Interactive Method for Service Design Using Computer Simulation

Makino, Furuta, Kanno, Yoshihara, and Mase: Interactive Method for Service Design Using Computer Simulation Service Science 1(2), pp. 121-134, ©2009 SSG

Interactive Method for Service Design Using Computer Simulation Yuki Makino, Kazuo Furuta, Taro Kanno The University of Tokyo 7-3-1 Hongo, Bunkyo-ku Tokyo, 113-8656, Japan [email protected], [email protected], [email protected]

Shigeki Yoshihara, Takamichi Mase ANA Strategic Research Institute Co., Ltd. 1-5-2 Higashi-shimbashi, Minato-ku Tokyo, 105-7133, Japan [email protected], [email protected]

A

n interactive method for service design has been proposed for services that heavily depend on human expertise and performance. In this method a simulation model of the service processes is to be constructed based on ethnographic field observation, and then the model is to be validated by showing simulation results to the field experts in a visualized form. In the course of proposing and assessing design options, opinions are repeatedly acquired from the field experts also by showing simulation results. Their expertise can be reflected thereby in the final design through such an interactive design process. In order to demonstrate the effectiveness and usefulness of the proposed design method, the method was applied to ground aircraft operations at a large airport. A simulation model of the service processes at Tokyo International (Haneda) Airport was constructed, and a simulation system was developed on a Java platform for Windows PCs. It was then demonstrated that the simulation could well replicate the observed data of ground aircraft operations. It was also shown that the proposed design method was useful to create new design options for ground aircraft operations and comparatively assess them for improving service performance. Key words: service design; interactive design method; computer simulation; ground aircraft operations; ethnography; visualization History: Received June 18, 2009; Received in revised form Sept. 22, 2009; Accepted Sept. 25, 2009; Online first publication Sept. 30, 2009

1. Introduction The center of industries is gradually shifting toward services rather than agriculture or manufacturing particularly in industrialized countries, and the productivity of services has a great impact on the international competitiveness of these countries (Council on Competitiveness 2004; Spohrer and Maglio 2008). The concept of a Product-Service System (PSS), which is an integrated form of products and services capable of fulfilling customer’s needs, is being accepted in the recent business scene (Oliva and Kallenberg 2003; Baines et al. 2007). Meanwhile some authors focus on the potential of PSSs for innovation by improving the sustainability or eco-efficiency of businesses (Manzini and Vezzoli 2003; Tukker 2004). Planning and designing service systems properly are therefore highly expected. Intangibility, perishability, simultaneity, and heterogeneity feature services compared with other industries or products. In addition, services usually rely heavily on human expertise and performance. These features make it difficult to apply the design methods developed for physical products directly to services; some new approach of service design is necessary. Performance measurement from a comprehensive viewpoint is another critical issue in service design. Neely et al. (1997 2005) have proposed a framework for designing performance measures and demonstrated the practical validity and utility of the framework (Neely et al. 1997 2005). How to model and account human performance in performance measurement is still a non-trivial issue. A variety of approaches have been proposed so far for analysis and design of service systems. Analysis methods based on video ethnography (Buur et al. 2000) or observation of human performance (Kumar 2004) are useful for understanding customers’ behavior in services. Shostack (1982 1984) proposed a method called service blueprint to 121

Makino, Furuta, Kanno, Yoshihara, and Mase: Interactive Method for Service Design Using Computer Simulation Service Science 1(2), pp. 121-134, ©2009 SSG

