chapter 04 forecasting 8th ed 2011 - university of nevada...
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© 2011 Pearson Education, Inc. publishing as Prentice Hall
SCM 352
4 Forecasting
Outline
• Global Company Profile: Disney World• What is Forecasting?• Types of Forecasts• Forecasting Approaches
– Overview of Qualitative & Quantitative Methods• Time-Series Forecasting• Monitoring and Controlling Forecasts
Famous Forecasting Quotes
"Those who have knowledge, don't predict. Those who predict, don't have knowledge. "
-- Lao Tzu, 6th Century BC Chinese Poet
"It is often said there are two types of forecasts ... lucky or wrong!!!! "
-- "Control" magazine (Inst. of Ops. Mgmt.)
(http://www.met.rdg.ac.uk/cag/forecasting/quotes.html)
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Forecasting at Disney World
• Global portfolio includes parks in Hong Kong, Paris, Tokyo, Orlando, and Anaheim
• Revenues are derived from people – how many visitors and how they spend their money
• Daily management report contains only the forecast and actual attendance at each park
• Disney generates daily, weekly, monthly, annual, and 5-year forecasts
• Forecast used by labor management, maintenance, operations, finance, and park scheduling
• Forecast used to adjust opening times, rides, shows, staffing levels, and guests admitted
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Forecasting at Disney World
• 20% of customers come from outside the USA• Economic model includes gross domestic product,
cross-exchange rates, arrivals into the USA• A staff of 35 analysts and 70 field people survey 1
million park guests, employees, and travel professionals each year
• Inputs to the forecasting model include airline specials, Federal Reserve policies, Wall Street trends, vacation/holiday schedules for 3,000 school districts around the world
• Average forecast error for the 5-year forecast is 5%• Average forecast error for annual forecasts is between
0% and 3%
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• Process of predicting a future event
• Underlying basis of all business decisions– Production– Inventory– Personnel– Facilities
Sales will be $200 Million!
What is Forecasting?
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• Short-range forecast• Up to 1 year, generally less than 3 months• Purchasing, job scheduling, workforce levels, job
assignments, production levels• Medium-range forecast
• 3 months to 3 years• Sales and production planning, budgeting
• Long-range forecast• 3+ years• New product planning, facility location, research
and development
Forecasting Time Horizons
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Types of Forecasts
• Economic forecasts– Address business cycle, e.g., inflation rate, money
supply, housing starts, etc.• Technological forecasts
– Predict rate of technological progress– Impacts development of new products
• Demand forecasts– Predict sales of existing products and services
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Strategic Importance of Forecasting
• Human Resources – Hiring, training, laying off workers
• Capacity – Capacity shortages can result in undependable delivery, loss of customers, loss of market share
• Supply Chain Management – Good supplier relations and price advantages
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Used when situation is stable & historical data exist
Existing productsCurrent technology
Involves mathematical techniques
e.g., forecasting sales of color televisions
Quantitative MethodsUsed when situation is vague & little data exist
New productsNew technology
Involves intuition, experience
e.g., forecasting sales on Internet
Qualitative Methods
Forecasting Approaches
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Overview of Qualitative Methods
• Jury of executive opinion– Pool opinions of high-level executives, sometimes
augment by statistical models– ‘Group-think’ disadvantage
• Sales force composite– Estimates from individual salespersons are
reviewed for reasonableness, then aggregated– Sales reps know customers’ wants
• Delphi method– Panel of experts, queried iteratively
• Consumer market survey– Ask the customer
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1. Naive approach
2. Moving averages
3. Exponential smoothing
4. Trend projection
5. Linear regression
Time-Series Models
Associative Model
Quantitative Approaches
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Time Series Forecasting
• Set of evenly spaced numerical data• Obtained by observing response variable at
regular time periods• Forecast based only on past values, no other
variables important• Assumes that factors influencing past and present
will continue influence in future
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Trend
Seasonal
Cyclical
Random
Time Series Forecasting
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Dem
and
for p
rodu
ct o
r ser
vice
| | | |1 2 3 4
Time (years)
Average demand over 4 years
Trend component
Actual demand line
Random variation
Seasonal peaks
Components of Demand
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Naive Approach
• Assumes demand in next period is the same as demand in most recent period– If May sales were 48, then June sales will be 48
• Sometimes can be cost effective and efficient• Can be good starting point
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Moving Average Method
• MA is a series of arithmetic means • Used if little or no trend• Used often for smoothing
– Provides overall impression of data over time• Equation
Moving average =∑ demand in previous n periods
n
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Potential Problems With MA
• Increasing n smooths the forecast but makes it less sensitive to changes
• Do not forecast trends well• Require extensive historical data
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(12 + 14 + 16)/3 = 14(14 + 16 + 18)/3 = 16(16 + 18 + 23)/3 = 19
JanuaryFebruaryMarchApril 16May 18June 23July 26
Actual 3-MonthMonth Shed Sales Moving Average
101214
Moving Average Example
(10 + 12 + 14)/3 = 12
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• Used when trend is present – Older data usually less important
• Weights based on intuition– Ranges between 0 & 1, & sum to 1.0
• Equation
WMA = Σ(Weight for period n) (Demand in period n)
ΣWeights
Weighted Moving Average Method
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(12*0.2 + 14*0.3 + 16*0.5) = 14.6(14*0.2 + 16*0.3 + 18*0.5) = 16.6(16*0.2 + 18*0.3 + 23*0.5) = 20.1
JanuaryFebruaryMarchApril 16May 18June 23July 26
Actual 3-MonthMonth Shed Sales Moving Average
101214
Weighted Moving Average Example
Weights: heaviest weights applied to most recent month – 0.5, 0.3, 0.2
(10*0.2 + 12*0.3 + 14*0.5) = 12.6
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Exponential Smoothing Method
• Form of weighted moving average– Weights decline exponentially– Most recent data weighted most
• Requires smoothing constant (α)– Ranges from 0 to 1– Select the value of α that gives us the lowest
forecast error (MAD or MSE)• Involves little record keeping of past data
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• Ft = Ft-1 + α(At-1 - Ft-1)– Use for computing forecast
• Ft = αAt-1 + α(1-α)At-2 + α(1- α)2·At-3
+ α(1- α)3At-4 + ... + α(1- α)t-1·A0
– Ft = Forecast value – At = Actual value – α = Smoothing constant
• What happens when α = 1?
