Airport management: The impact of public announcements on security clearing time

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Master Thesis

Airport management: The impact of

public announcements on security

clearing time

By

Durk A. Rienks

December, 2017 Supervisor: Dr. M.J. Land Supervisor: Dr. G. Pang

University of Groningen

Faculty of Economics and Business

&

Newcastle University

Business School

DD-MSc Technology and Operations Management

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List of tables

Table 2.1: Demand management strategies (Adapted from Klassen and Rohleder, 2002) 4

Table 2.2: Airport performance indicators (Adapted from Guide to Airport 6

Performance Measures, 2012)

Table 3.1:Characteristics of cases 8

List of figures

Figure 2.1: Conceptual model 7

Figure 3.1: Research framework 10

Figure 4.1: Arrival before departure times (Case 1) 11

Figure 4.4: Expected effect of the public announcement with regard to forecast 13

Figure 4.5: Comparison input with forecast without announcement (Case 1) 14

Figure 4.6: Comparison input with forecast with announcement (Case 1) 14

Figure 4.8:Comparison input with forecast with announcement (Case 2) 15

Figure 4.10: Comparison input with forecast with announcement (Case 3) 16

Figure 4.11: Expected effect of the public announcement on input and forecast in percentages 18

Figure 4.12: Comparison inputs and forecast in percentages with and 19

without announcement 00:00 – 12:00 (Case 1)

Figure 4.18: Queue length and security clearing time without announcement (Case 1) 23

Figure 4.19: Queue length and security clearing time with announcement (Case 1) 23

Figure 4.20:Queue length and security clearing time without announcement (Case 2) 24

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Figure 4.22: Queue length and security clearing time without announcement (Case 3) 25

Figure 4.23: Queue length and security clearing time with announcement (Case 3) 25

Abbreviations

AAS Amsterdam Airport Schiphol

SSBPC Self Service Boarding Pass Check

IATA International Air Transport Association

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Acknowledgements

This study is conducted to graduate for my Dual Master Degree program in Technology and Operations Management at the University of Groningen and the Newcastle University Business School. The journey has been challenging but also rewarding. I would like to express my gratitude to my supervisor M. Land for the feedback which significantly improved the quality of this research.

Even though there were some struggles with the acquirement of the data, this study would not have been possible without the input from the airport studied in this research. Therefore, I would like to thank Amsterdam Airport Schiphol, who participated in this research. Without Schiphol, this research could not have been performed.

Finally, I will express my profound gratitude to my family and friends for providing me with support and continuous encouragement throughout my years of study. This accomplishment would not have been possible without them. Thank you. I hope that you will enjoy reading this dissertation and that it will give insight into the effect of public announcements for airports.

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Abstract

Purpose - Around the world, airlines ask passengers to arrive earlier at the airport than usual when the

airport is crowded. Due to increasing travel demand it is likely that such announcements increase in frequency. However, it is unclear what the effect of these announcements are on the processes at airports, especially at the security check as it is assumed that this process acts as the bottleneck. This study aims to identify what the effect of the public announcements is on the security clearing time at airports.

Methodology – A multiple embedded case study is used in this research to answer the research question,

give practical insights and enlarge the theoretical body of knowledge regarding airport management. The study includes 3 cases and time series intervention analysis is used to answer the research question.

Findings – The findings showed that the public announcements did not have any effect on neither the

forecast nor the timing of the demand. Furthermore, the security clearing time did not increase on the different days.

Research Limitations/Implications – Only 3 embedded cases were used in this study which limits the

generalizability of the study to other airports. Furthermore, the research was focussed on the security process due to the availability of the data. However, the public announcements may affect the preceding check-in procedure.

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Chapter 1 INTRODUCTION

Due to increasing traffic demands and a lack of sufficient airport capacity, problems arise in the form of congestion and delays at airports around the world (Madas & Zografos, 2008). As air traffic continues to grow, the congestion and delay problems are expected to deteriorate even further. Congestion is a significant problem for thousands of passengers flying in and out of major airports each day (Solak, Clarke & Johnson, 2009). A mismatch between passenger volume and available capacity will lead to long queues and waiting times (in case volume is larger than capacity) or to unnecessary high costs (in case volume is less than capacity) (Helm, Urban, Werner et al., 2013). These issues have put pressure on airport authorities and airlines to come up with innovative measures to enhance capacity and align it with demand.