describe activities in a chart to show the cooperation between service providers and customers. Arai and Shimomura (2004) proposed a service CAD (Computer-Aided Design) system where one can define and describe an overview of service flow to design and assess service systems. As for modeling human performance in computer simulation, Baines and Benedettini (2007) proposed a practical framework to incorporate workers’ performance into discrete event simulation for manufacturing systems design. Though these methods are useful for understanding the reality and architecture of services, they are so weak in describing dynamic service processes that they are sometimes little useful for detailed design or quantitative assessment of service systems. In addition, they prescribe neither methodological guidelines for acquiring human expertise that often plays crucial roles in services, nor exact models for relating human performance to the influencing factors of human performance. An example of service where an effective design method is desired is operation of a large airport. The quality of airport service has some problems recently such as increasing delays in arrival and departure of aircraft due to heavy air traffic. Extension of runways, terminal buildings, and other ground facilities is planned or under way at many large airports worldwide. The expanded capacity, however, does not function as expected, unless operations of airport service are appropriately designed and managed. Though computerization and automation have already been introduced into airport service, its considerable parts still depend on human expertise and performance, because a lot of contextual factors affect airport service. This situation makes it impossible to model the service processes mathematically and then to apply conventional optimization methods. The aim of this work is to develop an interactive method for service design, where human expertise and performance play crucial roles. We will propose a human-centered design process, where intensive use of computer simulation enables one to design service systems interactively with the help of field experts. In addition to ethnographic analysis, which is suitable for revealing the reality of service processes and extracting knowledge from the field experts, an agent-based approach for modeling and simulation is used. Agent-based simulation can replicate dynamic service processes with relatively less approximation and the model used can be validated in comparison with field data. We will then demonstrate the effectiveness and usefulness of the proposed method by applying it to airport service: aircraft operations on the ground of a large airport. In aerospace industries, human performance modeling and simulation environments have been developed for design, visualization, and assessment of complex humanmachine systems for cockpit design (NASA 2009), but such technologies have hardly been applied to airport service where interactions among many actors play crucial roles. Some previous studies dealt with ground aircraft operations in preparation for extension of the runway capacity (Kazda and Caves 2007). Almost no studies, however, considered microscopic interactions of aircraft like delays caused by inappropriate ordering of pushback operations in departure. Our interview to the field experts of an airline suggested that such interactions influence considerably the efficiency of aircraft operations. In this study, we will try to construct a simulation model for ground aircraft operations from a microscopic view so that we can assess and design the service system considering dynamic and more detailed service processes. The service design method to be proposed will be explained in the next chapter and then construction of a simulation model for the application domain will be discussed in Chapter 4. The next chapter discusses implementation of the simulation model and then shows the results of test simulation to demonstrate the validity of simulation. In Chapter 6, how the simulation system developed in this study is used for proposing and assessing new design options will be presented, and finally conclusions will be given.

2.

Interactive Design Method

2.1 Overview We will propose a human-centered and interactive design method for service systems where the expertise of service providers can be reflected in service design. As shown in Figure 1, the method consists of three phases: service modeling, service simulation, and service planning. 2.1.1 Service Modeling Firstly a model of the service processes is to be constructed through ethnographic field observation. Ethnomethodology is a study method of sociology to find out some implicit orders, rules, or norms behind human behavior through observation in the actual work environment. Suchman (1987) pointed out that an ethnographic approach is required when a question is to be answered what knowledge and experience people use in a cooperative work. Service modeling should start by recording tasks, decisions, behavioral patterns of service staff in the actual 122

Makino, Furuta, Kanno, Yoshihara, and Mase: Interactive Method for Service Design Using Computer Simulation Service Science 1(2), pp. 121-134, ©2009 SSG

service operations, interviewing the staff, and analyzing the obtained data. This approach enables one to extract the knowledge of field experts that is required to create, modify, and improve the model. It is a key process for humancentered design of service systems, where human factors should be properly considered and reflected. 2.1.2 Service Simulation In service simulation, the whole service processes are replicated in a virtual environment within a time period shorter than the real time. Various data obtained from the simulation in addition to the simulation model itself are visualized in order to easily understand problems in the service system. Showing the visualized simulation results to the field experts belonging to different sections of service provider will enhance understanding and sharing of the problems among them. Solution of the problems from a comprehensive viewpoint is thereby expected. Such an interactive and cooperative process is very useful for decision-making in service design. It is also effective for validation of the simulation model from a viewpoint of the field experts. 2.1.3 Service Planning In the final phase of service planning, possible solutions are to be proposed to the problems found in the previous phase, assessed by simulation, and design decisions will be made. Design options are assessed from various viewpoints: customer and staff satisfaction, task difficulty, costs, and so on. The result of assessment is to be reviewed by the field experts again. As a consequence, new problems such as external costs and safety concerns may be found and further improvement may be added to early options. It is sometimes necessary to repeat this process to gradually brush up design options to be adopted finally. The model constructed at the beginning will undergo modification and improvement from the needs of field experts. It is realized by repeating simulation, presentation of the results to the field experts, and modification of the model considering the obtained feedbacks. As described in the above, the design method proposed in this work is not straight forward, but it is carried out as a cycle of proposal, assessment, and review. Simulation and presentation of its results to the field experts are repeated in this cycle, and we call the method interactive in this sense. Figure 1 Interactive Design Method for Service Systems

2.2 Model Development An agent-based approach (Ferber 1999) has been adopted for modeling the service processes. An agent-based approach is a method of studying phenomena in a complex system by observing the macroscopic system behavior that emerges from microscopic interactions between agents. An agent is defined as an autonomous entity that can perceive the surrounding environment, make a decision, and change behavior adapting to the environment. The environment here stands for the world outside of the system. A collective system modeled as a set of many agents is called a multi-agent system. 123

