Demand forecasting of established products Mechanical extrapolation techniques or time series analysis
Barometric (Or, Leading Indicator Technique)
Statistical Methods
Econometric Models
Simultaneous Equation Method

Demand Forecasting of new Products :

Demand Forecasting of new Products Survey of buyers’ intensions
Test marketing
Life cycle segmentation analysis
Bounding curves method

Mechanical extrapolation techniqueor Time series analysis :

Mechanical extrapolation techniqueor Time series analysis Meaning :-
Whenever data are collected and arranged in accordance with time, in the form of series is known as time series or trend projection method.

Slide 5:

Assumption :-
It is based on the assumption that future events are a continuation of the past and, therefore, historical data can be used to predict the future.

Utility of time series analysis :

Utility of time series analysis It helps to understand the past behavior and present situation and also to predict the future values i.e., to find trend value for a specific year.
To find seasonal fluctuation in the variable.
To businessmen and industrialists the study of the time series of demand, supply, price, production, sales etc is important to make the necessary adjustments in their business policies.

Components of time series :

Components of time series Variations in time series
long term short term
variations variations
Trend cyclical seasonal irregular

Slide 8:

Hence the total
variation =
Trend + Seasonal variations + Cyclical variations + Irregular variation
i.e., O = T+S+C+I

Slide 9:

Trend variation :-
If a time series is observed, there may appear many ups and downs in the series. However if it is carefully examined, a persistent growth or decline may be found there. This tendency is known as trend.
Cyclical component :
It is a wavelike, repetitive moment fluctuating about trend of the series.
e.g., depression, recovery, prosperity and recession in general business activity repeat themselves after some time.
The minimum time period of a cycle is greater than one year.

Slide 10:

Seasonal variation :-
Fluctuations that always occur at a particular time of the year. e.g., sales of woolen clothes are higher during winter season. Sales of umbrella will be highest during monsoon.
Irregular variation :-
These are those variations which are left over after isolating the other components.
All type of variations other than trend, seasonal and cyclical variations can be termed as irregular variation. E.g., strikes, floods, natural calamities, war etc

Decomposition of time series :

Decomposition of time series The four components may be related to each other in an additive or multiplicative form :-
O = T+S+C+I (Additive model)
O= T * S * C * I (Multiplicative model)
We can convert multiplicative model into additive model by taking logarithms as:
log O = log T + log S + log C + log I

Graphical Method :

Graphical Method It is also known as Fitting Trend Line by Observation
Graph paper: (1) Arithmetic scale (2) Semi-logarithm scale

Slide 13:

Advantage Easy & Quick method
Disadvantage
Lacks of Scientific Temper

Least-Square Method :

Least-Square Method Statistical Method
Forecast Sales
Independent Variable: '' Time''
''naive''- because doesn't explains the reasons of change.
Least-Square Method:(1) Linear Trend (2) Non- linear Trend

Linear Trend Method :

Linear Trend Method Also known as Straight-line Trend S = a + b. T
where,
a & b = constant, T = Trend ƸS = Na + b Ƹ T (N = no. of. years) ƸST = a ƸT + b Ƹ T2

Non-Linear Method :

Non-Linear Method (a) Second & higher degree polynomial trends S = a + bT+cT2
(b) Third degree/ Cubic Trend S = a + bT+cT2 + dT3
Exponential Trend/ Semi-log trend S = a ebt
log S = log a + log bT
Iv Double-log Trend S = aT b
log S = log a + b logT

Slide 17:

Smoothing Methods

Slide 18:

In cases in which the time series is fairly stable
and has no significant trend,
seasonal, or cyclical effects,
one can use Smoothing methods
To average out the irregular components of the time series.

Slide 19:

Moving Average Method

Slide 20:

Moving Average Method
The moving average method consists of computing an average of the most recent n data values for the series and using this average for forecasting the value of the time series for the next period..

