Here are the steps to forecast attendance for 2000 using exponential smoothing with α = 0.3:
1) Set the initial forecast F0 = actual attendance for the first period, which is 50
2) Plug into the exponential smoothing equation:
F1 = α*A1 + (1-α)*F0
= 0.3*60 + (1-0.3)*50 = 54
3) Continue using the exponential smoothing equation for subsequent periods:
F2 = 0.3*70 + (1-0.3)*54 = 58.8
F3 = 0.3*80 + (1-0.3)*58.8 = 63.72
4) The forecast for
Introduction to Health Economics Dr. R. Kurinji Malar.pptx
Demand forecasting methods2 gp
1. Demand Forecasting Methods
Professor & Lawyer.Professor & Lawyer.
Puttu Guru Prasad,Puttu Guru Prasad,
M.Com. M.B.A., L.L.B., M.Phil, PGDFTM, APSET.M.Com. M.B.A., L.L.B., M.Phil, PGDFTM, APSET.
ICFAI TMF, (PhD) at JNTUK,ICFAI TMF, (PhD) at JNTUK,
Expert Resource person at APHRDI, Bapatla,Expert Resource person at APHRDI, Bapatla,
Senior Faculty for Management Studies,Senior Faculty for Management Studies,
Certified NSS Program Officer,Certified NSS Program Officer,
Coordinator – College BeautificationCoordinator – College Beautification
S&HS&H Department,Department, VVIT,VVIT, Nambur,Nambur,
93 94 96 98 98, 807 444 95 39,93 94 96 98 98, 807 444 95 39,
My Blog: puttuguru.blogspot.inMy Blog: puttuguru.blogspot.in
2. MGMT6020Forecast
Learning Objectives
When you complete this chapter, you should be
able to :
Identify or DefineIdentify or Define::
ForecastingForecasting
Categories of forecastsCategories of forecasts
Time horizonsTime horizons
Approaches to measure forecastsApproaches to measure forecasts
Explain and Apply:Explain and Apply:
Moving averagesMoving averages
Exponential smoothingExponential smoothing
Trend and seasonal projectionsTrend and seasonal projections
Measures of forecast accuracyMeasures of forecast accuracy
3. MGMT6020Forecast
Why Forecasting?
Forecasting lays a ground for reducing the risk inForecasting lays a ground for reducing the risk in
all decision making because many of the decisionsall decision making because many of the decisions
need to be made under uncertainty.need to be made under uncertainty.
In business applications, forecasting serves as aIn business applications, forecasting serves as a
starting point of major decisions in finance,starting point of major decisions in finance,
marketing, productions, and purchasing.marketing, productions, and purchasing.
Under what condition there is no value forUnder what condition there is no value for
forecasting?forecasting?
4. MGMT6020Forecast
Decisions Relevant to Demand
Forecasts
Select product portfolioSelect product portfolio
Predicting new facility locationPredicting new facility location
Anticipating capacity needsAnticipating capacity needs
Identifying labor requirementsIdentifying labor requirements
Projecting material requirementsProjecting material requirements
Developing production schedulesDeveloping production schedules
Creating maintenance schedulesCreating maintenance schedules
5. MGMT6020Forecast
Forecasting at Tupperware
Via forecasting, managers make importantVia forecasting, managers make important
decisionsdecisions
Each of 50 profit centers around the world isEach of 50 profit centers around the world is
responsible for computerized monthly,responsible for computerized monthly,
quarterly, and 12-month sales projectionsquarterly, and 12-month sales projections
These projections are aggregated by productThese projections are aggregated by product
family and region, then globally, atfamily and region, then globally, at
Tupperware’s World HeadquartersTupperware’s World Headquarters
Tupperware uses all techniques discussed in textTupperware uses all techniques discussed in text
6. MGMT6020Forecast
Successful Forecasting
= Science + Art
"Science" implies that the body of the forecasting"Science" implies that the body of the forecasting
knowledge lies on the solid ground of quantitativeknowledge lies on the solid ground of quantitative
forecasting methods and their correct utilization forforecasting methods and their correct utilization for
various business situations.various business situations.
"Art" represents a combination of a decision"Art" represents a combination of a decision
maker's experience, logic, and intuition tomaker's experience, logic, and intuition to
supplement the forecasting quantitative analysis.supplement the forecasting quantitative analysis.
Both the science and art of forecasting are essentialBoth the science and art of forecasting are essential
in developing accurate forecasts.in developing accurate forecasts.
