Business Planning in Transport

By Adam Simmons

If you work in operations, finance or marketing in any part of the public transport sector, this book was written for you. Adam Simmons provides a set of practical business planning tools aimed at developing a business in the public transport sector, primarily – railways, ferry, long distance bus/coach and air transport services. Business

• Language: English
• Publisher: U P Publications Ltd
• Release date: May 29, 2017
• ISBN: 9781908135834

Adam Simmons

ADAM SIMMONS MBA MSc BSc has combined a global career in the transportation sector, for public and private sector clients (working on the financial and economic aspects of projects worldwide), with almost 30 years in academia. He has taught on a range of quantitative and qualitative subjects, at post-graduate level.

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2

PRINCIPLES OF DATA PRESENTATION

2.1 Introduction

Acquiring data is important but how the information is presented is equally important. If you cannot get your message across, then much of your effort will simply have been wasted.

Figure 2-1 shows a table of data. Although such tables can provide precise numbers, they are not especially good at explaining trends and comparing one set of numbers with another. To illustrate the value and power of charts, we will use data from the Canary Islands. For now, do not be concerned with the ‘real GDP’ column, as this will be explained in detail in Section 7.3; we will just say that it is a measure of the total wealth generated by a country or, in this case, region.

Figure 2-1: Dataset used to demonstrate Data Presentation

2.2 Presentation of Data

The first rule of data presentation is to make sure that a chart is not cluttered. If a chart has too many lines or blocks, then the chart will be like a piece of modern art: nice to look at but useless for presenting a business case! I hope that Figure 2-2, taken from a professional publication, which shall remain nameless, illustrates the point.

Figure 2-2: A Useful Chart or a Piece of Modern Art?

This figure shows eleven variables on a chart, which is simply too many.

The solution in this case would be to either delete variables which are less important or to split the graph into three separate ones, with three to four variables on each.

2.3 What is the Message?

Going back to our dataset, how we present the data will vary depending on what message we wish to convey to our audience and Figure 2-3 presents the same information but in different ways.

Figure 2-3: Passengers by Mode

Figure 2-3 shows the number of trips by mode over the thirteen years studied. If you are trying to understand how the airlines are competing in this market, then this figure is helpful; it shows that, up until 2006, air travel increased whilst ferry travel declined but, in the last couple of years, ferry travel has increased its dominance in this market. What it does not really show us is the size of the overall market.

Figure 2-4: Stacked bar version of passenger journeys

Figure 2-4 alleviates this problem by using a stacked bar chart. We can now see that the overall market for travel between Gran Canaria and Tenerife declined from 2007 but has been reasonably stable since 2009.

2.4 Plotting Data with Different Orders of Magnitude

Next, we will plot the total number of trips against GDP. Our first attempt, using the same axis, is unsatisfactory, as it does not show us the movements in GDP because the vertical axis cannot cope simultaneously with both numbers in the thousands (GDP in €m) and in millions (number of trips).

We can see the result in Figure 2-5.

Figure 2-5: Initial Plot of Trips against GDP

To get around this problem, Excel allows us to create a secondary axis. Now, let us draw a line graph of passenger journeys using these indices and we get Figure 2-6 as a result.

Figure 2-6:Trips and GDP using Two Axes

This chart is an improvement; in it we can see movement in both trips and GDP. However, using different vertical axes for the two sets of data can be somewhat confusing and Figure 2-6 also has the problem that most of the chart area is blank.

To overcome this problem, we can use a process of indexation. For both trips and GDP, we fix the base year as 100 and subsequent years show the changes in relation to 100.

We have also eliminated the blank space problem we encountered in Figure 2-6 by forcing the vertical axis to start at 90 rather than 0.

Figure 2-7: Index Version of Trips vs GDP

Although we no longer have the absolute values of the number of trips and GDP, this should not be a problem provided that, together with charts developed from the data, we also present the data from Figure 2-1.

One revealing point from 2-7 is that there seems to be a relationship between the number of trips and GDP over time, as they increase and decline in tandem, albeit at different rates.

It is easier to discern these trends if we use a metaphorical magnifying glass on the data, as we have done here by reducing the range of the vertical axis by removing the blank space at the bottom of Figure 2-6.

We will explore the extent of this relationship in Chapter 8.

2.5 Chapter 2 Exercises

Question 1 Suggest at least TWO ways in which the following data for the demand by various modes over a specific corridor can be presented graphically and then develop the charts. Which method do you prefer and why? What information do your charts provide?