Simple Moving Average :

Simple Moving Average Include n most recent observations
Weight equally
Ignore older observations weight today 1 2 3 ... n 1/n

Slide 23:

Issues with moving average forecasts:
All n past observations treated equally;
Observations older than n are not included at all;
Requires that n past observations be retained;
Problem when 1000's of items are being forecast.

Slide 24:

Exponential Smoothing

Slide 25:

Include all past observations Weight recent observations
much more heavily
than very old observations

Exponential Smoothing :

Exponential Smoothing weight today Decreasing weight given
to older observations

Exponential Smoothing :

Exponential Smoothing weight today Decreasing weight given
to older observations

Exponential Smoothing :

Exponential Smoothing weight today Decreasing weight given
to older observations

Exponential Smoothing :

Exponential Smoothing weight today Decreasing weight given
to older observations

Exponential Smoothing :

Exponential Smoothing weight today Decreasing weight given
to older observations

Slide 31:

Ft+1= α Xt + (1- α) Ft α is the smoothing constant (a number between 0 and 1) Ft is the forecast for period t Ft +1 is the forecast for period t+1 Xt is the actual data value for period t

Slide 33:

For Exponential Smoothing,
how do I determine the best value of α

Slide 34:

Find Out Ft Then Find Out Ft-Xt Find Square of Ft-Xt Make Total Of above Less Total=Preferable Weight

Used & Useful When Short-Term Forecasting
is to be made Inherrent Patern
of Trend is Highly Complex
& Difficult

Slide 38:

5 Step Process Removal Of Trend Parameter Estimation Model Identification Verification Forecasting

Slide 39:

Removal Of Trend Useful for Stationary Time Series
Remove Trend Component
by Differencing Methods

Slide 40:

Model Identification Find Combinations from below 3
1.Order of Involvment of Autoregressive terms
2.No.of Differences to remove orignal trend
3.Use Moving Average Terms
Whichever fits best to the Time Series

Slide 41:

Parameter Estimation Find a Particular Combinations
Use Method of Least Square
Coefficients are thus obtained

Slide 42:

Verification Check the Goodness of the Fit of Model
Do Residuals show specific Pattern?
-Yes - Go Back to Step2
-No - “Good” Fit

Slide 43:

Forecasting Arrive at Good fit
Use Coefficient to Forecast Use of Softwares for forecasting

Slide 44:

Limitations Of Softwares Very Expensive
Requires Experts & Qualified Forecasters
Difficulty in Operation of this Model

BEROMETRIC TECHNIQUE :

BEROMETRIC TECHNIQUE

WHAT IS BAROMETER? :

WHAT IS BAROMETER?

Barometer is an instrument used to measure the atmospheric pressure.Like that this method is used to forecast the demand of established product. :

Barometer is an instrument used to measure the atmospheric pressure.Like that this method is used to forecast the demand of established product.

ASSUMPTIONS :

ASSUMPTIONS Relationship can exist among various economic time series. For e.g.
Industrial production overtime and industrial loans by commercial banks overtime.
2. Future can be predicted from certain events occurring in the present. For e.g. Birth rate at present & demand for seat in school

UEFULNESS :

UEFULNESS PROVIES AN INDICATION FOR DIRECTION OF CHANGE

3 Indicators :

3 Indicators There are 3 indicators in barometric technique which act as a complement to each other. i.e.
1. Leading indicator
2. Coincident indicator
3. Lagging indicator

Leading Indicator :

Leading Indicator Variables whose movement precede the movement of some other variable
For e.g.
Series of Birth rate & series for enrolment in schools
Application for amt of housing loan & demand for construction material

Coincident Indicator :

Coincident Indicator When data of one variable moves along with data of another variable
for e.g.
Series of data on national income & series of data on employment in an economy

Lagging Indicators :

Lagging Indicators Where data moves behind the series being compared.
For e.g.
Series of number of babies , lagging series for the series of marriage

Forecasting stages :

Forecasting stages To locate the leading indicator for the variable to be forecast
To estimate a mathematical relationship
To find out the forecasted values of the variable and
If possible, to verify the validity of the forecast

For e.g. :

For e.g. X is a number of children born on particular day in a town, and Y is the demand for seats in schools (where the required age at the time of admission is exactly 5 years) in a town.

