1. Demand Planning and Forecasting
Session 3
Demand Forecasting Methods-1
By
K. Sashi Rao
Management Teacher and Trainer

2. Forecasting in Business Planning
Inputs
Market Conditions
Competitor Action
Consumer Tastes
Products’ Life Cycle
Season
Customers’ plans
Economic Outlook
Business Cycle Status
Leading Indicators-Stock
Prices, Bond Yields, Material
Prices, Business Failures, money
Supply, Unemployment
Forecasting
Method(s)
Or Model(s)
Outputs
Estimated Demands
for each Product
in each Time Period
Other Outputs
Management Team
Other Factors
Legal, Political, Sociological,
Cultural
Forecast
Errors
Feedback
Processor
Sales Forecast
Forecast and Demand
for Each Product
In Each Time Period
Production Capacity
Available Resources
Risk Aversion
Experience
Personal Values and
Motives
Social and Cultural
Values
Other Factors

3. Forecasting Methods
Forecasting
Quantitative
Or
Statistical
Qualitative
Or
Judgmental
Projective
Causal

4. Forecasting Basics
• Types
– Qualitative --- based on experience, judgment, knowledge;
– Quantitative --- based on data, statistics;
• Methods
– Naive Methods --- using ball-park numbers; or assuming
future demand same as before
– Formal Methods --- systematic methods thereby reduce
forecasting errors using:
– time series models (e.g. moving averages and exponential
smoothing);
– causal models (e.g. regression)

5. Forecasting Approaches(1)
• JUDGEMENTAL APPROACHES: The essence of the judgmental approach is to address the
forecasting issue by assuming that someone else knows and can tell you the right answer.
They could be experts or opinion leaders.
• EXPERIMENTAL APPROACHES: When an item is "new" and when there is no other
information upon which to base a forecast, is to conduct a demand experiment on a small
group of customers and extrapolated to the wider population. ‘Test marketing’ is an
example of this approach.
•
RELATIONAL/CAUSAL APPROACHES: There is a reason why people buy our product. If we
can understand what that reason (or set of reasons) is, we can use that understanding to
develop a demand forecast. They seek to establish product -demand relationships to relevant
factors and/or variables e.g. hot weather to cold drinks consumption.
•
TIME SERIES APPROACHES: A time series is a collection of observations of well-defined
data items obtained through repeated measurements over time.

6. Forecasting Approaches(2)
• In general, judgment and experimental approaches tend be
more qualitative
• While relationship/causal and time series approaches tend be
more quantitative
• Still, these qualitative methods are also scientifically done
with results that are expressed in indicative numbers and
broad trends
• Time series/causal methods are completely based on
statistical methods and principles

7. Qualitative Approach
• Qualitative Approach
Usually based on judgments about causal factors that underlie the demand of particular
products or services
Do not require a demand history for the product or service, therefore are useful for new
products/services
Approaches vary in sophistication from scientifically conducted surveys to intuitive hunches
about future events. The approach/method that is appropriate depends on a product’s life
cycle stage
• Qualitative Methods
Educated guess
Executive committee consensus
Delphi method
Survey of sales force
Survey of customers
Historical analogy
Market research

8. Forecasting Methods
-judgmental approach(a)
• Surveys - this involves a ‘bottom up’ method where each
individual/respondent contributes to the overall result; this could be for
product demand or sales forecasting ; also for opinion surveys amongst
employees, citizen groups or voter groups for election polls
• Sales Force Composites- where the similar ‘bottom up approach’ is used
for building up sales forecasts on any criteria like region-wise or product
wise sales territory groupings from sales force personnel
• Consensus of Executive Opinion -normally used in strategy formulation by
sought opinions from key organizational stakeholders- managers, suppliers,
customers, bankers and shareholders
• Historical analogy- used for forecasting new product demand as similar to
the previously introduced new product benefiting from its immediacy that
same demand influencing factors will apply

9. Forecasting Methods
-judgmental approach(b)
• Consensus thro “Delphi method ‘ especially for new product developments and
technology trends forecasting
• It is the most formal judgmental method and has a well defined process and
overcomes most of the problems of earlier ‘consensus by executive opinion’
• This involves sending out questionnaires to a panel of experts regarding a forecast
subject. Their replies are analyzed, summarized, processed and redistributed to the
panel for revisions in light of other’s arguments and viewpoints. By going thro such
an iterative process say 3-4 times, the final panel forecast is considered as fairly
accurate and authentic
• Yet, difficulties do exist in planning, administering and integrating member views
into a meaningful whole
• Course Booklet has a separate chapter on the Delphi method( page 107 onwards)

