An excellent piece of work which meets the criteria for M2 - Unit 7 Management Accounting. BTEC Level 3 Business. M2 - Analyse the importance of accounting data and statistical information to assess and predict business performance.
(i) Identify the current trend in sales using moving averages.
(ii) Forecast the sales figures for each quarter of 2018.
(iii) Present a time series graph for 2014 to 2018 to include the sales data and the
trend.
(b) Index Numbers
You have been provided with the Cash Budget for 2017 for Sophie’s Spring Water (Appendix
2).
The finance office prepares forecast cash budget figures. These are adjusted for changes in
the rate of inflation each year, as measured by the Retail Price Index. The Cash Budget for
2017 was based on the inflation rate - RPI - for 2015.
You have obtained the RPI figures for the last 2 years:
RPI for 2015 - 247
RPI for 2016 - 262
Using the figures given in the cash budget for 2017 and the information relating to the RPI,
prepare the forecast cash budget for 2018, adjusting the figures for the changes in RPI.
(c) Prepare a memo for the management of Sophie’s Spring Water enclosing:
the forecast sales figures for 2018
forecast cash budget for 2018
,Conor Cunningham P3 P4 M2
Explain why each of the two methods you used to forecast the sales and cash budgets were
the appropriate ones to use in each case.
Explain how the forecasts will be used in the business for planning and monitoring the
business activities in the coming year.
This provides evidence for P3, P4 and M2.
P3
Please find excel sheet attached.
P4
Please find excel sheet attached.
M2
Forecasting is the use of historic data to determine the direction of future trends. Businesses
utilise forecasting to determine how to allocate their budgets or plan for anticipated expenses
for an upcoming period of time. This is typically based on the projected demand for the goods
and services they offer. Forecasting addresses a problem or set of data. In accounting, we
can make assumptions regarding the situation being analysed. The data is analysed and the
forecast is determined. After this happens, most businesses such as Sophie’s Spring Water
should carry out a verification period where the forecast is compared to the actual results in
order to establish a more accurate model for forecasting in the future.
Time series analysis
A time series analysis is a collection of observations made sequentially through time. The
objectives of a time series analysis are to;
Describe patterns over time
See if the pattern observed can be explained in terms of factors or causes
Used for prediction (forecasting) as past records can help us predict what will happen in
the future
Improve the past system/behaviour such as taking action to improve the system
A time series analysis was a very appropriate technique used to forecast sales figures for
Sophie’s Spring Water in 2018. The time series collected data over a period of two years. This
can be used by management to make current decisions and plans based on long-term
forecasting. We would assume that past patterns and sales patterns in the business will
continue into the future. For example, the sales of Sophie’s Spring Water in July of 2016 would
, Conor Cunningham P3 P4 M2
be the same as those sales in July 2018. A time series analysis is essential to allow sufficient
time for the manufacturing, sales, finance and other departments of a company to develop
plans for possible new plants, financing, development of new products, and new methods of
production. Forecasting the level of sales, both short-term and long-term, is dictated by the
very nature of the business. Below are the sales figures for 2017 and the predicted sales figures
for 2018 using a time series analysis. This allowed Sophie’s Spring Water to predict their sales
based on the sales in previous years of trading. As seen below the figures are very similar and
Sophie would have been happy with the results of this analysis as sales are typically set to
increase in 2018.
Time series analysis can be useful to see how a given asset or sales are set to change over time.
It can also be used to examine how the changes associated with the chosen data point compare
to shifts in other variables over the same time period. This can be useful for Sophie and her
business however it is also useful for investors who will want to see predicted sales before
investing their money into a company. This is why this is such an appropriate method of
forecasting sales as it can give results for a number of different stakeholders and can help a
business change operations quickly if they are required to do so.
Time series analysis allows Sophie’s Spring Water to forecast her sales for 2018 from the results
of previous years. This will allow her to make changes to current business operations if she
needs to. When comparing her 2018 Q1 forecasted figure of 302 with her 2015 Q1 figure of
280, she will notice that her business’ output is increasing. This may inform her that for
example her premises are becoming too small to cope with the increase in production and that
she needs to increase the size of her factory. This forecast will also monitor how the business is
changing over a number of months and years and Sophie will be able to see if her business is
actually growing or becoming smaller. If the company’s sales were falling, Sophie will be able to
change some aspects of her business operations such as a change in the price of her products
and this shows how time series analysis is very useful for planning and monitoring business
activities in the coming year of 2018. Sophie may decide that a particular quarter of the year
her sales are predicted to be a lot lower and this could mean that Sophie will change her plans
to include more marketing in this month. Time series analysis can be used in many different
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