6.6. Appendix: More About the Fliers Model

A complete equation-by-equation description of the Fliers Model is available within the Fliers Mini-Sim in the CD folder for Chapter 6. Select 'browse model' from the list of buttons on the left of the opening page. A diagram similar to Figure 6.11 will appear. Then click on any model icon to view the accompanying equation and documentation.

The formulations in the market sector of the Fliers model need more explanation than is available on the CD. The equations for available passenger miles are shown in Figure 6.17. Essentially, the more planes owned by easyJet, the more passenger miles the airline can fly each year. Planes are represented as a stock accumulation with an initial value of 12. There is no inflow or outflow to this stock because, in the Fliers model, we are specifically interested in easyJet's $500 million start-up gamble to purchase and fill 12 new planes. Available passenger miles are defined as the product of planes, passenger miles per plane and service days per year. Passenger miles per plane is a measure of the daily travel capacity of a short-haul plane flying between European destinations. For example, a typical 737 aircraft carries 150 passengers and makes three flights of 1000 miles per day for a total travel capacity of 450 000 passenger miles per day. Service days per year is assumed to be 360, meaning that the typical new plane flies almost every day of the year. Hence, the total available passenger miles from easyJet's fleet of 12 planes is 12 * 450 000 * 360, which is roughly 1.9 billion passenger miles per year.

The rest of the equations in the market sector convert this travel capacity to a potential market size expressed as the maximum passenger miles flown by all airlines that share the routes operated by easyJet. The formulations are shown in Figure 6.18. Maximum passenger miles is the product of available passenger miles, average carriers per route, market share limit and maximum market size multiple. The reasoning is as follows. We assume that, on average, four carriers operate on the same routes as easyJet, including easyJet itself. So, as a rough approximation the potential market size is four times easyJet's available passenger miles. Of this market it is reasonable to assume a market share limit of 50 per cent (0.5) for any one airline. Moreover the existence of low fares could eventually double the market size. So the maximum market available to easyJet, with its fleet of 12 planes is 1.9 * 4 * 0.5 * 2 = 7.6 billion passenger miles per year. This is the number that determines the limits to growth of potential passengers in the Fliers model.

Figure 6.17. Equations for available passenger miles

Figure 6.18. Equations for maximum passenger miles

6.6.1. Back to the Future – From easyJet to People Express

The formulations for easyJet's maximum market may look overly complex. Why not simply set up a stock of unaware customers who, through word-of-mouth and marketing, are converted to potential passengers for easyJet? The initial value of this stock would then be the maximum market size. Over time, these unaware customers are converted, through word-of-mouth and marketing, to potential passengers for easyJet. As the number of remaining unaware customers falls ever closer to zero, the conversion process slows. A single graphical converter linking unaware customers to the increase of potential passengers will do the job. As the number of unaware customers approaches zero, only the diehards remain and they cannot be easily moved. Thus, as unaware customers become fewer, their effect is to slow the rate of increase in potential passengers. Eventually, the flow is choked-off entirely. I leave this formulation (involving a graph function) as an exercise for the reader. Note that the elegant Bass 'contagion formulations' would work too.

So why use eight concepts when two or three would do?^[] The reason is that the Fliers model was originally designed with two pedagogical purposes in mind. As we have seen in this chapter, it investigates the feasibility of easyJet filling 12 planes – Stelios' $500 million gamble. The model was also designed, however, so that it could be extended to investigate the rise and fall of People Express airlines – a fascinating dynamic phenomenon in the same low-cost airline industry, but from an earlier era in the US market. For this additional purpose it is necessary to examine the growth of aircraft and staff and their joint effect on the service reputation of the airline. As the number of aircraft grows then the maximum market grows too (on the assumption that a growing low-cost airline establishes new routes as it acquires extra planes, thereby expanding the number of would-be fliers in the catchment area it serves). The formulations for the maximum market readily allow for this expansion of model purpose and boundary.

^[] Modellers normally try to adhere to 'Occam's Razor' – only add new concepts to the model if they are absolutely essential for the model's purpose – 'what can be done with fewer assumptions is done in vain with more' (William of Ockham 1285–1345 – Occam is the Latin spelling).

Readers are invited to build for themselves a People Express model using the airline model components in the CD folder for Chapter 6. More information on how to proceed is provided there. However, it is a good idea, before plunging into this exercise, to read Chapter 7 about managing business growth. Here, we examine the managerial policies for building and coordinating strategic resources in a growth company in order to successfully and sustainably deliver new products and services to discerning customers. Coordination of growth was the key challenge facing Don Burr, the founder and CEO of People Express, in his company's bid to become a dominant player in the US domestic airline market and to change forever the competitive structure of the industry.^[],^[]

^[] There has been much debate about whether the rise and fall of People Express in the 1980s constituted a business failure. Those who disagree argue that in its brief lifetime the company changed the US airline industry beyond recognition. It also provided a memorable business experience for those involved – even those who suffered burn-out toward the end. Judge for yourself the degree of failure or success and consider the following anecdote. A London Business School Executive MBA, who attended the programme in the early 1990s, had previously worked with People Express, through good times and bad. When asked if she would relive that part of her life again she always replied yes, because of the atmosphere of excitement created by Don Burr and his top management team.

^[] The dramatic story of People Express is told in a now-famous Harvard Business School case study (Whitestone, 1983). The case became famous in part because of its widespread use in strategy courses alongside the People Express Management Flight Simulator (Sterman, 1988), which is itself based on the case. The model behind Sterman's vivid system dynamics gaming simulator is quite detailed and carefully calibrated to the case facts. Readers should be aware that Sterman's model is considerably more sophisticated than the airline model components in the CD folder for Chapter 6. Nevertheless, both models can be viewed as variations on the 'growth and underinvestment' structure that is covered in Chapter 7. As far as I know, they share in common their formulations for rivals' fare (in terms of an asset stock adjustment process to represent restructuring of costs by traditional full-service airlines).