Question 2 The Table on the following page shows the number of tourists per month visiting the island of Tenerife from January 2011 to June 2015 from the United Kingdom and mainland Spain. Chart this data and discuss what trends and fluctuations you see in the data.

Source of data: ISTAC (http://www.gobiernodecanarias.org/istac/)

3

MARKETS, SUPPLY AND DEMAND

3.1 Introduction

In these next three chapters, we will be going through some principles of economics that will be applied in later chapters. These include demand and supply, industry structure, generalised journey time, price setting and elasticity. We will also discuss precisely what we mean by cost later, in this book.

3.2 Markets

Much of economic theory and its application is about markets, but what do we mean by this? In the transport sector, defining a market can be challenging!

Let us examine the market between Malmö and Stockholm, approximately 610km apart. If we examine the suppliers of air transport between Malmö and Arlanda (Stockholm’s principal airport, which is 40km from the centre), we would conclude that there was a duopoly, as only Norwegian and SAS offer service on this route.

Next, we can bring into play Bromma airport in Stockholm, which is much closer to central Stockholm (10km). A third airline, Malmö Aviation, flies several times a day between the Malmö and Bromma. Flights between the two cities take a little over one hour.

The route from Malmö has been used as an example because of the unusual nature of the city’s relationship with its airport. Although Malmö airport is 30km from the centre of the city,

Copenhagen airport is just 2km farther and is much faster to reach by public transport from Malmö than the local airport.

Copenhagen has a somewhat higher frequency of flights provided by Norwegian and SAS but also a daily flight by Ryanair between Copenhagen and Skavsta airport, which is some 100km to the southwest of Stockholm.

It is acknowledged that the Ryanair flight does somewhat strain the definition of ‘Stockholm’, however!

Let us now expand the market still farther. There are hourly trains between Malmö and Stockholm which complete the journey in just under 4½ hours. Finally, we can add long distance buses (these are infrequent and take around 8 hours) and, of course, the private car, with an expected journey time of 5½ to 6 hours. So, even in this one corridor, we can see that defining the market is rarely straightforward. Also, bear in mind that not only have we expanded the size of the Malmö-Stockholm market, but its nature has changed from being a duopoly to one with quite intense competition, once competing airports and other modes are taken into account.

3.3 Demand

3.3.1 Definition

Economists have a very precise definition of demand, which is the relationship between the quantity of goods or service consumers will purchase and the prices charged for that commodity. Demand is not simply a quantity consumers wish to purchase such as ‘5 day returns’ or ‘17 shares of Microsoft’, because demand represents the entire relationship between quantity desired of a good and all possible prices charged for that good.

The specific quantity desired for a good at a given price is known as the quantity demanded. Typically, a time period is also given when describing quantity demanded.

For example, when the price of a railway ticket is £2.50, the expected quantity demanded is 100 tickets per day.

If the price of this ticket is cut to, say £2.25, we would expect that the quantity demanded will rise above 100 tickets. Therefore, price and demand should be inversely related and we will now explore this relationship further.

3.3.2 The Law of Demand

The law of demand states that, ceteris paribus (Latin for ‘assuming all else is held constant’), the quantity demanded for a good rises as the price falls.

In other words, the quantity demanded and price are inversely related.

Demand curves are drawn as ‘downward-sloping’ due to this inverse relationship between price and quantity demanded.

We will illustrate this with a simple demand schedule that lists the possible prices for a good and service and the associated quantity demanded. Figure 3-1 shows how the demand schedule for day returns could look (in part):. Figure 3-1 shows how the demand schedule for day returns could look (in part):

Figure 3-1: Demand Schedule for Return Tickets

The combination of price and quantity demanded for the highest and lowest prices in the schedule is illustrated in Figure 3-2.

Figure 3-2: Demand Curve with Prices and Quantities

3.4 Supply

3.4.1 Introduction

A key objective of an operator is to make profits for its shareholders and, even if the operator is in the public sector, it is usually expected to behave commercially. If an operator is able to charge a higher price for its tickets, it will have an incentive to supply more seats and frequencies on routes where profitability can be increased.

We see this phenomenon in all sorts of business sectors as well as transport. For example, with agricultural or mining products, increased demand will induce the plantation of more crops or increase the number of shifts in the mine.

Note that in all these examples, there may be a time lag between wanting to increase supply and being able to do so; for train operating companies, for instance, there will be a gap between ordering and receiving new trains.

