Demand Management Demand Management and Forecastingand ForecastingChapter 15

Learning ObjectivesLearning Objectives1. Understand the role of forecasting as a basis for

supply chain planning.2. Compare the differences between independent

and dependent demand.3. Identify the basic components of independent

demand: average, trend, seasonal, and random variation.

4. Describe the common qualitative forecasting techniques such as the Delphi method and Collaborative Forecasting.

5. Show how to make a time series forecast using regression, moving averages, and exponential smoothing.

Demand ManagementDemand ManagementStrategic forecasts: forecasts used to

help set the strategy of how demand will be met

Tactical forecasts: forecasted needed for how a firm operates processes on a day-to-day basis

The purpose of demand management is to coordinate and control all sources of demand

Two basic sources of demand◦ Dependent demand: the demand for a product

or service caused by the demand for other products or services

◦ Independent demand: the demand for a product or service that cannot be derived directly from that of other products

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Demand ManagementDemand Management

A

Independent Demand:Finished Goods

B(4) C(2)

D(2) E(1) D(3) F(2)

Dependent Demand:Raw Materials, Component parts,Sub-assemblies, etc.

Components of Components of DemandDemandAverage demand for a period of

timeTrendSeasonal elementCyclical elementsRandom variationAutocorrelation

Finding Components of Finding Components of DemandDemand

1 2 3 4

x

x xx

xx

x xx

xx x x x

xxxxxx x x

xx

x x xx

xx

xx

x

xx

xx

xx

xx

xx

xx

x

x

Year

Sal

es

Seasonal variation

Linear

Trend

Types of ForecastsTypes of ForecastsQualitative (Judgmental)

Quantitative◦ Time Series Analysis◦ Causal Relationships◦ Simulation

Qualitative MethodsQualitative Methods

Grass Roots

Market Research

Panel Consensus

Executive Judgment

Historical analogy

Delphi Method

Qualitative

Methods

Delphi MethodDelphi Methodl. Choose the experts to participate. There

should be a variety of knowledgeable people in different areas.

2. Through a questionnaire (or E-mail), obtain forecasts (and any premises or qualifications for the forecasts) from all participants.

3. Summarize the results and redistribute them to the participants along with appropriate new questions.

4. Summarize again, refining forecasts and conditions, and again develop new questions.

5. Repeat Step 4 if necessary. Distribute the final results to all participants.

Time Series AnalysisTime Series AnalysisTime series forecasting models try

to predict the future based on past data.

You can pick models based on:1. Time horizon to forecast2. Data availability3. Accuracy required4. Size of forecasting budget5. Availability of qualified personnel

Time Series AnalysisTime Series AnalysisShort term: forecast under

three months◦Tactical decisions

Medium term: three months to two years◦Capturing seasonal effects

Long term: forecast longer than two years◦Detecting general trends◦Identifying major turning points

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Simple Moving Average Simple Moving Average FormulaFormulaThe simple moving average model assumes an

average is a good estimator of future behavior. The formula for the simple moving average is:

F = A + A + A +...+A

ntt-1 t-2 t-3 t-n

Ft = Forecast for the coming period N = Number of periods to be averagedA t-1 = Actual occurrence in the past period for up to “n” periods

Simple Moving Average Simple Moving Average Problem (1)Problem (1)

Question: What are the 3-week and 6-week moving average forecasts for demand?

Assume you only have 3 weeks and 6 weeks of actual demand data for the respective forecasts

Week Demand1 6502 6783 7204 7855 8596 9207 8508 7589 892

10 92011 78912 844

F = A + A + A +...+A

ntt-1 t-2 t-3 t-n

Week Demand 3-Week 6-Week1 6502 6783 7204 785 682.675 859 727.676 920 788.007 850 854.67 768.678 758 876.33 802.009 892 842.67 815.33

10 920 833.33 844.0011 789 856.67 866.5012 844 867.00 854.83

F4=(650+678+720)/3

=682.67F7=(650+678+720 +785+859+920)/6

=768.67

Calculating the moving averages gives us:

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Weighted Moving Weighted Moving Average FormulaAverage Formula

n-tn3-t32-t21-t1t Aw+...+Aw+A w+A w=F

w = 1ii=1

n

While the moving average formula implies an equal weight being placed on each value that is being averaged, the weighted moving average permits an unequal weighting on prior time periods.

wt = weight given to time period “t” occurrence. (Weights must add to one.)

The formula for the moving average is:

Choosing WeightsChoosing WeightsExperience and trial-and-error

are the simplest waysGenerally, the most recent past

is the best indicatorWhen data are seasonal, weights

should be established accordingly

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Weighted Moving Average Weighted Moving Average Problem (1) DataProblem (1) Data

Weights: t-1 .5t-2 .3t-3 .2

Week Demand1 6502 6783 7204

Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4?

Note that the weights place more emphasis on the most recent data, that is time period “t-1”.

