mba2038 1t10 wk4 forecasting
TRANSCRIPT
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MBA 2038
Operations Management
Engr. Jovenal M. Arnaiz, PME,MMBMNov 2011
Forecasting
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Operations Management
Module 4: Forecasting
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Learning Objectives
Identify or Define:
Forecasting & strategic importance
Types of forecasts
Time horizons
Approaches to forecasts
Moving averages
Exponential smoothing Trend projections
Regression and correlation analysis
Measures of forecast accuracy
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What is Forecasting?
Art and science ofpredicting a futureevent.
Underlying basis ofall business decisions
Production
Inventory Personnel
Facilities
??
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ResourcePlanning
Sales and operationsplanning
Demandmanagement
Master productionscheduling
Detailed capacity
planning
Detailed material
planning
Material andcapacity plans
Shop-floorsystems
Suppliersystems
Front End
Engine
Back end
Manufacturing Planning and Control System
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Strategic Importance ofForecasting
Human Resources Hiring, training,laying off workers
Capacity Capacity shortages can resultin undependable delivery, loss ofcustomers, loss of market share
Supply-Chain Management Good
supplier relations and price advance
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Long-range forecast 3+ years
New product planning, facility location,research and development
Medium-range forecast 3 months to 3 years
Sales and production planning, budgeting
Short-range forecast Up to 1 year, generally less than 3 months
Purchasing, job scheduling, workforcelevels, job assignments, production levels
Forecasting Time Horizons
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n uence o ro uct eCycle
Introduction and growth require longerforecasts than maturity and decline
As product passes through life cycle,forecasts are useful in projecting
Staffing levels Inventory levels
Factory capacity
Introduction Growth Maturity Decline
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Product Life Cycle
Product designand developmentcritical
Frequent product
and processdesign changes
Short productionruns
High productioncosts
Limited modelsAttention toquality
Introduction Growth Maturity Decline
OM
Strategy/Iss
ues
Forecasting critical
Product andprocess reliability
Competitiveproductimprovements andoptions
Increase capacity
Shift towardproduct focus
Enhancedistribution
Standardization
Less rapid productchanges moreminor changes
Optimum capacity
Increasing stabilityof process
Long productionruns
Productimprovement andcost cutting
Little productdifferentiation
Costminimization
Overcapacityin the industry
Prune line toeliminate itemsnot returninggood margin
Reducecapacity
Figure 2.5
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Types of Forecasts
Economic forecasts
Address business cycle inflationrate, money supply, housing starts,etc.
Technological forecasts
Predict rate of technological progress
Impacts development of new products
Demand forecasts
Predict sales of existing product
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Seven Steps in Forecasting
o Determine the use of the forecast
o Select the items to be forecasted
o Determine the time horizon of the
forecasto Select the forecasting model(s)
o Gather the data
o Make the forecasto Validate and implement results
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The Realities!
Forecasts are seldom perfect
Most techniques assume anunderlying stability in the system
Product family and aggregatedforecasts are more accurate thanindividual product forecasts
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Forecasting Approaches
Qualitative Methods
Used when situation is vague and little data exist,i.e. new products, new technology
Involves intuition, experience, e.g., forecasting saleson Internet
Used when situation is stable and historical dataexist, i.e. existing products, current technology
Involves mathematical techniques, e.g., forecastingsales of color televisions
Quantitative Methods
O i f Q lit ti
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Overview of QualitativeMethods
Jury of executive opinion
Delphi method
Sales force composite
Consumer Market Survey
Nave
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Overview of QuantitativeApproaches
Moving averages
Exponentialsmoothing
Trend projection
Linear regression
Time-SeriesModels
Associative Model
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Set of evenly spaced numerical data
Obtained by observing response
variable at regular time periods
Forecast based only on past values
Assumes that factors influencing pastand present will continue influence infuture
Time Series Forecasting
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Components of Demand
Dema
ndforproductorservice
| | | |1 2 3 4
Year
Average
demand overfour years
Seasonal peaks
Trendcomponent
Actualdemand
Randomvariation
Figure 4.1
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A series of arithmetic means
Used if little or no trend
Used often for smoothing Provides overall impression of data
over time
Moving Average Method
Moving average = demand in previous n periods
n
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Used when trend is present
Older data usually less important
Weights based on experience andintuition
Weighted Moving Average
Weightedmoving average=
(weight for period n)x (demand in period n)
weights
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Exponential Smoothing
New forecast = last periods forecast+ a(last periods actual demand
last periods forecast)
Ft = Ft 1 + a(At 1 - Ft 1)
where Ft = new forecastFt 1 = previous forecast
a = smoothing (or weighting)constant (0 a 1)
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Trend Projections
Fitting a trend line to historical data points toproject into the medium-to-long-range
Linear trends can be found using the leastsquares technique
y = a + bX^
where y = computed value of the variable to bepredicted (dependent variable)
a = y-axis interceptb = slope of the regression lineX = the independent variable
^
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Least Squares Method
Time period
Values
ofDependent
Variable
Figure 4.4
Deviation1
Deviation5
Deviation7
Deviation2
Deviation6
Deviation4
Deviation3
Actual observation(y value)
Trend line, y = a + bx^
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Least Squares Method
Time period
Values
ofDependent
Variable
Figure 4.4
Deviation1
Deviation5
Deviation7
Deviation2
Deviation6
Deviation4
Deviation3
Actual observation(y value)
Trend line, y = a + bx^
Least squares methodminimizes the sum of the
squared errors (deviations)
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LeastSquares Method
Equations to calculate the regression variables
b =Sxy - nxySx2 - nx2
y = a + bX^
a = y - bx
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Common Measures of Error
Mean Absolute Deviation (MAD)
MAD = |actual - forecast|
n
Mean Squared Error (MSE)
MSE = (forecast errors)2
nMean Absolute Percent Error (MAPE)
MAPE =
100 |actuali - forecasti|/actuali
nn
i = 1
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Associative Forecasting
Used when changes in one or more independentvariables can be used to predict the changes in
the dependent variable
Most common technique is linearregression analysis
We apply this technique just as we did inthe time series example
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Associative Forecasting
Forecasting an outcome based on predictorvariables using the least squares technique
y = a + bX^
where y = computed value of the variable to bepredicted (dependent variable)
a = y-axis interceptb = slope of the regression linex = the independent variable though to
predict the value of the dependentvariable
^
Standard Error of the
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Standard Error of theEstimate
where y = y-value of each data point
yc
= computed value of the dependentvariable, from the regressionequation
n = number of data points
Sy,x
=(y - yc)
2
n - 2
Standard Error of the
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Standard Error of theEstimate
Computationally, this equation isconsiderably easier to use
We use the standard error to set upprediction intervals around the point
estimate
Sy,x =y2 - ay - bxy
n - 2
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How strong is the linear relationship between thevariables?
Correlation does not necessarily imply causality!
Coefficient of correlation, r, measures degree ofassociation
> Values range from -1 to +1
Correlation
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Correlation Coefficient
r =nSxy - SxSy
[nSx2 - (Sx)2][nSy2 - (Sy)2]
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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, theforecast has a bias error
Monitoring and ControllingForecasts
Tracking Signal
M i i d C lli
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Monitoring and ControllingForecasts
TrackingSignal
RSFEMAD
=
Trackingsignal =
(actual demand inperiod i -
forecast demandin period i)
(|actual - forecast|/n)
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Tracking Signal
Tracking signal
+
0 MADs
Upper control limit
Lower control limit
Time
Signal exceeding limit
Acceptablerange
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End of module 4