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.
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