Makino, Furuta, Kanno, Yoshihara, and Mase: Interactive Method for Service Design Using Computer Simulation Service Science 1(2), pp. 121-134, ©2009 SSG

An agent-based approach has the following merits as a method of study. Firstly, it can deal with the phenomena emerging from microscopic behavior of system components. Some order exists in emergent behavior of a complex system even without any central or top-down controls. It is usually the case with service systems and an agent-based approach is suited for modeling service systems. Secondly, a multi-agent system can be a natural representation of a complex system when agents can be defined corresponding to actual system components. As for service systems, it is comprehensible if each provider, receiver, and resource is modeled as an agent. Finally, a multi-agent system is easy to modify and expand, because agents can be developed, tested, added and modified separately. It is preferable not only for rapid prototyping of the model but also for the interactive design process. Model development progresses in seven steps shown in Figure 2. Each step is performed as explained below. (1) Definition of System Boundary The boundary that separates the target service system and the environment is to be defined. The world outside of the boundary is described with boundary conditions, i.e., fixed simulation scenarios. (2) Identification of Agents The persons and organizations that have relations to the target service are to be identified, defined, and represented as agents. Service providers and receivers are the people to be considered first, but other stakeholders may exist. Figure 2 Progress of Model Development

(3) Identification of Services Services exchanged within the system are to be identified and described. Intra-organizational services should be included as well. The providers and receivers for each service, the roles they play, the channels and media they use are to be identified and described. (4) Identification of the Environment The components of the environment that exist outside of the system boundary but affect the service processes are to be identified and described. Their behavior is given as prescribed scenarios, or they are often modeled as static agents. 124

Makino, Furuta, Kanno, Yoshihara, and Mase: Interactive Method for Service Design Using Computer Simulation Service Science 1(2), pp. 121-134, ©2009 SSG

(5) Description of Agent Behavior Behavior of each agent is to be described. An agent usually has some internal mechanism to change actions of its own depending on the status of the environment and other agents. The rules used for selecting actions and for changing the internal state of agent are described. (6) Description of Communication Communication among agents is to be described. Every interaction among agents that occurs to achieve specific goals in the service processes is represented as communication. (7) Model Validation The simulation model is to be validated by implementing the model as an executable simulation program, carrying out simulation, and checking obtained simulation results. Visualization of simulation results is very useful also for model validation.

3. Service Modeling for Ground Aircraft Operations The proposed method was applied to ground aircraft operations at a large airport, Tokyo International (Haneda) Airport, for demonstration of its effectiveness and usefulness. In this chapter, it is explained how a model of the service processes was constructed for the concrete application domain. 3.1 Knowledge Acquisition Through Field Visit and Observation Through visit to Haneda Airport and interview to the staff in various sections there, we investigated the actual service processes and their situations at the site of work. The places visited include ground facilities, the operation control center of an airline, towing cars, and so on. The interviewees were operators of towing cars, pilots, and operation controllers. As a result, the flow of activities and the roles of field staff were clarified. Documents on the layout and specs of ground facilities were separately obtained. In next step, we video-recorded the movements of aircraft from different locations, in particular around the second terminal building of Haneda Airport, and voice-recorded the radio communication between pilots and ground controllers. From the video and voice data, the time required for arrival and that for departure were evaluated and the taxiing routes instructed by ground controllers were identified. These data were then transcribed and used as a basis for acquiring the rules that pilots and ground controllers use in their work. Since an aircraft cannot move backward by itself in departure from the spot, a towing car must push it back. This operation is called “pushback.” The pushback patterns that are used for each spot following ground controller’s instruction were examined and tabulated together with their application conditions. Preliminary results of simulation were shown to operation controllers, pilots, ex-pilots, and ex-controllers for qualitative check in the course of model development by prototyping. The obtained comments were fed back for redesign of the simulation model. This process was repeated a few times. 3.2 Simulation Model for Ground Aircraft Operations The service processes were next modeled based on the data obtained through the field visit and observation. The agents identified in the application domain are aircraft (pilots), ground controllers (air traffic controllers), operation controllers, and passengers. An overview of the ground aircraft operations can be illustrated in Figure 3, which consists primarily of air traffic control service, transportation service, and in-company operation service. Service here stands for some sort of activities through which the provider changes the physical or mental state of the receiver to fulfill receiver’s needs, and a service agent is the provider or receiver of the service. Every service is performed between providers and receivers. As for the air traffic control service, the providers are ground controllers and the receivers are aircraft. Radio is used as the physical communication channel, where messages are exchanged between the providers and receivers. It is the basic pattern of communication in this service that aircraft request permission to make a move and the ground controllers give instructions on the move. An aircraft plays a role of the receiver in the air traffic control service, but it provides the transportation service to passengers at the same time. An aircraft is the medium of this service, with which physical transportation of passengers is realized. Since assessment of passenger satisfaction was beyond the scope of this study, we did not explicitly model the transportation service and passengers. The operation controllers of an airline provide the operation service to aircraft. They also use radio as the physical communication channel for this service. The operation controllers determine the timing of departure considering the flight schedule and the status of aircraft. If the pilot of departing aircraft has received an instruction 125

