Forecasting Final

Meaning of Sales ForecastingWhy Study forecasting ?Types of ForecastsCategorization of Sales ForecastingFacts in ForecastingLimitations of Demand ForecastingSteps in ForecastingSales forecasting TechniqueForecast Accuracy

“Contrive” “Before”

[the fact]؟؟

Meaning of Demand Forecasting

Meaning of Demand ForecastingDemand forecasting is the scientific and

analytical estimation of demand for a product (service) for a particular period of time.

Allows managers to plan personnel, operations of purchasing & finance for better control over wastes inefficiency and conflicts.

Why Study forecasting ?

Setting Sales Targets, Pricing policies, establishing controls and incentives.

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Why Study Why Study forecasting ?forecasting ?

Reduce the cost for purchasing raw material, Increased revenue

Improved customer service (efficiency)

Effective forecasting builds stability in operations.

Measure as a barometer of the future health of a company.

The ability to plan for production avoid the problem of over-production & problem of short supply…………. Sales Maximization.

The ability to identify the pattern or trend of sales knowing when and how much to buy--- better marketing positioning.

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Types of ForecastsEconomic forecastsEconomic forecasts

–Address the future business conditions (e.g., inflation rate, money supply, etc.)

Technological forecastsTechnological forecasts–Predict the rate of technological progress–Predict acceptance of new products

Demand forecastsDemand forecasts–Predict sales of existing products

Facts in ForecastingMain assumption: Past pattern repeats itself into the

future.Forecasts are rarely perfect: Don't expect forecasts to

be exactly equal to the actual data.The science and art of forecasting try to minimize, but

not to eliminate, forecast errors.

Forecasts for a group of products are usually more accurate than these for individual products.

A good forecast is usually more than a single number.

The longer the forecasting horizon, the less accurate the forecasts will be .

Limitations of Demand Forecasting

Qualities of Good Forecasting

1) Simple

2) Economy of time

3) Economy of money

4) Accuracy

5) Reliability

Steps in ForecastingDetermine the purpose of the forecast.

Select the items to be forecasted.

Gather the data.

Determine the time horizon of the forecast.

Select the forecasting model(s).

Make the forecast.

Validate and implement results.

Sales forecasting Process

Setting Goals Forecasting

Gathering data

Analysis of data

Evaluating of forecasting outcomes

Choosing The Best Model For

ForecastingForecasting

METHODS OF FORECASTING• Forecasting methods are classified into two

groups:

Qualitative Analysis Quantitative Analysis

Time Series Analysis

Causal Analysis

Customer Survey

Sales Force Composite

Executive Opinion

Delphi Method

Test Marketing

ExponentialSmoothing

Regression

AnalysisLeast

squares

Moving Average

Naïve approach

Weighted Moving

Forecasting Technique

Sales forecasting Technique

It is generally recommended to use a combination of quantitative and

qualitative techniques.

Forecasting Techniques

Qualitative (Subjective) Methods

1- Consumers’ Opinion Survey (Buyer’s expectation Method )

Advantages : Simple to administer and comprehend.

Forecasting Reveals general attitude and feeling about products from potential users.

Technique is very effective to determine demand for a new product when no past data available.

Suitable for short term decisions regarding product and promotion.

1- Consumers’ Opinion Survey (Buyer’s expectation Method )

1- Consumers’ Opinion Survey : (Buyer’s expectation Method)

2- Sales Force Composite Method Salespersons are in direct contact with the customers.

Each salespersons are asked about estimated sales targets in their respective sales territories in a given period of time.

These forecasts are then reviewed to ensure they are realistic, then combined at the district and national levels to reach an overall forecast.

In this method sales people put their future sales estimate either alone or in consultation with sales manager.

2- Sales Force Composite Method

3- Jury of Executive Opinion:

TO make the judgemental forecasts more realistic by minimising bias method is used.

In this method a panel of experts are asked sequential questions in which the response to one questionare is used to produce next questionare.

The information available to one experts are made available to other experts.

4- Experts’ Opinion MethodDelphi Technique:

Conclusions, insights, and expectations of the experts are evaluated by the entire group resulting in shared more structured and less biased estimate of the future.

There are three different types of participants in the Delphi process:

decision makers, staff personnel, and respondents.


4- Delphi Technique:

assist the decision makers by preparing, distributing, collecting, and summarizing a series

of questionnaires and survey results.

are a group of people whose judgments are valued. This group provides inputs to the decision makers before the forecast is made.

(Sales)?

(What will sales be? Administering survey)

)People who can make valuable judgments

Sales will be 45, 50, 55)

Evaluate responses and make Decision

(Sales will be 50)!


