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4.3 Sales Forecasting 2014 Syllabus

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Deborah Kelly

on 9 November 2016

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Transcript of 4.3 Sales Forecasting 2014 Syllabus

4.3 Sales Forecasting
(HL only)

Calculate up to a four part moving average
plot the trend lines
prepare a forecast (including seasonal, cyclical and random variation)
examine the benefits and limitations of sales forecasting

Key concept: Strategy
Sales Forecasting
Sales forecasting
- predicting future sales levels and sales trends.

It is impossible to predict the future with complete certain but most business will attempt to forecast future sales - if only for a short time into the future.

Potential Benefits:

If marketing managers were able to predict the future sales accurately, the risks of business operations and business strategic decisions would be much reduced.

The operations department would know how many units to produce and what quantity of materials to order and how much stock level to hold.
The marketing department would be aware of how many products to distribute and whether changes to the marketing mix are needed.
Human resources workforce plan would be more accurate.
Finance could plan cash flows with much greater accurate amd make accurate profit forecasts.
Strategic decision-making such as developing new products or entering new markets would become much better informed.
In reality, such precision in forecasting is impossible to achieve, because of external factors that can influence sales performance.

Market forecasts form an essential part of the market planning process and of the screening process before new products are launched on to the market. These forecasts will be based on
market research data
, gained from both
primary
and
secondary sources
. A common way of assessing future demand for a product yet to be fully launched is to use test marketing in one particular area.

For existing products sales forecasts are commonly based on past sales data.
Quantitative sales forecasting methods - time-series analysis
This method of sales forecasting is based entirely on
past sales data
. Sales records are kept over time and , when they are presented in chronological order, they are referred to as "time series'.
Extrapolation
Extrapolation involves basing future predictions on past results. When actual results are plotted on a time-series graph, the line can be extended, or
extrapolated
, into the future along the trend of the past data. This simple method assumes that sales patterns are stable and will remain so in the future. It is ineffective when this is not true.
Moving Averages
This method is more complex than simple graphical extrapolation. It allows the identification of underlying factors that are expected to influence future sales.These are the trend,
seasonal variations
,
cyclical variations
and
random variations
.

Seasonal variations
: regular and repeated variations that occur in sales data withing a period of 12 months or less.
Examples - clothing winter and summer, Flights (holidays), textbooks.

Cyclical variations
: variations in sales occurring over periods of time of much more than a year - they are related to the business cycle.

Random variations
: may occur at any time and will cause unusual and unpredictable sales figures, eg exceptionally poor weather or negative public image following a high profile product failure.

The moving average is used to analyze these. This technique "smooths out" the fluctuations in time-series data and allows managers to identify the trend more easily.
CYCLICAL EXAMPLE
TREND, SEASONAL AND RANDOM VARIATION EXAMPLES
Trend: underlying movement of the data in a time series.
Calculating Moving Averages - Simple Example three-year moving average
Yearly sales of a calculator manufacturer:
Year
Sales (US$000)
1
400
2
600
3
800
4
650
5
700
6
850
7
950
8
1200
Steps calculate a three year moving average:

1. Calculate the mean sales for the first 3 years, then the second three sets and so on.
400 + 600 + 800
3
= 600
Years 1, 2, 3:
Years 2, 3, 4:
Years 3, 4, 5:
Years 4, 5, 6:
Years 5, 6, 7:
600 + 800 +650
3
800 + 650 + 700
3
650 + 700 + 850
3
850 + 950 + 1200
3
= 683.333
= 716.667
= 733.333
= 1,000.000
Remember in US$ 000
Sales revenue with the three-year moving average (trend): (in US$ 000)
Year 1 2 3 4 5 6 7 8
Sales 400 600 800 650 700 850 950 1200
Trend 600 683.333 716.667 733.333 833.333 1000
Years 6, 7, 8:
700 + 850 +950
3
= 833.333
Now what:

1. Plot the actual
sales revenue
and the
trend line (moving average)
on a time series graph.
Title
Sales
Years
2.
Extrapolate
- extend the trend line to predict future sales using a
line of best fit
.
PRACTICE: Do 3-period moving average practice questions A and B.
The benefits and limitations of sales forecasting
Benefits:
Better cash flow managemen
t - by taking into consideration cyclical and seasonal variation factors, financial managers can better plan to improve the liquidity position of a business.