Makino, Furuta, Kanno, Yoshihara, and Mase: Interactive Method for Service Design Using Computer Simulation Service Science 1(2), pp. 121-134, ©2009 SSG

from the operation controller, he will request a departure clearance to the ground controller. The operation controller carries out his/her tasks following some scheduling policy of the airline. Figure 3 Overview of the Service System.

Operation controller

Ground controller Operation service

Air traffic control service Aircraft

Service

Weather Transportation service Ground

Service agent Environment

Passenger

3.3 Agent Model Relevant actors in the service system are modeled as agents. Each agent has some internal states defined by the agent class, and an agent changes behavior in different states. One can handle the action rules of agent in a modular form by defining agent’s internal states. The action rules as well as the state transition rules were extracted from some documents or from the data obtained through the field observation and implemented into the agent models. State transition is determined by the current state of agent and the event that triggers the transition, i.e., the message that the agent receives. The agent is therefore modeled as an automaton. At the moment, errors or malfunctions are not considered in modeling agent behavior, because the simulator first aims at replicating system performance under normative conditions. Service resources such as space, time, manpower, and equipment are not considered explicitly in decision-making of agents, but some constraints on these resources are reflected in simulation scenarios. The spot already occupied by some aircraft, for example, can never be assigned as the destination of an arriving aircraft in duplicate. Neither the cost nor the priority of tasks except the length of taxiing route is considered, either, but the action rules and state transition rules obtained from the field data implicitly reflect these factors. Explanations of the primary agent models are given below. 3.3.1 Aircraft Model The agent representing an aircraft has nine internal states: landing, arrival, stay, towing, pushback, stay after pushback, departure, lineup, and takeoff. An arriving aircraft is generated as an aircraft agent at the end of runway, and its state is set “landing” at the beginning. Having exited from the runway, the aircraft agent requests the taxiing route to the ground controller agent, changes its state to “arrival,” and moves along the taxiing route to the spot of destination. When the aircraft agent arrives and stops at the final destination, it changes its state to “stay.” A staying aircraft agent does nothing, but it requests a clearance to the ground controller agent and changes its state if it receives any instruction from the operation controller agent. An operation to move an aircraft from the spot staying at present to another using a towing car is called towing. If a staying aircraft agent receives a towing instruction, it is towed in the state of “towing” to the spot of destination and stays there again. If the operation controller agent gives a pushback instruction, a staying aircraft agent changes its state to “pushback” and it is pushed back following the pushback pattern instructed by the ground controller agent. Having 126

Makino, Furuta, Kanno, Yoshihara, and Mase: Interactive Method for Service Design Using Computer Simulation Service Science 1(2), pp. 121-134, ©2009 SSG

