estimating demand for a new regional transport aircraft (a)

IMB 365

Jayarama Holla, Rishikesha Krishnan and Srinivas Prakhya prepared this case for class discussion. This case is not intended to serve as an endorsement, source of primary data, or to show effective or inefficient handling of decision or business processes.

Copyright 2012 by the Indian Institute of Management Bangalore. No part of the publication may be reproduced or transmitted in any form or by any means electronic, mechanical, photocopying, recording, or otherwise (including internet) without the permission of Indian Institute of Management Bangalore.

JAYARAMA HOLLA, RISHIKESHA KRISHNAN AND SRINIVAS PRAKHYA

ESTIMATING DEMAND FOR A NEW AIR TRAVEL OFFERING (A)

In late 2007, buoyed by the explosive growth of the civil aviation industry in India, the countrys National Aerospace Laboratories (NAL) was keen to take its civil aircraft development program to the next level. It had already developed a 2-seater trainer aircraft Hansa and was at an advanced stage in the development of a 14-seater transport aircraft Saras. However, to be a part of the boom in domestic civil aviation services, it needed to develop a larger aircraft that could connect small towns with each other and with larger cities.

Although the Government of India (GoI) was supportive of efforts to develop and manufacture aircraft within the country, design and development of such an aircraft would be a much bigger project than anything that NAL had undertaken before. Besides, as a Research and Development organization, NAL had to bring on board partners for manufacturing. Globally, the trend was to involve a range of strategic partners for critical sub-systems of the aircraft such as engines, avionics, and airframe right from the design phase. Although broad numbers and trends suggested the existence of a large market for civil aircraft in India, NAL realized that they needed a more robust estimate of market demand if they were to convince the GoI and potential strategic partners to support the project.

Kota Harinarayana, who joined NAL as an advisor to the Civil Aircraft Development Program, had a long and successful career in the aerospace industry, first in Indias largest aerospace company Hindustan Aeronautics Ltd., and later as the head of an ambitious project to build an Indian Light Combat Aircraft (LCA). As head of the LCA program, Kota had built a successful network of research and academic institutions and sought to replicate this in NALs civil aircraft development program. On the market estimation dimension, he roped in two of Indias premier educational institutions, the Indian Institute of Technology Bombay (IITB) and the Indian Institute of Management Bangalore (IIMB). The IIMB team was of the opinion that the new aircraft could be used to provide an air travel offering that could compete with high end rail and road travelmodes. Demand for the new aircraft would then be derived from the demand for the new air travel offering and the first step would be to estimate countrywide demand for the latter. Although, IIMB would be involved in demand estimation at the level of individual routes, the estimates so generated would be fed into a larger model built by IITB to estimate aircraft demand for the country.

The challenge before the IIMB team was to design a cost-effective and reasonably quick method of demand estimation at the route level.

CIVIL AVIATION IN INDIA

With a large geography and several areas with difficult topography, civil aviation might have been expected to be a growth sector in India. However, until the early 1990s, civil aviation grew only at the rate of the gross domestic product (GDP) growth because it was seen as a luxury rather than an essential transportation service and was monopolized by the state-owned airlines, Indian Airlines (domestic), and Air India (international). A first phase of deregulation in 1993 that saw the entry of some private airlines gave a hint of the potential of the civil aviation industry, but ended prematurely with the bankruptcy of many of the new airlines. A second phase of growth was spurred by the entry of Indias first low-cost carrier (LCC) Air Deccan in 2003. By this time, India was on a clear post-liberalization growth trajectory and ready for disruptive growth models. The entry of Air Deccan and other

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Estimating Demand for a New Air Travel Offering (A) Page 2 of 26

LCCs such as IndiGo and SpiceJet and the resultant decrease in fares saw the demand for civil aviation grow at about 25% per annum, i.e. at about three times the growth of GDP.

Although, the sudden growth of civil aviation resulted in the growth of several auxiliary services and employment for thousands of people, Indias R&D, design and manufacturing sectors missed out as the aircraft were purchased or leased from the large international manufacturers such as Airbus, Boeing, ATR, or Embraer. The boom in civil aviation was driven by travel between Indias six metropolitan cities (New Delhi, Mumbai, Chennai, Bangalore, Hyderabad, and Kolkata) though a whole set of Tier II cities were fast becoming an important part of the network. There were about 46 active airports in India. However, more than 300 small towns had airstrips, a legacy of British rule in India and these could be potential sources of growth in the future.

