a) Explain what information you would need to forecast demand and determine the amount, type and timing of resources that would be needed.
The building of a stadium and the desire of the local council to promote public transport creates immeasurable opportunities for Train Operating Companies in the area to develop a rail system to service the stadium. If such a system was implemented properly, it could prove greatly successful.
The demand for passenger mobility has grown exponentially in Europe in the past twenty five to thirty years. Policy makers place great emphasis on the ability to predict, analyse and manage the growth of passenger mobility. Large infrastructural projects, such the proposed rail system are expensive and have long planning horizons. Ideally planners would like to know what the future demand will be for mobility so that they can have the right infrastructure in place. Unfortunately, with such a long planning horizon, it is not possible to know with certainty what the future demand will be. Forecasting demand for a new service has always been inherently problematic. Trends, trend breaks, or combinations thereof influence the demand. It is by nature difficult or impossible to identify these trends and to quantify them. Therefore, building a dynamic and consistent model for forecasting demand that takes into account all the relevant factors and interactions, is a complicated task (Malone, et. al, 2001).
The main obstacle to the prediction of demand is the limited vision of planners. Semon (1995) says ‘it is hard for us to realise how severely we are hemmed in by assumptions of the persistence of current patterns’. The reality of it is that any business that forecasts demand is just guessing. However, in modern business, to survive, demand must be more or less accurately predicted. The price of inaccuracy in most industries is high: surplus is a wasted resource; shortfall is a wasted opportunity (Hennel, 2002).
Forecasting demand must be coordinated by a consensus methodology, a process that brings finance, marketing, sales and production together to agree on a single forecast. The key to success is an operational perspective. That is, each group must seek the best forecast for meeting demand with the narrowest margin of error (Hennel, 2002).
However, there is also a growing recognition of the need for an integrated approach to transport to extend beyond integration within transport and between transport systems and services. It must also include the integration of transport with the environment, land-use planning and policies for education, health and wealth creation. In short, transport is inextricably linked to society and lifestyles; and the linkage is two way (Lyons, 2004).
Thus, to build a rail system to service the stadium, planners must have resources that will:
- Forecast demand
- Link the system with internal operations in the organisation
- Link the system with external factors outside the organisation
The information and the amount, type and timing of resources needed will be expanded upon on section c.
b) Assess the possible implications for existing services, especially in relation to peak problems.
It would be fair to expect that transport plays a fundamental role in the daily lives of all residents within the area that the stadium is to built. It provides them with means of access to essential goods and services, employment and leisure activities. Business may be dependent upon transport to deliver supplies and to ensure access to suppliers and customers. The consequences of heavy traffic flows and congestion experienced as a result of the building of the new stadium could lead to a steady degradation in the quality of the environment through an increase in air pollution, noise and severance of local communities. Road safety would become a major concern.
In the UK in 2002, twenty eight professors of transport submitted jointly an open letter of concern to the Secretary of State for Transport, Alistair Darling MP. In this carefully worded letter, the thrust of the message was clear: in areas of heavy congestion, a combination of selective road building and improvements to alternative means of transport to the car will not improve travel conditions unless accompanied by traffic restraint (Lyons, 2004).
Traffic restraint may occur naturally in the area where the stadium is to be built. Increasing levels of congestion could influence residents’ decision not to own a car (or reduce its usage) or not to use over-crowded buses. However traffic restraint through congestion is both inefficient and uneconomic. What is required is a planned approach to restraint which is fair and equitable and which brings about significant improvements in the quality of the local environment.
The limitation on parking within the stadium is an encouraging development. This initiative seeks to restrain the use of unnecessary vehicles, especially private cars in the area. However, it may not be enough. Motorists may not be deterred from driving to the stadium. The operation of rail service, however, would provide an incentive to many to leave their cars at home. It would discourage private car journeys which are not essential and which could reasonably be made by public transport. This, of course, is provided that the service was relatively affordable, reliable, efficient and safe. The public tend not to detracted from their cars where there is no satisfactory public transport alternative. If the rail service did prove satisfactory, however, other local services in the area (roads and buses) would for the most part remain unaffected by the building of the stadium. They would be prioritised and preserved for usage by local people.
c) Using an appropriate model, analyse the market for this service and discuss promotion opportunities.