All managers are forecasters!All managers are forecasters!
7. MGMT6020Forecast
Forecast Categories
TYPESTYPES
QualitativeQualitative Executive opinionsExecutive opinions
Sales force surveysSales force surveys
Delphi methodDelphi method
Consumer surveysConsumer surveys
QuantitativeQuantitative Times series methodsTimes series methods
Associative (causal)Associative (causal)
methodsmethods
8. MGMT6020Forecast
Forecast Categories
TIME HORIZONTIME HORIZON
Long-termLong-term For duration of 3-5For duration of 3-5
years or more (onyears or more (on
annual basis)annual basis)
Medium-termMedium-term For duration of up to threeFor duration of up to three
years (usually on quarterlyyears (usually on quarterly
or monthly basis)or monthly basis)
Short-termShort-term Up to one year, usually lessUp to one year, usually less
than three months (onthan three months (on
daily, weekly)daily, weekly)
9. MGMT6020Forecast
Facts in Forecasting
Main 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. Forecast errors mean
the difference between actual and forecasted values.
Forecasts for a group of products are usually more
accurate than these for individual products; a shorter
period tend to be more accurate.
Computer and IT are critical parts of the modern
forecasting in large corporations.
10. MGMT6020Forecast
Seven Steps in Forecasting
(Demands)
Determine the use of the forecastDetermine the use of the forecast
Select the items to be forecastSelect the items to be forecast
Determine the time horizon of the forecastDetermine the time horizon of the forecast
Select the forecasting model(s)Select the forecasting model(s)
Gather the dataGather the data
Make the forecastMake the forecast
Validate and implement resultsValidate and implement results
11. MGMT6020Forecast
Quantitative Methods
A time series is an uninterrupted set of dataA time series is an uninterrupted set of data
observations that have been ordered inobservations that have been ordered in
equally spaced intervals (units of time).equally spaced intervals (units of time).
Associative (causal) forecasting is based onAssociative (causal) forecasting is based on
identification of variables (factors) that canidentification of variables (factors) that can
predict values of the variable in question.predict values of the variable in question.
12. MGMT6020Forecast
Quantitative Forecasting Models
·· Time Series ModelsTime Series Models
Naive ForecastNaive Forecast
Simple Moving AveragesSimple Moving Averages
Weighted Moving AveragesWeighted Moving Averages
Simple Exponential SmoothingSimple Exponential Smoothing
Exponential Smoothing withExponential Smoothing with
TrendTrend
Linear Trend ProjectionLinear Trend Projection
Time Series DecompositionTime Series Decomposition
·· Associative (Causal)Associative (Causal)
ModelsModels
Simple Linear RegressionSimple Linear Regression
Multiple Linear RegressionMultiple Linear Regression
Nonlinear RegressionNonlinear Regression
13. MGMT6020Forecast
Time Series Pattern: Stationary
The result of manyThe result of many
influences that actinfluences that act
independently so as toindependently so as to
yield nonsystematicyield nonsystematic
and non-repeatingand non-repeating
patterns about somepatterns about some
average value.average value.
Forecasting methods:Forecasting methods:
naive, moving average,naive, moving average,
exponential smoothingexponential smoothing
14. MGMT6020Forecast
Time Series Pattern: Trend
It represents a generalIt represents a general
increase or decrease inincrease or decrease in
a time series overa time series over
several consecutiveseveral consecutive
periods (some sourcesperiods (some sources
present six-seven orpresent six-seven or
more periods).more periods).
Forecasting methods:Forecasting methods:
linear trend projection,linear trend projection,
exponential smoothingexponential smoothing
with trend, etc.with trend, etc.
15. MGMT6020Forecast
Time Series Pattern: Seasonal
Seasonal PatternsSeasonal Patterns
represent patterns thatrepresent patterns that
are periodic andare periodic and
recurrent (usually on arecurrent (usually on a
quarterly, monthly, orquarterly, monthly, or
annual basis).annual basis).
Forecasting methods:Forecasting methods:
exponential smoothingexponential smoothing
with trend andwith trend and
seasonality, time seriesseasonality, time series
decomposition, etc.decomposition, etc.
16. MGMT6020Forecast
Time Series Pattern: Cyclical
The result of economic andThe result of economic and
business expansions (increasingbusiness expansions (increasing
demand) and contractionsdemand) and contractions
(recessions and depressions)(recessions and depressions)
and usually repeat every two-and usually repeat every two-
five years. Cyclical influencesfive years. Cyclical influences
are difficult to forecast becauseare difficult to forecast because
cyclical demands are recurrentcyclical demands are recurrent
but not periodic (they happen inbut not periodic (they happen in
different intervals of time withdifferent intervals of time with
great variability of demands).great variability of demands).
