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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.