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Learning ObjectivesLearning ObjectivesRecommend the appropriate forecasting model for a given
situation.Conduct a Delphi forecasting exercise.Describe the features of exponential smoothing.Conduct time series forecasting using exponential
smoothing with trend and seasonal adjustments.
DEMAND MANAGEMENTDEMAND MANAGEMENT
DEMAND MANAGEMENT marketing finance operations human resources
TYPES OF DEMAND TYPES OF DEMAND
Independent or dependent demandDemand for outputs or inputsAggregate versus item demand
TIME DIMENSIONTIME DIMENSION
short term- 15-30 daysmedium term -6-12 months long term - 10-20 years
LEAD TIME REQUIREMENTS LEAD TIME REQUIREMENTS
Make to stock - short lead timeMake parts-to-stock/assemble -to-order industryMake-to-order industry - long lead time
Data sourcesData sources
Marketing projectionsEconomic projectionsHistorical demand projections
ForecastingForecasting
FORECASTING FOR SUPPORT SERVICES Hiring Layoffs and reassignments Training Payroll actions Union contract negotiations
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FORECAST ERROR Et = Dt - Ft Et = error for period t Dt = actual demand that occurred in period t Ft = forecast for period t
Period t depends on the purpose of the forecast
Cont…Cont…
MEAN ABSOLUTE DEVIATION (MAD): simplest way of calculating average error
MAD = ΣEt n
HISTORICAL DEMAND PROJECTIONSHISTORICAL DEMAND PROJECTIONS
By time series we mean a series of demands over time. The main recognizable time-series components are: Trend, or slope, defined as the positive or negative shift in series
value over a certain time period Seasonality, usually occurring within one year and recurring
annually Cyclical Pattern, also recurring, but usually spanning several
years Random Events: explained, such as effects of natural disasters
or accidents Unexplained, for which no known cause exists
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Forecasting ModelsForecasting ModelsSubjective Models
Delphi MethodsCausal Models
Regression ModelsTime Series Models
Moving AveragesExponential Smoothing
NAIVE METHOD OF FORECASTING NAIVE METHOD OF FORECASTINGuse the most recent period’s actual sales jury of executive opinionprompted by lack of good demand data
MULTIPERIOD PATTERN PROJECTIONMULTIPERIOD PATTERN PROJECTION
MEAN AND TREND used when the historical demand lacks trend and is not inherently
seasonal
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SEASONAL : often an item showing a trend also has a history of demand seasonality, which calls for the seasonal index method of building seasonality into a demand forecast Seasonal Index: example in handout Seasonally adjusted trends: example in handout
PATTERNLESS PROJECTIONPATTERNLESS PROJECTION
These techniques make no inferences about past demand data but merely react to the most recent demands.
These techniques – moving average, exponential smoothing, and simulation – typically produce a single value, which is the forecast for a single period into the future.
Moving AverageMoving Average
It is the arithmetic mean of a given number of the most recent actual demands
3 period moving average - exhibit 4-13 (handout)Mean absolute deviation (MAD) - exhibit 4-13
EXPONENTIAL SMOOTHINGEXPONENTIAL SMOOTHING
Most widely used quantitative forecasting techniquesmoothes the historical demand time seriesassigns different weight to each period’s data; lower to points further away
Ft+1 = Ft + α(Dt - Ft) Ft+1 = forecast for period t+1α = smoothing constantDt = actual demand that occurred in period tFt = forecast for period t
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next period forecast = last period forecast + α(last period demand - last period forecast)
the future forecasts are being adjusted for the forecast error in the last period
exhibit 4-16 (handout)small α means each successive forecast is close to its
predecessor - stable demand large α means large up and down swings of actual
demand - unstable demand
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note - how the exponential smoothing extends back into the past indefinitely, that is, the adjustments made in the past are carried forward in a diminishing manner
problem of startup forecastmoving average and exponential smoothing are based
on the assumption that past demand data is the best indicator of the future
problem in exhibit 4.16 (handout)
ADAPTIVE SMOOTHINGADAPTIVE SMOOTHING
used as an extension of exponential smoothing forecasters may adjust the value of smoothing
coefficient α if cumulative forecast error gets too large, thus adapting the forecasting model to changing conditions
running sum of forecast error is used for signaling, whether α needs to be changed
TRACKING SIGNAL = RSFE MAD
If RSFE is getting larger in the positive direction, implying, that actual demand is higher than the forecasted demand, then you want to increase the next period forecasted value. This can be done by increasing the value of α; and vice versa.
FORECASTING BY SIMULATION FORECASTING BY SIMULATION
using distributions of each variable, simulated runs are generated - suggesting the forecasted values.
forecast error is calculated by subtracting the actual demand from the forecasted demand
CORRELATIONREGRESSION
QUESTIONS TO PONDERQUESTIONS TO PONDER
1. What are the purposes of demand management?2. What are the short, medium, and long term purposes of
demand forecasting?3. How is forecast error measured? What are the limitations of
this measure?4. What is a time series? What are its principle components?5. How is one forecasting model compared with another in
selecting a model for future use?6. Make sure you know how to do the problems.