Forecasting methods: timeForecasting methods: time
series decomposition, multipleseries decomposition, multiple
regressionregression
17. MGMT6020Forecast
Product Demand Charted over 4
Years with Trend and Seasonality
Year
1
Year
2
Year
3
Year
4
Seasonal peaks Trend component
Actual
demand line
Average demand
over four years
Demandforproductorservice
Random
variation
18. MGMT6020Forecast
Set of evenly spaced numerical dataSet of evenly spaced numerical data
Obtained by observing response variable atObtained by observing response variable at
regular time periodsregular time periods
Forecast based only on past valuesForecast based only on past values
Assumes that factors influencing past andAssumes that factors influencing past and
present will continue influence in futurepresent will continue influence in future
ExampleExample
Year:Year: 19931993 19941994 19951995 19961996 19971997
Sales:Sales: 78.778.7 63.563.5 89.789.7 93.293.2 92.192.1
What is a Time Series?
19. MGMT6020Forecast
Naïve Approach
Assumes demand in next period is the same asAssumes demand in next period is the same as
demand in most recent perioddemand in most recent period
e.g., If May sales were 48, then June sales will bee.g., If May sales were 48, then June sales will be
around 48around 48
Sometimes it is effective & cost efficientSometimes it is effective & cost efficient
e.g. when the demand is steady or changes slowlye.g. when the demand is steady or changes slowly
when inventory cost is lowwhen inventory cost is low
when unmet demand will not losewhen unmet demand will not lose
20. MGMT6020Forecast
MA is a series of arithmetic meansMA is a series of arithmetic means
Used if little or no trend, seasonal, and cyclicalUsed if little or no trend, seasonal, and cyclical
patternspatterns
Used oftenUsed often for smoothingfor smoothing
Provides overall impression of data over timeProvides overall impression of data over time
EquationEquation
MAMA
nn
nn
== ∑∑ Demand inDemand in PreviousPrevious PeriodsPeriods
Moving Average Method
23. MGMT6020Forecast
Moving Average Solution
Time Response
Yi
Moving
Total
(n=3)
Moving
Average
(n=3)
1995 4 NA NA
1996 6 NA NA
1997 5 NA NA
1998 3 4+6+5=15 15/3 = 5
1999 7 6+5+3=14 14/3=4 2/3
2000 NA
24. MGMT6020Forecast
Moving Average Solution
Time Response
Yi
Moving
Total
(n=3)
Moving
Average
(n=3)
1995 4 NA NA
1996 6 NA NA
1997 5 NA NA
1998 3 4+6+5=15 15/3=5.0
1999 7 6+5+3=14 14/3=4.7
2000 NA 5+3+7=15 15/3=5.0
26. MGMT6020Forecast
Used when trend is presentUsed when trend is present
Older data usually less importantOlder data usually less important
Weights based on intuitionWeights based on intuition
Often lay between 0 & 1, & sum to 1.0Often lay between 0 & 1, & sum to 1.0
EquationEquation
WMA =WMA =
ΣΣ(Weight for period(Weight for period nn) (Demand in period) (Demand in period nn))
ΣΣWeightsWeights
Weighted Moving Average
Method
27. MGMT6020Forecast
Actual Demand, Moving Average,
Weighted Moving Average
0
5
10
15
20
25
30
35
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Month
SalesDemand
Actual sales
Moving average
Weighted moving average
28. MGMT6020Forecast
IncreasingIncreasing nn makes forecast less sensitivemakes forecast less sensitive
to changesto changes
Do not forecast trend well due to the delayDo not forecast trend well due to the delay
between actual outcome and forecastbetween actual outcome and forecast
Difficult to trace seasonal and cyclicalDifficult to trace seasonal and cyclical
patternspatterns
Require much historical dataRequire much historical data
Weighted MA may perform betterWeighted MA may perform better
Disadvantages of Moving
Average Methods
29. MGMT6020Forecast
Form of weighted moving averageForm of weighted moving average
Weights decline exponentiallyWeights decline exponentially
Most recent data weighted mostMost recent data weighted most
Requires smoothing constant (Requires smoothing constant (αα))
Ranges from 0 to 1Ranges from 0 to 1
Subjectively chosenSubjectively chosen
Involves little record keeping of past dataInvolves little record keeping of past data
Exponential Smoothing Method
42. MGMT6020Forecast
Ft = α At - 1 + α(1- α)At - 2 + α(1- α)2
At - 3 + ...