finished the pushback operation, the aircraft agent changes its state to “stay after pushback.” After separation of the towing car and start-up of engines in this state, the aircraft agent requests a taxi clearance to the ground controller agent. A departing aircraft agent that has been instructed the taxiing route changes its state to “departure,” and starts to move along the taxiing route. When the aircraft agent enters a runway after having been approved entrance to runway, it lines up and waits for a takeoff clearance in “lineup.” Having obtained the clearance, the aircraft agent changes its state to “takeoff” and starts a takeoff action. When the aircraft agent in takeoff reaches the system boundary, it is eliminated from the system model. An aircraft agent holds state variables like the current position, moving direction, and velocity in addition to static parameters like the hardware specs of aircraft. An aircraft agent moves on taxiways and runways following instructions from the ground controller agent. An aircraft agent moves toward the preliminary destination point along the taxiing route, and it moves further to the next destination point if it has passed the previous one. Such behavior to follow consecutive points along the taxiing route is consistent with the actual movement of an aircraft. The steering and velocity control model determines movement of an aircraft agent. An aircraft agent takes obstacle avoidance behavior to slow down its velocity if another aircraft agent enters the area of pilot’s cognition, which is defined for each aircraft agent. The velocity control model used in this work is basically equivalent to the optimal velocity model (Bando et al. 1995), which has been proposed as a microscopic velocity control model of vehicles in traffic congestion. 3.3.2 Ground Controller Model Seven states are defined for the ground controller agent: standby, arrival, pushback, departure, towing, approach, and takeoff. Shifting among these states, the ground controller agent instructs many aircraft agents to move on the ground of airport without any interference. In “standby,” the ground controller agent has no specific tasks but monitoring the status of aircraft and the ground. The controller agent is continuously scanning the current locations of aircraft agents and predicting their movements. If the controller agent receives a request for taxi from an arriving aircraft agent, it plans the taxiing route toward the arriving spot and gives necessary instructions in “arrival.” When the aircraft agent that has got ready to depart requests a pushback clearance, the ground controller agent determines the pushback pattern to be used and gives a pushback instruction in “pushback.” The selection rules of pushback pattern acquired from the field observation implicitly reflect the taxiing cost afterwards. Having been requested a clearance for taxi, the controller agent plans the taxiing route to the runway and gives instructions on the route in “departure.” The controller agent that has received a towing request plans the towing route and gives instructions on the route in “towing.“ The controller agent gives permission to enter the runway in “approach,” and then gives a takeoff clearance to an aircraft agent awaiting takeoff in “takeoff.” The agent representing ground controllers performs its tasks in three steps: recognition of the environment, prediction of aircraft movements, and decision-making for instruction. The controller agent recognizes the present situation based on the information collected from the external environment: the status of the ground, locations of aircraft agents, weather conditions, messages from aircraft agents, and so on. It then predicts aircraft movements in the near future, makes a decision by the internal reasoning mechanism, and then gives instructions to aircraft agents. The reasoning mechanism is based on a conventional rule-base system that uses the action rules extracted from the field observation. In addition, the controller agent plans taxiing routes by A* algorithm considering route length as the taxiing cost, but taxiing routes are restricted within specific alternatives that are usually used in the actual operations. The controller agent handles ground traffic in a FCFS (First Come First Served) style, which is the same as in the actual operations. This service style together with the obstacle avoidance behavior of aircraft agents can implicitly resolve the constraints on space and time. 3.3.3 Operation Controller and Environment Model The operation controller agent gives instructions to aircraft agents referring to the flight schedule and considering the present status of aircraft agents. Two types of instructions are possible: departure and towing instruction. The operation controller agent of this study decides the start timing of move with a simple rule to follow the flight schedule. The operation controller agent therefore is just an event generator, and the priority of flights is implicitly assumed in the prescribed flight schedule. The environment model consists of the ground model and the weather model. It represents the field and the surroundings where the airport service is provided. The ground model consists of various ground facilities of airport: runways, taxiways, and spots. A taxiway is a path where aircraft move between runways and spots. Each taxiway consists of many path segments and joints. A spot is a location where an aircraft parks for embarkation, 127

Makino, Furuta, Kanno, Yoshihara, and Mase: Interactive Method for Service Design Using Computer Simulation Service Science 1(2), pp. 121-134, ©2009 SSG

disembarkation, or maintenance. The wind direction is the only component of the weather model at present. It affects the usage of runways, and the ground controller agent decides how to use runways depending on the current status of wind direction. Two patterns are now in use for the runways at Haneda Airport: one for a north wind and the other for a south wind. 3.3.4 Service Process Model Having designed relevant agents for the service system, the protocol and processes of communication were determined. Aircraft agents carry out the service processes by radio communication with the ground controller and operation controller agent. It is the basic process of the air traffic control service that aircraft agents request service to the ground controller agent, and the latter delivers instructions in reply. In addition, the ground controller agent may spontaneously gives instructions judging from the situation of aircraft agents on the ground. Figure 4 shows the flow of service processes.

