chapter 15

Demand Management and Forecasting

1. 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. Show how to make a time series forecast using moving averages and exponential smoothing.

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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Not much a firm can do about dependent demand

◦ It is demand that must be met There is a lot a firm can do about

independent demand1. Take an active role to influence demand2. Take a passive role and respond to demand

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Time series analysis is based on the idea that data relating to past demand can be used to predict future demand

Components of demand◦ Average demand for a period of time◦ Trend◦ Seasonal element◦ Cyclical elements◦ Random variation

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

ntt-1 t-2 t-3 t-nF =

A + A + A +...+A

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

The simple moving average model assumes an average is a good estimator of future behavior

The formula for the simple moving average is:

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” periodsIf N=1 we have the “naïve forecast” – the forecast equals the current period’s actual

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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-nF =

A + A + A +...+A

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

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

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

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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:

©The McGraw-Hill Companies, Inc., 2004

8

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500550600650700750800850900950

1 2 3 4 5 6 7 8 9 10 11 12

Dem

and

Week

Demand3-Week6-Week

Plotting the moving averages and comparing them shows how the lines smooth out to reveal the overall upward trend in this example

Plotting the moving averages and comparing them shows how the lines smooth out to reveal the overall upward trend in this example

Note how the 3-Week is smoother than the Demand, and 6-Week is even smoother

Note how the 3-Week is smoother than the Demand, and 6-Week is even smoother

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Week Demand1 8202 7753 6804 6555 6206 6007 575

Question: What is the 3 week moving average forecast for this data?

Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts

Question: What is the 3 week moving average forecast for this data?

Assume you only have 3 weeks and 5 weeks of actual demand data for the respective forecasts

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Week Demand 3-Week 5-Week1 8202 7753 6804 655 758.335 620 703.336 600 651.67 710.007 575 625.00 666.00

F4=(820+775+680)/3

=758.33F6=(820+775+680 +655+620)/5 =710.00

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

constant smoothing Alpha

period epast t tim in the occurance ActualA

period past time 1in alueForecast vF

period t timecoming for the lueForcast vaF

:Where

1-t

1-t

t

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Week Demand1 8202 7753 6804 6555 7506 8027 7988 6899 775

10

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

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

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Week Demand 0.1 0.61 820 820.00 820.002 775 820.00 820.003 680 815.50 793.004 655 801.95 725.205 750 787.26 683.086 802 783.53 723.237 798 785.38 770.498 689 786.64 787.009 775 776.88 728.20

10 776.69 756.28

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

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

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500550600650700750800850

1 2 3 4 5 6 7 8 9 10

Demand

Week

Demand

0.1

0.6

Note how that the smaller alpha results in a smoother line in this example

Note how that the smaller alpha results in a smoother line in this example

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Question: What are the exponential smoothing forecasts for periods 2-5 using a =0.5?

Assume F1=D1

Question: What are the exponential smoothing forecasts for periods 2-5 using a =0.5?

Assume F1=D1

Week Demand1 8202 7753 6804 6555

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Week Demand 0.51 820 820.002 775 820.003 680 797.504 655 738.755 696.88

F1=820+(0.5)(820-820)=820 F3=820+(0.5)(775-820)=797.75

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MAD = A - F

n

t tt=1

n

MAD =

A - F

n

t tt=1

n

1 MAD 0.8 standard deviation

1 standard deviation 1.25 MAD

The ideal MAD is zero which would mean there is no forecasting error

The larger the MAD, the less the accurate the resulting model

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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?Question: What is the MAD value given the forecast values

in the table below?

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MAD = A - F

n=

40

4= 10

t tt=1

n

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

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

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Mean Absolute Percentage Error (MAPE) is another measure often used to evaluate forecasting accuracy

n

actual

forecastactual

100MAPE

n

1i i

ii

A MAPE of under 8% is acceptable for most applications

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Time ACTUAL FORECAST ERROR ABS ERROR APE1 820 820.00 --- --- ---2 775 820.00 -45.00 45.00 5.813 680 815.50 -135.50 135.50 19.934 655 801.95 -146.95 146.95 22.445 750 787.26 -37.26 37.26 4.976 802 783.53 18.47 18.47 2.307 798 785.38 12.62 12.62 1.588 689 786.64 -97.64 97.64 14.179 775 776.88 -1.88 1.88 0.2410 776.69

61.91 8.93MAD MAPE