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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