By:Ankit Saxena

INTRODUCTION Forecasting plays a crucial role in the

development of plans for the future It is an essential tool for the

organization to know what level of activities one is planning before investment in inputs i.e. man, machines and materials be made

INTRODUCTIONBefore making an investment decision, many questions

will arise like: What should be the size or amount of capital required? How large should be the size of the work force? What should be the size of the order and safety stock? What should be the capacity of the plant?

The answer to above question depends upon the forecast for the future level of operations.

CONTD.. Forecasting as defined by American

Marketing Association is: “An estimate of sales in physical units (or monetary value) for a specified future period under proposed marketing plan or program and under the assumed set of economic and other forces outside the organization for which the forecast is made”.

FORECASTING Vs PREDICTIONPrediction: Prediction is an estimate of future

event through subjective considerations other than just the past data.

For prediction, a good subjective estimation is based on managers skill, experience and judgment.

CONTD..Forecasting: Forecasting is based on the historical

data and it requires statistical and management science techniques.

It is an estimate of future event achieved by systematically combining and casting forward in a predetermined way data about the past.

NEED FOR SALES FORECATING Majority of the activities of the industries depend upon the

future sales. Projected sales for the future assists in decision-making

with respect to investment in plant and machinery, market planning programs.

To schedule the production activity to ensure optimum utilization of plant’s capacity.

To prepare material planning to take up the replenishment action to make the materials available at right quantity and right time.

To provide an information about the relationship between sales for different products as a function of time.

Forecasting is going to provide a future trend which is very much essential for products design and development.

LONG TERM AND SHORT TERM FORECASTING

Forecasts which cover the period of less than 1 year are called as short term forecasting

Short term forecasts are made for the purpose of materials control, loading and scheduling and budgeting

Forecast which cover the period of more than 1 year (5 years or 10 years) are termed as long term forecasting

Long term forecast are made for the purposes of product diversification, sales and advertising budgets, capacity planning and investment planning.

FACTORS AFFECTING SALES FORECASTING

External Factors

Relative state of the economy Direct and indirect competition Styles or fashions Consumer earnings Population changes Weather

FACTORS AFFECTING SALES FORECASTING

Internal Factors Labour problems Inventory shortages Working capital shortage Price changes Change in distribution method Production capability shortage New product lines

CLASSIFICATION OF FORECASTING METHODS

Judgmental (subjective method) Timer series (based on past data

arranged in a chronological order) Econometric (cause and effect

relationship)

JUDGMENTAL TECHNIQUES Opinion survey method Executive opinion method Customer and distributor surveys Marketing trials Market research Delphi technique

TIME SERIES ANALYSIS Based on the past data arranged in chronological

order as a dependent variable and time as an independent variable

For e.g. sales of TV sets for last four years are:

Time series method does not study the factors that influence the demand, in this method all the factors that shape the demand are grouped into one factor-time and demand is expressed as a series of data with respect to time.

YEAR 1993-94 1994-95 1995-96 1996-97

NO. OF TV SETS 20 30 40 58

FOUR COMPONENTS OF TIME SERIES ANALYSIS

1. Trend(T) 2. Cyclical fluctuation(C)

3. Seasonal variation (S)

1. Irregular variations(R)

Most commonly used expression for a time series forecast is:

Y=TCSRWhere, Y= Forecasted value T= Secular trend C= Cyclic variations S= Seasonal variations R= Irregular fluctuations

FOUR COMPONENTS OF TIME SERIES ANALYSIS

MOVING AVERAGES The sales results of multiple prior periods

are averaged to predict a future period Called ‘moving’ because it is continually recomputed as new data becomes available, it progresses by dropping the earliest value and adding the latest value.

EXPONENTIAL SMOOTHING Similar to moving average method Used for short run forecasts Instead of weighing all observations equally in

generating the forecast, exponential smoothing weighs the most recent observations heaviest

Next year’s sale=a(this year’s sale) + (1-a)(this year’s forecast)

a is smoothing constant taken in scale 0-1

MARKET TEST METHOD Used for developing one time forecasts

particularly relating to new products A market test provides data about consumers'

actual purchases and responsiveness to the various elements of the marketing mix.

On the basis of the response received to a sample market test, product sales forecast is prepared.

REGRESSION ANALYSIS Identifies a statistical relationship between

sales(dependent variable) and one or more influencing factors, which are termed the independent variables.

When just one independent variable is considered (eg. population growth), it is called a linear regression, and the results can be shown as a line graph predicting future values of sales based on changes in the independent variable.

When more than one independent variable is considered, it is called a multiple regression

BENEFITS OF SALES FORECASTING

Better control of Inventory Staffing Customer Information Use for Sales People Obtaining Financing

LIMITATIONS OF SALES FORECASTING

Part hard fact, part guesswork Forecast may be wrong Times may change

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