Analysing the Market
Successful demand forecasting has two fundamental objectives: to identify the key variables that underlie demand within a particular service area, and to understand how and why these variables might change overtime. Accomplishing these objectives requires a systematic analytical process that ensures all aspects of potential demand are assessed. Finarelli & Johnson (2004) identify a nine-step process, that if followed, can create a database and framework for evaluating key variables and testing assumptions, and provide the necessary basis for accurately forecasting demand. The model they developed was with specific reference to the health care industry. Yet, its underlying lessons and challenges can be considered of extreme importance to the building of the proposed rail system. At a glance, the nine steps are as follows:
1. Assemble historical data
2. Analyse historical trends
3. Identify key demand drivers
4. Identify relevant benchmarks
5. Model existing conditions
6. Develop core assumptions for population based demand
7. Develop core assumptions for provider level demand
8. Create a baseline forecast of future demand
9. Test sensitivity of projections to changes in core assumptions
It is necessary to examine each of these steps in detail:
Step 1: Assemble historical data:This data should reflect current and historical demand for the service. Due to the fact that the rail service is a new venture, however, current and historical demand for similar services should be examined. Yet caution should be exercised here. Data from multiple sources may vary significantly because each source collects and analyzes different statistics for different reasons. Moreover, many external
databases have incomplete, inconsistent, or out of date information. It is therefore best to
compare several data sources, if possible, to identify the most appropriate set of historical demand data.
Step 2: Analyse historical trends:Examine the data to identify key trends, for example, absolute change, percentage change, and average annual percentage change.
Step 3: Identify key demand drivers: Causal and influencing factors should be carefully reviewed and discussed to ensure that all key demand drivers have been considered and that the relationships between the drivers and service demands are well understood. Predicting passenger mobility, especially fifteen or more years into the future, requires taking into account the most relevant demand drivers in the modelling process. Examples include accounting directly for the effects of the diversification of leisure time or the increase in individualization, and their consequent (potential) impact on the demand for mobility. These trends are difficult to quantify. However, the ability to account for them and other important variables is important in the process of modelling the demand for mobility (Malone et. al, 2001).
Step 4: Identify relevant benchmarks: Benchmarks provide a point of reference for determining the extent to which demand trends are in line with broader marketplace or national trends. Relevant benchmarks include use rates in comparable markets, established best practices or service-specific guidelines and performance measures. Without relevant benchmarks, it could easily be assumed that recent trends in other areas will continue for the foreseeable future, when actually current practice patterns may be poised to shift. It may therefore be worthwhile to investigate alternate scenarios.
Step 5: Model existing conditions: If the model cannot replicate existing conditions, it cannot be used to predict future demand. Thus, a spreadsheet model should be developed that best replicates the latest verifiable market data and utilisation statistics, and that best projects the trends that have occurred since. Historical data and historical trends should be used to develop the most reasonable combination of assumptions about current conditions for the key demand drivers.
Step 6: Develop core assumptions for population based demand: While this step is obviously an essential one for the healthcare industry, it is also very important for the building the stadium. It is after all the public that will be availing of the services built up around the stadium. Thus, socio-economic factors affecting demand might include population growth, aging, and use rates. Also, in relation to public transport we may include factors such as:
- travel demand (kilometres driven by mode etc.);
- travel supply (developments in infrastructure, levels of service);
- supply and demand equilibrium (travel times); and
- car ownership (number of cars).
These factors are often called external factors because they are outside of the organisation’s control. Information for making assumptions about population growth and aging generally are available from demographic firms or from national, state, or local governmental agencies.