Exponential Smoothing Equations
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Problem 4.6, Page 140
Month SalesJanuary 20February 21March 15April 14May 13June 16July 17August 18September 20
October 20November 21
December 23
(b) What is the forecast for January?
[iv] Exponential smoothing, α = 0.3FSep = 18FOct = 18 + 0.3(20-18) = 18.6FNov = 18.6 + 0.3(20-18.6) = 19.02FDec = 19.02 + 0.3(21-19.02) = 19.6FJan = 19.6 + 0.3(23-19.6) = 20.62
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Fitting a trend line to historical data points to project into the medium-to-long-range
Linear trends can be found using the least squares technique
y = a + bx^
where y = computed value of the variable to be predicted (dependent variable)
a = y-axis interceptb = slope of the regression linex = the independent variable
^
Trend Projections
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Equations to calculate the regression variables
b =Σxy - nxyΣx2 - nx2
y = a + bx^
a = y - bx
Least Squares Method
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• Example: Sales (y) & advertising (x)• Slope (b)
– Estimated y changes by b for each 1 unit increase in x• If b = 2, then sales (y) is expected to increase
by 2 for each 1 unit increase in advertising (x)• Y-intercept (a)
– Average value of y when x = 0• If a = 4, then average sales (y) is expected to
be 4 when advertising (x) is 0
Interpretation of Coefficients
^
^
^
^
^
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• You want to achieve:– No pattern or direction in forecast error
• Error = (At - Ft) = (Actual - Forecast)• Seen in plots of errors over time
– Smallest forecast error• Mean square error (MSE)• Mean absolute deviation (MAD)
Selecting a Forecasting Model
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Measuring Forecast Error
Mean Absolute Deviation (MAD)
MAD =∑ |actual - forecast|
n
Mean Squared Error (MSE)
MSE =∑ (forecast error)2
n
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Comparison of Forecast Error
Rounded Absolute Rounded AbsoluteActual Forecast Deviation Forecast Deviation
Tonnage using for using forQuarter Unloaded Model A Model A Model B Model B
1 180 179 1772 168 167 1713 159 160 1564 175 184 172
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Forecast Error - MAD
Rounded Absolute Rounded AbsoluteActual Forecast Deviation Forecast Deviation
Tonnage with for with forQuarter Unloaded Model A Model A Model B Model B
1 180 179 1 177 32 168 167 1 171 33 159 160 1 156 34 175 184 9 172 3
12 12
MAD =∑ |deviation|
n
= (1+1+1+9)/4= 12/4 = 3
For Model A
= (3+3+3+3)/4= 12/4 = 3
For Model B
Model A and Model B have the same MAD values.
© 2011 Pearson Education, Inc. publishing as Prentice Hall
Forecast Error - MSE
Rounded Absolute Rounded AbsoluteActual Forecast Deviation Forecast Deviation
Tonnage with for with forQuarter Unloaded Model A Model A Model B Model B
1 180 179 1 177 32 168 167 1 171 33 159 160 1 156 34 175 184 9 172 3
12 12
= (1+1+1+81)/4= 84/4 = 21
For Model A
= (9+9+9+9)/4= 36/4 = 9
For Model B
MSE =∑ (forecast error)2
n
Model B has a smaller MSE (=9) than Model A (=21)
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• Tracking signal• Measures how well the forecast is
predicting actual values• Ratio of running sum of forecast errors
(RSFE) to mean absolute deviation (MAD)• Good tracking signal has low values• If forecasts are continually high or low, the
forecast has a bias error
Monitoring & Controlling Forecasts
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Tracking signal
RSFEMAD=
Tracking signal =
∑(actual demand in period i -
forecast demand in period i)
(∑|actual - forecast|/n)
Monitoring & Controlling Forecasts
What’s the interpretation of a positive or negative RSFE?
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Tracking signal
Signal exceeding limit
+
0 MADs
–
Upper control limit
Lower control limit
Time
Acceptable range
Tracking Signal
Thank You
Questions? ?
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