AVIATION R&D AND MANUFACTURING CAPABILITIES IN INDIA

Indias aircraft manufacturing industry pre-dated Indias independence. Hindustan Aeronautics Ltd. (HAL) was set up in 1942 by H. Walchand, a prominent Indian industrialist. HAL was nationalized after independence and became a vertically integrated aircraft manufacturer catering primarily to Indias strategic needs. Most aircraft manufactured by HAL were built under license from foreign aircraft manufacturers. HAL concentrated on the requirements of the Indian Air Force, Navy, and Army. HAL had recently moved into the manufacture of indigenously developed aircraft such as the light combat aircraft (LCA) and the Advanced Light Helicopter. HAL had done some civil aircraft work in the past it manufactured the HS-748 and Dornier 228 aircraft under license. However, it remained primarily a defense aircraft manufacturer. The National Aerospace Laboratories (NAL) was set up as part of a national program to create advanced science and technology capabilities. Under independent Indias first Prime Minister, Jawaharlal Nehru, networks of research laboratories were set up under umbrella bodies such as the Council of Scientific & Industrial Research (CSIR), Indian Council of Agricultural Research (ICAR) and the Defence R&D Organization (DRDO). NAL was an important constituent of the CSIR and was one of the prime repositories of aeronautical R&D capability in the country. However, Indias most successful R&D efforts were arguably in the field of space and atomic energy where the Indian Space Research Organization (ISRO) and the Atomic Energy Commission (AEC) had successfully built vertically integrated capabilities in their respective fields. The Indian aerospace community was keen to emulate ISRO in the civil aeronautics field.

THE OPPORTUNITY

The rapid increase in demand for air travel, sparked by the entry of new LCCs into the civil aviation sector, steps taken to create new airports in metros, and plans to improve the civil aviation infrastructure in other towns and cities, pointed to steady growth of the civil aviation sector in the years ahead. One of the LCCs, Air Deccan, started routes to small towns that were hitherto not on the civil aviation network. As the growth in civil aviation is closely linked to economic growth, with India projected to continue its impressive growth record in the years ahead, there was reason to believe that there would be a market for a variety of aircraft in India. Already, Indian carriers had announced orders and options for hundreds of aircraft from the mainstream aircraft manufacturers, Airbus and Boeing. At the same time, it was recognized that a new aircraft takes a long time to develop, involves considerable upfront investment, and the aircraft itself typically has a long useful life. Long-term demand projections were therefore important to get a sense of the viability of a new aircraft program. The regional aircraft project taken up by NAL intended to target developing small aircraft to service small airports located in the smaller towns of India. In total, there were more than 334 (in 2002) civilian airports in India 238 with paved runways and 108 with unpaved runways. As a follow-up to the development of Hansa and Saras, NAL was exploring the possibility of taking a lead in the development of a new aircraft with a nominal capacity of 70 seats for use primarily in the civil aviation sector. The regional aircraft project was likely to cost about Rs. 40 (1$ = Rs. 52, in 2012) billion in development and the aircraft could roll out for certification and air worthiness trials by 20142015. The 70-seater aircraft was expected to serve the domestic airline services market on regional routes, covering a range of about 600800 km. Discussions with the existing operators indicated the need for an aircraft with 30% more fuel efficiency, 25% reduction in cost of ownership, with an ability to fly faster than the ATR 72/42 and land in small airfields (30005000 ft) with minimum facilities.




ESTIMATING DEMAND

The broad methodology for the estimation of total market demand for any industry is well-established. It consists of the following steps (Barnett 1988).

Define the market Divide total industry demand into its main constituents Forecast the drivers of demand in each segment and project how they are likely to change Conduct sensitivity analyses to understand the most critical assumptions and to gauge risks to the baseline

forecast

The market demand projections of three major aircraft manufacturers Airbus, Boeing, and Bombardier wereavailable in the public domain (Airbus 2006; Boeing 2006; Bombardier 2006). Based on the documents available, and some other secondary material, Exhibit 1 describes the process followed by Airbus and Boeing. In addition to these forecasts, elaborate demand forecasting exercises were periodically undertaken by the state transportation departments, and the commissions for planning and developing airport systems and road networks. Until 2010, air travel demand forecasting usually used quantitative indicators (population, gross national product, retail sales, etc.), derived socioeconomic factors (propensity to travel, lifestyles, etc.), and supply factors (fare, frequency, etc.). Primary forecasts were measures of air activity such as passenger traffic and freight traffic that could further lead to estimates of number of aircraft required. Two approaches were prevalent in aviation forecasting. The top-down approach developed aggregate national demand forecasts using economic data. These forecasts were then broken down to regions using past data and projected growth rates. The bottom-up approach generated regional forecasts, which were aggregated to obtain aggregate national forecasts. Forecasting methods included time trends, econometric models, and gravity models. Judgmental forecasts of experts refined using Delphi techniques for obtaining consensus were also used.