10. Forecasting Methods
-judgmental approach(c)
Method
Short term Medium
accuracy
term
accuracy
Long term
accuracy
Cost
Personal
insights
POOR
POOR
VERY POOR
VERY LOW
Panel
consensus
POOR TO FAIR
POOR TO FAIR
POOR
LOW
Market survey
VERY GOOD
GOOD
FAIR
VERY HIGH
Historical
analogy
POOR
FAIR TO GOOD
FAIR TO GOOD
MEDIUM
Delphi method
FAIR TO GOOD
FAIR TO GOOD
FAIR TO GOOD
HIGH

11. Forecasting Methods
- experimental approach
• Customer surveys- thro extensive formal market research using personal or mail interviews, and newly
thro internet modes; also build demand models for a new product by an aggregated approach
• Consumer panels- particularly used in initial stages of product development and design to match
product attributes to customer expectations
• Test marketing- often used after product development but before national launches by starting in a
selected target market/geography to understand any problems or issues to fine-tune marketing plans
and avoid costly mistakes before going in a big way
• Customer buying data bases- based on selected and accepted individuals/families on their buying
behavior , patterns and expenditures captured using electronic means direct from retailer sales data;
gives extensive clues on buying factors, customer attitudes, brand loyalty and brand switching and
response to promotional offers

12. Forecasting Methods
- relationship/causal approach(1)
• Its basic premise is that relationships exist between various independent demand
variables( like population, income, disposable incomes, age, sex etc to consumer
needs/wants/expectations( dependent variables)
• Before linking these, we need to find the nature and extent of these
causes/relationships in mathematical terms as regression( linear/multiple)equations
• Once done, they can be used to forecast the dependent variable for available
independent variables
• Various types of causal methods follow in next slide

13. Forecasting Methods
- relationship/causal approach(2)
• Econometric models like discrete choice and multiple regression models
used in large-scale or macro-level economic forecasting
• Input-output models used to estimate the flow of goods between markets
and industries, again in macro-economic situations
• Simulation models used to establish raw materials and components
demand based on MRP schedules , driven by keyed-in product sales
forecasts; to reflect market realities and imitate customer choices
• Life-cycle models which recognize product demand changes during its
various stages(i.e. introduction/growth/maturity/decline) particularly in
short life cycle sectors like fashion and technology

14. Forecasting Methods
- time series approach(1)
• Fundamentally, uses historical demand/sales data to determine future
demand
• Basic assumptions are that :
• Past data/information is available
• This data/information can be quantified
• Past patterns will continue into the future and projections made( though in reality
may not always be the case !)
• They involve statistical methods of understanding and explaining
patterns in time series data( like constant series e.g. annual rainfall;
trends e.g. growing expenditure with incomes; seasonal series e.g.
umbrella demand during rainy season; and any random/unexplained
‘noise’ where actual value= underlying pattern+ random noise)

15. Forecasting Methods
-time series approach(2)
• Static elements:
• Trend
• Seasonal
• Cyclical
• Random
• Adaptive elements:
– Moving average
– Simple exponential smoothing
– Exponential smoothing (with trend)
– Exponential smoothing (with trend and seasonality)

16. Time Series
-static elements
• Trend component- persistent overall downward or upward
pattern; due to population, technology or long term movement
• Seasonal component- regular up and down fluctuations due to
weather and/or seasons whose pattern repeats every year
• Cyclical component- repeated up and down movements; due to
economic or business cycles lasting beyond one year but say every
5-6 years
• Random component- erratic, unsystematic, residual fluctuations
due to random events or occurrences like one –time drought or
flood events

17. Forecasting Methods
- time series approach(3)
• Basic concepts involved are those of moving averages and exponential smoothing
• A ‘simple average’ forecast method is usable if past pattern is very stable, but very few time
series are stable over long periods, hence are of limited use
• A ‘moving average’ takes the average over a fixed number( by choice) of previous periods
ignoring older data periods giving a sense of immediacy to the data used e.g. taking only past 3
months data as relevant for forecasting for next quarter with same weightage; later improved by
weighted moving averages with unequal weightage
• All moving averages suffer in that(a) all historically used data are given same /unequal weight
and (b) works well only when demand is relatively constant. Its handicaps are overcome by
exponential smoothing
• Exponential smoothing is based on idea that as data gets older it becomes less relevant and
should be given a progressively lower weightage on a non-linear basis

18. Forecasting Examples
• Examples from Projects:
– Demand for tellers in a bank;
– Traffic flow at a major junction
– Pre-poll opinion survey amongst voters
– Demand for automobiles or consumer durables
– Segmented demand for varying food types in a restaurant
– Area demand for frozen foods within a locality
• Example from Retail Industry: American Hospital Supply Corp.
– 70,000 items;
– 25 stocking locations;
– Store 3 years of data (63 million data points);
– Update forecasts monthly;
– 21 million forecast updates per year.

