Improvement of basic principles for planning of capacity and investments
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Management summary
Amsterdam Airport Schiphol (AAS) is one of the leading airports in Europe and continuously has to analyse their demand and supply of capacity to maintain their competitive position. Investments in the passenger and baggage facilities, will directly account to the visit costs and there by to the ticket prices. Capacity planning will be helpful to determine how much capacity is needed and to prevent for unnecessary investments.
In the past, different ways of capacity planning were used. The capacity planning was adapted a few times, because air transport market is continuously developing. Current developments lead to the presumption that the current planning method has to be adapted to find a better match between demand and supply. The following developments did contribute to this presumption:
• More competition of other airports • Market changes
Other airports have improved their competitive position and because airlines work at low profits, the visit costs are becoming more important. Besides that, the share of leisure passengers will increase, because of the cheaper tickets and higher welfare. The larger amount of leisure passengers will result in higher peaks around the holidays. Besides that, due to the increase of welfare and the globalizing world, flying by airplanes did become a very common transport mode. These developments have resulted in an increased need for capacity. However, AAS has to prevent for high visit costs and new investments therefore have to be well founded by an extended plan. Current planning is based on a ‘typical week’ (result from timetables for next years and of historical statistics). It is well known that single processes have peak volumes at different times. Therefore, it is assumed that improving the current planning method will result in a better match between demand and supply. The main research question therefore is formulated as follows:
The research is divided in the following three main parts: system description, data collection and analysis and solution generation. The last chapter contains of the overall conclusions and recommendations.
Can the current planning methods at AAS be adapted to improve peak demand predictions and investment planning; what measures can be implemented to reduce these peak demands?
Thesis report Dirk Voogd
- vi - System description
The first phase of this research covers the description of the studied system. The system is demarked to only the passenger and baggage process within the terminal. Those processes are located at landside. Forced by the European Law (2320/2002) the airport is divided into an airside part and a landside part. Airside will be left out of scope. The passenger and baggage processes consist of different subprocesses. These processes are performed by different stakeholders, which make use of different facilities. Nine different stakeholders are identified, namely Amsterdam Airport Schiphol, the airlines, the Koninlijke Marechaussee (immigrations), security companies, customs, the passengers, the neighbours, governmental bodies and international authorities. AAS is the provider of all facilities and responsible of the proper execution of the regulations set by the governmental bodies and international authorities, but has outsourced the execution of the screening tasks to the security guards of a private company. Users are the passengers.
Current method of capacity planning makes use of different divisions of the airlines and AAS. Based on the timetable airlines are intended to fly and make use of historical statistics an expectation is been made how much passenger will fly via AAS for one year. From this annual volume a distribution to months, weeks and days is been made. From the distributions a typical week is analysed, a week with relative high day volumes. This typical week is used to predict passenger volume per hour and quarter of a day. The maximum hourly volumes are used to determine the amount of capacity is needed for the studied years. A capacity planning is made for five years in advance.
It is concluded that the current capacity is determined using the volumes of a typical week and not the using the maximum peak hour volumes, which could be expected over a year. Analysis should be performed to prove if there would be differences between the volumes.
Data collection and analysis
Using the daily passenger numbers (realized numbers) comparisons are made between the planning day volume and realized day numbers. The differences are not very high, but could result in capacity problems. The forecasted data is rather accurate and are used in the simulation model, called the Operationeel Model. From this model, the hourly volumes are used to do the same analysis and find out how the peak hours are distributed. It is shown that daily peaks can still grow and it is expected this will be the case. Six facilities, both from passengers and baggage, are selected to analyse the hourly volumes. From the hourly peak volumes, it is concluded that peak hours of the different
Improvement of basic principles for planning of capacity and investments
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facilities will occur at different dates. Differences between the planning hour volumes and expected maximum peak hour are also relatively small, but should be taken into account. If those peaks are known, measures can be taken to prevent for large consequences.
Solution generation
Two types of adaptations to the current capacity planning are suggested. The first one is to eliminate the differences between the dates of the peak volumes. A peak factor is suggested to convert current volumes of the typical week to the maximum peak demand. These peak factors are determined from the results of the Operationeel Model. This method has to be used for every single facility. Dependent of the importance of the process and other factors, a converting ratio is determined. This ratio is called the CD-ratio. The ratio converts the maximum peak demand into the capacity requirements for the facility.
Another way to handle peak hour demand is to spread the peak over a day or of days over a week. This process is called peek shaving. Peek shaving can be achieved by the application of measures. All kind of measures are presented, summarizing in measure to limit the overall demand for services at the airport and measures to limit the demand for a specific facility.
Conclusions & recommendations
The current capacity planning can indeed be improved. Two adaptations are suggested to the capacity requirement determination process. These adaptations are firstly a peak factor, to convert the maximum peak demand of the typical week to the expected maximum peak demand for the planning period. The second adaptations is the introduction of a CD-ratio to convert the calculated maximum peak demand to the capacity requirement per facility. A suggestion of the ratios is showed in the table below. Facility Planning volume Peak factors Expected
peak volume CD-ratios
Needed capacity Check-in desks 1 1,650 1.10-1.15 1,800-1,900 0.95-1.00 1,700-1,900 Check-in desks 2 2,050 1.25-1.30 2,550-2,650 0.95-1.00 2,300-2,500 Baggage system hall 1 1,450 1.10-1.15 1,6001,650 0.90-0.95 1,450-1,550 Departure filter 1 2,000 1.25-1.30 2,500-2,600 0.90-0.95 2,150-2,350 Transfer filter From NS-S 1,300 1.10-1.15 1,400-1,500 0.85-0.90 1,150-1,250 Baggage system transfer traffic 5,800 1.00-1.05 5,800-6,050 0.80-0.85 4,600-5,150
Thesis report Dirk Voogd
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These ratios can be used by AAS to get a quick overview of the maximum peak demand and the capacity requirements of the different facilities without running a simulation run.
Besides having a capacity planning method to handle the peak demand volumes, measures to limit peak demands can be helpful to limit the consequences. A long list of measures has been found. Two types of measures are designed, one to limit the overall demand for the airport and one type to limit the demand for a single facility. Currently, no study has been done on the performance of the different measures. If such study will be performed, a selection can be made and an implementation plan can be designed.
Besides the further research on the measures, it is recommended to do real measurements in the terminal to get real data. This data can be used to make a simulation model of the terminal and the facilities. By simulating flows at busy days, consequences of capacity shortages can be visualized. More data also contributes to improve the reliability of the peak factors and CD-ratios. In addition, more research can be done on other facilities then those that were chosen and on cargo at AAS. Finally, the developed method can be adapted to make it suitable in the determination of capacity for new facilities.