Forecast Effects of
Smoothing Constant α
Weights
Prior Period
α
2 periods ago
α(1 - α)
3 periods ago
α(1 - α)2
α=
α= 0.10
α= 0.90
10%
43. MGMT6020Forecast
Ft = α At - 1 + α(1- α) At - 2 + α(1- α)2
At - 3 + ...
Forecast Effects of
Smoothing Constant α
Weights
Prior Period
α
2 periods ago
α(1 - α)
3 periods ago
α(1 - α)2
α=
α= 0.10
α= 0.90
10% 9%
44. MGMT6020Forecast
Ft = α At - 1 + α(1- α)At - 2 + α(1- α)2
At - 3 + ...
Forecast Effects of
Smoothing Constant α
Weights
Prior Period
α
2 periods ago
α(1 - α)
3 periods ago
α(1 - α)2
α=
α= 0.10
α= 0.90
10% 9% 8.1%
45. MGMT6020Forecast
Ft = α At - 1 + α(1- α)At - 2 + α(1- α)2
At - 3 + ...
Forecast Effects of
Smoothing Constant α
Weights
Prior Period
α
2 periods ago
α(1 - α)
3 periods ago
α(1 - α)2
α=
α= 0.10
α= 0.90
10% 9% 8.1%
90%
46. MGMT6020Forecast
Ft = α At - 1 + α(1- α) At - 2 + α(1- α)2
At - 3 + ...
Forecast Effects of
Smoothing Constant α
Weights
Prior Period
α
2 periods ago
α(1 - α)
3 periods ago
α(1 - α)2
α=
α= 0.10
α= 0.90
10% 9% 8.1%
90% 9%
47. MGMT6020Forecast
Ft = α At - 1 + α(1- α) At - 2 + α(1- α)2
At - 3 + ...
Forecast Effects of
Smoothing Constant α
Weights
Prior Period
α
2 periods ago
α(1 - α)
3 periods ago
α(1 - α)2
α=
α= 0.10
α= 0.90
10% 9% 8.1%
90% 9% 0.9%
48. MGMT6020Forecast
You want to achieve:You want to achieve:
Smallest forecast errorSmallest forecast error
Mean square error (MSE)Mean square error (MSE)
Mean absolute deviation (MAD)Mean absolute deviation (MAD)
No pattern or direction in forecastNo pattern or direction in forecast
errorerror
Error = (Error = (YYii -- YYii) = (Actual - Forecast)) = (Actual - Forecast)
Seen in plots of errors over timeSeen in plots of errors over time
Guidelines for Selecting
Forecasting Model
^
49. MGMT6020Forecast
How to Choose α
Seek to minimize the Mean Absolute Deviation (MAD)
If: Forecast error = demand - forecast
Then:
n
errorsforecast∑
=MAD
Note that the sum of all weights in exponential
smoothing equals to 1. It is popular because of the
simplicity of data keeping.
50. MGMT6020Forecast
Measuring Forecast Accuracy
Mean Squared Error (MSE)Mean Squared Error (MSE)
represents the variance of errors in a forecast. This criterionrepresents the variance of errors in a forecast. This criterion
is most useful if you want to minimize the occurrence of ais most useful if you want to minimize the occurrence of a
major error(s).major error(s).