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public announcements is on the different components of an airport, is an interesting topic for research. Therefore, the research question is as follows, where the focus of this research is on the security check. How do the public announcements, as a demand management strategy, influence the security clearing

time at airports?

This research is conducted at Amsterdam Airport Schiphol (AAS), one of Europeans main hub airport. (Veiligheid vliegverkeer Schiphol, 2007). AAS processes nearly 64 million passengers a year from different cultural backgrounds with destinations all over the world. Amsterdam Airport Schiphol is one of the largest airports of the world in terms of market share. It is among the busiest airports in Europe. In 2016, the number of airline passengers dealt by Schiphol rose with 9.2% compared to 2015. In addition, in 2017 there is an expected growth of another 5% to 6%. This research contributes to the existing body of knowledge by providing insight into the effect of the public announcements on the throughput of departing passengers.

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Chapter 2 RESEARCH FRAMEWORK

Operations management defines capacity as the process’ maximum number of output units in a certain time frame. However, capacity for a manufacturing organisation might not be measured the same way as for a service organisation. For example, a manufacturing company might measure its capacity in the number of products it can produce in a certain period and service organisations in the number of customers it can serve. An airports’ capacity can be defined as the maximum number of operations (departures/arrivals) in a specific time interval (e.g. one hour) at a certain airport under given conditions and circumstances (e.g. runway conditions and weather conditions) (Gilbo, 1993). Though, airport capacity might be measured in aircrafts (e.g. arrivals, departures, movements) or by their load (passengers, cargo, baggage) per time period depending on the capacity component. An additional element that influences the capacity of airports is the level of service at which the airport would like to operate (Brunetta, Righi and Andreatta, 1999). The level of service is determined by the perception of quality and conditions of the passenger. Standard measures of the level of service are waiting time,

processing time, etc. (Brunetta, Righi and Andreatta, 1999).This means that there are several different

capacities due to the different components within the airport.

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airport capacity. This paper focusses on the third category that Hamzawi (1992) presents as the public announcements are an attempt to manage the demand.

2.1 Demand management

In contrast to capacity management, which is a response to demand, demand management is an attempt to shift demand (Klassen and Rohleder, 2002). In other words, capacity management are means to ensure that sufficient capacity is present so that demand can be met, while demand management attempts to control when and how much customers use the service (Pullman and Rodgers, 2010). Capacity enhancements often require a longer time frame and more investment of resources in comparison to demand managing strategies (Vaze and Barnhart, 2012). Demand management can be defined as a collection of different administrative and economic measures to ensure that demand is kept at a manageable level (Fan and Odoni, 2001). Demand management is concerned with the prediction of demand and synchronizing this with the capabilities of the supply chain (Croxton, Lambert and García-Dastugue, 2002). This also includes increasing flexibility and reducing variability in the supply chain. It enables organisations to be proactive towards anticipated demand and reactive towards unanticipated demand (Croxton, Lambert and García-Dastugue, 2002). However, organisations should take into account that customer-driven demand will always exhibit some variability. Developing appropriate strategies should begin with an understanding of the patterns and factors that govern demand in a given point of time (Lovelock, 1984). After an understanding of the patterns and factors, strategies can be developed to tailor to the demand. After reviewing the literature, Klassen and Rohleder (2002) identify a number of different strategies that can be applied in service organisations. The strategies are divided into explicit and implicit strategies. Explicit strategies involve scheduling of customers to some degree and allow for greater control over demand patterns. Implicit strategies may or may not influence demand as the choice for using the ‘service’ rests with the customer. The strategies are outlined in table 2.1.

Explicit strategies Explanation

Reserve/Schedule customers Schedule customers in order to know when they will arrive

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Implicit strategies Explanation

Price differentials Different prices for different customers

Service differentials Different services for different customers

Complementary services Additional services for customers

Substitute services Replacing services for customers

Inform and educate customers Informing customers when it is busy and vice versa

Advertising Advertising to increase demand or reach certain

level of demand

Table 2.1: Demand management strategies (Adapted from Klassen and Rohleder, 2002)

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2.2 Quality of service

Quality has become a recognized strategic tool to increase the efficiency and improved performance in both the goods and service sector (Jain and Gupta, 2004). Over time it has been shown that quality exhibits a positive relationship with improved profits and customer satisfaction. It has also been argued that organisations with superior quality outperform the organisations with inferior quality (Jain and Gupta, 2004). Furthermore, it is an opportunity for organisations to differentiate themselves from fierce competition. Service quality has been differently defined from “conformance to requirements” to “one that satisfies the customer” (Jain and Gupta, 2004).