Increased efficiency
- sales forecasting greatly assists the production department in knowing the number of goods to produce and in planning for the amount of stock required in the future.

Better workforce planning
- accurate sales forecasting can help the human resources department in succession planning regarding the number of staff required in the future.

Improved market planning
- marketers will gain greater awareness of future trends and be able to adjust their marketing strategies accordingly in an effort to increase their market share.
Limitations:
Sales forecasting is time-consuming
- it takes a long time to calculate due to its complex nature, especially when considering the calculation of average seasonal variations in each quarter over a number of years.

Sales forecasting ignores qualitative external factors.
A number of political, social and economic factors can influence the accuracy of sales predictions,
for example political instability, changes in consumer taste and preferences and exchange rate fluctuations.
To what extent does knowing assist us in predicting?

How do we know that our predictions are reliable?


"
Prediction is very difficult, especially if it is about the future
" Neils Bohr, Nobel Laureate

This statement highlights some of the problems of using mathematics in forecasting.

Do you think there is any point in
managers forecasting future sales?
Calculating a four-year moving average
Calculating the four-year moving average is a bit more complex than calculating a three-year moving average. Four-year moving average is the most widely used technique as it is often used when forecasting from quarterly data. Much business data is released quarterly.

In this case, it makes use of
centering
. This involves the use of a four-year and a eight year moving total to establish a mid-point. This is because if a four-quarter moving total was divided by four in order to calculate the average it would not lie alongside any one quarter. It would not make sense to have a result which did not belong to any time period. This is overcome by
centering
the average so that it lies alongside one actual quarter. This is done by adding two four-quarter moving totals together. This is divided by eight to give the moving average.
TOK
Four-year moving average using the previous example:
Yearly sales of a calculator manufacturer:
Year
Sales (US$000)
1
400
2
600
3
800
4
650
5
700
6
850
7
950
8
1200
Steps to calculate a four-year moving average:

1. Four-year moving total

Sum the sales of year 1, 2, 3 and 4. (400 + 600 + 800 + 650 = 2450)
Sum the sales of year 2, 3, 4 and 5. (600 + 800 + 650 + 700 = 2750)
.... (continue for years 3-6, 4-7 and 5-8)

2. Calculate eight-year moving total

Add 2 sets of 4 year moving totals = 2450 + 2750 = 5200
.... continue

3. Calculate the four-year centered moving average.

Divide the eight-year moving total by 8. 5200/8 = 650. Place this in the line where year 3 (or Quarter 3 is positioned).Continue using same approach.

Complete the four-year moving average in table format.
Practice 4-year moving average:

Do C on 4.3 exercises.
Calculating Variations
Variation - the difference between actual sales and trend values (moving average)
Continuing with the example:
Year

Sales (US$ 000)

Trend - 4-year moving average

Variation in each year
1 2 3 4 5 6 7 8

400 600 800 650 700 850 950 1200

650 718.75 768.75 856.25



150 -68.75 -68.75 -6.25


1. Calculate the
variation
: Sales - Trend. (800-650 = 150) ...
Average cyclical variation:
This is calculated as the sum of the variations over the period divided by the number of years within the period.
sum of variations
number of years
Example:
150000 + (-68750) + (-68750) + (-6250)
4
= 1,562.50
BE CAREFUL WITH ADDING + AND - NUMBERS. THE SIGN MUST BE CORRECT
=
IF you were reading off the trend line for year 9 you would
adjust
it for the average seasonal or cyclical variation. If it was 1350000 then it would be 1350000 - 1562.50 = 1348437.5.
3.
Calculate
the variation.
Sales - trend.
DO:

MORE Practice -
4 period
moving average
Headline: October 16th, 2015

Market Trends

Bloomberg Business

"US Retail Sales Rose Less than Forecast."
Full transcript