4.3 - Sales Forecasting

sales forecasting

quantative technique used to predict the level of sales revenue a firm expects to earn over a cetain period of time. neccesary to help an organisation plan for functions like recruitment, production levels, stock control, and cash flow forecasts

seasonal variation

expected periodic fluctuations in sales reveues over a given time period such as peak trading periods during certain times of the year. variations are caused by enviro & cultural factiors which cause different people to have different level of demand at different times of the year

how do u calculate variation

managers find the numerical difference between observed data values on a trend line. can be shown as a $ or % deviation from the trend

cyclical variation

the reccurent fluctuations in sales revenue linked to bus cycle like boomsor slumps in the ecnomy. unlinke seasonal variations, cyclical variation can last months

random variation

upredictable and erratic fluctuations in sales revenue caused by irregular and unexpected factors like natrual diasters, war, disease, corporate scandal, etc. these can occur at any time so theres no specidic mehtod to isolate and identify the deviations

benefits of sales forecasting

• helps managers to identigy trend by smoothing out seasonal, cyclical or random variations in the data set

• useful planning tool to help mangaers reduce uncertainties and risks in the future

• identifying a trend enables bus to extrapolate or predict future sales revenues as a basis for strategic and financial plannign

• can enable managers to allocate more realistic budgets for the different functional areas of bus

dis of sales forecasting

• only sales forecasting for a short period of time are likley to be accurate to usefulness is limites

• assumes past will repeat itself,but this may be unrealistic in an environment where change is inevitable. extrapolated results can be overly simplistic

• needs reliable data and indo which isnt easy of cheap

• can be accurate for predicting sales of a single product but less accurate from big MNC’s that sell a broad range of products

• not suited for all business like product oriented bua with very dynamic customer prefrences like fast fashion and high tech industires

• qualitative factors like sales rev arent easy to incorperate into slaes forecasting - less accurate results

• variations of sales are also liekly to occur in reality. categoried as cyclical and random

example of seasonal variation

products during christmas, easter, halloween

extrapolation

forecasting technique identifies the trend by using past data and extending this trend to predict future sales.

market research

Identifying and

forecasting the

buying habits of

consumers can be

vital to a firm’s

prosperity and

survival.

time series anylasis

technique

attempts to predict

sales levels by

identifying the

underlying trend from

a sequence of actual

sales figures.

• The three main

elements to time

series analysis are: cyclical, random, and seasonal variations

charcateristics of sales forecast

typically presented in a form of time series data, uses extrapolation, generally based on recent trends, market analysis of the industry and state of economy