3.4.2 Supply Curve and Schedule

A producer will be willing to supply more quantity to the market as the price increases as profitability per unit of output increases. Let us take the same price points as we saw in the demand schedule and we can observe the willingness of the supplier to increase production as prices increase.

Figure 3-3: Notional Supply Schedule for Return Tickets

The supply curve is shown below in Figure 3-4 with the same two quantities as we illustrated in the demand curve.

Figure 3-4: Supply Curve with Prices and Quantities

3.5 Equilibrium Point

So, the objective of the operator is, wherever possible to match supply with demand. Where the two lines cross is called the point of equilibrium - see Figure 3-5.

Figure 3-5: Equilibrium Point for Supply and Demand

In the example above, we can see that the capacity demanded and supplied are equal at a point where 60 tickets are made available at a price of £3. If the price is lower, there is a possibility of insufficient capacity as demand surges whereas if the price increases, the trains or buses could be carrying more air than passengers! Please note that the revenue in the above case is £180 (£3 * 60).

3.6 Changes in Demand

Let us now assume that demand is subject to an external ‘shock’. This shock, also known as an exogenous effect, could be a reduction in income, an increase or taxation or a fall in the price of a substitute product (for example, demand for a railway service will decline if a competing bus service slashes its fares).

We can see in Figure 3-6 that the entire demand curve shifts downwards (or to the left – both expressions are common). Revenue will now be approximately £2.75 * 55 = £151 (approx.). This is a significant change on the original revenue shown in Figure 3-5, as both quantity and demand are lower.

Figure 3-6: Demand Curve Shifts Downwards

3.7 Changes in Supply

What happens when the supply curve shifts? Let us assume a negative shift, so that the producer’s costs increase or subsidies are reduced. In other words, at any given level of supply, the producer will need more revenue to consider supplying a given quantity.Please note that a supply line will only shift in this manner if there is an exogenous effect which impacts on the market, such as oil price changes. The entire supply curve shifts upwards because, for any given quantity that the operator is willing to supply, the price at which any quantity will be offered is higher than before. We can see how this affects supply by examining the dotted blue line in Figure 3-7.

Figure 3-7: Supply Curve Shifts Upwards

The equilibrium point has now changed and the market is in balance when the supply is for 50 units at a price of £3.50 each. Before the revenue was £180 but is now a little lower (£3.50 * 50 = £175).

Although demand is lower, the price per unit is higher, which offsets the reduced demand. Note, however, that this shift does not tell us anything about the operator’s profitability; using the oil price example, all we know is that costs per unit output will be higher than before.

A similar analysis can be undertaken if, due to reductions in costs, the supply curve shifts downwards. Note that when the demand curve shifts, price and demand will move in the same direction, whereas when the supply curve moves up or down, price and demand will move in opposite ones. So, when the demand curve shifts, the range of outcomes is higher.

3.8 Luxury Goods

So far, we have described what happens to demand and supply for normal goods. However, luxury goods behave differently.

A return economy fare between London and New York costs between €500 and €600, whereas a First Class return ticket costs over €3,000. Now, imagine that a new entrant into the market sets a first-class price of under €2,000. One set of customers would undoubtedly be keen to try such a product, but others will be deterred by the low price.

The reason for the negative reaction from some parts of the market is because what is perceived as a luxury product will be considered as undervalued. For many people who buy luxury goods, what is important is image and cachet rather than price.

3.9 Chapter 3 Exercises

Exercise 1 In this Chapter, we discussed how demand curves can shift up or down. Supply curves are subject to similar shifts; what factors can induce these curve shifts?

Exercise 2 On a single chart, draw demand curves or lines for:

A railway line, used principally for commuting;

An airline route used principally by holidaymakers;

First class travel on a long-haul airline route.

Comment on your results. Do not worry about units on the price or quantity scales

4

INDUSTRY STRUCTURE

4.1 Overview

In this Chapter, we are going to look at some theory and rather more practice on how different industries are organised. First, we will examine the four main structures of

perfect competition;

oligopoly;

duopoly; and

monopoly.

We will then see how well these structures apply to the transport sector using examples from various modes and regions throughout the world and then examine the role of economic regulation in the transport sector.

4.2 Perfect Competition

Much economic theory has been developed using the assumption of perfect competition.

Key features of perfect competition include:

Each business in the sector offers an identical product;

It is not possible to charge different prices to different segments of the markets;

All firms charge the same price; and

Entering and exiting the market is easy (there are few or no barriers to entry or exit).

