OR/MS Today - October 1997 - Airline Information Technology

OR/MS Today, October 1997

Journey Management

By Patrick Bitauld, Ken Burch, Soad El-Taji, Elena Fanucchi, Mario Montevecchi, Jim Ohlsson, Anthony Palella, Russ Rushmeier, Jane Snowdon

IBM and Air Canada team up to develop a simulation model aimed at delivering a positive experience to airline passengers as they proceed through airport processes.

Have you noticed that airports seem a little more crowded lately and lines at the ticket, check-in and security counters are longer? That's because the number of flights into and out of hub-and-spoke airports is increasing, disgorging dramatically more passengers into an airport system in North America and Europe that has expanded little in recent years. IBM Research and IBM's Worldwide Travel and Transportation Industry Solution Unit (T&T ISU) are helping airlines and airports use advanced information technology to get passengers through check-in, security and boarding faster, and to ensure planes and flight crews are scheduled for best service and efficiency.

We recently began applying simulation modeling techniques, used for decades to streamline manufacturing lines, to the airport congestion problem with promising results. Simulation models are useful in helping airlines understand what impact new technologies (such as self-service kiosks, voice-recognition check-in, smart cards and electronic ticketing) will have on bottlenecks, personnel needs and customer service levels. IBM is developing a library of building blocks and templates, called the IBM Journey Management Library, for use with a simulation tool to describe airline processes (e.g., check-in) and related new technologies.

Air Canada and IBM have piloted the new modeling approach to analyze domestic passenger processes at Toronto airport. This "as-is" model will serve as a baseline for performing "what-if" studies, which will involve examining the effect of new technology on improving the traveler's experience at Toronto and other airports.

Airline Information Technology
The travel industry has been a leader in the use of information technology for more than 30 years. Around the world, the travel industry is investing more than $10 billion a year on information technology. Government agencies are also investing heavily in information technology. For example, customs and immigration in Europe, the United States and elsewhere around the world have become highly automated, handling a volume of travel which would otherwise be unmanageable.

Continuing worldwide deregulation is helping fuel rapidly growing passenger volumes that must be accommodated in a much slower growing airport infrastructure. With increasing airport congestion and strain on existing terminal facilities, a revolution has begun in the travel industry to make processes more efficient for making reservations, booking tickets or simply seeking information. The end result will be the ability to offer customers a more attractive service, and at the same time, reduce costs and enhance revenue.

Some of the major technology enablers for achieving these results include electronic commerce, electronic tickets, smart cards and self-service kiosks. Airline ticket reservations can now be made and paid for online. Electronic ticketing is gaining popularity as many airlines offer fliers the option of booking flights with electronic tickets. Air Canada successfully implemented electronic ticketing in December 1995.

Smart card technology adds another dimension to travel. Smart cards look like credit cards and contain an embedded computer chip. Lufthansa has used smart cards for frequent fliers on German domestic routes. American Express Company and IBM began piloting the world's first multipurpose corporate smart card in October 1996. The two companies are utilizing this smart card technology combined with electronic tickets in 21 United States' airports using American Airlines' enhanced gate readers. Self-service kiosks deliver fast and direct services to travelers right at the point of service. Asiana Airlines recently launched a new service of automatic reservation, ticketing and check-in using kiosks installed at Kimpo International Airport to enable domestic passengers with carry-on luggage to reserve tickets, buy tickets and check-in.

While airlines recognize that the adoption of new technologies can achieve higher levels of service, they have a difficult time quantifying service level and economic impact of the interactions between new technologies and process changes. By prototyping complex processes and identifying the determining factors, simulation provides an important bridge between proposed ideas and implementation.

Journey Management
Airline "journey management" is concerned with the ability to deliver a positive experience to airline passengers as they proceed through the airport processes. Improved journey management and the associated improvement in customer service extends to airlines the promise of higher customer satisfaction and the capability to cost-effectively process larger numbers of passengers through existing airports. Achieving this promise requires the effective integration, coordination and optimization of information technology into journey management processes.

The primary driver of airport events is the flight schedule. The flight schedule for major hub airports consists of a number of waves of closely spaced arrivals and departures. The passenger load on each flight varies with the type of aircraft, the origin of an arriving flight, the destination of a departing flight, the day of week and the time of day. These factors combine to produce significant peaks in activity in the weekday prime travel periods of early morning and late afternoon.