EquationYt = f (Xt -s)where, t refers to current years ands refers to length of the lag :

EquationYt = f (Xt -s)where, t refers to current years ands refers to length of the lag

Limitations of leading indicator :

Limitations of leading indicator Not easy to identify a proper leading indicator
It tells us about the direction of change and not about the magnitude of change.

Index numbers :

Index numbers Diffusion indices (group of indicators)
Composite indicators (weighted avg)

Slide 59:

STATISTICAL
METHODS OF
FORCASTING

Slide 60:

(1) NAÏVE MODELS (2)CORRELATION AND
REGRESSION METHOD

Slide 61:

3-61 Naive Forecasts The forecast for any period equals the previous period’s actual value.

Slide 62:

Quantitative forecasting methods in library management 62 Naive forecasts NF1 and NF2 Naive forecasts: (a “folk forecasting technique”)
NF1 means “The value tomorow will be the same as today”.
Example: Number of library visitors today was 120. Forecast NF1 for tomorow: 120.
NF2 means “The value tomorow will be less /greater by …10% ”.
Example: Average temperature this month is 20 degrees. Forecast NF2 for the next month: Temperature will be 25 degrees (increase of 25%).

Slide 63:

Simple to use
Virtually no cost
Quick and easy to prepare
Easily understandable
Can be a standard for accuracy
Cannot provide high accuracy Naive Forecasts

Slide 64:

Stable time series data
F(t) = A(t-1)
Seasonal variations
F(t) = A(t-n)
Data with trends
F(t) = A(t-1) + (A(t-1) – A(t-2)) Uses for Naive Forecasts

Slide 65:

MOST POPULAR METHODS
UNLIKE TIME SERIES ANALYSIS CORRELATION AND REGRATION ANALYSIS DOSE NOT LIMIT IT SELF TO “TIME” AS THE INDEPENDENT VARIABLE CORRELATION
AND REGRESSION METHODS

Slide 66:

MEANING- THE RELATIONSHIP BETWEEN TWO VARIBLES SUCH THAT A CHANGE IN ONE VARIBLE RESULT IN A POSITIVE OR NEGATIVE CHANGE IN THE OTHER VARIABLE
OR
DIRECT OR INDIRECT CAUSE AND EFFECT RELATIONSHIP BETWEEN TWO AND MORE VARIABLES IS CALLED CORRELATION CORRELATION ANALYSIS

Slide 67:

POSITIVE CORRELATION— TWO SERIES VARYING IN THE SAME DIRECTION (r>0)
NEGATIVE CORRELATION— TWO SERIES VARYING IN THE OPPOSITE DIRECTION (r<0)`

TWO TYPES OF CORRELATION ANALYSIS :

TWO TYPES OF CORRELATION ANALYSIS SIMPLE OR PARTIAL CORRELATION WHEN ONLY ONE INDEPENDENT VARIABLE IS TAKEN TO EXPLAIN VARITIONS IN DEPENDENT VARIABLE
E.G-SALES & PRICE MULTIPLE CORRELATION WHEN THERE ARE MORE THAN ONE INDEPENDENT VARIABLE IS TAKEN TO EXPLAIN VARITIONS IN DEPENDENT VARIABLE
E.G-SALES & PRICE,INCOME ADVERTISEMENT

COEFFICIENT OF CORRELATION :

COEFFICIENT OF CORRELATION IT IS USED TO KNOW CLOSENESS OF VARIABLES

Slide 70:

REGRESSION EQUTION
METHODS (1) FITTING SIMPLE LINEAR REGRESSION
(a) GRAPHICAL METHOD
(b)LEAST SQUARES METHOD
(2) FITTING SIMPLE NON-LINEAR REGRESSION EQUTION
(a)LOGARITHMIC MODEL
(b)PARABOLIC REGRESSION MODEL
(C) MULTIPLE REGRESSION ANALYSIS

