Forecating dr. sameh mousa
Transcript of Forecating dr. sameh mousa
AgendaMeaning 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]؟؟
؟؟
Sales Mantra“Hope is not a sales Strategy”
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.
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
Why Study Why Study forecasting ?forecasting ?
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 Market Positioning
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
Categorization of Demand Forecasting
: (3 months to 2 years): for production planning, purchasing, and distribution. Sales & production planning, budgeting : (2 years and more) for capacity planning, and investment decisions New product planning, facility location
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.
Facts in ForecastingForecasts 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
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 forecastSelect the items to be forecastedGather the dataDetermine the time horizon of the
forecastSelect the forecasting model(s)Make the forecastValidate and implement results
Sales forecasting Process
Setting Goals Forecasting
Gathering data
Analysis of data
Evaluating of forecasting outcomes
Choosing The Best Model
For Forecasting
Forecasting
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
Sales forecasting Technique
It is generally recommended to use a combination of quantitative and
qualitative techniques.
Principles of Supply Chain Management: A Balanced Approach by Wisner, Leong, and Tan. © 2005 Thomson Business and Professional Publishing
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
1- Consumers’ Opinion Survey (Buyer’s expectation Method )
Advantages :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 )
Principles of Supply Chain Management: A Balanced Approach by Wisner, Leong, and Tan. © 2005 Thomson Business and Professional Publishing
1- Consumers’ Opinion Survey : (Buyer’s expectation Method)
2- Sales Force Composite MethodSalespersons 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.
2- Sales Force Composite MethodThese 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
2- Sales Force Composite Method
2- Sales Force Composite Method
Principles of Supply Chain Management: A Balanced Approach by Wisner, Leong, and Tan. © 2005 Thomson Business and Professional Publishing
2- Sales Force Composite Method
3- Jury of Executive Opinion:
3- Jury of Executive Opinion:
3- Jury of Executive Opinion:
3- Jury of Executive Opinion:
It includes successive sessions of brainstorming among highly specialized experts.
Answers of questions in the first round are summarized and form the base of the second round
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- Experts’ Opinion MethodDelphi Technique:
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)!
4- Experts’ Opinion MethodDelphi Technique:
4- Experts’ Opinion MethodDelphi Technique:
4- Experts’ Opinion MethodDelphi Technique:
5- Test Marketing
5- Test Marketing
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
Principles of Supply Chain Management: A Balanced Approach by Wisner, Leong, and Tan. © 2005 Thomson Business and Professional Publishing
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.
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Product Life Cycle
Time
Sales
Intr
oduc
tion
Mat
urit
y
Dec
line
Gro
wth
BCG Growth – Share Matrix
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: SeasonalPatterns 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
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
1- Naive Approach
It is convenient for long term periods
YearSales% change
2007527
20086390.2
2009467- 0.3
20107950.7
20118530.1
20129850.2Forecast for 2013
= 985 × 1.2 = 1182
Actual Sales Previous
Sales
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
3- Moving Average Method
MAMA nnnn Demand in Demand in Previous Previous PeriodsPeriods
3- Moving Average…… Example
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 Jun.
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.
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
4- Weighted Moving AverageWeights are used to give more values to recent valueThis makes the techniques more responsive to changes because latter periods may be more heavily waitedMost recent observation receives the most weight, and the weight decreases for older data values
4- Weighted Moving Average
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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
4- Weighted Moving Average
5- 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 + (At-1 - Ft-1)
Ft= forecast for this period Ft-1 = forecast for the
previous period
At-1= Actual demand for
the previous period
Smoothing constant (0 to 1)
6- Exponential Smoothing Method
Ft = Ft-1 + (At-1 - Ft-1)
Ft= forecast for 2013 Ft-1 = forecast 2012
At-1= Actual sales 2012Smoothing constant (0 to 1)
6- 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 +
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)
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)
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)
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)
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)
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)
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)
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)
6- Exponential Smoothing Method
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
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
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
At = actual demand for
Period t
Ft = forecast for Period t
et = At - Ft
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
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
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
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
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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