The passenger service activity at the terminal is tied closely with the flight schedule. Some arriving passengers will be terminating their trip, and need baggage claim and ground transport services. Others will be connecting passengers who will proceed to the gate for connecting flight check-in and boarding. Originating passenger appearances at the terminal are timed ahead of their flight to allow them sufficient time for check-in processing, security checks and boarding.

Since there are definite peaks and valleys of demand for service it would be inefficient to have agents and other resources all available throughout the day. Instead, the opening and closing of facilities such as gates and baggage claim areas is timed to the flight schedule. This idea extends to the use of personnel resources as well.

IBM and Air Canada formed a partnership to develop a baseline simulation model of Air Canada's domestic journey management processes at Toronto airport. The scope of the baseline model consists of ticketing, coach passenger check-in, premium passenger (e.g., first class, elite frequent flyers) check-in, special assistance (e.g., unaccompanied minors, people requiring wheelchairs), special services (e.g., oversized baggage, pets) and gate control (e.g., gate check-in, coupon lift, close-out /reconciliation) processes. This journey management model is intended for use in analyzing the impact of introducing advanced information technology capabilities and evaluating their improvement on customer service.

Modeling Challenges
In the simulation model, the key entities are passengers who move through a set of processes and activities that consume resources. Constructing a suitable model of this airport passenger service process presents a number of challenges in the application of simulation technology.

In many simulation models, the entity arrival process is easily captured by specifying a parameterized distribution such as the Poisson process with a given rate. However, the dependence of passenger appearance on the flight schedule prohibited using this approach. In our model a set of flight pre-departure events was generated at a fixed time interval before the departure time of each flight in the schedule. During the simulation, each of these flight pre-departure events in turn simultaneously kicks off the generation of each passenger on the flight. Each passenger entity is then assigned a terminal appearance event time based on the appropriate distribution of time ahead of flight. The pre-departure event is also used to key the timing of process activities.

Another challenge in this simulation effort was to accurately capture the complexity of the passenger mix and its impact on the requirements for airport services. For example, a different distribution of number of bags needed to be applied to business and leisure passengers. Such distinctions were further broken down by type of travel (domestic versus regional) and even time of day. Immediately upon generation and prior to terminal appearance, each passenger type, as determined by the enplanement forecast, is assigned some key attributes. Based on statistical information about the percentage of each category, a passenger is assigned either as originating or connecting. These attributes work with the processing logic to model the flow of the passenger through the process.

A different challenge was involved in the modeling of agents at the counters. Both full-time and part-time agent schedules are phased in and out over the course of the day to maximize productivity by approximating the peaks and valleys in passenger activity. The result is a resource profile that can vary significantly in each 15-minute interval throughout the workday. The model dealt with this complexity in two ways. The first was to take advantage of the modeling tool's ability to group individual resources into workgroups. Secondly, in the course of the study, a method of transforming agent schedules from manpower planning worksheets into simulation resource downtime schedules was devised. This allowed the modification of resource schedules to be accomplished outside of the simulation interface using either a text editor or spreadsheet application.

Another resource-related modeling challenge was making adjustments in the allocation of resources to activities based on observations of the system. For example, one step of the processing logic is to serve regular customers "unless you see a line forming" at the priority counter. In the model, this procedure was handled by giving priority passengers priority access to a "swing" counter. The definition of the resources for the swing counter then needed to be modeled in more detail and separately from the other counters. Such balance decisions will likely continue to be difficult to capture in process modeling regardless of the type of simulation technology.

Study Results
Air Canada's industrial engineering and quality control organizations provided input data to the model from both current observations and historical patterns. After validating the model with actual data collected for the day of May 2, 1997, we conducted multiple simulation runs for each day of the week of July 7-13, 1997. The runs used forecasted data as input and collected performance measures for all major processes. The measures included peak and average wait times, peak and average number of passengers waiting in line, resource utilization, etc.

The primary objective of these runs was to assess whether predefined standards for passenger service levels were attained. Ticketing standards stipulate that 80 percent of passengers should wait in queue less than five minutes. Similar standards that 90 percent of coach check-in passengers wait less than five minutes and 90 percent of premium check-in passengers wait less than two minutes also hold.

As such, each run took a snapshot of system conditions at each instance when a passenger experienced a service level that did not meet Air Canada's standards. These conditions include time of day, type of passenger, number of passengers in queue, wait time, resources availability and resources utilization.