n
e
=
n
)2
Ft-At(
n
1=t=MSE
2
t
n
1=t
∑∑
52. MGMT6020Forecast
Ft = α(Actual demand this period)
+ (1- α)(Forecast last period+Trend estimate last period)
Ft = α(At-1) + (1- α)Ft-1 + Tt-1
or
Tt = β(Forecast this period - Forecast last period)
+ (1- β)(Trend estimate last period
Tt = β(Ft - Ft-1) + (1- β)Tt-1
or
Exponential Smoothing with
Trend Adjustment - continued
53. MGMT6020Forecast
FFtt = exponentially smoothed forecast of the= exponentially smoothed forecast of the
data series in perioddata series in period tt
TTtt = exponentially smoothed trend in period= exponentially smoothed trend in period tt
AAtt = actual demand in period= actual demand in period tt
αα = smoothing constant for the average= smoothing constant for the average
ββ = smoothing constant for the trend= smoothing constant for the trend
Exponential Smoothing with
Trend Adjustment - continued
55. MGMT6020Forecast
Used for forecasting linear trend lineUsed for forecasting linear trend line
Assumes relationship between responseAssumes relationship between response
variable,variable, Y,Y, and time,and time, X,X, is a linearis a linear
functionfunction
Estimated by least squares methodEstimated by least squares method
Minimizes sum of squared errorsMinimizes sum of squared errors
iY a bXi= +
Linear Trend Projection
57. MGMT6020Forecast
Slope (Slope (bb))
EstimatedEstimated YY changes bychanges by bb for each 1 unitfor each 1 unit
increase inincrease in XX
IfIf bb = 2, then sales (= 2, then sales (YY) is expected to increase) is expected to increase
by 2 for each 1 unit increase in advertisingby 2 for each 1 unit increase in advertising
((XX))
Y-intercept (Y-intercept (aa))
Average value ofAverage value of YY whenwhen XX = 0= 0
IfIf aa = 4, then average sales (= 4, then average sales (YY) is expected to) is expected to
be 4 when advertising (be 4 when advertising (XX) is 0) is 0
Interpretation of Coefficients
58. MGMT6020Forecast
How to Find a and b: Least
Squares Equations
Equation: ii bxaYˆ +=
Slope:
22
1=
1=
−∑
−∑
=
xnx
yxnyx
b
i
n
i
ii
n
i
Y-Intercept: xbya −=
Criteria of finding a and b:
59. MGMT6020Forecast
Measuring Forecast Accuracy
Mean Absolute Deviation (MAD)Mean Absolute Deviation (MAD)
measures the average absolute error of a forecast. A sign ofmeasures the average absolute error of a forecast. A sign of
an error, which represents over- or underestimation, isan error, which represents over- or underestimation, is
really not important in most cases; we are rather concernedreally not important in most cases; we are rather concerned
with the value of deviation.with the value of deviation.
where:where:
AAtt = actual value in period t,= actual value in period t,
FFtt = forecasted value in period t,= forecasted value in period t,
eett = forecast error in period t,= forecast error in period t,
n = number of periods.n = number of periods.
n
|e|
=
n
|F-A|
=MAD
t
n
1=t
tt
n
1=t
∑∑
60. MGMT6020Forecast
Measuring Forecast Accuracy
Mean Squared Error (MSE)Mean Squared Error (MSE)
represents the variance of errors in a forecast. This criterionrepresents the variance of errors in a forecast. This criterion
is most useful if you want to minimize the occurrence of ais most useful if you want to minimize the occurrence of a
major error(s).major error(s).
n
e
=
n
)2
Ft-At(
n
1=t=MSE
2
t
n
1=t
∑∑
61. MGMT6020Forecast
Include seasonal and cyclical patternsInclude seasonal and cyclical patterns
In decomposition, a time series is described as a function ofIn decomposition, a time series is described as a function of
four components:four components:
Y = T*C*S*IY = T*C*S*I multiplicative model (commonly used)multiplicative model (commonly used)
Y = T+C+S+IY = T+C+S+I additive modeladditive model
where:where:
Y =Y = actual value of time seriesactual value of time series
T =T = trend componenttrend component
C =C = cyclical componentcyclical component
S =S = seasonal componentseasonal component
I =I = irregular (random) componentirregular (random) component
General Description of TS Models:
Time Series Decomposition
62. MGMT6020Forecast
Multiplicative Seasonal Model
FindFind average historical demandaverage historical demand for eachfor each
“season”“season” by summing the demand forby summing the demand for
that season in each year, and dividing bythat season in each year, and dividing by
the number of years for which you havethe number of years for which you have
data.data.
Compute theCompute the average demand over allaverage demand over all
seasonsseasons by dividing the total averageby dividing the total average
annual demand by the number of seasons.annual demand by the number of seasons.
63. MGMT6020Forecast
Multiplicative Seasonal Model
Compute aCompute a seasonal indexseasonal index by dividingby dividing
that season’s historical demand (fromthat season’s historical demand (from
step 1) by the average demand over allstep 1) by the average demand over all
seasons.seasons.
Estimate next year’s total demand byEstimate next year’s total demand by
using smoothed linear trend projectionusing smoothed linear trend projection
modelmodel
Divide this estimate of total demand byDivide this estimate of total demand by
the number of seasons, then multiply itthe number of seasons, then multiply it
by the seasonal index for that season.by the seasonal index for that season.