For airports, service quality is focussed on how passengers perceive the quality of service as well as objective measures of service delivery (Airports Council International, 2014). This is an important area of airport performance and it reflects the activities of the airport in order to deliver a higher standard of service on a continuous basis, ranging from airport cleanliness to waiting times. The International Air Transport Association (IATA) together with the Airports International Council (ACI) and key aviation stakeholders have developed a reference manual which provide performance measures for airports. These measures are categorised in Core, Safety and Security, Cost Effectiveness/Productivity, Environmental, Financial/Commercial and Service Quality. Each of these performance measures are divided into key performance indicators. The Service Quality key performance indicators are outlined in the table below (Table 2.2), for a complete list of all the indicators in the other categories see Guide to Airport Performance Measures (2012).

Indicator Definition by ACI

Practical Hourly Capacity Maximum aircraft movements per hour assuming average delay of no more than four minutes, or such other number of delay minutes as the airport may set.

Gate Departure Delay Average gate departure delay per flight in minutes—measured from scheduled departure time at average and peak times.

Taxi Departure Delay Average taxi delay for departing aircraft per flight in minutes -- measured by comparing actual taxi time versus unimpeded taxi time at average and peak times.

Customer Satisfaction Overall level of passenger satisfaction as measured by survey responses.

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Security Clearing Time Average security clearing time from entering queue to completion of processing -- measured at average and peak times.

Border Control Clearing Time Average border control clearing time from entering queue to completion of processing -- measured at average and peak times.

Check-in to Gate Time Average time from entering the check-in queue to arrival at the boarding gate -- measured at average and peak times.

Table 1.2: Airport performance indicators (Adapted from Guide to Airport Performance Measures, 2012)

One of the key indicators in the Service Quality is the security clearing time. This indicator is measured from the moment a passenger enters the queue before the security until the moment the passenger is cleared through security. Within the Airport development reference manual is stated that the security clearing time indicator should be measured both on average days and peak days.

2.3 Conceptual framework

The effect of the public announcements on the security clearing time is unclear. Demand management is concerned with the ability to accurately forecast, increase flexibility and decrease the variability in the demand. It is expected that the public announcement will influence the timing of the demand and due to these change the forecast may become inaccurate. This in turn may affect the security clearing time as an inaccurate forecast could result in capacity problems. Therefore, the conceptual model of this study is as follows.

The objective of this study is to find out if a relationship between the public announcement and the security clearing time exists and to which extent the public announcement has effect. Therefore, the research question that underlies this conceptual model and objective is:

Public announcement

Forecast Timing of demand

Security clearing time

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How do the public announcements, as a demand management strategy, influence the security clearing time at airports?

For this study, a number of propositions are formulated in order to test these with the analysis of the data. The first proposition is that public announcement will influence the arrival time of passengers at the airport. This means that passengers arrive at the airport with more time to spare before their flight departures on its scheduled time, which is the purpose of the public announcement. It is expected that the public announcements influence the arrival time in such a way that the passengers arrive an hour earlier at the airport as this is recommended in the announcements. The second proposition is that the change in the arrival time of passengers will affect the accuracy of the forecast as it is expected that the forecast does not account for these changes. This is due to the fact that the forecast is made by the airport and the public announcement is made by the airlines. As a consequence, the expectation is that the security clearing time increases due to the changes in the timing of the demand and insufficient capacity as the forecast becomes inaccurate. In summary:

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Chapter 3 RESEARCH DESIGN

In the effort to research the influence of public announcements as a demand management strategy on the security clearing time, a case study has been executed. A case study allows the phenomenon to be studied in its natural setting and gain a full understanding of the nature and complexity of the phenomenon (Yin, 2012). Furthermore, since this study addresses the ‘How’ question, the case study is most relevant (Yin, 2012). In addition, a case study lends itself for exploratory research and due to the exploratory nature of this study, case study seems, even more, an appropriate fit. For this reason, a multiple (embedded) case study is selected, due to the exploratory nature and since the aim of the study is to identify a relationship between the variables, e.g. building theory. The case study is conducted at a single organisation (AAS) at which multiple cases can be identified due to the different paths passengers follow depended on their destination. The multiple case study allows for more robust conclusions in comparison with a single case study since it allows for clarification whether the observed result is replicated by some cases (Yin, 2012). Furthermore, the generalisability of the research findings is higher when using multiple cases.

