Sales forecastingDefinition:

What decisions could ride on sales forecasting?

What problems could a business face if they do not effectively forecast sales?

How can a business construct sales forecasts?

What factors affect sales forecasts?

Forecasting:

– Time series data analysis

Definition:

Key aspects to consider:

• The trend

• Seasonal fluctuations

• Cyclical fluctuations

• Random fluctuations

– Extrapolation

Forecast versus actual sales

Show that you can interrogate this data by calculating the percentage change between 2012 sales and 2013 sales.

Analyse your results.

2013 2012

Maths tip

• Sales forecasting necessarily involves statistics. It can be useful to show percentage changes in business data.

• Example:

2012 sales = £400,000 2013 sales = £450,000

Sales have increased by 12.5% between 2012 and 2013.

This is percentage change is found by:

Value of the change in sales

x 100

Value of the original sales

50,000

x 100 = 12.5%

400,000

Fluctuations

Seasonal fluctuations

• Suggest 5 other products whose sales could be affected by seasonal fluctuations.

• Can you think of any strategies a business could use to flatten the fluctuations?

• Attempt Q2 on gas and

electricity consumption

Factors affecting sales forecasting

Consumer trends

• Seasonal variations• Fashion• Long term trends

Economic variables

• Economic growth• Interest rates• Inflation• Unemployment• Exchange rates

Actions of competitors•Marketing campaigns•Product development

Benefits of forecasting

• Inform cash flow forecasts

• Plan the ordering of supplies and components

• Enable the correct staffing levels

• Ensure the business has enough capacity to meet demand

Stop and think…

• Explain how TWO of the Heinz managers could be helped by two weeks’ warning that sales are forecast to rise by 15%.

Difficulties of sales forecasting

• Volatile consumer tastes and preferences

• Range of data

• Subjective expert opinion

Five whys and a how

1. Why do firms need sales forecasts?

2. Why forecast, if the forecast is unlikely to be correct?

3. Why is extrapolation a widely used, but clearly flawed, method of forecasting?

4. Why should firms be aware of ‘unknown unknowns’?

5. Why may it be quite easy for Heinz to forecast UK sales of its ketchup?

6. How can a brand new business hope to make an accurate sales forecast?

Questions

Answers

Five whys and a how

1. Because they are the starting point for all planning eg financial, HR or marketing plans

2. If you have worked out the impact of x000 sales on your finances and staffing it is easier to cope if sales are 10% lower or higher than this figure.

3. Extrapolation can only be correct if the future works out exactly the same as the past. When tastes/habits/economic variables change it can go hopelessly wrong.

4. Because then firms can develop contingency plans – especially a financial cushion.

5. It is a long established product with a dominant market position, unlikely to be much affected by market ups and downs.

6. It is often nearly impossible to do. Best you may be able to do is to work out the average sales within a market and assume those will be true.

1. Why do firms need sales forecasts?

2. Why forecast, if the forecast is unlikely to be correct?

3. Why is extrapolation a widely used, but clearly flawed, method of forecasting?

4. Why should firms be aware of ‘unknown unknowns’?

5. Why may it be quite easy for Heinz to forecast UK sales of its ketchup?

6. How can a brand new business hope to make an accurate sales forecast?

Questions

Answers

Knowledge check – without looking at your notes!

• Why might a business want to predict future sales?• State 3 advantages of sales forecasting• What is ‘time series data’?• What is meant by a trend?• State 3 economic variables that could affect the sales

forecasts of a business• Give three reasons why consumer trends might change• State 3 difficulties in accurately forecasting sales

Useful resources

• Slides 1 – 10 of this (although using American terminology) could be useful. Illustrates the different fluctuations quite well. http://www.slideshare.net/BabasabPatil/analyzing-and-forecasting-time-series-data-ppt-bec-doms