CASH MANAGEMENT Forecasting Future Cash Receipts and Payments

Information Sales information Production information Accounting information Forecast information

Forecasting Future Cash Receipts and Payments Time series analysis Graphical presentation Calculating a trend using moving averages Calculating seasonal variations Mark ups and margins (already done!) Using time series analysis in cash budgeting Dealing with inflation in cash budgeting Using indices.

Time series analysis When preparing cash budgets a large number of figures are estimated – Sales – Purchases – Wages etc By looking at the past we can estimate the future trends and patterns A time series is simply a record of previous figures over a period of time

Time series analysis Analysing these graphically can help visualise these figures MonthSales £'000 January2,030 February1,570 March1,620 April2,100 May2,080 June1,740 July1,690 August2,190 September2,150 October1,830 November1,780 December2,200

Time series analysis

The trend line

Moving averages The trend line previously shown could be extended into future months for an indication of future trends More technical methods are available Moving averages is the average of each successive group Can be taken using either an odd or even number of figures for each average For this syllabus we use odd, a bit easier!

Moving averages From the previous example we calculate: – Three period moving average for sales Jan – March – Then the three period moving average for sales Feb – April – Then the three period moving average for sales March – May etc. – The moving average is placed against the period which is the mid point of the range

Moving averages MonthSales £'000Moving average £’000 January2,030 February1,5701,740 March1,6201,763 April2,1001,933 May2,0801,973 June1,7401,837 July1,6901,873 August2,1902,010 September2,1502,057 October1,8301,920 November1,7801,937 December2,200

Moving averages From the previous data we can: – See a clear upward trend as time advances – Calculate the average increase across each month – (1,937 – 1,740) ÷ 9 – £22,000 per month Task – review example page 30, complete activity 1 page 31, activity 2 page 32

Seasonal variations Figures in a time series are made up of a number of different elements: – Trend – general movement we have seen in the time series – Cyclical variation – long term movements in the economy – Random variation – unexplained random events – Seasonal variation – patterns during successive periods (Christmas, bonfire night!)

Seasonal variations - additive model Each figures in a time series is made up as follows: A=T+SA=T+S A=Actual figure T=Trend figure S=Seasonal variation ∴S=A-T∴S=A-T

Using time series analysis in cash budgeting We have seen how to calculate a trend Now we must use this in cash budgeting We estimate future figures Based upon past performance Known as EXTRAPOLATION

Using time series analysis in cash budgeting Using the example before we calculated the trend as a monthly increase of £22,000 To calculate January – April figures we add multiples of £22,000 January’s figure is calculated by adding 2 x £22,000 to the November figure Each subsequent month by adding £22,000 to the result of the preceding month

Using time series analysis in cash budgeting 20X9PeriodTrend £’000 January ((1,937 + (2 x 22))1,981 February (1, )2,003 March (2, )2,025 April (2, )2,047

Using time series analysis in cash budgeting Seasonal variation JanuaryFebruaryMarchApril £’000 Seasonal variation Note we now have to take into account the seasonal variations Under the additive model the variation is either added to, or subtracted from the trend figure (variation figures are given below):

Using time series analysis in cash budgeting 20X9PeriodTrend £’000Seasonal variation £’000 Estimated sales £’000 January ((1,937 + (2 x 22))1, ,155 February (1, )2, ,833 March (2, )2, ,790 April (2, )2, ,278

Using time series analysis in cash budgeting Problems using time series analysis include: – The less historical data available the less reliable the results will be – The further into the future we forecast the less reliable the results will be – Assumes trend and seasonal variations from the past will continue – Cyclical and random variations have been ignored.

Seasonal variations - additive model Example page 32 Activity 3 page 34 Activity 4 page 35 Workbook Activity 6 page 43 Task 2 - Handout

Indices A way of expressing price changes/figures and fluctuations over time Compared to a base year Base Year is given an index of 100 First step is to determine the base year If price is greater, then index will be > 100 If price is less, then index will be < 100

Indices A specific price index relates to a specific item A general price index measures a variety of goods Retail price index gives a good indicator of inflation in the economy, base month of January 1987 – January 2012 figure was (link)(link) Index numbers can be used to forecast future data to be included in cash flows.

Indices - example Given the base price and the RPI we can estimate future months. General overheads in December £75k, increase in line with general inflation defined by the RPI. In December the RPI stood at In January it is anticipated to be and in February What are the estimated overhead costs in these coming 2 months?

Indices - example Current Index x Base Cost Base Index x 75,000 to calculate January x 75,000 to calculate February 196.6

Indices – further example given prices we can use a similar method for calculating index values (calculate % change over time) Cost price of commodity YearUnit price £CalculationIndex value 20X314.60Base year X /14.60x X /14.60x X /14.60x X /14.60x X /14.60x Current Costs x 100 Base Costs

Student Tasks Look at Example page 36 Example page 38 Activity 5 p 39 Test your Knowledge – page 43 and 44 Test your Learning – BPP p 66-69