This provides theThis provides the seasonal forecastseasonal forecast..
64. MGMT6020Forecast
Example of Multiplicative
Seasonal Model
The following trend projection is used to predict quarterly
demand: Y = 350 - 2.5t, where t = 1 in the first quarter of
1998. Seasonal (quarterly) relatives are Quarter 1 = 1.5;
Quarter 2 = 0.8; Quarter 3 = 1.1; and Quarter 4 = 0.6. What is
the seasonally adjusted forecast for the four quarters of 2000?
(10%)
Period Projection Adjusted
9 327.5 491.25
10 325 260
11 322.5 354.75
12 320 192
65. MGMT6020Forecast
Seven Steps in Forecasting
(Demands)
Determine the use of the forecastDetermine the use of the forecast
Select the items to be forecastSelect the items to be forecast
Determine the time horizon of the forecastDetermine the time horizon of the forecast
Select the forecasting model(s)Select the forecasting model(s)
Gather the dataGather the data
Make the forecastMake the forecast
Validate and implement resultsValidate and implement results
66. MGMT6020Forecast
Past Data of Nurse Demand: What
patterns can be observed?
0
5
10
15
20
1
3
5
7
9
11
Time Period
NumberofNurses
Number of
Nurses
67. MGMT6020Forecast
Forecasting Issues During a
Product’s Life
Introduction Growth Maturity Decline
Standardization
Less rapid product
changes - more minor
changes
Optimum capacity
Increasing stability of
process
Long production runs
Product improvement
and cost cutting
Little product
differentiation
Cost minimization
Over capacity in the
industry
Prune line to eliminate
items not returning
good margin
Reduce capacity
Forecasting critical
Product and process
reliability
Competitive product
improvements and
options
Increase capacity
Shift toward product
focused
Enhance distribution
Product design and
development critical
Frequent product and
process design changes
Short production runs
High production costs
Limited models
Attention to quality
Best period to
increase market
share
R&D product
engineering
critical
Practical to change
price or quality image
Strengthen niche
Cost control
critical
Poor time to change
image, price, or quality
Competitive costs
become critical
Defend market position
OMStrategy/IssuesCompanyStrategy/Issues
HDTV
CD-ROM
Color copiers
Drive-thru restaurants Fax machines
Station
wagons
Sales
3 1/2”
Floppy
disks
Internet
68. MGMT6020Forecast
Measures how well the forecast is predictingMeasures how well the forecast is predicting
actual valuesactual values
Ratio of running sum of forecast errorsRatio of running sum of forecast errors
(RSFE) to mean absolute deviation (MAD)(RSFE) to mean absolute deviation (MAD)
Good tracking signal has low valuesGood tracking signal has low values
Should be within upper and lower controlShould be within upper and lower control
limitslimits
Tracking Signal
( )
MAD
errorforecast
ˆ
1 ∑∑
=
−
== =
MAD
yy
MAD
RSFE
TS
n
i
ii
69. MGMT6020Forecast
Plot of a Tracking Signal
Time
Lower control limit
Upper control limit
Signal exceeded limit
Tracking signal
Acceptable range
MAD
+
0
-
71. MGMT6020Forecast
Predicting Cyclical Factors
Leading indicatorsLeading indicators
Investment (public/private)Investment (public/private)
ExportExport
Business purchasingBusiness purchasing
Consumer confidenceConsumer confidence
Government expendingGovernment expending
72. Advanced Forecasting Methods
To improve the accuracy, more complicatedTo improve the accuracy, more complicated
models might be required. For example,models might be required. For example,
Adaptive smoothingAdaptive smoothing
Focus forecastingFocus forecasting
Hinweis der Redaktion
e.g. weather forecast, predict traffic jam, election result, or stock price.
Almost all OM decisions requires forecast as the input.
The emphasis in this chapter will be on demand forecasting.
Recently, Boeing’s stock drop, and airline laying off.
Bottom-up forecasting for quarterly demands.
This definition is varied among different industries
Pooling effect is to eliminate pure randomness.
In this class, associative models will focus on computer-aided application but time-series model will require hand-on exercise. In practice, associated model usually for larger scale and centralized decision making such as economic trends or air travel demand.
Fortunately, cyclical pattern often is important for strategic decisions in longer term and is responsibility for executives. For most manager, even things went very wrong, you are not along.
Time series indicates the type of data required for forecasting.
Assume the patterns will exist in the future
Inventory cost includes holding and parish cost
It is popular because of the simplicity of data keeping.