Demand forecasting involves determining customer demand for products and services in terms of what, where, when, and how much is needed. It is a foundational process that supports planning activities like capacity, inventory, and business planning. There are qualitative and quantitative forecasting methods. Qualitative methods include surveys, expert opinions, and market experimentation, while quantitative methods analyze past demand trends and patterns using techniques like time series analysis, regression, and exponential smoothing. Accurate demand forecasting provides benefits like higher revenues, improved production planning, and better customer service.

1. Demand ForecastingDemand Forecasting

2. SO
WHAT
“IS”
DEMAND
FORECASTING?

3. Forecasting customer demand for
products and services is a proactive
process of determining what products
are needed where, when, and in what
quantities. Consequently, demand
forecasting is a customer–focused
activity.
Demand forecasting is also the
foundation of a company’s entire
logistics process. It supports other
planning activities such as capacity
planning, inventory planning, and even
overall business planning.

4. Characteristics of demand
5 main characters of demand are-
Average
Demand tends to cluster around a specific level.
Trend
Demand consistently increases or decreases over time.
Seasonality
Demand shows peaks and valleys at consistent intervals. These
intervals can be hours, days, weeks, months, years, or seasons.
Cyclicity
Demand gradually increases and decreases over an extended period
of time, such as years. Business cycles (recession/expansion)
product life cycles influence this component of demand.
Elasticity
Degree of responsiveness of demand to a corresponding
proportionate change in factors effecting it.

5. TYPES OF FORECASTS
PASSIVE FORECASTS
Where the factors being forecasted
are assumed to be constant over a
period of time and changes are
ignored.
ACTIVE FORECASTS
Where factors being forecasted are
taken as flexible and are subject
to changes.

6. Why Study forecasting?
Reduces future uncertainties, helps study markets
that are dynamic, volatile and competitive
Allows operating levels to be set to respond to
demand variationsAllows managers to plan personnel, operations of
purchasing & finance for better control over wastes
inefficiency and conflicts.
Inventory Control-reduces reserves of slack resources
to meet uncertain demand
Effective forecasting builds stability in operations.
Setting Sales Targets, Pricing policies, establishing
controls and incentives

7. THE FORECAST
How?
Step 6 Monitor the forecast
Step 5 Prepare the forecast
Step 4 Gather and analyze data
Step 3 Select a forecasting technique
Step 2 Establish a time horizon
Step 1 Determine purpose of forecast

8. LEVELS OF FORECASTING
AT FIRMS LEVEL
AT INDUSTRY LEVEL
AT TOTAL MARKET LEVEL

9. KEY FACTORS FOR SELECTING A RIGHT METHOD
TIME PERIOD
SHORT TERM
3-6 Months, Operating Decisions,
E.g- Production planning
MEDIUM TERM
6 months-2 years, Tactical Decision
E.g.- Employment changes
LONG TERM
Above 2 years, Strategic Decision
E.g.- Research and Development

10. DATA REQUIREMENTS
Techniques differ by virtue of how
much data is required to successfully
employ the technique.
Judgmental techniques require little or
no data whereas methods such as
Time series analysis or Regression
models require a large amount of past
or historical data.

11. PURPOSE OF STUDY
It means the extent of explanation
required from the study. Some
techniques are based purely on the
pattern of past data and may do quite
well at forecasting, whereas many a
times these are not useful by themselves
since it is difficult to explain the causal
factors underlying the forecast.

12. PATTERN OF DATA STUDIED
The pattern of historical data is an
important factor to consider. Though
most of the times, the major pattern is
that of a trend, there are also cyclic and
seasonal patterns in the data. Certain
techniques are best suited for capturing
the different patterns in the data.
Regression methods incorporates all
these variations in data whereas trend
analysis methods study these factors
individually.

13. SO ,WE KNOW WHAT IT’S ALL
ABOUT!!!
NOW LETS ANALYSE THE
METHODS OF
DEMAND
FORECASTING.

14. 2 MAIN CATEGORIES
MICROECONOMIC METHODS
(QUANTITATIVE)
- involves the prediction of activity of particular firms,
branded products, commodities, markets, and industries.
- are much more reliable than macroeconomic methods
because the dimensionality of factors is lower and often
can easily be incorporated into a model.
MACROECONOMIC METHODS
(QUALITATIVE)
- involves the prediction of economic aggregates such as
inflation, unemployment, GDP growth, short-term interest
rates, and trade flows.
- is very difficult because of the complex interdependencies
in the overall economic factors