19. Components of an Observation
Observed demand (O) =
Systematic component (S) + Random component
Level (current deseasonalized demand)
(R)
Trend (growth or decline in demand)
Seasonality (predictable seasonal fluctuation)
• Systematic component: Expected value of demand
• Random component: The part of the forecast that deviates
from the systematic component
• Forecast error: difference between forecast and actual demand

20. Time Series
Forecasting Methods
• Goal is to predict systematic component of
demand
– Multiplicative: (level)(trend)(seasonal factor)
– Additive: level + trend + seasonal factor
– Mixed: (level + trend)(seasonal factor)
• Static methods
• Adaptive forecasting

21. Static Methods
• Assume a mixed model:
Systematic component = (level + trend)(seasonal factor)
Ft+l = [L + (t + l)T]St+l
= forecast in period t for demand in period t + l
L = estimate of level for period 0
T = estimate of trend
St = estimate of seasonal factor for period t
Dt = actual demand in period t
Ft = forecast of demand in period t

22. Adaptive Forecasting
• The estimates of level, trend, and seasonality are
adjusted after each demand observation
• General steps in adaptive forecasting
• Moving average
• Simple exponential smoothing
• Trend-corrected exponential smoothing (Holt’s model)
• Trend- and seasonality-corrected exponential smoothing
(Winter’s model)

23. Moving Averages(1)
• This is the simplest model of extrapolative forecasting
• Since demand varies over time, only a certain amount of historical data is relevant to the
future, implying that we can ignore all observations older than some specified age
• A moving average uses this approach by taking average demand over a fixed number of
previous periods( say 3 as in below example)
• Example: If product demand is 150, 130 and 125 over the last 3 months, then forecast for 4th
month is (150+130+125)/3= 135. If actual demand in 4th month is 135 as forecasted( their
differences are forecasting errors which will discuss later), then forecast for 5th month is
(130+125+135)/3= 130; and this process is repeated for subsequent periods
• In above example, all past periods were given equal weightage; which can then be
differentially weighted to give more importance to most recent periods

24. Moving Averages(2)
• Used when demand has no observable trend or seasonality
• Systematic component of demand = level
• The level in period t is the average demand over the last N periods (the N-period
moving average)
• Current forecast for all future periods is the same and is based on the current
estimate of the level
Lt = (Dt + Dt-1 + … + Dt-N+1) / N
Ft+1 = Lt and Ft+n = Lt
After observing the demand for period t+1, revise the estimates as follows:
Lt+1 = (Dt+1 + Dt + … + Dt-N+2) / N
Ft+2 = Lt+1

25. Moving Averages(3)
• Include n most recent observations
• Weight equally
• Ignore older observations
weight
1/n
n
...
3
2
1
today

26. Moving Averages(4)
• Forecast Ft is average of n previous observations or actual
Dt :
• Note that the=past D + D +equally weighted.
n 1 ( observations are + D
Ft +1
t
t−
1
t + −n )
1
n average forecasts:
• Issues with moving
1 t
– All n past +1 =
Ft observations treated equally;
D
∑n i
n i =t +1−
– 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.

27. Moving Averages(5)
Internet Unicycle Sales
n=3
450
400
350
Units
300
250
200
150
100
50
0
Apr-01
Sep-02
Jan-04
May-05
Oct-06
Feb-08
Month
Jul-09
Nov-10
Apr-12
Aug-13

28. Simple Moving Averages(6) example
Month
Actual Sales
Forecast
Chosen 3 months moving
average
Jan
24500
Feb
27000
Mar
19950
Apr
26000
23817
May
21200
24317
June
18900
22383
July
17500
22033
Aug
19000
19200
Sep
18525
18467

29. Weighted Moving Averages(1)
• This is to overcome the lacuna of ALL past periods being given SAME
importance
• Here, different past periods are given different weightage
•
In same earlier example, let us take past periods weightage as 0.60, 0.30 and 0.10(
totaling 1 or 100%) ; then forecast for 4th month is ( 125x0.60+ 130x0.30+
150x0.10)= 75+39+15= 129; and further forecast for 5th month as
(129x0.60+125x0.30+130x0.10)= 127.9; and so on……..
• Idea is to give more importance to most recent observations
• But problems relate to the logic of deciding the number of past periods and
the given differential weightage
• Generally, if the demand is stable, then larger n values are chosen; if not,
then a smaller n and using weightage factors is better