After analyzing all collected statistics, we focused on investigating conditions where wait time exceeded Air Canada's targets. Those areas for which the target service levels appeared to be inconsistent were the ticketing process and the coach check-in process. Output from the model indicated that a minimum of 87 percent and a maximum of 93 percent of all passengers waiting in the ticketing queue and a minimum of 81 percent and a maximum of 93 percent of all passengers waiting in the coach check-in queue waited less than the target five minutes.

However, a detailed analysis of the ticketing counter process showed that those passengers whose wait time exceeded the service standard experience a consistent and significant clustering of excessive waiting time during a period of about one hour in the morning. A closer scrutiny of the clustering revealed that almost 60 percent of those passengers whose wait time exceeds five minutes waited more than twice the desired standard with about 20 percent of passengers waiting between 25 and 30 minutes. Hence, the ticketing process represents an opportunity to investigate the integration of self-service kiosks to improve the throughput.

In contrast, a more detailed data analysis at the coach check-in process portrayed a different picture. In this case, passengers whose wait time exceeds five minutes seldom do so by more than seven minutes.

This type of observation provides a base for further simulations that seek to integrate new technology enablers into streamlined processes.

Future Directions
We designed the model architecture to facilitate model usage at multiple airports and the incorporation of future enhancements. We made the simulation granular to encompass additional distinctions of such things as passenger type (e.g. multiple levels of elite or frequent flyer passengers). We made the simulation expandable to include additional processes, processing locations or areas of operations. We made the simulation agile to substitute various process alternatives easily to support "what-if" studies. We made the simulation flexible to easily maintain the model for running future studies. Finally, we made the simulation reusable to facilitate the incorporation of model processes for other studies with only minor modifications. Of course, the effective integration of technology into Air Canada's journey management processes at various airports must address both human behavior and industrial engineering considerations.

Human behavior considerations lead to marketing issues because behavior varies as a function of passenger segment. For example, given an understanding of what needs frequent business flyers find important, what features should the new technology-based journey management capabilities offer to satisfy these needs? Needs could include reducing wait time in lines, rapidly changing travel plans in response to irregular operations (e.g., canceled or delayed flights), generating a receipt for expense reimbursement from a self-ticketing kiosk, and using a kiosk for seat selection or gate check-in. The human behavior considerations help define the size of passenger flows in different journey management processes that are simulated. For example, how many eligible passengers will choose to acquire an electronic ticket, and how many will subsequently choose to use self-service devices for check-in?

Industrial engineering considerations must account for the particular characteristics of individual airport facilities when developing scenarios for kiosk placement, queue management, passenger flow facilitation, staff scheduling and staff utilization. Parametric data, which describes the features and usage times associated with new information technology capabilities, impacts these considerations and can be varied to answer "what-if" scenarios. For example, what is the impact to passenger flow in an airport check-in area where self-service check-in kiosks generate baggage tags compared to a scenario where tagging bags is handled at another step in the process?

By constructing reusable process and information technology building blocks within the IBM Journey Management Library, parametric changes to the baseline simulation model enable quick model construction and analysis of alternative scenarios. New models of other airports would require minimal customization. The experience gained in this project reinforces our belief that simulation will continue to play a key role in efforts to structure advanced journey management processes that can accommodate expanding passenger volumes.

REFERENCES

1. Gogg, Thomas J., and Jack R. A. Mott (1995), "Improve Quality & Productivity with Simulation," JMI Consulting Group, Palos Verdes, Calif.

Acknowledgments

The authors acknowledge the help of Air Canada's Quality Assurance Team at Toronto Airport, Sugato Bagchi, Brenda Dietrich and William Tulskie from IBM Research, and Gary Cross, Claude Guay and Ian Smith from the IBM Travel and Transportation Industry Solution Unit.

Ken Burch, Mario Montevecchi and Anthony Palella are senior consultants in IBM's Travel and Transportation Industry Solution Unit. Russell Rushmeier and Jane Snowdon are Research Staff Members in the Mathematical Sciences Department at the IBM T. J. Watson Research Center. Soad El Taji is a Senior Operations Research Analyst; Elena Fanucchi is the Business Innovation Manager; Patrick Bitauld is the Manager of Airport Customer Service Planning; and Jim Ohlsson is the Director of Operations Research and Business Innovation Solutions — all at Air Canada in Montreal.

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