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ForecastingLecture 10
Objective
To know the concept of forecasting To know the difference between a quantitative
and qualitative forecasting
To know the basic concepts of the statisticalforecasting techniques
Forecasting
Is an activity that calculates/predicts some future eventsor conditions, usually as a result of rational study or
analysis of pertinent data
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Qualitative and
Quantitative Forecasting
Qualitative is an intuitive and educated guess Quantitative is based on some deterministic or statistical
model and historical data
Requirements in
Forecasting
Sophisticated modeling Efficient data architecture, warehouse Computing technology Computational statistics The data/time series
Sources of Data
POS database Credit history Usage history Economic indicators Consumer panel survey Sales monitoring Retail audit Data retailers Meta-analysis
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Demand Forecasting
New product is launched/Existing one is relaunched Increase demand? Coping with the demand Strategic planning
Demand is not limited There is competition Regulations
Demand Forecasting
Can we keep up with the demand? How do we create new demand?
Activation Promotion Loyalty incentives
Sales Performance
Historical vs Drivers of Sales Historical
Patterns Seasonality Trend Cycle Irregular Patterns
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Sales Performance
Drivers of Sales Marketing Distribution Availability Pricing Consumers Capability Consumers Behavior
Roles of Forecasting
Models
Customer Relations Management Activation Promotion Loyalty Pricing Strategy
Business Insights (minimize the pains of trial and errors) Maintain C, Expand DE, Attract AB
Roles of Forecasting
Models
Consumer Insights (deliver the right goods and services) Preference Profile Needs
Implications Minimize adhoc, routine studies More in-house research capabilities required
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Features Common to All
Forecast
Forecasting techniques generally assume that the sameunderlying causal system that existed in the past will
continue to exist in the future
Forecasts are rarely perfect; actual results usually differfrom predicted values
Features Common to All
Forecasting
Forecasts for groups of items tend to be more accuratethan forecasts for individual items because forecasting
errors among items in a group usually have a canceling
effect
Forecast accuracy decreases as the time period coveredby the forecastthe time horizonincreases
Elements of a Good
Forecast
Features Common to All Forecast The forecast should be accurate and the degree of
accuracy should be stated
The forecast should be reliable The forecast should be expressed in meaningful units The forecast should be in writing The forecasting technique should besimple to understand
and use
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Forecast Based on Time
Series
A time series is a time-ordered sequence of observationstaken at regular intervals over a period of time (e.g.,
hourly, daily, weekly, monthly, quarterly, annually).
Analysis of time series data requires the analyst toidentify the underlying behavior of the series. This can
often be accomplished by merelyplotting the data and
visually examining the plot.
Trend
Trend refers to a long-term upward or downwardmovement in the data. Population shifts, changing
incomes, and cultural changes often account for such
movements
Forecasting System
Cycles are wavelike variations of more than one yearsduration. These are often related to a variety of economic,
political, and even agricultural conditions.
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Exponential Smoothing
Exponential smoothing is a sophisticated weightedaveraging method that is still relatively easy to use and
understand. Each new forecast is based on the previous
forecast plus a percentage of the difference between that
forecast and the actual value of the series at that point:
Exponential Smoothing
Next Forecast = Previous forecast + !(Actual Previous forecast)
where (Actual = Previous forecast) represents the forecast error and
= is a percentage of the error
Ft =Forecast for period t Ft-1 = Forecast for the previous period != Smoothing constant At-1 = Actual demand or sales for the previous period
Ft = (a!!)F
t!1+!A
t!1
Exponential Smoothing
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Trend Equation
Trend Equation
yt =
a+bt
b =n ty! " t! y!n t2! " t!( )
2
a =y
! "b t
!n
t = specified number of time periods from t = 0
yt = forecast for the period t
a = value of yt at t = 0
b = slope of the line
n = Number of periods
y = Value of the time series
Trend Equations
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Simple Linear Regression
b =n xy!( )" x!( ) y!( )
n x2!( )" x!( )2
a =y! " b x!
n
n = number of paired observations
Correlation
A measure of the strength and direction of relationshipbetween two variables
can range from -1.00 to +1.00 +1.00 indicates that changes in one variable are always
matched by changes in the other
-1.00 indicates that increases in one variable are matchedby decreases in the other
Correlation
0 = indicates little linear relationship between twovariables
r =
n xy!( )" x!( ) y!( )n x
2!( )" x!( )2
# n y2!( )" y!( )2
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Assumptions on Linear
Regression Analysis
Variations around the line are random Deviations around the line should be normally distributed Predictions are being made only within the range of
observed values
Guidelines in Getting the
Best Result
Always plot the data to verify that a linear relationship isappropriate
The data may be time-dependent A small correlation may imply that other variables are
important
Weaknesses of Linear
Regression
Simple linear regression applies only to linearrelationships with one independent variable
One needs a considerable amount of data to establish therelationshipin practice, 20 or more observations
All observations are weighted equally
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Measuring Forecast
Accuracy
Forecast accuracy is a significant factor when decidingamong forecasting alternatives. Accuracy is based on the
historical error performance of a forecast
Mean Absolute Deviation (MAD) Mean Squared Error (MSE)
MAD and MSE
MAD=Actual =Forecast!
n
MSE =Actual"Forecast( )
2
!n"1
CONTROLLING THE
FORECAST
It is necessary to monitor forecast errors to ensure that theforecast is performing adequately.
The model may be inadequate due to (a) the omission of animportant variable, (b) a change or shift in the variable that
the model cannot deal with (e.g., sudden appearance of a
trend or cycle), or (c) the appearance of a new variable (e.g.,
new competitor).
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CONTROLLING THE
FORECAST
Irregular variations may occur due to severe weather orother natural phenomena, temporary shortages or
breakdowns, catastrophes, or similar events
The forecasting technique may be used incorrectly or theresults misinterpreted
There are always random variations in the data.Randomness is the inherent variation that remains in the
data after all causes of variation have been accounted for
TRACKING SIGNAL
TRACKING_ SIGNAL =Actual!Forecast( )"
MAD
Control Limits
UCL = 0+z MSE
LCL = 0!
z MSE
Where:
Square Root of MSE = standard deviation
z = Number of standard deviations; 2 and 3 are the typical
values
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Summary
Summary