forecasting final
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pptTRANSCRIPT
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Meaning of Sales ForecastingWhy Study forecasting ?Types of ForecastsCategorization of Sales ForecastingFacts in ForecastingLimitations of Demand ForecastingSteps in ForecastingSales forecasting TechniqueForecast Accuracy
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“Contrive” “Before”
[the fact]؟؟
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؟؟
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Meaning of Demand Forecasting
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Meaning of Demand ForecastingDemand forecasting is the scientific and
analytical estimation of demand for a product (service) for a particular period of time.
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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
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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 .
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Limitations of Demand Forecasting
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Limitations of Demand Forecasting
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Qualities of Good Forecasting
1) Simple
2) Economy of time
3) Economy of money
4) Accuracy
5) Reliability
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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.
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Sales forecasting Process
Setting Goals Forecasting
Gathering data
Analysis of data
Evaluating of forecasting outcomes
Choosing The Best Model For
ForecastingForecasting
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METHODS OF FORECASTING• Forecasting methods are classified into two
groups:
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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
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Sales forecasting Technique
It is generally recommended to use a combination of quantitative and
qualitative techniques.
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Forecasting Techniques
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Qualitative (Subjective) Methods
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1- Consumers’ Opinion Survey (Buyer’s expectation Method )
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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 )
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1- Consumers’ Opinion Survey : (Buyer’s expectation Method)
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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.
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2- Sales Force Composite Method
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2- Sales Force Composite Method
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2- Sales Force Composite Method
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2- Sales Force Composite Method
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3- Jury of Executive Opinion:
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3- Jury of Executive Opinion:
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3- Jury of Executive Opinion:
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3- Jury of Executive Opinion:
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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:
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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- Experts’ Opinion MethodDelphi Technique:
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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.
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(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)!
4- Experts’ Opinion MethodDelphi Technique:
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4- Experts’ Opinion MethodDelphi Technique:
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4- Experts’ Opinion MethodDelphi Technique:
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5- Test Marketing
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5- Test Marketing
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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
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Qualitative Analysis Quantitative Analysis
Time Series Analysis
Causal Analysis
Customer Survey
Sales Force Composite
Executive Opinion
Delphi Method
Test Marketing
ExponentialSmoothing
RegressionAnalysis
Least squares
Moving Average
Naïve approach
Weighted Moving
Forecasting Technique
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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.
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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.
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46
Product Life Cycle
Time
Sales
Intr
oduc
tion
Mat
urit
y
Dec
line
Gro
wth
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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?
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Time SeriesD
eman
d fo
r pr
oduc
t or
se
rvic
e
2007 2008 2009 2010 Time2011
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Time Series Components
RandomRandomSeasonalSeasonal
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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
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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
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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.
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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
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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
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Qualitative Analysis Quantitative Analysis
Time Series Analysis
Causal Analysis
Customer Survey
Sales Force Composite
Executive Opinion
Delphi Method
Test Marketing
ExponentialSmoothing
RegressionAnalysis
Least squares
Moving Average
Naïve approach
Weighted Moving
Forecasting Technique
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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
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2- Moving Average…… Example
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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.
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3- 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 6+5+3=14 14/3 = 4.7 Jun.
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3- 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=5.0 May 7 6+5+3=14 14/3=4.7 Jun 5+3+7=15 15/3=5.0
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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.
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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
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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
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4- Trend Projection Method
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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
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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
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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
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© 1995 Corel Corp.
6- Exponential Smoothing……Example
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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 +
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6- Exponential Smoothing Solution
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)
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MGMT 6020 Forecast
6- Exponential Smoothing Solution
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)
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MGMT 6020 Forecast
6- Exponential Smoothing Solution
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)
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MGMT 6020 Forecast
6- Exponential Smoothing Solution
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)
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6- Exponential Smoothing Solution
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)
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6- Exponential Smoothing Solution
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)
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6- Exponential Smoothing Solution
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)
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6- Exponential Smoothing Solution
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)
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6- Exponential Smoothing Method
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Qualitative Analysis Quantitative Analysis
Time Series Analysis
Causal Analysis
Customer Survey
Sales Force Composite
Executive Opinion
Delphi Method
Test Marketing
ExponentialSmoothing
RegressionAnalysis
Least squares
Moving Average
Naïve approach
Weighted Moving
Forecasting Technique
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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
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Regression Analysis Method
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Regression Analysis Method
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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
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Time periodTime periodValu
es o
f Dep
ende
nt V
aria
ble
DeviationDeviation11
((errorerror))
DeviationDeviation55
DeviationDeviation77
DeviationDeviation22
DeviationDeviation66
DeviationDeviation44
DeviationDeviation33
ActualActual observationobservation
Trend lineTrend line
forecast error
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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.
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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
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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
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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
1010-20-205805808280828000
0.26390.2639
MAD=58
Forecast Accuracy
MAD= [E]n
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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
Forecast Accuracy
MSE = [E2]n
MSE=8280
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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
Forecast Accuracy
MAPE= n
[ E/]A × 100 %
MAPE=2.64%
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Excel Chart Methods
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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
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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
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• 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
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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:
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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