Amsterdam Airport Schiphol is selected for this study as this airport, and the airlines at this airport, make public announcements towards its passengers via the media and experienced queues at the security lanes. Furthermore, the media also addressed long waiting times at the security checks at times the public announcement were made. Amsterdam Airport Schiphol processes nearly 64 million passengers each

year. It is the 3rd largest airport in Europe and an important hub in the aviation industry with 322 direct

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Cases Type

Case 1 Schengen

Case 2 Non-Schengen

Case 3 Non-Schengen

Table 2.1: Characteristics of cases

3.1 Data collection

The data is collected from different data systems of Schiphol. It is quantitative data and concerned with the time passengers arrive at the airport before their flight departures, the number of passengers forecasted, number of passengers that actually passed through the security check (Input and Output) and the security clearing time of passengers. The data is arbitrarily chosen due to the expected duration of the effect of the public announcement and covers 16 days in total of which 8 days in a holiday period without announcements and 8 days in a holiday period with announcements. The reason for both periods being a holiday is to ensure comparison between similar days since the public announcement is made in a Dutch holiday period. The data gathered from the systems are counts of passengers which are merged into 15 minutes’ intervals. For example, at 10:00 AM, there are 150 passengers going through the Self-Service Boarding Pass Check (SSBPC), which is the start of the security check (See Appendix A). At 10:15 there are 200 passengers going through the SSPBC. This means that between 9:45 and 10:00, 150 passengers have gone through the SSPBC and between 10:00 and 10:15, 200 passengers. In cumulative this would mean that, at 10:15, 350 passengers have entered the security check since 9:45. This is a limitation of the study as some patterns might get lost due to these 15 minutes’ blocks. An excerpt of the data that is used is given in Appendix B. The excerpt shows data for security filter 2.

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3.2 Data analysis

The method of time series analysis is used in this study to analyse the quantitative data. More specifically, intervention analysis is performed. Intervention analysis evaluates the impact of an intervention by comparing the performance on the security clearing time in a condition in which the intervention is absent with one in which the intervention is present. Figure 3.1 shows the steps this research follows in consecutive order. The first step is to measure the extent to which the intervention had effect. In this case, it refers to whether or not the passengers respond to the public announcement and how much earlier the passengers arrive at the airport because of these announcements. Therefore, the distribution of the arrival time of passengers before departure of the flight is analysed in percentages. The percentages are taken because the total number of passengers differ per day and this enables to compare different days with each other.

The times are given in intervals due to the nature of the data. The next step is the analysis of the actual input of passengers compared to the forecast. This is done to identify whether or not passengers arrive earlier than the forecast which may result in complications in the capacity deployment at the security. The analysis is made in cumulative numbers. The third step is to identify changes in the patterns of the demand that could affect the security clearing time. This is done by comparing the input and forecast on a day with the announcement with the input and the forecast on a day without the announcement. The analysis is done in cumulative percentages as the total number of passengers processed differs per day and the cumulative percentages allow for pattern identification. The third step in this research is to analyse the performance of the security clearing time with the public announcement and without the public announcement. The data is used to construct cumulative throughput diagrams. These diagrams allow to identify where queues are starting to build up and how much passengers are waiting in line. Furthermore, the diagrams show the security clearing time. However, due to the structure of the data being in intervals of 15 minutes, the security clearing time, using the diagrams, can only be measured by guessing or interpolation. For this study, however the security queue length is compared with the BLIP data as this allows to identify times within the 15 minute intervals and are more accurate. In addition, it contributes to the triangulation of data.

