2.
What is a Forecast?
• A guess about what is going to happen in the future.
• An integral part of almost all business enterprise
• Logical and rational, but still a guess.
• Objective is to minimize error (as you will always be wrong!)
• Could be a complicated or simple process
3.
Market Size
• The number of buyers and sellers in a particular market. This
is especially important for companies that wish to launch a
new product or service, since small markets are less likely to
be able to support a high volume of goods. Large markets
could bring in more competition.
• The number of individuals in a certain market who are
potential buyers and/or sellers of a product or service.
Companies are interested in knowing the market size before
launching a new product or service in an area.
4.
Forecasting Models
Forecasting
Techniques
Qualitative
Models
Time Series
Methods
Delphi
Method
Jury of Executive
Opinion
Sales Force
Composite
Consumer Market
Survey
Naive
Moving
Average
Weighted
Moving Average
Exponential
Smoothing
Trend Analysis
Causal
Methods
Simple
Regression
Analysis
Multiple
Regression
Analysis
Seasonality
Analysis
Multiplicative
Decomposition
5.
Qualitative & Quantitative Forecasting
Methods
QualitativeA.Executive Judgement
B. Sales Forse Composite
C.Market Research/Survey
D.Delphi Method
QuantitativeA. Time Series Models
a.Naïve
b.Moving Average
1.Simple 2.Weighted
c.Exponential Smoothing- 1.Level 2.Trend 3. Seasonality
B.Regression Models
6.
Jury of Executive Opinion
Involves small group of high-level experts and managers
Group estimates demand by working together
Combines managerial experience with statistical models
Relatively quick
‘Group-think’
7.
Sales Force Composite
• Each salesperson projects his or her sales
• Combined at district and national levels
• Sales reps know customers’ wants
• Tends to be overly optimistic
8.
Delphi Method
• The Delphi Method is a group
decision process about the
likelihood that certain events
will occur.
• Today it is also used for
environmental, marketing and
sales forecasting.
• The Delphi Method uses a panel
of experts.
• Expert responses to a series of
questionnaires are anonymous.
• Each round of questionnaires
results in a median answer.
• The process guides the group
towards a consensus.
9.
Consumer Market Survey
• Ask customers about purchasing plans.
• What consumers say, and what they actually do are often
different.
• Sometimes difficult to answer.
10.
Demand Patterns in Time Series Model
• Time Series: The repeated observations of demand for a service
or product in their order of occurrence.
• There are five basic patterns of most time series• Horizontal- The fluctuation of data around a constant mean.
• Trend- The systematic increase or decrease in the mean of the
series over time.
• Seasonal- A repeatable pattern of increases or decreases in
demand, depending on the time of day, week, month, or season.
• Cyclical-The less predictable gradual increases or decreases over
longer periods of time (years or decades).
• Random- The unforecastable variation in demand
12.
Naive Approach
• Demand in next period is the same as demand in most recent
period
• Assumes demand in next period is the same as demand in most
recent period
•
e.g.- If May sales were 48, then June sales will be around
48.
• Sometimes it is effective & cost efficient
•
e.g.- when the demand is steady or changes slowly
•
when inventory cost is low
•
when unmet demand will not lose
13.
Moving Average Method
•
•
•
•
•
MA is a series of arithmetic means
Used if little or no trend, seasonal, and cyclical patterns
Used often for smoothing
Provides overall impression of data over time
Equation
MA
Demand in Previous n Periods
n
14.
Moving Average Example
• S.K. Patel is manager of a museum store that sells historical
replicas. You want to forecast sales of item (123) for 2000 using a
3-period moving average.
1995 4
1996 6
1997 5
1998 3
1999 7
18.
Exponential Smoothing Method
• Form of weighted moving average
• Weights decline exponentially
• Most recent data weighted most
• Requires smoothing constant ( )
• Ranges from 0 to 1
• Subjectively chosen
• Involves little record keeping of past data
19.
Exponential Smoothing Equations
Ft = Ft-1 + (At-1 - Ft-1)
= At-1 + (1 - ) Ft-1
F = Forecast value
At = Actual value
= Smoothing constant
t
Ft = At - 1 + (1- )At - 2 + (1- )2·At - 3
+ (1- )3At - 4 + ... + (1- )t-1·A0
Use for computing forecast
20.
Regression Analysis as a Method for Forecasting
• Regression analysis takes advantage of the relationship between
two variables. Demand is then forecasted based on the
knowledge of this relationship and for the given value of the
related variable.
• Ex: Sale of Tires (Y), Sale of Autos (X) are obviously related
• If we analyze the past data of these two variables and establish a
relationship between them, we may use that relationship to
forecast the sales of tires given the sales of automobiles.
• The simplest form of the relationship is, of course, linear, hence it
is referred to as a regression line
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