30. Weighted Moving Averages(2)-example
Month
Actual Sales
Forecast
Chosen 3 months moving
average
Weightage- immediate
past as 0.45, then 0.30
and then 0.25
Jan
24500
Feb
27000
Mar
25500
Apr
26000
25700
May
21200
26100
June
18900
23715
July
17500
21365
Aug
19000
18845

31. Moving Averages- closing remarks
• All moving average methods( besides exponential smoothing to
be taken up later) focus on short term forecasting and provide
such capability without consideration of any time series patterns
• But when medium term( say 1 year) or long term( 5 years or
more) forecasting needed, then time series data patterns need
looking into
• These data patterns relate to trend, cyclical, seasonal and
random forms( as introduced earlier)
• Once these patterns are extracted from a given time series data ,
they can be used for forecasting

32. Time Series Patterns(1)
50,000
40,000
30,000
20,000
10,000
97
,2
97
,3
97
,4
98
,1
98
,2
98
,3
98
,4
99
,1
99
,2
99
,3
99
,4
00
,1
0

33. Time Series Patterns(2)
50000
Demand
40000
30000
Dt
Dt-bar
20000
10000
0
1 2 3 4 5 6 7 8 9 10 11 12
Period

34. Time Series Patterns(3)

35. Time Series Patterns(4)

36. Causal Forecasting(1)
• Basic idea is to use a cause or a relationship
between and amongst variables as a
forecasting method e.g. product sales is
dependent on its price
• Need to identify the independent and
dependent variables
• Causal forecasting is illustrated by linear
regression

37. Linear Regression
• It looks for a relationship of the form:
• Dependent variable(P)= q+ r multiplied by
independent variable (S) or P= q+ r S where:
• q= intercept and r= gradient of the line
….
•
Dependent
Variable “P”
…………………………
Gradient “r” ( >0)
“r”(<0)
Intercept “q”
Independent variable “S”

38. Linear Regression - example
• A manufacturer of critical components for two
wheelers is interested in forecasting the trend in
demand during the next year as a key input to its
annual planning exercise. Information on past
demand is available for last three years( next slide).
We need to develop a linear regression equation to
extract the trend component of the time series and
use it for predicting the future demand for
components

39. Linear Regression – example(contd.)
ACTUAL DEMAND FOR LAST THREE
YEARS( in ‘000 units)
PERIOD
Period Number(X)
ACTUAL DEMAND(Y)
Year 1- Q1
1
360
Year 1- Q2
2
438
Year 1- Q3
3
359
Year 1- Q4
4
406
Year 2- Q1
5
393
Year 2 -Q2
6
465
Year 2- Q3
7
387
Year 2- Q4
8
464
Year 3- Q1
9
505
Year 3- Q2
10
618
Year 3- Q3
11
443
Year 3- Q4
12
540

40. Linear Regression – example(contd.)
Period
X
Y
XY
XX
PERIOD
PERIOD Number
ACTUAL DEMAND(Y)
Year 1- Q1
1
360
360
1
Year 1- Q2
2
438
876
4
Year 1- Q3
3
359
1078
9
Year 1- Q4
4
406
1625
16
Year 2- Q1
5
393
1965
25
Year 2 -Q2
6
465
2790
36
Year 2- Q3
7
387
2709
49
Year 2- Q4
8
464
3712
64
Year 3- Q1
9
505
4545
81
Year 3- Q2
10
618
6180
100
Year 3- Q3
11
443
4873
121
Year 3- Q4
12
540
6480
144
SUM
78
5379
37193
650

41. Linear Regression – example(contd.)
• Linear regression equation P= q+ rS
• Using method of least squares, the regression coefficients are worked out as X= 78/12=
6.50 and Y= 5379/12= 448.25
• Then the gradient “r”= 37193-(12x6.50x448.25)/650-(12x6.50x6.50)= 2229.5/143= 15.59
• The ‘intercept ’q’= 448.25-15.59x6.50= 346.91
• Final regression equation is P= 346.91+ 15.59S
• Thus Forecast for Year 4 Q1= 346.91+ 15.59x13= 550
•
Forecast for Year 4 Q2= 346.91+ 15.59x14= 565
•
Forecast for Year 4 Q3= 346.91+ 15.59x15= 581
•
Forecast for Year 4 Q4= 346.91+ 15.59x16= 596

42. Multiple Regression
• When there are many independent variables involved which influence a
dependent variable then issues become complicated
• Then not only linear regression equations are required but also multiple
regression analysis is involved where the interdependency of the various
independent variables are taken into account
• These involve complex statistics beyond the scope of this course
• For their practical use, advanced techniques and tools are available thro
MS Excel tools, SPSS and other software packages