Step 1

Arrival analysis

Step 2

Absolute forecast analysis

Step 3

Relative forecast analysis

Step 4

Security clearing time analysis

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3.3 Quality criteria

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Chapter 4 ANALYSIS

This chapter presents the findings of the different cases in this study. For the study a total of 16 days are analyzed, 8 days without the announcement and 8 days with the announcement. A Sunday is used as example due to the reason that, on the Saturday before, the public announcements hit the national news and therefore should show the largest impact on this Sunday.

4.1 Arrival before departure times

A demand management strategy can be either implicit or explicit (Klassen and Rohleder, 2002). Due to the implicit nature of the public announcements studied here, the first step is to analyze whether passengers respond to the announcement. This section presents the findings of the shift in the amount of time a passenger arrives at the airport before departure of their flight. The following graphs shows, for each of the three cases shown in chapter 3, the arrival time of passengers at the start of the security-check in relation to the scheduled departure time of their flight.

Figure 4.1: Arrival before departure times (Case 1)

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Case 1 is a Schengen filter and it is normally recommended for passengers to arrive 2 hours before departure of their flight at the airport. The other two cases are Non-Schengen security filters and for both it is normally recommended to arrive 3 hours before departure of their flight. From the graphs, it can be seen that there is a shift in the arrival time of passengers with respect to the departure time of their flight. However, the shift is not by an hour as expected due to the recommendation in the announcement to arrive an hour earlier. In Figure 4.1 the median on the day without the announcement lies in the interval of 01:30 to 01:45. After the announcement, the median shifts to the 02:00 to 02:15 interval. For case 2 (Figure 4.2), the median shifts from the 01:45 - 02:00 interval to the 02:15 - 02:30 interval. In Figure 4.3, the median shifts from the 01:45 - 02:00 interval to the 02:00 - 02:15 interval. The shift holds for all days that are analyzed. However, they differ in the extent of the shift. The graphs shown here reflect the Sunday, where the largest shift of the median was observed for each of the three cases.

4.2 Absolute forecast analysis

The public announcement as a demand management strategy is made by the airlines with the purpose to shift the demand to other times of the day. The responsibility of performing the security check lies at the airport. The forecast for each of the security filters is made by the airport on which it relies to deploy the right amount of capacity in time so that the passengers can be processed in reasonable time. Due to the fact that the public announcement is made by the airlines, it may be that the forecast for the security does not take the attempt to shift the demand into account which consequently results in capacity problems at the security filter. Therefore, it is expected that the public announcement shows the effect on the input in relation to the forecast as illustrated in Figure 4.4 and defined by the proposition in

section 2.3.This would suggest that the public announcement had effect and the forecast did not take

this into account, which could result in capacity problems at the security filter.

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The following graphs show the input at the security check in relation to the forecast on a Sunday for each of the different filters

Figure 4.5: Comparison input with forecast without announcement (Case 1)

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4.3 Relative forecast analysis

The confirmation that passengers arrive with more time to spare before their flight departures, as found in section 4.1 may smooth or disrupt the arrival pattern at the security filter. However, there was no pattern to be identified (Apart from the fixed pattern found in case 2 and 3) in the relation to the forecast on days without and with the announcement. As explained in section 4.2, this may be due to the significant differences in the forecast in relation to the input, which hinders the ability to identify patterns. Therefore, the input in relation to the forecast is analyzed in percentages so that patterns and differences can be identified. It is expected that the effect of the public announcement is as shown in Figure 4.11 and defined in the proposition in section 2.3. The figure shows that the passengers arrive earlier on the day with the announcement both in comparison with the forecast for that day. Furthermore, it is also expected that passengers arrive earlier than the input and forecast of the day without the announcement. This would indicate that the public announcement had an effect.

