29 Dec DECS 310 Operations Management
Operations Management Case Study- Forecasting Models
DECS 310 Operations Management
Case Study 2, Fall 2019
As a result of Expo 2020, airport terminals in the UAE are expecting increased waiting times and longer queues at security gateways. Historically, when major events were held, he waiting times have been sometimes hours and waiting lines have extended to hundreds of meters in length. In order to reduce waiting times and decrease queue lengths during Expo 2020, airports began analyzing their security processes and started to search for operations management solutions. Experts recognized that one of the main components of any plan to improve the operation at airport terminals is forecasting the number of passengers arriving at checkpoints so that the required number of checkpoints and employees can be determined. At the Dubai airport, operation analysts would like to forecast the number of passengers at the arrival terminal for next November. The objective is to determine how many checkpoints they should operate during the month to ensure that waiting times and queue lengths are within limits. Historical data shows that the demand for airline travel has been following an upward trend during the last 3 years. There are two terminals at the considered airport, West and East, each designated for different carriers. The data below displays the number of passengers arriving at the West terminal for 10 days (randomly selected) in two-hour periods from 4:00 am to 10:00 pm for the past 3 years for the month of November. Experts are expecting arrivals will surge 50% during November 2020, compared to historical levels, due to Expo 2020.
1. Develop a forecasting model for the number of passengers arriving at the West terminal for each time interval for November in year 2020.
2. Discuss different forecast models that could be used to solve this case and evaluate the accuracy of the selected model.
3. Other than the information that was provided to you in the case, what information would likely improve the accuracy of your forecasts?
Day
4-6 AM
6-8 AM
8-10 AM
10-noon
noon-2 PM
2-4 PM
4-6 PM
6-8 PM
8-10 PM
1
2400
2700
3200
1400
1700
1800
1600
800
200
2
1900
2500
3100
1600
1800
2000
1800
900
300
3
2300
3100
2500
1500
1500
1800
1900
1100
200
4
2200
3200
3100
2200
1900
2400
2100
1200
400
Year 1
5
2400
3300
3400
1700
2200
2100
2000
1000
600
6
2600
2800
3500
1500
1700
1900
1500
1100
300
7
1900
2800
3100
1200
1500
2000
1400
900
400
8
2000
2700
2500
1500
2000
2300
1900
1000
200
9
2400
3200
3600
1600
2100
2500
1800
1400
200
10
2600
3300
3100
200
2500
2600
2400
1100
400
11
3100
3900
4100
2200
2600
2300
2500
1100
300
12
2800
3400
3900
1900
2100
2500
2000
1200
300
13
2700
3800
4300
2100
2400
2400
2400
1200
400
14
2400
3500
4100
2400
3000
3200
2600
1200
700
Year 2
15
3300
3700
4000
2600
2600
2700
2900
1000
300
16
3500
4000
3800
2300
2700
3100
3000
900
200
17
2900
4100
3900
2400
3000
3200
2500
1100
500
18
3400
3800
4200
2000
2500
3000
2200
1000
300
19
3600
3600
4000
2300
2600
2800
2600
1200
200
20
3700
3700
4000
2200
2600
2700
2400
1200
200
21
4400
4400
4500
2600
3300
3400
3000
1200
400
22
4200
4500
4300
2500
3400
3600
3100
1400
300
23
4500
4500
4700
2700
3400
3500
2900
1200
300
24
4600
4600
4600
2500
3200
3500
2800
1300
300
Year 3
25
4500
4300
4400
2900
3300
3300
3300
1500
400
26
4200
4300
4500
3000
4000
3400
3000
1500
600
27
4500
4500
5100
3300
4000
3700
3100
1200
300
28
4300
4200
4300
2800
3500
4000
3300
1100
400
29
4900
4100
4200
3100
3600
3900
3400
1400
500
30
4700
4500
4100
3000
4000
3700
3400
1200
500
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