15. QUALITATIVE METHODS
- SURVEY OF BUYERS INTENSIONS
- EXPERTS OPINION METHOD
- DELPHI METHOD
- MARKET EXPERIMENTATION METHOD
- COLLECTIVE OPINIONS METHOD
QUANTITATIVE METHODS
- TIME SERIES MODELS
- TREND ANALYSIS
- MOVING AVERAGES METHODS
- EXPONENTIAL SMOOTHING
- CAUSAL MODELS
- REGRESSION MODELS

16. BUYERS INTENSION SURVEY
FEATURES
 EMPLOYS SAMPLE SURVEY TECHNIQUES
FOR GATHERING DATA.
 DATA IS COLLECTED FROM END USERS OF
GOODS - CONSUMER, PRODUCER,MIXED.
 DATA PORTRAYS BIASES AND
PREFERENCES OF CUSTOMERS.
 IDEAL FOR SHORT AND MEDIUM TERM
DEMAND FORECASTING, IS COST
EFFECTIVE AND RELIABLE.

17. ADVANTAGES
 HELPS IN APPROXIMATING FUTURE
REQUIREMENTS EVEN WITHOUT
PAST DATA.
 ACCURATE METHOD AS BUYERS
NEEDS AND WANTS ARE CLEARLY
IDENTIFIED & CATERED TO.
 MOST EFFECTIVE WAY OF
ASSESSING DEMAND FOR NEW
FIRMS

18. LIMITATIONS
People may not know what they
are going to purchase
They may report what they
want to buy, but not what they
are capable of buying
Customers may not want to
disclose real information
Effects of derived demand may
make forecasting difficult

19. EXPERTS OPINION METHOD
FEATURES
PANEL OF EXPERTS IN SAME FIELD WITH
EXPERIENCE & WORKING KNOWLEDGE.
COMBINES INPUT FROM KEY
INFORMATION SOURCES.
EXCHANGE OF IDEAS AND CLAIMS.
FINAL DECISION IS BASED ON MAJORITY
OR CONSENSUS, REACHED FROM
EXPERT’S FORECASTS

20. ADVANTAGES
CAN BE UNDERTAKEN EASILY
WITHOUT THE USE OF ELABORATE
STATISTICAL TOOLS.
INCORPORATES A VARIETY OF
EXTENSIVE OPINIONS FROM EXPERT
IN THE FIELD.

21. LIMITATIONS
JUDGEMENTAL BIASES
FOR EXAMPLE
Availability heuristic
Involves using vivid or accessible events
as a basis for the judgment.
Law of small numbers
People expect information obtained
from a small sample to be typical of
the larger population

22. COMPETATIVE BIASES
Over reliance on personal opinions.
Possibility of undue influence in
certain cases.
STATISTICAL INADEQUACY
Lack of statistical and quantifiable
data or figures to substantiate the
forecasts made.

23. DELPHI METHOD
PANEL OF EXPERTS IS SELECTED.
ONE CO-ORDINATOR IS CHOSEN BY
MEMBERS OF THE JURY
ANONYMOUS FORECASTS ARE
MADE BY EXPERTS BASED ON A
COMMON QUESTIONNAIRE.
CO-ORDINATOR RENDERS AN
AVERAGE OF ALL FORECASTS
MADE TO EACH OF THE MEMBERS.

24. 3 CONSEQUENCES- DIVERSION,
CONSENSUS OR NO AGREEMENT.
2 TO 3 CYCLES ARE UNDERTAKEN.
CONVERGENCE AND DIVERSION IS
ACCEPTABLE.
FORECASTS ARE REVISED UNTIL
A CONSENSUS IS REACHED BY
ALL.

25. ADVANTAGES
ELIMINATES NEED FOR GROUP
MEETINGS.
ELIMINATES BIASES IN GROUP
MEETINGS
PARTICIPANTS CAN CHANGE
THEIR OPINIONS ANONYMOUSLY.

26. LIMITATIONS
TIME CONSUMING -REACHING
A CONSENSUS TAKES A LOT
OF TIME.
PARTICIPANTS MAY DROP OUT.

27. MARKET EXPERIMENTATION
INVOLVES ACTUAL EXPERIMENTS & SIMULATIONS.
COUPONS ARE ISSUED TO FEW SELECT
CUSTOMERS.
SELECTED CUSTOMERS PURCHASE THE PRODUCTS.
PROXIMITY WITH CONSUMERS MAKES
INFORMATION COLLECTED RELIABLE.
INFORMATION FROM INTERACTIONS BETWEEN
SALES PERSONNEL & CUSTOMERS IS USED FOR
FORECASTING.
BEST USED IF SALES PERSONNEL ARE HIGHLY
SPECIALISED AND WELL TRAINED.

