decision - making techniques Flashcards
why is sales forecasting important?
it underpins most of the forward planning needed to run a business
what is the most common method of forecasting for an existing business and what three quantitative sales forecasting techniques help to understand them?
to identifying past trends:
- moving averages
- extrapolation
- correlation (scatter graphs)
moving average
a quantitative method used to identify underlying trends in a set of raw data
what specific data is moving averages key for identifying the underlying trends
data with seasonal variations or an erratic pattern
the moving average helps to understand the long-term trend when data seems erratic, what is the formula?
add the first three months together (centred three month total) then divide that by 3 to find the average (centred three month average)
how do businesses predict the future?
by assuming that past trends will continue, the future will be just like the past
time series dat
is a series of figures covering an extended period of time (long term trends identified in time series data is extended into the future)
Extrapolation
means predicting by projecting past trends into the future
what is the main challenge with extrapolation
when a trend is not clear cut - there will be cases where a forecaster must decide where a recent downturn/upward trend will continue for the foreseeable future
correlation
expresses a relationship between two variables
why is scatter graphs useful to a forecaster
the relationship between the two variables can provide great insight relating to the extend to which those variables are linked
common variable links
- sales and advertising expenditure
- sales and temperature
- sales and number of stores open
- sales and level of staff bonuses available
what do scatter graphs always need to be drawn on in order to show the trend clearly
a line of best fit
what are the two limitations of quantitative sales forecasting
- the future may not be like the past (there can be unpredictable external events that cause changes)
- the forecast is reliant on the forecaster to interpret the data (lots of decision making is needed and a good understanding variable relationships that may not always be accurate)