Personally, I have only ever seen one example of perfect competition, which was in Jemaa-el-Fnaa Square in Marrakech. Within this square, there are over 50 vendors of freshly squeezed orange juice and all sell their product at the same price. If one vendor, say, tried to charge 11 dirham rather than 10 for a glass of juice, the theory says that demand for his product would fall to zero, because everybody else is selling the identical product for less.

The start-up costs of this orange juice business are small and, if the vendor wishes to leave, he can take his equipment out of the square and set up business elsewhere. We can also assume that there are too many vendors to establish a price cartel.

On any route, by any mode, the conditions of perfect competition rarely apply.

Figure 4-1 provides some examples of how transport operators differentiate their product to be able to charge prices which differ from their competitors.

Figure 4-1: Differentiating Factors by Mode

We will use schedule as an example of a differentiated product. On Monday, August 31st 2015, there were 11 nonstop flights between Frankfurt and Palma de Mallorca. This route was selected at random and a date four weeks ahead was chosen.

Figure 4-2: Single fares Frankfurt-Palma on 31/08/2015

Source: www.skyscanner.es Fares reviewed on July 20th 2015

Although we would never expect to see a perfect match between fares and schedule, what Figure 4-2. does imply is that people are willing to pay less for the inconvenience of early morning flights and, similarly, people prefer not to arrive too late at their holiday destination.

In this one day sample, the most expensive departures are the most convenient, leaving Frankfurt mid- or late-morning.

Lufthansa and Air Berlin are full service carriers whilst Condor, Germania and Tuifly are charter/low cost operators, which goes some way to explaining the fare differentials.

We will explore these issues in more detail in the next Section.

4.3 Oligopoly

4.3.1 Bangkok-Singapore Air Market

For our purposes, we will define an oligopoly as a market in which three or more firms operate. We will use the air route between Bangkok and Singapore to demonstrate an oligopolistic market.

There were seven airlines operating the route on a non-stop basis at least once per day during the summer of 2015 with an average 29 flights per day each way between the two cities, which are just over 1,400km apart. Flight times were, unsurprisingly, very similar, between 2 hours 15 minutes and 2½ hours.

4.3.2 Perfect Competition or Oligopoly

To understand why this market is more oligopolistic than one exhibiting perfect competition, let us examine Figure 4-3.

Figure 4-3: Airlines Operating between Bangkok and Singapore

Note: schedules are from August 2015. Source: Skyscanner, airline schedules

Under perfect competition, the prices for a one-way trip on each of these airlines would be identical or at least similar. However, in the first week of August 2015, a one-way price ranged from €43 (Air Asia) to €280 (Singapore Airlines). This market breaches perfect competition in various ways:

Low cost carriers. Four of the seven competitors offer high density seating and lower prices. Therefore, the products cannot be said to be identical;

Loyalty programme. Frequent flyer rewards are a means of rewarding more loyal passengers but they also serve to ‘lock in’ customers who would not gain air miles if they switched to a competing airline;

Frequency: an airline offering a higher frequency is providing a higher quality product and service than one offering a low frequency. We would normally expect a superior product to translate into higher prices, but Air Asia offers the highest frequency and lowest prices on the route;

Airport. Two of the seven airlines operate from Don Muang airport, around 25km north of Bangkok, whereas the remainder uses Suvarnabhumi, 32km to the east. Don Muang is used exclusively by low cost carriers offering point to point traffic, so Bangkok provides a similar comparison with Heathrow versus Stansted in London.

It is worth pointing out that of the 202 weekly frequencies shown, 82 are flown by either Singapore Airlines, a full-owned subsidiary (Scoot) or an airline in which Singapore Airlines is the largest shareholder (Tiger Air).

4.3.3 How Competitive is this Oligopoly

In the Bangkok-Singapore example above, the top three firms operate around 56% of the total frequency.

If, however, we include Scoot and Tiger Air in the Singapore Airlines grouping, the top three concentration ratio increases to 80%.

Whilst the concentration ratio can provide a useful indication of market power, the test is by no means conclusive. This is the case here, as it is unclear to what extent Tiger Air, for example, is an independent player, given its ownership by Singapore Airlines. What we can infer, from the range of prices on offer, is that there is no evidence of collusion.

Another test we could run is to benchmark this route against similarly competitive ones in the region. For example, Kuala Lumpur to Jakarta uses different countries and airports, with a route length of 1,140km.