5- Test Marketing

Type Characteristics Strengths WeaknessesExecutive opinion

A group of managers meet & come up with a forecast

Good for strategic or new-product forecasting

One person's opinion can dominate the forecast

Market research

Uses surveys & interviews to identify customer preferences

Good determinant of customer preferences

It can be difficult to develop a good questionnaire

Delphi method

Seeks to develop a consensus among a group of experts

Excellent for forecasting long-term product demand, technological changes, and

Time consuming to develop



Causal Analysis

Customer Survey


Executive Opinion

Delphi Method

Test Marketing


RegressionAnalysis

Least squares

Moving Average

Naïve approach

Weighted Moving


Forecasting Techniques Quantitative forecasting

Uses mathematical models and historical data to make forecasts.

Used when situation is stable & historical data existExisting products

Time series models are the most frequently used among all the forecasting models.

Quantitative forecastingTime Series

ModelsCasual Models

Only independent variable is the timebased on the assumption that the future is an extension of the past .

assumes that one or more factors other than time predict future demand.

46

Product Life Cycle

Time

Sales

Intr

oduc

tion

Mat

urit

y

Dec

line

Gro

wth

A collection of data recorded over a period of time (weekly, monthly, quarterly).

an analysis of its history can be used by management to make current decisions and plans based on long-term forecasting.

Forecast based only on past values Assumes that factors influencing past and present will continue influence in future

ExampleYear: 2007 2008 2009 2010 2011Sales: 78.7 63.5 89.7 93.2 92.1

What is a Time Series?

Time SeriesD

eman

d fo

r pr

oduc

t or

se

rvic

e

2007 2008 2009 2010 Time2011

Time Series Components

RandomRandomSeasonalSeasonal

change occurring consistently over a long time and is relatively smooth in its path.

either increasing or decreasing.

Forecasting methods: linear trend projection, exponential smoothing

Time Series Pattern: Secular Trend

Time Series Pattern: Seasonal Patterns of change in a time

series within a year which tend to repeat each year.

Due to weather, customs, etc.

Occurs within 1 year

Forecasting methods: exponential smoothing with trend

have a tendency to recur in a few years usually repeat every two-five years.

Repeating up & down movements

Due to interactions of factors influencing economy.

Time Series Pattern: Stationaryor Irregular Variation or Random events

have no trend of occurrence hence they create random variation in the series.

(due to unexpected or unpredictable events).

Short duration & non-repeating.

Forecasting methods: naive, moving average, exponential smoothing

Product Demand Charted over 4 Years with Trend and Seasonality

Dem

and

for

prod

uct

or

serv

ice

Year1

Year2

Year3

Year4

Random variation

Seasonal peaks Trend component

Actual demand line

Average demand over

four years



Causal Analysis

Customer Survey


Executive Opinion

Delphi Method

Test Marketing


RegressionAnalysis

Least squares

Moving Average

Naïve approach

Weighted Moving


Can be defined as the summation of demands of total periods divided by the total number of periods.

Useful if we can assume that market demands will stay fairly

steady over time.

It is convenient for short term periods

1- Moving Average Method

MAMA nnnn Demand in Demand in Previous Previous PeriodsPeriods

2- Moving Average…… Example

2- Moving Average Solution

Time

Actual Sales

Moving Total (n=3)

Moving Average

(n=3) Jan. 4 Feb. 6 Mar. 5 April 3 4+6+5=15 15/3 = 5 May 7 Jun.


Time

Actual Sales

Moving Total (n=3)

Moving Average

(n=3) Jan. 4

Feb. 6 Mar. 5 April 3 4+6+5=15 15/3 = 5 May 7 6+5+3=14 14/3 = 4.7 Jun.


Time

Actual sales

Moving Total (n=3)

Moving Average

(n=3)

Jan. 4 Feb. 6 Mar. 5 April 3 4+6+5=15 15/3=5.0 May 7 6+5+3=14 14/3=4.7 Jun 5+3+7=15 15/3=5.0

3- Weighted Moving AverageWeights are used to give more values to recent value

This makes the techniques more responsive to changes because latter periods may be more heavily waited

Most recent observation receives the most weight, and the weight decreases for older data values.

