Forecasting Demand for Services

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Forecasting Demand for Services
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Learning Objectives

• Recommend 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.

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Forecasting Models

• Subjective Models Delphi Methods
• Causal Models Regression Models
• Time Series Models Moving Averages Exponential
  Smoothing

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N Period Moving Average
Let MAT The N period moving average at the
end of period T AT Actual
observation for period T Then MAT (AT AT-1
AT-2 .. AT-N1)/N Characteristics
Need N observations to make a forecast
Very inexpensive and easy to understand
Gives equal weight to all observations
Does not consider observations older than N
periods
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Moving Average Example
Saturday Occupancy at a 100-room Hotel

Three-period Saturday
Period Occupancy Moving Average
Forecast Aug. 1 1
79 8
2 84 15
3 83 82
22 4
81 83 82 29 5
98 87 83 Sept. 5
6 100 93 87
12 7 93


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Exponential Smoothing
Let ST Smoothed value at end of period T
AT Actual observation for period T FT1
Forecast for period T1 Feedback control
nature of exponential smoothing New value
(ST ) Old value (ST-1 ) observed error

or
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Exponential SmoothingHotel Example
Saturday Hotel Occupancy ( 0.5)
Actual
Smoothed Forecast
Period Occupancy
Value Forecast
Error Saturday t
At St
Ft At -
Ft Aug. 1 1
79 79.00 8 2
84 81.50 79 5 15
3 83 82.25
82 1 22 4
81 81.63 82 1 29
5 98 89.81
82 16 Sept. 5 6
100 94.91 90 10

MAD 6.6



Forecast Error (Mean Absolute
Deviation) SlAt Ftl/n

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Exponential SmoothingImplied Weights Given Past
Demand
Substitute for
If continued
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Exponential Smoothing Weight Distribution
Relationship Between and N
(exponential smoothing constant) 0.05 0.1
0.2 0.3 0.4 0.5 0.67 N (periods
in moving average) 39 19 9
5.7 4 3 2
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Saturday Hotel Occupancy
Effect of Alpha ( 0.1 vs. 0.5)
Actual
Forecast
Forecast
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Exponential Smoothing With Trend Adjustment
Commuter Airline Load Factor Week Actual
load factor Smoothed value Smoothed
trend Forecast Forecast error t
At St
Tt
Ft At - Ft 1
31 31.00
0.00 2 40
35.50 1.35
31 9 3
43 39.93 2.27 37 6 4
52
47.10 3.74 42
10 5 49
49.92 3.47
51 2 6
64 58.69
5.06 53
11 7 58
60.88 4.20
64 6 8
68 66.54
4.63 65
3

MAD
6.7
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Exponential Smoothing with Seasonal Adjustment
Ferry Passengers taken to a Resort Island
Actual
Smoothed Index Forecast
Error Period t At
value St It
Ft At - Ft

2003 January 1 1651
.. 0.837
.. February 2
1305 ..
0.662 .. March
3 1617 ..
0.820 .. April
4 1721 ..
0.873 .. May
5 2015 ..
1.022 .. June
6 2297 ..
1.165 .. July
7 2606 ..
1.322 .. August
8 2687 ..
1.363 .. September
9 2292 ..
1.162 .. October 10
1981 ..
1.005 .. November 11
1696 ..
0.860 .. December 12 1794
1794.00 0.910
..
2004 January 13
1806 1866.74 0.876 -
-
February 14 1731
2016.35 0.721 1236 495 March
15 1733 2035.76
0.829 1653 80
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Topics for Discussion

• What characteristics of service organizations
  make forecast accuracy important?
• For each of the three forecasting methods, what
  are the developmental costs and associated cost
  of forecast error?
• Suggest independent variables for a regression
  model to predict the sales volume for a proposed
  video rental store location.
• Why is the N-period moving-average still in
  common use if the simple exponential smoothing
  model is superior?
• What changes in a, ß, ? would you recommend to
  improve the performance of the trendline seasonal
  adjustment forecast shown in Figure 11.4?

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Interactive Exercise Delphi ForecastingQuestion
In what future election will a woman become
president of the united states?
Year 1st Round Positive Arguments 2nd Round Negative Arguments 3rd Round
2008
2012
2016
2020
2024
2028
2032
2036
2040
2044
2048
2052
Never
Total