4.3.4 Price Setting in an Oligopoly

As this Section is concerned with the impact of price changes, let us narrow down the market a little for this discussion to the LCCs operating in the Bangkok-Singapore market.

There are three players with a roughly equal share of weekly departures (Air Asia, Jetstar, Tiger Air) and one low frequency operator (Scoot). It is a fair assumption that passengers using these four carriers are somewhat more price-sensitive than those using full service carriers. Let us first assume that overt collusion via a cartel is illegal and that airlines would face severe penalties if they attempted to act in this manner. An overt cartel is not the only means of fixing price, however. If Jetstar, for example, were to raise its prices on the route by 10%, competing firms would have three broad options:

Raise prices by the same amount. If the other firms respond to the price signal sent out by Jetstar, their market shares on the route should stay more or less the same, but the overall market size will be less, as the supply curve shifts upwards and demand would decline (recall that an upward shift means that prices are higher for all quantities offered);

Raise prices by less, say 5%. The other firms will take some market share from Jetstar because their price increases are less severe. The market size will be a little smaller than before the price rises but larger than if all airlines raised their fares by 10%. The effect on Air Asia is a little more complicated to predict, as it uses a different airport in Bangkok; or

Competitors’ prices remain unchanged. Under the rules of perfect competition, Jetstar’s position would be untenable and its market share would fall to zero. Such a scenario is unlikely because certain people will prefer Jetstar’s schedule or service quality, or else there may be customers locked into Qantas’ (Jetstar’s owner) loyalty programme. In extremis, Jetstar may have to reverse its price increases if it lost sufficient market share so that the price increase meant a net loss of revenue to the company on this route.

Needless to say, this range of possible outcomes makes life much more complicated! To forecast a company’s future revenue and profitability, we require a more complex method of forecasting and we will discuss one such tool, known as scenario planning, in Section 19.6

4.4 Duopoly

4.4.1 Overview

The example in the previous Section had seven players in the market. As market size declines, the number of viable competitors declines and so having just two operators is quite commonplace. From an operator’s or terminal owner’s viewpoint, the range of outcomes is somewhat easier to predict as there is only one other competitor.

From the user’s viewpoint, however, the risk of (tacit) collusion increases; a simple way to think of this is that it is easier for a committee of two to reach an agreement on something than a committee of seven.

There are many instances of duopolies (and indeed oligopolies) being subject to legal and economic regulation. As the number of players in a market declines, the need for regulation to protect consumers generally increases along with the likelihood of collusion.

4.4.2 Jersey - Gatwick Market

British Airways (six daily weekday flights) and easyJet (three) competed on the Jersey-London route using identical aircraft (Airbus A319) in 2015 and Gatwick accounted for around 90% of total demand.

The other operations included easyJet flights into Southend three times per week and Blue Islands Air, operating into London City three times per day but using smaller and slower aircraft.

Using their dominance of the market, two types of collusive behaviour are possible¹.

First, the two dominant operators could raise fares. As Blue Islands operates smaller aircraft into a different airport, BA and easyJet may be able to make these price rises stick.

Alternatively, if the dominant operators wish to drive out a small operator, they could tacitly collude to reduce fares in order to make business difficult for the smaller third operator (this is known as predatory pricing). BA and easyJet, given their sizes (hundreds of aircraft), can clearly sustain a price war on a single route for much longer than a smaller operator with just five.

4.4.3 Tests for Collusion

How can we test to see whether a market is acting in a competitive manner? This is an extremely complex area but there are nonetheless some simple tests that can be carried out. Using our Jersey-London example above, one test is to see how the price per kilometre compares to similar routes served by BA and easyJet, taking into account differences in airport charges, number of competitors, etc.

A second test would be to see how prices between the two airlines move over time. We would expect variations in fuel prices to have an impact on fares offered so, ideally, research should be undertaken over a period when crude oil prices are stable.

A final test is to test the sensitivity of prices to a new operator. Blue Islands started operations in March 2011 between Jersey and London City airport. To what extent, if any, did prices between Jersey and Gatwick fall because a third operator entered the London market?

However, it should be pointed out that it is very difficult to determine whether any price reductions were due to a functioning market or an attempt to put pressure on the new entrant through predatory pricing.

4.5 Monopoly

4.5.1 Occurrence of Monopolies

Monopolies, as defined in this Section are either pure, when there is only one operator,