28. ADVANTAGES
USES KNOWLEDGE OF THOSE CLOSEST
TO THE MARKET.
HELPS ESTIMATING ACTUAL POTENTIAL
FOR FUTURE SALES.
PROVIDES FEEDBACK FOR IMPROVING
CUSTOMIZING & OFFERING MADE TO
CUSTOMERS.

29. COLLECTIVE OPINIONS METHOD
OPINIONS FROM MARKETING &
SALES SPECIALISTS ARE COMPILED.
2 TYPES OF TARGETS ESTIMATED-
AMBITIOUS TARGETS.
CONSERVATIVE TARGETS.
COMBINES EXPERTISE OF HIGHER
LEVEL MANAGEMENT & SALES
EXECUTIVES.

30. LIMITATIONS
POWER STRUGGLES MAY OCCUR
BETWEEN SPECIALISTS.
CONSENSUS MAY NOT BE
REACHED IN GOOD TIME.
DIFFERENCES AND PREJUDICES
IN OPINIONS MAY ALSO EXIST.

31. “ARE YOU STILL THERE??”

32. THAT FINISHES THE QUALITATIVE
METHODS.
NOW LETS LOOK AT THE
“QUANTITATIVE
METHODS”

33. TIME SERIES MODELS
Past data is used to make future
predictions .
Known or Independent variables are
used for predicting Unknown or
dependent variables, using the trend
equation- “ Predictive analysis”
Based on trend equation, we find
‘Line of Best Fit’ and then it is
projected in a scatter diagram,
dividing points equally on both sides
TREND ANALYSIS

34. TREND EQUATION
Y^ = a + bX + E
Y^ = Estimated value of Y
a = Constant or Intercept
b = slope of trend line
X = independent variable
E = Error term

35. = EXPLAINED VARIATION
1 - = UNEXPLAINED VARIATION
Explained variation - means the
extent to which the independent
variable explains the relative
change in the dependent variable.
Higher the explained variation,
lower the error value leading to
accurate forecast

37. MOVING AVERAGE METHOD
Data from a number of consecutive
past periods is combined to provide
forecast for coming periods.Higher
the amount of previous data, better
is the forecast.
Since the averages are calculated
on a moving basis, the seasonal and
cyclical variations are smoothened
out.

38. EXPONENTIAL SMOOTHING
Used in cases where the variable
under forecast doesn’t follow a
trend.
2 Types- Simple and Weighted
Simple smoothing- simple average of
specific observation called order.
Weighted smoothing- weights
assigned in decreasing order as
one moves from current period
observations to previous
observations.

39. The equation for exponential
smoothing follows a Geometric
Progression.Values may be written as-
a, a (1-a), a(1-a)^2….. a(1-n) where,
a = value of weight assigned
to the observation
a(1-a) = weight assigned to 1 period
previous observation
a(1-a)^2 = weight assigned to 2
periods previous observation
Sum of all weights always equals Unity.

40. CASUAL MODELS
It is a statistical technique for
quantifying the relationship between
variables. In simple regression analysis,
there is one dependent variable (e.g.
sales) to be forecast and one independent
variable. The values of the independent
variable are typically those assumed to
"cause" or determine the values of the
dependent variable.
REGRESSION MODEL

41. For example
Assuming that the amount of
advertising dollars spent on a
product determines the amount
of its sales, we could use
regression analysis to quantify
the precise nature of the
relationship between
advertising and sales. For
forecasting purposes, knowing
the quantified relationship
between the variables allows us
to provide forecasting estimates

42. STEPS IN REGRESSION ANALYSIS
1.Identification of variables
influencing demand for product
under estimation.
2.Collection of historical data on
variables.
3.Choosing an appropriate form of
function
4.Estimation of the function.

43. REGRESSION EQUATION
Y = xβ+α
Where
Y= value being forecasted
= constant value
= coefficients of regression
= independent variable
α
xβ
β
x
x
α

44. HIGHER REVENUES
SALES MAXIMIZATION
REDUCED INVESTMENTS FOR SAFETY
STOCKS
IMPROVED PRODUCTION PLANNING
EARLY RECOGNITION OF MARKET TRENDS
BETTER MARKET POSITIONING
IMPROVED CUSTOMER SERVICE LEVELS
BENEFITS OF EFFECTIVE DEMAND
FORECASTING