3- Weighted Moving Average

321

Last month agoTwo month agoThree month ago

Sum of the weights6

MonthActual salesThree month moving averageJan10Feb12

March13

April166 = 12.17

13 ) ×(3

12) × + (2 10)×( +1

MonthActual salesThree months moving average

Jan10

Feb12

March13

April16(3× 13)+) 2× 12)+) 1× 10)/6 = 12.17

May19(3× 16)+) 2× 13)+) 1× 12)/6 = 14.33

June23(3× 19)+) 2× 16)+) 1× 13)/6 = 17

July26(3× 23)+) 2× 19)+) 1× 16)/6 =20.5

3- Weighted Moving Average

4- Trend Projection Method

YY = bX + a

a = Y-interceptX

Changein Y

Change in Xb = Slope

Linear Equationsde

pend

ent v

aria

ble

valu

e

a = value of (Y) when (X) equals zero

independent variable value

Ft = Ft-1 + (Dt-1 - Ft-1)

Ft= forecast for this period Ft-1 = forecast for the

previous period

Dt-1= Actual demand for

the previous period

Smoothing constant (0 to 1)

6- Exponential Smoothing Method

Ft = Ft-1 + (Dt-1 - Ft-1)

Ft= forecast for 2013 Ft-1 = forecast 2012

Dt-1= Actual sales 2012Smoothing constant (0 to 1)

- Exponential Smoothing Method

© 1995 Corel Corp.

6- Exponential Smoothing……Example

TimeTime ActualActual ForecastForecast, F t( αα= = .10.10((

20082008 180 175.00 (Given)175.00 (Given)20092009 16816820102010 15915920112011 17517520122012 19019020132013 NANA

6- Exponential Smoothing Solution

Ft = Ft-1 + (At-1 - Ft-1)

175.00 +175.00 +


TimeTime ActualActual ForecastForecast, F t(αα = = .10.10((

20082008 180180 175.00 )Given(175.00 )Given(20092009 168168 175.00 + 175.00 + .10.10))

20102010 15915920112011 17517520122012 19019020132013 NANA

Ft = Ft-1 + (At-1 - Ft-1)

MGMT 6020 Forecast


TimeTime ActualActual Forecast, Forecast, FFtt

))αα = = .10.10((

20082008 180180 175.00 )Given(175.00 )Given(20092009 168168 175.00 + 175.00 + .10.10))180180 - -

20102010 15915920112011 17517520122012 19019020132013 NANA

Ft = Ft-1 + (At-1 - Ft-1)

MGMT 6020 Forecast


Time ActualForecast, Ft

)αα = = .10.10((

20082008 180180 175.00 )Given(175.00 )Given(20092009 168168 175.00 + 175.00 + .10.10))180180 - 175.00 - 175.00((

20102010 15915920112011 17517520122012 19019020132013 NANA

Ft = Ft-1 + (At-1 - Ft-1)

MGMT 6020 Forecast


Time ActualForecast, Ft

)αα = = .10.10((

20082008 180180 175.00 )Given(175.00 )Given(20092009 168168 175.00 + 175.00 + .10.10))180180 - 175.00- 175.00(( = = 175.50175.50

20102010 15915920112011 17517520122012 19019020132013 NANA

Ft = Ft-1 + (At-1 - Ft-1)


Time ActualForecast, F t

(α = .10)

20082008 180 175.00175.00 )Given(

20092009 168168 175.00175.00 + .10) + .10)180180 - 175.00( = - 175.00( = 175.50175.50

20102010 159159 175.50 175.50 + .10)+ .10)168168 - 175.50( = - 175.50( = 174.75174.7520112011 17517520122012 19019020132013 NANA

Ft = Ft-1 + (At-1 - Ft-1)


TimeTime ActualActual ForecastForecast, F t(α = .10)

2008 180 175.00175.00 )Given(

20092009 168168 175.00175.00 + .10) + .10)180180 - 175.00( = - 175.00( = 175.50175.5020102010 159159 175.50 175.50 + .10)+ .10)168168 - 175.50( = - 175.50( = 174.75174.7520112011 175175 174.75 174.75 + .10)+ .10)159159 - 174.75( = - 174.75( = 173.18173.1820122012 19019020132013 NANA

Ft = Ft-1 + (At-1 - Ft-1)


Time Actual Forecast, F t(α = .10)

2008 180 175.00175.00 )Given(

20092009 168168 175.00175.00 + .10) + .10)180180 - 175.00( = - 175.00( = 175.50175.5020102010 159159 175.50 175.50 + .10)+ .10)168168 - 175.50( = - 175.50( = 174.75174.7520112011 175175 174.75 174.75 + .10)+ .10)159159 - 174.75( = - 174.75( = 173.18173.1820122012 190190 173.18 173.18 ++ .10.10))175 175 - 173.18- 173.18(( = 173.36= 173.3620132013 NANA

Ft = Ft-1 + (At-1 - Ft-1)


TimeTime ActualActual ForecastForecast, F t(α = .10)

20082008 180180 175.00 )Given(175.00 )Given(20092009 168168 175.00 + .10)180 - 175.00( = 175.50175.00 + .10)180 - 175.00( = 175.5020102010 159159 175.50 + .10)168 - 175.50( = 174.75175.50 + .10)168 - 175.50( = 174.7520112011 175175 174.75 + .10)159 - 174.75( = 173.18174.75 + .10)159 - 174.75( = 173.1820122012 190190 173.18 + .10)175 - 173.18( = 173.36173.18 + .10)175 - 173.18( = 173.3620132013 NANA 173.36173.36 + + .10.10))190190 - 173.36- 173.36( = ( = 175.02175.02

