Forecasting is the process of making predictions of the future based on past and present data and analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. After gathering information about various aspects of the market and demand from primary and secondary sources, an attempt may be made to estimate future demand.

1. Market And Demand
Analysis - 2
Presented By:
Rashedul Kabir
Sr. Asst. Secretary, R&D
SEIP Project, BKMEA
Theoretical Part
Lecture : 6
5-August-2017

2. Pattern Prediction
Predict the next number in the pattern
• 1, 4, 7, 10, 13, ………?
•
• 1, 3, 9, …………..?
...?

3. Pattern Prediction
• Predict the next number in the pattern (Solution):
1.
2.
3.

4. Demand Forecasting Methods
Forecasting
Methods
Quantitative
Casual
Consumption Level
Chain Ratio
End Use
Bass Diffusion
Leading Indicator
Econometric
Time Series
Smoothing
Moving Average
Weighted Moving Average
Exponential Smoothing
Trend
Projection
Trend Projection
Adjusted for
Seasonal
Influence
Qualitative
Delphi
Expert
Judgment
Scenario
Writing
Intuitive
Approaches

5. Forecasting Horizon
Range Horizon Application Method
Long >5 years
1. Facility planning
2. Capacity planning
3. Product planning
1. Economic
2. Demographic
3. Market
Information
Medium 1-5 years
1. Staffing plan
2. Aggregate
production plan
1. Time series
2. Regression
analysis
Short
1 day to 1 year
1. Purchasing
2. Detail job
schedule
1. Exponential
2. Smoothing
3. Trend exploration
4. Graphical
methods

6. Forecasting Methods
Qualitative method Quantitative method
1. Characteristics Based on human
judgment, opinions;
subjective and
nonmathematical
Based on mathematics;
quantitative in nature
2. Strength Can incorporate latest
changes in the
environment and “inside
information.”
Consistent and objective;
able to consider much
information and data at
one time
3. Weakness Can bias the forecast and
reduce forecast accuracy
Often quantifiable data are
not available. Only as good
as the data on which they
are based
Forecasting methods are classified into two groups

7. Quantitative Methods
1. Time Series Methods
a) Trend projection method: Trend projection method
involves
• Determining the trend of future value by analyzing
past value statistics
• Projecting future value by extrapolating the trend
Linear relationship is used as most commonly
employed relationships i. e.
Calculation.xlsx
tbXa tY
Continued

8. Quantitative Methods
1. Time Series Methods
b)Trend Projection Adjusted Seasonal Influence
[Use SPSS software]
Y(2016)Q1(1st Quarter)=7.91000+(-.44427)
=7.4673
Sales Price forecast for Year 2016, 1st Quarter
Continued

9. Quantitative Methods
1. Time Series Methods
c) Smoothing Model:
i) Exponential smoothing method:
Note that the value we entered in the Damping
factor box is α=0-1; forecasts for other
smoothing constants can be computed easily
by entering a different value for 0-1 in the
Damping factor box.
Continued

10. Quantitative Methods
1. Time Series Methods
c) Smoothing Model:
ii) Moving average method: As per the moving average
method of sales forecasting the forecast for the next
period is equal to the average of the sales for several
preceding periods.
Where Ft+1 is the forecast for the next period, St is the
sales for the current period, and n is the period over which
averaging is done.
n
SS ntt 11t
1t
....S
F 



Continued

11. Quantitative Methods
1. Time Series Methods
c) Smoothing Model:
iii) Weighted Moving Average :
Weighted moving average involves selecting a
different weight for each data value and then
computing a weighted average of the most
recent n values as the forecast.
e.g. WMA=1/6*(B2)+2/6*(B3)+3/6*(B4)

12. Quantitative Methods
2. Causal Methods
a) Chain Ratio Method
The potential sales of a product may be estimated by applying a series of
factors to a measure of aggregate demand.
For instance a firm planning to manufacture T-shirt in Bangladesh tried to
estimate its potential sales in the following manner –
Adult male population in the country :150 million
Proportion of adult male wearing T-shirt : 60%
So Adult male population wearing T-shirt : 90 million
Number of T-shirt one buys per year: 15 piece
Total T-shirt needed per year: 1315 million
Proportion of market capture capacity : 9%
Potential sales : 121.5 million piece per year
It is a simple analytical approach to demand estimation but its success rate significantly
depended on the information that uses in the estimation process.
Continued

13. Quantitative Methods
2. Causal Methods
b) Consumption level method
This types of estimation is useful for a product that is directly consumed. The method
is basis of elasticity coefficients, the important ones being the income elasticity of
demand and the price elasticity of demand.
• Income Elasticity of Demand: The income elasticity of demand reflects the responsiveness of
demand to variation in income. Mathematically –
• Price Elasticity of Demand: The price elasticity of demand reflects the responsiveness of
demand to variation in price. Mathematically –
-The price elasticity coefficient is applicable to only small variations
-The price elasticity measure assumes that the pattern of consumer behavior remain unchanged
12
12
12
12
/ QQ
II
II
QQ
iE 