MARKET DEMAND IN INDIA

Both the Airbus and Boeing estimates were built ground-up based on individual routes in specified markets and treating demand growth as a function of economic growth with adjustments for expected structural changes in the markets.

Although, the IIMB team believed that the basic methodology to be followed may not be too different, it felt that primary demand generation for air travel was possible by competing with all existing modes of travel. Hence, the following issues needed to be addressed.

Generation of primary demand would require design of a new air travel offering that could compete with rail and road; hence substitution between air, rail, and road needed to be understood well.

In an effort to stem the loss of traffic to LCCs, the Indian Railways had become more dynamic in their strategies and this could have an impact on the growth of air traffic.

Announcements such as superfast highway corridors between selected pairs of cities could have a long-term effect on the demand for air travel, particularly since new airports in some cities were located far outside the city centers and reaching the airport may take the same amount of time as air travel transit time to a nearby location. At the same time, cities were planning new mass transit systems that could help people reach the airport quickly.

Differential economic development growth rates across regions. Many of the routes to Tier II cities and smaller towns had been started just then and it was too early to see a

trend. Possible delays in creation/upgrade of airport infrastructure owing to resource constraints and the impact on

congestion in air and on ground. Changes in market structure consequent to the entry of new players needed to be better understood. Since

these players did not have a long history, analogs from other countries could help to model the expected behavior of the new entrants. There was also a question of the viability and sustainability of the price-cutting strategies followed by the low-cost carriers.




Exhibit 1 summarizes the demand projection for regional jets according to the existing estimates made by other manufacturers. Exhibit 1a shows the air network and the traffic volume at 46 top airports in India and a comparison of passenger traffic by train and air. However, demand for a specific aircraft such as the proposed 70-seater (Exhibit 2) would be determined by the credibility of the manufacturer, and the specific value proposition of the particular aircraft.

ESTIMATING THE DEMAND POTENTIAL

Against this backdrop, the IIMB team wondered about the best way to estimate the demand for air travel and the yet-to-be-designed aircraft. Clearly, any method adopted would have to be cost-effective and reasonably quick. It would not be possible to study each and every route, so there was a need to identify representative routes that could be used to extrapolate broader demand. In India, air travel was mostly between major cities and was historically driven by business travel. The availability of an entire set of unused airfields presented a potential opportunity for generating air travel demand in these locations. This would require a thorough understanding of the existing travel options and designing an air travel offering that could successfully compete with the existing options. The teams objective was to arrive at an assessment of the extent of potential demand in this context.

This forecasting exercise was divided into two distinct phases. In the first phase, one pair of origin and destination airports would be identified for in-depth study. The Phase I study was further split into an exploratory phase to arrive at the important attributes that influence travel mode choice and a final phase that would fine tune the attribute set and examine the tradeoffs between these attributes. In the second phase, the focus would be on developing a cost-effective approach to estimate countrywide demand.

PRIMARY DEMAND CREATION PHASE I EXPLORATORY STAGE

The objective was to assess the potential for air travel between hitherto unused airfields using small aircraft. In 2007the travel modes available between such locations were restricted to rail and road. A new alternative such as air travel would compete with the existing travel modes. In 20082009, passenger movement in India by air was less than 0.4% of that by rail. This represented a huge opportunity for an air travel offering that could effectively compete with rail and road travel modes. Hence, it was important to ascertain the benefits and tradeoffs contained in the travel modes. This information would be useful in designing a new offering. Accordingly, in this phase, the focus was on rail, road, and air as the primary travel modes leading to the following specific questions.

Can a new air travel product that can compete with road and rail be developed? What benefits are required of such a new offering? At what price would such an offering be attractive?

In the new product development process, such questions are the focus of the product design phase, which follows the identification of opportunity. In the product design phase, precise measurement of consumer preferences for products, which are bundles of benefits, is required.