Step 7: Develop core assumptions for provider level (TOC) demand: Factors that determine demand at the provider level include market share, customer mix or flow patterns, and operational performance. These factors are often called controllable factors because they can be affected by specific actionsof the service provider. Developing these factors are of extreme importance as they indicate the level of demand an organisation can sustain. When developing market-share assumptions, for example, the potential impact of new competitors in the market place (e.g. national niche providers) should not be overlooked. Many organisations lose demand to their competitors.
Step 8: Create a baseline forecast of future demand: This forecast should combine the core assumptions for both population-based and provider-level demand. Typically, a baseline forecast includes:
- Moderate assumptions for external factors to create the baseline forecast of population-based demand
- Moderate market-share targets to create the baseline forecast of provider-specific utilization levels
- Aggressive performance-improvement targets to develop the baseline workload projections
Using the most aggressive performance targets is considered good business planning and tends to moderate the increase in resource requirements (e.g., staffing levels, service capacity) that might otherwise accompany projected increases in customer volumes.
Step 9: Test sensitivity of projections to changes in core assumptions: The need to explore the response not only to changes in infrastructural supply but also to other mechanisms to manage the demand for transport, such as pricing mechanisms, leads to the need for models to explore ‘what if?’ scenarios (Malone et. al, 2001). Alternative scenarios with different sets of assumptions should be considered. Such scenarios might include:
- Low and high rates of change in population based use rates
- More dramatic shifts in market share
- Results that, for whatever reason, fall short of achieving projected operational efficiencies or other performance-improvement targets
During the process of developing scenarios, a strong cause-and-effect reasoning should be used. This logical approach can also be employed to think through the consequences of possible developments (Malone et. al, 2001). It may be useful to consider a best-case scenario, with more favourable assumptions (such as higher population based demand, greater market share growth) than were used in the baseline forecast. It is also much more important, however, to test downside sensitivity by using less favourable (or more conservative) assumptions about use rates, market share, or performance improvement. Decisions relating to the proposed rail service may hinge on the risk associated with not achieving targeted utilisation levels.
Sensitivity analysis, however, is not intended to introduce so much uncertainty that decision makers are afraid to act. Instead, it should provide a reasonable estimate of the risk and reward associated with the rail service by considering realistic combinations of internal and external factors that might cause future demand to diverge from the baseline forecast. Malone et. al (2001) say that the use of scenarios can assist planners in the following three ways:
1. They assist in structuring, understanding and thinking through a changing situation.
2. They focus attention on the structural uncertainty of the most critical factors.
3. They increase the capability of the developers to understand the environment in which the transport system functions.
When these nine steps are completed the process must not stop according to Lapside (1997). He says that although demand forecasting is largely about understanding the reasons behind fluctuations in demand, it is just as important to recognize and cope with the uncertainty that remains. This involves such measures as contingency planning, inventory safety stock, and excess capacity which are designed to deal with uncertainty in demand.
Hence, the nine steps outlined above (plus the additional one provided by Lapside) provide a solid framework for systematically and thoughtfully tracking and explaining potential future changes in demand, both expected and unexpected. The dynamics of the models are highly flexible and aggregated, and therefore are very suitable for analysing scenarios where a variety of interrelated long-term developments have to be dealt with. This, it provides a sound model on which to base the demand surrounding the proposed rail system.
Promotion of Rail Service
Promoting the proposed rail service is of extreme importance. If the public are unaware of the benefits associated with using the service, they will, of course, be hesitant to use it. Advertising, undoubtedly, plays an important role. If a game is to be played at the stadium, the rail service should actively seek to advertise transport to this game alongside existing advertising for the game. The two advertisements should go hand in hand and reinforce one another.
However, advertising is only one aspect to promotion. It must be backed up with efficient support systems within the rail service itself. These may include measures that:
- improve reliability, service frequency, ticketing and information services, comfort and personal security;
- make services and associated infrastructure fully accessible;
- develop the present network of routes to create more capacity and improve the range of trip needs served.
In this way, the rail service will become an attractive, convenient, safe and accessible alternative to driving to the stadium.