12
12
12
12
/ QQ
PP
PP
QQ
pE 




Continued

14. Quantitative Methods
2. Causal Methods
c) End use method
Suitable for estimating the demand for intermediate products, the end
use method, also called the consumption behavior methods, involving the
possible steps:
• Identify the possible uses of the product
• Define the consumption coefficient of the product for various uses
• Project the output levels for the consuming industries
• Derive the demand for the product
Continued

15. Quantitative Methods
2. Causal Methods
d) Bass diffusion model
Developed by Frank Bass, the Bass diffusion model seeks to estimate the pattern of sales
growth for new products, in terms of two factors:
p: The coefficient of innovation. It reflects the likelihood that a potential customer would
adopt the product because of its innovative features.
q: The coefficient of imitation. It reflects the tendency of a potential customer to buy the
product because many others have bought it. It can be regarded as a network effect.
According to a linear approximation of the model:
Where nt is the sales in period t, p is the coefficient of innovation, N is the potential size of
the market, q is the coefficient of imitation, and Nt is the accumulative sales made until
period.
2
11 )()/()(   ttt NXNqNpqpNn
Continued

16. Quantitative Methods
2. Causal Methods
e) Leading indicator method
Steps:
• Identify the appropriate leading indicator(s): change
ahead of other variables
• The lead-lag relationship: lagging variables
For example, the change in the level of urbanization (a
leading indicator) may be used to predict the change in
the demand for air conditions (a lagging variable).
Continued

17. Quantitative Methods
2. Causal Methods
f) Econometric method
An econometric model is a mathematical representation of economic
relationship(s) derived from economic theory. The primary objective
of econometric analysis is to forecast the future behavior of the
economic variables incorporated in the model.
An example of the single equation model is given below:
Where Dt is demand for a certain product for year t, Pt is price for the
product in year t, and Nt=income in year t.
>The simultaneous equation model portrays economic relationships
in terms of two or more equations.
ttt NaPaaD 210 

18. Qualitative Methods
1. Jury of executive methods/Expert Judgment/Jury of Executive opinion method:
This method involves asking the opinions of a group of managers on expected future
sales and combining them in to a sales estimate.
2. Delphi method: This method is used for producing the opinions of a group of
experts with the help of a mail survey.
Steps of Delphi method:
• A group of experts is sent a questionnaire by mail and asked to express their views.
• Received responses are summarized without disclosing identity and send back to
the experts to prove further reasons.
• The process may be continued for one or more rounds till a reasonable agreement
emerges in the views of the experts
Continued

19. Qualitative Methods
3. Scenario Writing:
Different sets of assumptions lead to different scenarios. The
job of the decision maker is to decide how likely each scenario
is and then to make decisions accordingly.
4. Intuitive Approaches
Subjective or intuitive qualitative approaches are based on
the ability of human mind to process a variety of information
that, in most cases, is difficult to quantify. In brainstorming
sessions, individuals are freed from usual group restrictions of
peer pressure and criticism.

20. Uncertainties in Forecasting
(i) Data about past and present market
-Lack of standardization
-Few observations
-Influence of abnormal factors
(ii) Methods of forecasting
-Inability to handle unquantifiable factors
-Unrealistic assumptions
-Excessive data requirement
(iii) Environmental changes
-Technological change
-Shift in governmental policy
-Developments on the international scene
-Discovery of new sources of raw material
-Vagaries of monsoon

21. Coping with Uncertainties
• Conduct analysis with data based on uniform and standard
definitions.
• In identifying trends, coefficients, and relationships, ignore the
abnormal or out-of-the-ordinary observations.
• Critically evaluate the assumptions of the forecasting methods and
choose a method which is appropriate to the situation.
• Adjust the projections derived from quantitative analysis in the light
of a due consideration of unquantifiable, but significant influences.
• Monitor the environment imaginatively to identify important
changes.
• Consider likely alternative scenarios and their impact on market and
competition.
• Conduct sensitivity analysis to assess the impact on the size of
demand for unfavorable and favorable variations of the
determining factors from their most likely levels.

22. Group Assignment
Home Task.docx

23. Source/Reference
• PROJECS Planning Analysis, Selection, Financing, Implementation, and
Review (8th edition), by Prasanna Chandra
• Statistics for Business and Economics (11th edition), by Anderson, Sweeny,
and Williams
• Sourcing and Supply Chain Management (5th edition), by Handfield,
Monczka, Giunipero, and Patterson
• Fundamentals of Statistics (7th edition), S.C. Gupta
• Basic Econometrics (4th edition), by Damodar N. Gujarati
• Introductory Econometrics: A Modern Approach (5th edition), by Jeffrey
Wooldridge
• An Introduction to Statistics (4th edition) ,By Mian & Miyan
• Managerial Economics: Theory, Applications, and Cases (Eighth Edition), by
W. Bruce Allen (Author), Keith Weigelt (Author), Neil A. Doherty (Author),
Edwin Mansfield (Author)

24. Any ?
Thank You