4. Service Simulation 4.1 Architecture of Simulation System A simulation system has been developed for ground aircraft operations based on the agent-based simulation model explained so far. The simulator development was done on a Java platform for Windows PCs. Most of the information required for setting up simulation cases is given as input data to the simulator so that assumptions for simulation can be modified easily without changing the simulation model. The input data include the layout of airport facilities, flight schedule, weather conditions, and some of the action rules of agents. These data also specify design options of the airport service. The output data of simulator include chronological actions of agents. The simulation controller runs many agents in turn, but the cycle time of simulation, 100 ms, is short enough that agent actions including communication look asynchronous in a long time span. No task interruptions are considered, and the agents handle events sequentially. In addition, it is assumed that agent’s internal process is carried out within a single simulation cycle. These approximations, however, cause no limitations so far for the present simulation purposes, because agent actions are organized in a small unit compared with the whole duration of simulation scenario and because the time required for some object to move in space or for some condition to obtain dominates event timing. Results of simulation are visualized with a graphical user interface shown in Figure 5. Movements of aircraft are displayed by animation of icons on the airport map. Flight data including aircraft type, call sign, agent’s internal state, destination, and so on are shown in text near the icon. The area of pilot’s cognition and the planned taxiing route of each aircraft are also displayed. The messages exchanged between agents are listed in the separate communication window. The visualization makes it easy to find out relevant problems not only for the designers but also for the field experts as a sense of disagreement with the reality, which could not have been detected just by interview. It contributes to brush-up of the simulation model. For example, we modified the model so that unrealistic movements of aircraft and unrealistic controller’s instructions were fixed, and that the agents made decisions depending more on the ground situation than at the early stage of model development. We could also fix unrealistic design options that are unfeasible due to constraints just implicitly shared by the experts. The visualization is also effective to detect problems hardly recognized in specific field sections; one can notice new issues from a comprehensive viewpoint by looking at a variety of information provided by simulation. It therefore can be a good media through which staff members in different service sections communicate each other and share problems and solutions beyond the boundaries between different professions. 4.2 Test Simulation for Model Validation A test simulation was performed to check the validity of the simulation model. The time required for arrival and that for departure were chosen as the performance measure, because they determine whether or not flights are on time and then most affect passengers’ convenience in the airline business. Fuel savings from reduction of delays also contribute to cuts in CO2 emission as well as operation costs. The time required for arrival from entering the field boundary of observation to stopping at the spot, and that for departure from requesting pushback to exit from the field boundary were evaluated from the video taken at the airport for three hours during daytime, December 10, 2007. The simulation scenario used for the test was set up from the flight schedule and the weather conditions during the same time interval.

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Figure 4 Flow of Service Process Passenger

Aircraft (Pilot)

Landing

Operation controller

Ground controller

Landing

Arrival

request Request taxi

Plan taxiing route

Receive taxiing route

Instruct taxiing route delivery

Arrive at spot

Stay

Stop at spot

Embarkation or disembarkation

Standby

Instruct departure or towing

Receive instruction Towing

Pushback

request Request pushback

Plan pushback pattern

Receive pushback pattern

Instruct pushback pattern delivery

request Request taxi

Plan taxiing route

Receive taxiing route

Instruct taxiing route delivery

Arrive at runway end

Lineup

Departure

Stay after pushback

Separate towing car & start engines

Request entry to runway

request

Enter runway

delivery

Request takeoff

request

Entry clearance to runway

Takeoff clearance Takeoff

Takeoff

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delivery

Makino, Furuta, Kanno, Yoshihara, and Mase: Interactive Method for Service Design Using Computer Simulation Service Science 1(2), pp. 121-134, ©2009 SSG

Figure 5 Graphical User Interface of Simulator

Area of pilot's cognition Wind direction

Clock

Time control slider

Communication window

Airport map

Flight data

Planned taxiing route

Time predicted by simulation (s)

Figure 6 Comparison of Time Required for Arrival and Departure

300

200

100

y = 1.016 x r = 0.88 0 0

100 200 300 Time evaluated by field observation (s)

The time predicted by simulation was compared with that evaluated from the video. As mentioned before, the simulation model had been improved to eliminate unrealistic agent behaviors by showing simulation results to the field experts and collecting their comments until the final results were obtained. Figure 6 shows that a correlation 130

Makino, Furuta, Kanno, Yoshihara, and Mase: Interactive Method for Service Design Using Computer Simulation Service Science 1(2), pp. 121-134, ©2009 SSG

coefficient of 0.88 and a regression coefficient of 1.016 were attained between the simulated and measured data; the simulation could well predict the time required for arrival and departure. The simulation system can temporally simulate aircraft movements on the two-dimensional plane of airport and agent actions by the elementary task of ground aircraft operations. Model validation in a microscopic view is therefore possible, where the agent actions simulated are compared with those actually observed in the field from moment to moment. The macroscopic performance measure of time required for arrival and departure was adopted in this study, because the airline is highly concerned about this measure in terms of service quality and it could fulfill the preliminary objective of service design.

5. Service Planning The service simulation presented in the previous chapter revealed problems in the present situation of ground aircraft operations at Haneda Airport. The concave shape of the second terminal building and the four open spots, which are located apart from the building, cause interference between aircraft and consequent delays in arrival and departure of aircraft. Figure 7 schematically shows the layout of ground facilities in front of the second terminal building. The aircraft pushed back from any of the open spots obstruct the taxiway in front of the terminal building. To solve this problem, alternative design options were proposed for usage of the open spots from Spot 81 to 84 considering the comments from those who looked at the simulation results. The time required for arrival and departure was the performance measure also for assessing the proposed options by simulation. Here the time required for arrival was defined as that between exit from the runway and stop at the spot, and the time required for departure between requesting pushback and entrance to the runway. Figure 7 Layout of Ground Facilities in Front of the Second Terminal Building E6