CAPTURING CHOICE BEHAVIOR CONJOINT ANALYSIS

Traditional disjoint methods for ascertaining consumer preferences would consider (1) the value to the consumer and (2) the importance of each attribute/benefit at various levels. The two ratings would then be combined resulting in an overall utility. Conjoint analysis adopts a different approach and allows for assessment of tradeoffs. The approach involves developing sets of combinations of attribute/benefits and obtaining relative assessments of these combinations from the respondent. Conjoint analysis assumes that the utility associated with a product (total worth) is obtained by combining the separate amounts of utility provided by each attribute (part-worth), which could be present at one of the different levels. An appropriate analytic technique is then used to decompose this overall assessment of the combination of benefits into the graded value of each benefit to the respondent. Such an approach is preferred to the disjoint approach of obtaining an assessment of value associated with each benefit and the importance of this benefit in the overall assessment, since it is relatively less burdensome for the respondent and more representative of consumer behavior. The IIMB team decided to use the conjoint methodology to measure the customer preferences.




The IIMB team adopted the following process to design the conjoint experiment.

STEP 1: FORMULATE THE RESEARCH PROBLEM AND SELECT THE ATTRIBUTES AND THE LEVELS

Rajamundry, a small town with an airfield, in the state of Andhra Pradesh, was selected for the first phase of the study (Exhibit 3). It is close to Kakinada which has a port and was emerging as a center of economic activity owing to the potential for extracting natural gas in this region. It was also a transit point for religious tourism owing to the temples located in this region. RajamundryChennai was chosen as the origindestination pair in this study.

The first key decision in formulating a conjoint exercise was the selection of attributes and levels for these attributes. This decision involved careful consideration of all attributes that potentially created or detracted from the overall worth of the product. Further, the attributes had to be actionable and communicable. However, if the number of attributes and the levels were too many, the burden on the respondent increased resulting in possible data integrity issues.

After a quick review of the existing travel modes in India, the IIMB team decided to retain air, rail, and bus as the three main travel modes for inter-city travel. The team observed that travel by air-conditioned (AC) rail coach would compete with air travel. The AC rail coach had two classes, namely 2-tier and 3-tier, with 2-tier being more expensive because it provided more space to each traveler and was hence less crowded than a 3-tier coach. Similarly, the bus travel between these two cities provided two options, Volvo and Garuda, Volvo being more comfortable and more expensive.

The fare charged by different modes of transport was an important factor considered by the commuters. The team observed the various existing fares for rail and bus and decided to elicit responses to air travel by setting the lowest range of airfares at just beyond the highest of other modes of travel. To capture a non-linear pricing relationship, the team decided to consider three price points, spread sufficiently far enough.

The IIMB team thought that travel time would be a consideration in selecting the mode of travel. Air travel between these two cities could be done in about 1.5 hours, with rail and bus taking around 15 hours of travel. The short air-travel time was often offset by the distance to the boarding point as airports were usually located away from the city unlike rail and bus stations. Hence, last mile connectivity or accessibility of the boarding point was used as one of the attributes. The team wanted to gauge the response at the existing level of connectivity and improved level of connectivity to the boarding point.

Owing to the popularity and affordability of rail and bus travel, availability was an issue in these modes of travel. The team wanted to gauge the importance attached by the commuters to easy availability of tickets. The team decided to provide a choice between the next day availability and the need to book the tickets, a week in advance.

Accordingly, the attributes and levels chosen for each attribute were as shown in Exhibit 4.

STEP 2: METHOD OF DATA COLLECTION

In full profile approaches, respondents are asked to rank or rate all the profiles chosen, in what is called judgment data collection. Choice-based conjoint analysis is an alternative approach, in which the respondents are asked to choose only one among a subset of profiles that is presented. Choice-based conjoint analysis provides a more natural setting since in real life, consumers choose among the alternatives available in the market. Further, choosing one from a small set of options is easier for the respondent relative to rating all profiles. Hence, choice-based conjoint has been finding favor among market researchers. Respondents can choose a profile from each of the several sets of choice alternatives, which include a none of the above option. The IIMB team decided to use the choice-based conjoint method to collect the data.

