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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Delphi Forecasting
Question In what future election will a woman
become president of the united states?
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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
22 4 81
29 5
98 Sept. 5 6
100 12 7


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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 8 2
84 15 3
83 22 4
81 29 5
98 Sept. 5 6 100



MAD

Mean Absolute Deviation
(MAD)
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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

31 9 3
43 4 52
47.10
3.74 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 Period
t At value St
It Forecast Ft
At - Ft
1995 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 ..

1996 January 13 1806

.. February 14 1731 March
15 1733 April
16 1904 May 17
2036
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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.
• Suggest how the Delphi method can be incorporated
  into a cross-impact analysis.