E5

Taxiway

Spot

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56

J6

57 58

81

82

Z

83

R3

59

60

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84 R2

61

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68 67

64

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66

Second Terminal Building

Two alternatives were first examined: Plan A and B. In Plan A, Spot 82 and 83 are removed and converted into taxiways. In Plan B, all of the open spots are removed and the apace is used as pushback areas. Simulations were carried out for the two design options using the scenario based on the actual record of operations for about two hours on June 27th, 2008, which corresponds to rush hour of the day. Figure 8 shows the total reduction in time required for arrival and departure compared with the present operation scheme. The time required for arrival did not decrease greatly, but the total reduction in time required for arrival and departure was around 20 minutes with Plan A and around 34 minutes with Plan B. If the cost of reconstruction is ignorable, Plan B is more attractive than Plan A.

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23

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Figure 8 Time Required for Arrival and Departure (Plan A and B) 2000 Arrival 1200 184 Departure 1500 800

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7 9 11 13 15 Flight Number

The field experts pointed out, however, that the cost of reconstruction for adopting Plan A or B is considerable, and that reduction of available spots is unfavorable because of the planned extension of the airport. Two other alternative plans were therefore considered: Plan C and D. In Plan C, the space of the open spots is used as pushback areas for Spot 58 through 66 during daytime. In Plan D, the aircraft leaving from Spot 56 or 57 is pushed back to Spot 81, and that from Spot 67 or 68 to Spot 84, in addition to the same operation scheme of Plan C. Plan C and D were assessed with a simulation scenario of operations for about two hours on October 7th, 2008. The total reduction in time required for arrival and departure was about 11 minutes with Plan C, and about 20 minutes with Plan D as shown in Figure 9. It was confirmed by the computer simulation that use of the open spot space as pushback areas is effective to avoid interference of aircraft in front of the second terminal building and contributes to speedup of departure. The field experts who looked at the aircraft movements predicted for Plan D stated concerns on safety in terms of the aircraft pushed back to Spot 81 and 84. Consequently Plan C was adopted for further consideration, which is not only effective for efficient aircraft operations but also free from problems in terms of reconstruction cost and safety.

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Figure 9 Time Required for Arrival and Departure (Plan C and D) 1200 1500 Arrival 11 Departure 1000 800

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As described above, an interactive process of identifying problems in the present services, proposing alternative design options to solve the problems, assessing the alternatives, and checking the results of assessment is very effective for human-centered design of service systems. Computer simulation and visualization of its results are key 132

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technologies to realize this process, because the field experts as well as the designers can easily understand simulation results and recognize implicit problems in design options.

6. Conclusion A human-centered design method for service systems that consists of three phases of service modeling, service simulation, and service planning has been proposed in this study. This method relies on ethnographic field observation and computer simulation. An agent-based approach has been adopted for computer simulation, which approach is suitable for representing complex service systems. An interactive process where visualized simulation results are shown to the field experts repetitively enhances rapid prototyping and obtaining valuable feedbacks from them. Construction, modification, and validation of agent models are thereby performed considering the knowledge of field experts. In addition, identifying problems in the present service system, proposing alternative design options, and their assessment are also carried out in a similar interactive process. The proposed method was applied to a specific domain of ground aircraft operations at Haneda Airport for demonstration. A model of the service processes in the application domain was constructed by field visit and observation. The agents relevant to the services were defined and their internal mechanisms were implemented that fit to behaviors observed in the actual operations. A test simulation was performed and comparison of the simulation results with the field data demonstrated that computer simulation of ground aircraft operations is feasible. As a result of test simulation, it was also revealed that the present layout of ground facilities at the airport results in interference between aircraft. Four alternative solutions were proposed and assessed by simulation. It was evaluated that the design option finally chosen can reduce the time required for departure by about 11 minutes for two hours of rush hour operations without concerns on reconstruction cost and safety. This case study showed that the computer simulation was very useful for proposing and assessing design options to solve the problems identified. The interactive method was effective also in this course, because the tacit knowledge of field experts could be extracted and considered in design decisions. Though a specific domain, ground aircraft operations, was the only application domain in this study, the design method is sufficiently general so that it is expected applicable to a variety of service domains that depend on human expertise and performance. It will contribute to realizing rational service design that can consider various dynamic and microscopic factors affecting service processes and predicting service performance in advance. It will probably be a limitation of this method that it can hardly deal with infrequent or inexperienced cases of situations, because it heavily depends on ethnography and experts’ knowledge. Neither errors nor unanticipated events are considered in the present simulation model, either. Simulation will be possible even for such non-normal cases, but feedbacks from the field experts are less expectable and the validity of simulation will be imperfect. Since no other approaches exist, the interactive design method is still useful to a considerable extent within these limitations. References Arai, T., Y. Shimomura. 2004. Proposal of Service CAD System - A Tool for Service Engineering -. Annals of CIRP 53(1) 397-400. Baines, T. S., et al. 2007. State-of-the-art in product-service systems. IMechE Part B: Journal of Engineering Manufacture 221 1543-1552. Baines, T. S., O. Benedettini. 2007. Modelling human performance within manufacturing systems design: from a theoretical towards a practical framework. Journal of Computer Simulation 1(2) 121-130. Bando, M., K. Hasebe, A. Nakayama, A. Shibata, Y. Sugiyama. 1995. Dynamic model of traffic congestion and numerical simulation. Physical Review E51 1035-1042. Buur, J., T. Binder, E. Brandt. 2000. Taking Video beyond ‘Hard Data’ in User Centered Design. Participatory Design Conference 2000, 121-131. Council on Competitiveness. 2004. Innovate America. National Innovation Initiative Report. Ferber, J. 1999. Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison Wesley, Harlow UK. Kazda, A., R. E. Caves. 2007. Airport Design and Operation. Elsevier, Oxford UK. Kumar, V. 2004. User insights tool: a sharable database for global research. Institute of Design, Illinois Institute of Technology. 133

