Download - Chapter 17 Forecasting Demand for Services
![Page 1: Chapter 17 Forecasting Demand for Services](https://reader036.vdocuments.us/reader036/viewer/2022082402/568134ac550346895d9bc22a/html5/thumbnails/1.jpg)
Chapter 17Forecasting Demand for Services
Learning ObjectivesDemand characteristicsOverview of forecasting modelsCommon demand pattern for servicesLinear regression to account for trendSeasonality indices for seasonal demandCombination of trend and seasonality
17-1
![Page 2: Chapter 17 Forecasting Demand for Services](https://reader036.vdocuments.us/reader036/viewer/2022082402/568134ac550346895d9bc22a/html5/thumbnails/2.jpg)
Demand Characteristics
TimeTime(a) Trend(a) Trend
TimeTime(d) Trend with seasonal pattern(d) Trend with seasonal pattern
TimeTime(c) Seasonal pattern(c) Seasonal pattern
TimeTime(b) Cycle(b) Cycle
Dem
and
Dem
and
Dem
and
Dem
and
Dem
and
Dem
and
Dem
and
Dem
and
Random Random movemenmovementt
![Page 3: Chapter 17 Forecasting Demand for Services](https://reader036.vdocuments.us/reader036/viewer/2022082402/568134ac550346895d9bc22a/html5/thumbnails/3.jpg)
Forecasting Models
Subjective ModelsDelphi Methods
Causal ModelsRegression Models
Time Series ModelsMoving AveragesExponential Smoothing
17-3
![Page 4: Chapter 17 Forecasting Demand for Services](https://reader036.vdocuments.us/reader036/viewer/2022082402/568134ac550346895d9bc22a/html5/thumbnails/4.jpg)
yy = = aa + + bxbx
wherewherea a = intercept= interceptb b = slope of the line= slope of the linex x = time period= time periody y = forecast for = forecast for demand for period demand for period xx
Using Linear Regression to account for trend
b =
a = y - b x
wheren = number of periods
x = = mean of the x values
y = = mean of the y values
xy - nxy
x2 - nx2
xn
yn
![Page 5: Chapter 17 Forecasting Demand for Services](https://reader036.vdocuments.us/reader036/viewer/2022082402/568134ac550346895d9bc22a/html5/thumbnails/5.jpg)
Least Squares Examplexx(PERIOD)(PERIOD) yy(DEMAND)(DEMAND) xyxy xx22
11 3737 3737 1122 4040 8080 4433 4141 123123 9944 3737 148148 161655 4545 225225 252566 5050 300300 363677 4343 301301 494988 4747 376376 646499 5656 504504 8181
1010 5252 520520 1001001111 5555 605605 1211211212 5454 648648 144144
7878 557557 38673867 650650
![Page 6: Chapter 17 Forecasting Demand for Services](https://reader036.vdocuments.us/reader036/viewer/2022082402/568134ac550346895d9bc22a/html5/thumbnails/6.jpg)
x = = 6.5
y = = 46.42
b = = =1.72
a = y - bx= 46.42 - (1.72)(6.5) = 35.2
3867 - (12)(6.5)(46.42)650 - 12(6.5)2
xy - nxyx2 - nx2
781255712
Least Squares Example (cont.)
![Page 7: Chapter 17 Forecasting Demand for Services](https://reader036.vdocuments.us/reader036/viewer/2022082402/568134ac550346895d9bc22a/html5/thumbnails/7.jpg)
Linear trend line y = 35.2 + 1.72x
Forecast for period 13 y = 35.2 + 1.72(13) = 57.56 units
70 70 –
60 60 –
50 50 –
40 40 –
30 30 –
20 20 –
1010 –
0 0 –
| | | | | | | | | | | | |11 22 33 44 55 66 77 88 99 1010 1111 1212 1313
ActualActual
Dem
and
Dem
and
PeriodPeriod
Linear trend lineLinear trend line
![Page 8: Chapter 17 Forecasting Demand for Services](https://reader036.vdocuments.us/reader036/viewer/2022082402/568134ac550346895d9bc22a/html5/thumbnails/8.jpg)
Seasonal Adjustments
Repetitive increase/ decrease in demandRepetitive increase/ decrease in demand Use seasonal factor to adjust forecastUse seasonal factor to adjust forecast SSii = seasonality index of period i = seasonality index of period i
AAi(j)i(j) = demand in season i (in year j) = demand in season i (in year j)
Note: The method used here is different from Note: The method used here is different from the bookthe book
Seasonal factor = Seasonal factor = SSii = = AAii
AAijij
![Page 9: Chapter 17 Forecasting Demand for Services](https://reader036.vdocuments.us/reader036/viewer/2022082402/568134ac550346895d9bc22a/html5/thumbnails/9.jpg)
Seasonal Adjustment (cont.)
2005 12.62005 12.6 8.68.6 6.36.3 17.517.5 45.045.0
2006 14.12006 14.1 10.310.3 7.57.5 18.218.2 50.150.1
2007 15.32007 15.3 10.610.6 8.18.1 19.619.6 53.653.6
Total 42.0Total 42.0 29.529.5 21.921.9 55.355.3 148.7148.7
DEMAND (1000’S PER QUARTER)DEMAND (1000’S PER QUARTER)
YEARYEAR 11 22 33 44 TotalTotal
SS11 = = = 0.28 = = = 0.28 AA11
AAijij
42.042.0148.7148.7
SS22 = = = 0.20 = = = 0.20 AA22
AAijij
29.529.5148.7148.7
SS44 = = = 0.37 = = = 0.37 AA44
AAijij
55.355.3148.7148.7
SS33 = = = 0.15 = = = 0.15 AA33
AAijij
21.921.9148.7148.7
![Page 10: Chapter 17 Forecasting Demand for Services](https://reader036.vdocuments.us/reader036/viewer/2022082402/568134ac550346895d9bc22a/html5/thumbnails/10.jpg)
Forecast to account for both Trend and Seasonality
Step 1: Calculate the seasonal index for each season.Step 2: Use linear regression to forecast the total demand
for the following year to account for trend. (In the previous slide example, use the year as dependent variable, and yearly demand as independent variable)
a = 40.97, b = 4.30 (Note: 2005/6/7 are years a = 40.97, b = 4.30 (Note: 2005/6/7 are years 1/2/3)1/2/3)
F(2008)F(2008) = 40.97 + 4.30(4) = 58.17= 40.97 + 4.30(4) = 58.17
Step 3: Use the forecast total demand (obtained in Step 2) and multiply by the seasonal index to determine the forecast seasonal demand.SF1 = (S1) (F2008) = (0.28)(58.17) = 16.28SF1 = (S1) (F2008) = (0.28)(58.17) = 16.28 SF2 = (S2) (F2008) = (0.20)(58.17) = 11.63SF2 = (S2) (F2008) = (0.20)(58.17) = 11.63SF3 = (S3) (F2008) = (0.15)(58.17) = 8.73SF3 = (S3) (F2008) = (0.15)(58.17) = 8.73SF4 = (S4) (F2008) = (0.37)(58.17) = 21.53SF4 = (S4) (F2008) = (0.37)(58.17) = 21.53