Weighted Moving Average Weighted Moving Average Problem (1) SolutionProblem (1) Solution

Week Demand Forecast1 6502 6783 7204 693.4

F4 = 0.5(720)+0.3(678)+0.2(650)=693.4

Exponential SmoothingExponential Smoothing Most used of all forecasting techniques Integral part of all computerized

forecasting programs Widely used in retail and service Widely accepted because…

1. Exponential models are surprisingly accurate2. Formulating an exponential model is relatively

easy3. The user can understand how the model works4. Little computation is required to use the model5. Computer storage requirements are small6. Tests for accuracy are easy to compute

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Exponential Smoothing Exponential Smoothing ModelModel

Premise: The most recent observations might have the highest predictive value.

Therefore, we should give more weight to the more recent time periods when forecasting.

Ft = Ft-1 + (At-1 - Ft-1)

= smoothing constant

Exponential Smoothing Exponential Smoothing Problem (1) DataProblem (1) Data

Question: Given the weekly demand data, what are the exponential smoothing forecasts for periods 2-10 using =0.10 and =0.60?

Assume F1=D1

Week Demand1 8202 7753 6804 6555 7506 8027 7988 6899 775

10

Week Demand 0.1 0.61 820 820.00 820.002 775 820.00 820.003 680 815.50 820.004 655 801.95 817.305 750 787.26 808.096 802 783.53 795.597 798 785.38 788.358 689 786.64 786.579 775 776.88 786.61

10 776.69 780.77

Answer: The respective alphas columns denote the forecast values. Note that you can only forecast one time period into the future.

Exponential Smoothing Exponential Smoothing Problem (1) PlottingProblem (1) Plotting

500550600650700750800850

1 2 3 4 5 6 7 8 9 10

Demand

Week

Demand

0.1

0.6

Note how that the smaller alpha the smoother the line in this example.

Simple Linear Regression Simple Linear Regression ModelModel

Yt = a + bx0 1 2 3 4 5 x (Time)

YThe simple linear regression model seeks to fit a line through various data over time.

Is the linear regression model.

a

Yt is the regressed forecast value or dependent variable in the model, a is the intercept value of the the regression line, and b is similar to the slope of the regression line. However, since it is calculated with the variability of the data in mind, its formulation is not as straight forward as our usual notion of slope.

Simple Linear Regression Simple Linear Regression Formulas for Calculating “a” Formulas for Calculating “a” and “b”and “b”

a = y - bx

b =xy - n(y)(x)

x - n(x2 2

)

Simple Linear Regression Simple Linear Regression Problem DataProblem Data

Week Sales1 1502 1573 1624 1665 177

Question: Given the data below, what is the simple linear regression model that can be used to predict sales?

Week Week*Week Sales Week*Sales1 1 150 1502 4 157 3143 9 162 4864 16 166 6645 25 177 8853 55 162.4 2499

Average Sum Average Sum

b =xy - n(y)(x)

x - n(x=

2499 - 5(162.4)(3)=

a = y - bx = 162.4 - (6.3)(3) =

2 2

) ( )55 5 9

63

106.3

143.5

Answer: First, using the linear regression formulas, we can compute “a” and “b”.

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Yt = 143.5 + 6.3x

135140145150155

160165170175180

1 2 3 4 5Period

Sal

es

Sales

Forecast

The resulting regression model is:

Now if we plot the regression generated forecasts against the actual sales we obtain the following chart:

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Forecast ErrorForecast ErrorBias errors: when a consistent

mistake is madeRandom errors: errors that

cannot be explained by the forecast model being used

Measures of error◦Mean absolute deviation (MAD)◦Mean absolute percent error (MAPE)◦Tracking signal

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The MAD Statistic to The MAD Statistic to Determine Forecasting ErrorDetermine Forecasting Error

The ideal MAD is zero. That would mean there is no forecasting error.

The larger the MAD, the less the desirable the resulting model.

MAD = A - F

n

t tt=1

n

1 MAD 0.8 standard deviation

1 standard deviation 1.25 MAD

MAD Problem DataMAD Problem Data

Month Sales Forecast1 220 n/a2 250 2553 210 2054 300 3205 325 315

Question: What is the MAD value given the forecast values in the table below?

MAD Problem SolutionMAD Problem Solution

MAD = A - F

n=

40

4= 10

t tt=1

n

Month Sales Forecast Abs Error1 220 n/a2 250 255 53 210 205 54 300 320 205 325 315 10

40

Note that by itself, the MAD only lets us know the mean error in a set of forecasts.

Tracking Signal Tracking Signal FormulaFormula

The TS is a measure that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand.

Depending on the number of MAD’s selected, the TS can be used like a quality control chart indicating when the model is generating too much error in its forecasts.

The TS formula is:

TS =RSFE

MAD=

Running sum of forecast errors

Mean absolute deviation

ANY QUESTIONS?ANY QUESTIONS?