(1) FITTING SIMPLE LINEAR REGRESSION :

(1) FITTING SIMPLE LINEAR REGRESSION (a) GRAPHICAL METHOD

Slide 73:

(b)LEAST SQUARES METHOD
S=a+Bp
NORMAL EQUATION
∑S=na+b∑p
∑sp=a∑p+b∑p2
bsp=n∑SP- ∑S ∑P
n∑P2-(∑p)2
a= ∑S-b ∑P
n

(2) FITTING SIMPLE NON-LINEAR REGRESSION EQUTION (a)LOGARITHMIC MODEL
log S=a+b*p

(B)PARABOLIC REGRESSION MODEL :

(B)PARABOLIC REGRESSION MODEL

(C) MULTIPLE REGRESSION ANALYSIS :

(C) MULTIPLE REGRESSION ANALYSIS SALES= a, PRICE +b, ADVERTISING +c, RIVALS’ PRICE LEVEL +d, PERSONAL DISPOSABLE INCOME
+ u
WHERE a,b,c,d are partial regression coefficient

Slide 77:

LIMITATION
(i) BASED ON PAST DATA
(ii)ACCURACY OF MEASUREMENT OF INDEPENDENT VARIABLE DETERMINES THE DEGREE TO WHICH THE CONFIDENCE CAN BE PLACED IN THE FORECASTED VALUES OF THE DEPENDENT VARIABLE

ECONOMATRIC MODEL :

ECONOMATRIC MODEL The main difference between time series and econometric model is-
Time series- only time was taken as an independent variable
Econometric model- all ecomonic and demographic variables which influece forecasting variable are taken as independent variable.

:

Example
Qd=w + B1*P1T + B2*P2T + B3*A1T + B4*A2T + B5*WT + B6*YT + e
Q=q dd of air condition of a given brand
P1=price of given brand
P2=price of sustitute
A1=adv exp of a given brand
A2=adv exp of rival brand
W=weather condition
Y=aggregate personal disposable income
e=disturbance term

3 stages of econometric model :

3 stages of econometric model Identification of variables and the functional form
Estimation of parameters
Finding the forecast values

Classification of econometric models :

Classification of econometric models 1. Single equation models:
Only one equation is used
2. Simultaneous models:
Set of equations are used
a. Endogenous variables
values are determined by the model
b. Exogenous variables
values originate from outside the system

SIMULTANEOUS EQUATION MODEL :

SIMULTANEOUS EQUATION MODEL When variables are dependent and independent at the same time, this model is used.
Set of equations are used where a variable affects another variable and in turn is also affected by the other variable.
This method is more complex.
It is also known as COMPLETE SYSTEM APPROACH.

Example :

Example Simultaneous model is consist of 2 type of equation-
1.Identities: represent relationships that are necessarily true
2.Behavioural equations: represent the expected behaviour of physical process or individuals
E.g.:
Yt = Ct + Gt + It
Ct = b0 + b1*Yt + et
It = a0 - a1*it + a2*Pt-1 + et

Advantages :

Advantages Able to identify reason for the change
Possible to forecast the effect of policy variables by tracing them as an independent variables
Predicted values are compared with the actual data, it is possible to improve the accuracy of prediction of the model
We need to estimate the future values of only predetermined variables (exogenous)

DEMAMND FORECASTING FOR NEW PRODUCTS :

DEMAMND FORECASTING FOR NEW PRODUCTS Survey of buyer’s intention (or,consumer survey)
Test marketing
Life cycle segmentation analysis
Bounding curves method

3. Life Cycle Segmentation :

3. Life Cycle Segmentation

Stages in product life cycle :

Stages in product life cycle Introduction: Quality, advertising, price, service
Growth: Advertising, quality
Maturity: Price, advertising, quality, service
Saturation: product differentiation in quality, packaging, advertising
Decline: Find new product-uses and advetise them, quality and service, price

4. Bounding curves method :

4. Bounding curves method

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By: gguerrer (114 month(s) ago)

Would you send this ppt to my mail pls? [email protected]