Figure 4.11: Expected effect of the public announcement on input and forecast in percentages

The following graphs show the input of passengers on a day without the announcement in comparison with the input of passengers on a day with the announcement in cumulative percentages for each of the different cases. In addition, the cumulative forecast in percentages is also given. Each of the days are

Pe rc en ta ge Time of day

Percentage input and forecast (Expected)

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Figure 4.12: Comparison inputs and forecast in percentages with and without announcement 00:00 – 12:00 (Case 1) 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 00: 00 00: 30 01: 00 01: 30 02: 00 02: 30 03: 00 03: 30 04: 00 04: 30 05: 00 05: 30 06: 00 06: 30 07: 00 07: 30 08: 00 08: 30 09: 00 09: 30 10: 00 10: 30 11: 00 11: 30 12: 00 Pe rc en ta ge Time of day

Percentage input and forecast (Case 1)

Without announcement With announcement Forecast Forecast

45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% 12: 00 12: 30 13: 00 13: 30 14: 00 14: 30 15: 00 15: 30 16: 00 16: 30 17: 00 17: 30 18: 00 18: 30 19: 00 19: 30 20: 00 20: 30 21: 00 21: 30 22: 00 22: 30 23: 00 23: 30 00: 00 Pe rc en ta ge Time of day

Percentage input and forecast (Case 1)

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Figure 4.14: Comparison inputs and forecast in percentages with and without announcement 00:00 – 12:00 (Case 2) 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 00: 00 00: 30 01: 00 01: 30 02: 00 02: 30 03: 00 03: 30 04: 00 04: 30 05: 00 05: 30 06: 00 06: 30 07: 00 07: 30 08: 00 08: 30 09: 00 09: 30 10: 00 10: 30 11: 00 11: 30 12: 00 Pe rc en ta ge Time of day

Percentage input and forecast (Case 2)

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Figure 4.16: Comparison inputs and forecast in percentages with and without announcement 00:00 – 12:00 (Case 3) 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 00: 00 00: 30 01: 00 01: 30 02: 00 02: 30 03: 00 03: 30 04: 00 04: 30 05: 00 05: 30 06: 00 06: 30 07: 00 07: 30 08: 00 08: 30 09: 00 09: 30 10: 00 10: 30 11: 00 11: 30 12: 00 Pe rc en ta ge Time of day

Percentage input and forecast (Case 3)

45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% 12: 00 12: 30 13: 00 13: 30 14: 00 14: 30 15: 00 15: 30 16: 00 16: 30 17: 00 17: 30 18: 00 18: 30 19: 00 19: 30 20: 00 20: 30 21: 00 21: 30 22: 00 22: 30 23: 00 23: 30 00: 00 Pe rc en ta ge Time of day

Percentage input and forecast (Case 3)

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As can be seen from Figures 4.12 and 4.13, passengers arrived earlier on the day with the announcement than on the day without. However, this is also as expected. This can be seen with both the forecast lines, for the day with the announcement, the passengers were also forecasted to arrive earlier than the forecasted passengers for the day without the announcement. Therefore, there is no structural effect of the public announcements as the other days also do not show an effect of the public announcement. At case 2 (See Figure 4.14 and 4.15) passengers arrive earlier at the airport on days with the announcement than on days without the public announcement from 06:00 AM, as seen with the forecast in section 4.2. This is corrected for most days at 08:00 AM. After 08:00 AM the patterns appear to be random again. The same possible reason as given in section 4.2 may hold here; which is that passengers had to wait at the check-in procedure, therefore lost time and arrived later at the security-check which cancels out the effect of the public announcement. Apart from this observation, there is no other effect of the public announcement to be identified in case 2. Figures 4.16 and 4.17 show that, on the day with the announcement, passengers arrive significantly earlier at the airport. However, this is the only day were such a shift was observed. Each of the other days in the analysis show other random patterns (See for an example of another day, Appendix C). This suggests that there is no structural effect of the public announcement at case 3. In summary, Figures 4.12 till 4.17 each show different characteristics between the cases and this also holds for the other days that are analyzed. Due to the randomness in the patterns, it seems that there is no effect of the public announcements.

4.4 Security clearing time

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Figure 4.18: Queue length and security clearing time without announcement (Case 1)

Figure 4.19: Queue length and security clearing time with announcement (Case 1)

From Figure 4.18 and 4.19 can be seen that at times where there is no queue length, the security clearing time fluctuates between the 5 to 10 minutes. A build-up of the queue length results in an increase of the security clearing time. Furthermore, there is a 1 to 1 relation between the numbers of passengers waiting in line and the increase in the security clearing time. This is due to the variable capacity that is deployed and the achieved productivity of the filter. A higher number of passengers in the queue might result in an addition of capacity or an increase in productivity which results in a smaller increase in the security

