step is choosing the correct cycle length, this period should cover the whole pattern (for example quarterly), then calculate the total for the first cycle, calculate the average by dividing the total by the number of periods in the cycle. Then repeat the process for the next cycle, repeat this calculation for each successive cycle until the data has been fully analysed.
If the trend is constant, then extrapolation is easy to calculate as the increase between each value is the same. Once the trend has been identified it is possible to calculate the seasonal variations from the trend, this is how the data varies from the trend line. There are two methods:
- Additive model: variations expressed in absolute terms with above and below average figures.
- Multiplicative mode: variations are shown as a percentage of the trend
6.4 Forecasting
Once seasonal variations have been calculated they can be used to forecast future values. The trend line is extrapolated, and the variations are applied to the trend. Additive model forecast = T + S (where T is the trend line and S is the seasonal variation). Multiplicative model forecast is T x S (S is normally represented as a percentage).
The limitations of time series analysis:
- Assumption that what has happened in the past is a reliable guide to the future
- Assumption that a straight-line trend exists
- Assumption that seasonal variations are constant
6.5 Linear regression
The advantage of linear regression is that all data points are considered.
Correlation: two variables are said to be correlated if a change in one variable brings a change in another variable. Correlation measures the strength of the connection between two variables. A scatter graph or scatter chart can be drawn to see if any visible relationship exists. Variables can be perfectly correlated, partially correlated or uncorrelated, as well as being positive and negative correlations.
6.6 Correlation coefficient
This measures the strength of a linear relationship between two variables. It can therefore give an indication of how reliable the estimated linear function is for a set of data. The correlation coefficient can only take on values between -1 and 1:
- R = + 1 indicates perfect positive correlation
- R = 0 indicates no correlation
- R = -1 indicates perfect negative correlation
The coefficient of determination is the square of the correlation coefficient. This is a measure of how much variation in the dependent variable is explained by the variation of the independent variable. The variation not accounted for by variations in the independent variable will be due to random fluctuations, or to other specific factors that have not been identified.
The limitations are correlation could be misleading used on sample data, a correlation in a sample is not necessarily present in the population. Correlation and causality are not the same, a correlation does not automatically imply that one causes the other.
