Forecasting Demand for Services

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# Forecasting Demand for Services - PowerPoint PPT Presentation

Forecasting Demand for Services. Learning Objectives. Recommend the appropriate forecasting model for a given situation. Conduct a Delphi forecasting exercise. Describe the features of exponential smoothing.

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### Forecasting Demand for Services

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.
Forecasting Models
• Subjective Models Delphi Methods
• Causal Models Regression Models
• Time Series Models Moving Averages Exponential Smoothing
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-N+1)/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

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

Exponential Smoothing

Let : ST = Smoothed value at end of period T

AT = Actual observation for period T

FT+1 = Forecast for period T+1

Feedback control nature of exponential smoothing

New value (ST ) = Old value (ST-1 ) + [ observed error ]

or :

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

Forecast Error (Mean Absolute Deviation) = ΣlAt – Ftl/n

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

Saturday Hotel Occupancy

Effect of Alpha ( =0.1 vs. =0.5)

Actual

Forecast

Forecast

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

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

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 α, β, γ would you recommend to improve the performance of the trendline seasonal adjustment forecast shown in Figure 11.4?
Interactive Exercise: Delphi ForecastingQuestion: In what future election will a woman become president of the united states?