Ft = Ft-1 + (At-1 - Ft-1)

6- Exponential Smoothing Method



Causal Analysis

Customer Survey


Executive Opinion

Delphi Method

Test Marketing


RegressionAnalysis

Least squares

Moving Average

Naïve approach

Weighted Moving


Usually consider several variable that are related to the quantity being predicted.

once the related variable are found, statistical models are then built and used to forecast

Example: PC sales forecasts (dependent variable) could be correlated to advertising budget, promotions, prices, competitors prices (independent variables)

Causal Method

Regression Analysis Method

forecast errordefined as the difference between actual quantity and the forecast

The smaller the forecast error, the more accurate the forecast.

et = forecast error for Period t

Dt = actual demand for

Period t

Ft = forecast for Period t

et = Dt - Ft

Time periodTime periodValu

es o

f Dep

ende

nt V

aria

ble

DeviationDeviation11

((errorerror))







ActualActual observationobservation

Trend lineTrend line

forecast error

Forecast AccuracySeveral measures of forecasting accuracylarger the value the larger the forecast error

Mean absolute deviation (MAD)Sum of absolute values of individual

forecast errors / number of periods of data

The larger the MAD, the less the accurate the resulting model

MAD of 0 indicates the forecast exactly predicted demand.

Forecast AccuracyMean squared error (MSE)

Average of the squared differences between the forecasted and observed values

Mean absolute percentage error (MAPE)How many Percent the forecast is off from the

actual data

PeriodSales(A)Forecast E[E]E2[E/]A116001650-505025000.0313222002010190190361000.0864320002200-200200400000.100041600158020204000.012552500248020204000.0080

635003520-20204000.0057733003310-10101000.00308320032000000.0000939003850505025000.0128

1047004720-20204000.0043

1010-20-2058058082800828000.26390.2639

MAD=58 & MSE=8280 & MAPE=2.64%

Forecast Accuracy


635003520-20204000.0057733003310-10101000.00308320032000000.0000939003850505025000.0128

1047004720-20204000.0043

1010-20-205805808280828000

0.26390.2639

MAD=58

Forecast Accuracy

MAD= [E]n


635003520-20204000.0057733003310-10101000.00308320032000000.0000939003850505025000.0128

1047004720-20204000.0043

1010-20-2058058082800828000.26390.2639

Forecast Accuracy

MSE = [E2]n

MSE=8280


635003520-20204000.0057733003310-10101000.00308320032000000.0000939003850505025000.0128

1047004720-20204000.0043

1010-20-2058058082800828000.26390.2639

Forecast Accuracy

MAPE= n

[ E/]A × 100 %

MAPE=2.64%

Excel Chart Methods

XXX

YXXYb

2

• Identify dependent (y) and independent (x) variables

• Solve for the slope of the line

• Solve for the y intercept

• Develop your equation for the trend line

Y=a + bX

XbYa

22 XnX

YXnXYb

Linear regression

Linear Regression Problem: A maker of golf shirts has been tracking the relationship between sales and advertising. Use linear regression to find out what sales might be if the company invested 53,000 in advertising

next year.

22 XnX

YXnXYbSales (Y)Adv.

(X)XYX^2Y^2

1130324160230416,900

2151527852270422,801

3150507500250022,500

4158558690302524964

5153.8553

Tot58918928202925387165

Avg147.2547.25

153.85531.1592.9Y1.15X92.9bXaY

92.9a47.251.15147.25XbYa

1.1547.2549253

147.2547.25428202b 2

• Correlation coefficient (r) measures the direction and strength of the linear relationship between two variables. The closer the r value is to 1.0 the better the regression line fits the data points.

• Coefficient of determination ( ) measures the amount of variation in the dependent variable about its mean that is explained by the regression line. Values of ( ) close to 1.0 are desirable.

.964.982r

.98258987,1654*(189)-4(9253)

58918928,2024r

YYn*XXn

YXXYnr

22

22

22

22

Multiple Regression

• An extension of linear regression but:– Multiple regression develops a relationship

between a dependent variable and multiple independent variables. The general formula is:

108

Measuring Forecasting Accuracy

• Mean Absolute Deviation (MAD)– measures the total error in a forecast

without regard to sign• Cumulative Forecast Error (CFE)

– Measures any bias in the forecast

• Mean Square Error (MSE)– Penalizes larger errors

• Tracking Signal– Measures if your model is working

n

forecast - actualMSE

2

MADCFETS

nforecastactual

MAD

forecastactualCFE

Forecasting Final

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