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In the summer of 2001, the Organising Committee for the Athens 2004 Olympic Games (ATHOC) began a project using Systems Dynamics for the design of venue operations. The project team involved 4 analysts, led by two University Professors acting as scientific advisors. Using the iThink software, the team developed models aimed at helping different stakeholders reach an agreed position about the processes taking place in different venues and the resources required to meet expected levels of service. The Athens 2004 Olympic Games will represent, just as all previous Olympiads, a complex, large-scale competition event. Within a period of 16 days, 16,000 athletes from 36 different sports will take part in 300 events across 28 venues located in the Greater Athens area. They will be watched by an estimated 5 million ticketed spectators, together with over 20,000 journalists and broadcasters, and 2,500 members of international committees. With a budget of $5billion, and a workforce of over 175,000 for the duration of the Games, ATHOC's task is to ensure the efficient and effective running of the Games in all competition venues, in a fully co-ordinated manner with non-competition venues (e.g. airport, Olympic village etc) and the city's infrastructure (transportation, city operations etc). For each venue and for each day of the Games, venue operations need to satisfy both functional requirements (the necessary procedures and types of resources) and non-functional requirements (the quality of service provision). The design of venue operations is an activity that involves the majority of functional areas of ATHOC. Out of the total of 27 functional areas, those with most involvement in venue operations are: accreditation, security, technology, transportation, spectator services, venue staffing, logistics, catering, cleaning & waste, sponsors, ticketing, merchandising, broadcasting, press operations, medal ceremonies. We developed models focusing on specific problems areas, such as printing and distribution of competition results, or athletes' transportation, but also on broader problem areas such as the dynamic profiling of an entire venue for the duration of a whole day. These applications ranged from simple processes involving a small set of actors and resources to complicated ones involving venues with multiple stadia and many thousands of 'customers'. Customers may be spectators, athletes, Games staff, Olympic family, broadcasters, journalists, and many others. In this work we adopted a process-oriented approach to modelling, where we examine problems from a customer-oriented perspective. The contribution of various resources was examined at key stages of each process and many scenarios were developed in collaboration with representatives from the affected functional areas. The models were used to examine two facets of the
venue operations system, under certain assumptions: The first facet of the venue operations system, i.e. the behaviour of specific components of the system is examined by parts of the model like the fragment that follows. This fragment is about the various types of services spectators might want to use in a complex venue area, such as buying food at a catering stand or memorabilia at a merchandising stand, withdrawing money from an ATM etc. This issue is of course linked to the overall behaviour of spectators (not shown here), which includes activities such as circulating in the common venue area, queuing for ticket checking, or watching an event inside a stadium. The behaviour of the service model is determined by two issues: the demand for each type of service and the supply of service by the provider of each service type.
The demand is determined through the 'pct of specs per service type' variable. This is a user-defined parameter expressing the customers expected for each type of service facility as a percentage of total spectator presence. The supply is determined by two parameters: the number of 'Service Points' available (e.g. 10 stands selling food), and the 'specs per channel per minute' service rate (e.g. two spectators serviced per service point per minute). Both variables are user-defined, i.e. they can be set through the interface of the model. According to this representation, spectators arrive at the service facility ('going to facilities'), queue there for a while if no service point is available ('Specs Queue at Facilities'), and eventually get serviced ('servicing'). When simulating this model fragment, we get the results shown in the following screen capture, which focuses on the 'Merchandising' service for the duration of an entire day. The results are both graphical and numeric, and concern four separate service areas within the entire venue. The graphical results include spectators queuing at each moment (blue curve), and the waiting time (red curve), while numerical results include the mean and maximum waiting times, as well as the total number of spectators served throughout the day. This simulation panel also contains one of the parameters that determine the behaviour of the merchandising service, i.e. the number of service points in each area of the venue.
The
second facet of the venue operations system, i.e. the overall behaviour of the
system is presented in screens like the one that follows. The screen shows the
profile of spectators' behaviour for the entire day in four key points of the
venue system: arrival, presence in the common area, presence inside venues
(i.e. stadiums) and, finally, departure.
The models were subjected to testing through rapid simulation sessions, in workshops involving from 5 to as many as 40 participants. In all workshops the models together with the simulated runs, enabled all participants to reach a consensus about the underlying process and the implications that each choice would have on the overall system behaviour. The results concerning specific components of the system answered operational questions concerning the rational allocation of resources and the resulting service provision capabilities of the system. The results concerning overall system behaviour proved useful for understanding the overall venue 'profile' during an entire Games day, thus answering higher-level, management questions concerning spectator presence, arrival and departure patterns etc. We found that prior to our work, stakeholder workshops, facilitated on the basis of past experience, were only partially helpful. Architectural and topological designs imposed constraints on thinking about customer-oriented service provision. Textual requirements specification resulted in voluminous documentation with little chance for proper agreement, estimation of resources and planning for a co-ordinated implementation. Using stock and flows we overcame many of these problems. The stock-and-flow visual 'language' gave us clear semantics and intuitive syntax and this greatly enhanced visualisation of processes that in turn contributed to a more informed discussion and agreement between stakeholders. Overall, we found the utility of Systems Dynamics and that of the tool of a very high degree. Participants in the design process became increasingly involved as the process progressed and continue to work with us on new projects towards the full definition of designs for venue operations.
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