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Manzini, E., C. Vezzoli. 2003. A strategic design approach to develop sustainable product service systems: examples taken from the ‘environmentally friendly innovation’ Italian prize. Journal of Cleaner Production 11 851-857. NASA. 2009. MIDAS: Man-Machine Integration Design and Analysis System. Retrieved on June 4, 2009, http://humansystems.arc.nasa.gov/groups/midas/index.html Neely, A., H. Richards, J. Mills, K. Platts, M. Bourne. 1997. Designing performance measures: a structured approach. International Journal of Operations & Production Management 17(11) 1131-1152. Neely, A., M. Gregory, K. Platts. 2005. Performance measurement system design: A literature review and research agenda. International Journal of Operations & Production Management 25(12) 1228-1263. Oliva, R., R. Kallenberg. 2003. Managing the transition from products to services. International Journal of Service Industry Management 14(2) 160-172. Shostack, L. G. 1982. How to Design a Service. European Journal of Marketing 16(1) 49-63. Shostack, L. G. 1984. Design Services that Deliver. Harvard Business Review 62(1) 133-139. Spohrer, J., P. P. Maglio. 2008. The emergence of service science: Toward systematic service innovations to accelerate co-creation of value. Production and Operations Management 17(3) 1-9. Suchman, L. 1987. Plans and Situated Actions. Cambridge University Press, Cambridge UK. Tukker, A. 2004. Eight-Types of Product-Service System: Eight Ways to Sustainability? Experiences from SusProNet. Business Strategy and the Environment 13(4) 246-260. Biographical Notes Yuki Makino was graduated and obtained B.Eng. from the Department of Architecture, the University of Tokyo in 2007. He was graduated and obtained M.Eng. from the Department of Quantum Engineering and Systems Science, the University of Tokyo in 2009. He is now working for IBM Japan, Ltd. Kazuo Furuta was graduated from the Department of Nuclear Engineering, the University of Tokyo, and obtained B.Eng. in 1981, M.Eng. in 1984, and Dr.Eng. in 1986. He was a researcher, the Central Research Institute of Electric Power Industry (CRIEPI), a lecturer, and associate professor, the University of Tokyo. He has been a professor, the Graduate School of Engineering, the University of Tokyo since 2004. His research interests are now on cognitive systems engineering, social design for safety, and service design. Taro Kanno is an Associate Professor in the Department of Systems Innovation, the University of Tokyo. He received his M.Eng. and Dr.Eng. in cognitive systems engineering from the University of Tokyo. His research interests include human factors and cognitive ergonomics, service cognition, and the modeling and simulation of team and organizational cooperation and coordination for service systems design. Shigeki Yoshihara was graduated from Waseda University in corporate strategy and joined All Nippon Airways (ANA) Co., Ltd. in 1991. He was the manager of the head office on public relations, and then on government and industry affairs. He has been the deputy director on corporate planning since 2005 and at the same time belongs to the ANA Strategic Research Institute Co., Ltd. He is also a panel member of Japanese government on safety transportation of dangerous goods, the International Civil Aviation Organization (ICAO). Takamichi Mase is a chief researcher of the Division of Research and Development, ANA Strategic Research Institute Co., Ltd

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