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Figure 4.20 and 4.21 again show that the security clearing time fluctuates between the 5 to 10 minutes when there are no passengers in the queue, also seen at case 1. At most the security clearing time is fluctuating around the 15 minutes on the day without the announcement. On the day with the announcement, the security clearing time increases to around the 20 minutes. This is a small increase in the security clearing time. The relationship of que length and security clearing time again appears to be 1 on 1 and are closer to each other in comparison with case 1. As soon as the que length increases, the

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In this case, the security clearing time does not increase significantly as shown in Figure 4.22 and 4.23. The security clearing time fluctuates around the 15 minutes for both days with and without the announcement. There is one small peak where the security clearing time reaches the 20 minutes’ mark. On the other days, the security clearing time also fluctuates around 15 minutes and does not increase nor decrease with the announcement. Even though the forecast was significantly off as seen in section 4.2, the security clearing time did not increase.

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4.5 Summary of findings

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Chapter 5 DISCUSSION

This chapter discusses the findings following the 3 propositions formulated in Section 2.3.

Proposition 1: Passengers arrive earlier at the airport before departure of their flight

Klasssen and Rohleder (2002) identified that informing and educating customers is an implicit demand management strategy. Meaning that the choice to respond to the information lies with the customer. This is also true for the public announcements. The findings show that passengers arrived earlier at the airport before departure of their flight. However, the extent of the shift in the median differ per day as seen in section 4.1. Overall can be said that the proposition can be confirmed. Passengers indeed arrive earlier at the airport with more time to spare before the scheduled departure of their flight.

Proposition 2: The forecast does not take the shift in the demand into account

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Proposition 3: Security clearing time increases because of above propositions

The service quality indicator Security clearing time as defined in the Guide to airport performance measures (2010), did neither increase nor decrease. The findings show that the security clearing time in only one instance increased by a marginal 5 minutes. Furthermore, the patterns identified at the case 2 and 3 did not have a relation to this increase of 5 minutes. It appears that the public announcement does not have an effect on the cases studied here and the proposition is not confirmed.

5.1 Managerial implications

This study showed valuable insights for practitioners to manage the security check at airports. Based on literature and empirical insights it is shown that the public announcements do not increase the security clearing time at airports. Furthermore, the study shows that an inaccurate forecast does not relate to an increase in the security clearing time. This indicates that for these subjects there is no action that has to be undertaken for practitioners regarding the public announcements and the security check.

5.2 Research implications

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Chapter 6 CONCLUSION

Conclusively, this study adopted an exploratory approach to the effect of public announcements as a demand management strategy on the security clearing time at airports. A multiple embedded case study was conducted at a single airport to answer the following research question.

How do the public announcements, as a demand management strategy, influence the security clearing time at airports?

It was found that the public announcements had no effect on the security clearing time for the cases studied here. The findings did show that there was a shift in the time passengers arrived at the airport before departure of their flight. They had more time left, which indicated an effect of the public announcement. However, the extent of the differed per day and passengers did not arrive an hour earlier as recommended in the public announcements. From literature was deducted that demand management influences the ability to forecast and changes the timing of the demand. However, no relation was found between the effect of the public announcement on the forecast and the timing of the demand. This was due to the inaccuracy of the forecast. Therefore, an analysis was performed using percentages. This analysis showed random patterns for each of the different days both within the cases and across the cases. The findings showed that passengers arrived on different times (earlier, later or same time) in comparison with the forecast for that day and the arrival times and forecast on days without the announcement. Furthermore, an increase in the security clearing time by a margin of 5 minutes was found, however this could not be related to the public announcements.

6.1 Limitations and future research

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Press articles

Drukte meivakantie groeit Schiphol boven het hoofd. (2017). [Online] Available at:

https://www.telegraaf.nl/nieuws/133368/drukte-meivakantie-groeit-schiphol-boven-het-hoofd [Accessed May 8, 2017]

Weer grote drukte op Schiphol door meivakantie. (2017). [Online] Available at:

https://www.rtlnieuws.nl/nederland/weer-grote-drukte-op-schiphol-door-meivakantie [Accessed May 8, 2017]

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Chapter 8 APPENDICES

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Appendix C

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 100% Pe rc en ta ge Time of day

Percentage input and forecast (Case 3)

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Airport management: The impact of public announcements on security clearing time