STEP 3: DESIGN THE STIMULI

There are two approaches to eliciting travel mode preferences. In the pair-wise approach, the respondent may be asked to evaluate two attributes at a time. In the full profile approach, complete profiles are constructed and




presented to the respondent. Further, in a full profile approach, a full factorial design consists of all possible combinations of attributes and their levels. It is possible to estimate the main effects and interactions between attributes using a full factorial design. It is not always feasible to use a full factorial design as the number of profiles may be too large. For example, Exhibit 5 shows the linear factor design for this problem; there are 15 attributes with two levels each and one with three levels. A full factorial design would contain possible combinations, making the respondents task of providing relative evaluations very difficult. Instead, a fractional factorial design where a reduced number of combinations are arrived at by ignoring certain combinations or interaction effects can be used. A fractional factorial design needs to be balanced and orthogonal. When each level of every attribute occur equal number of times, then the design is balanced. A design is orthogonal when every pair of levels belonging to a pair of attributes occurs equal number of times. A design which is both orthogonal and balanced is 100% efficient. When it is not practical to generate an optimal design, different algorithms are used to select the most optimal design possible which seeks to minimize the variance of estimated parameters. Randomizing a design involves arranging the product combinations or the choices in a random order. Statistical software such as SAS and SPSS has tools that enable designing such experiments.

The IIMB team decided to adopt a full profile approach to create the stimuli and use a fractional factorial design. The process followed to generate the fractional factorial design using SAS macros (detailed in Exhibits 6 & 7)contains the SAS code for implementing these macros. Given the attributes and levels, the macro %MktRuns is used to arrive at the number of profiles for orthogonal and balanced designs and the macro %MktExe is used to create an efficient and randomized design. %MktEval is used to evaluate the generated designs and finally %MktKey and %MktRoll are used to roll out the conjoint design. The output from running these macros is presented in Exhibit 8.An orthogonal and balanced design with 24 combinations was chosen.

A sample card presented to the respondents is shown in Exhibit 9. The respondent is required to choose one of the six options presented in 24 such cards. The survey was administered to 30 respondents in the initial exploratory phase. The data is available in the excel sheet phase1_exploratory_data_case_A.

STEP 4: ANALYSIS OF DATA

Although, choice-based conjoint reduces the respondents burden, the analysis is more complicated requiring the use of discrete choice models since the dependent variable is nominal. When the dependent variable is metric, a multiple regression model can be used to assess the marginal effects of an increase in independent variable on the dependent variable. However, when the dependent variable is nominal, the relationships between the probabilities of the choices and the independent variables have to be estimated. This is done by specifying utilities for each choice in terms of the independent variables and estimating the importance weights of the independent variables in determining choice using the observed data. The basic description of the choice model used is provided in Annexure 1.

The importance weights were estimated using the SAS code detailed in Exhibit 10 and the results of estimation are presented in Exhibit 11.

STEP 5: MANAGERIAL ANALYSIS

Analysis of these estimates suggests that:

Price and availability are the critical benefits. Within a mode, time taken is not important. Last mile connectivity is not important.

Here, only importance weights estimated with high precision (p


The probability of choosing the air travel option is

= 0.022682.

Further, if the airfare is reduced to Rs. 1,500, utility of the air travel offering increases to 1.755. The probability of traveling by air when other choices remain the same and the airfare is reduced to Rs. 1,500 is

= 0.116698.

Thus, the market share of the air travel mode increases from around 2.3% to 11.7% when the fare decreases from Rs. 2,900 to Rs. 1,500.

The IIMB team faced the task of redesigning the conjoint experiment for data collection on a large representative sample. What are the attributes that should be retained and what are the levels that would be appropriate for understanding travel mode competition and enabling design of an attractive new air travel offering? Once these decisions were made, an efficient factorial design would be required. What other demographic and socio-economic data would help in segmenting the market?




Annexure 1Multinomial Logit

The basic assumption of discrete choice models is that the choice is based on the principle of utility maximization. Let indicate the utility perceived by the respondent by a choice alternative . The utility specification consists of a deterministic component that is a weighted combination of attributes present in the alternative and a random component that is known to the respondent but not to the analyst. Thus,

where are the attributes of alternative and is the random portion of the utility.

The probability that an individual will choose alternative among the alternatives in the choice set is given by

If the error distribution is Gumbel or Type I extreme value distribution

the probability that an individual would choose an alternative is given by the multinomial logit form

where is the vector of attributes describing alternative i.

Given a series of choices among the choice sets presented, the parameter can be estimated by maximizing the likelihood of the observed choices. The IIMB team decided to do an aggregate level analysis using a multinomial logit model.

Here, the utility derived from an alternative i by a respondent n on choice occasion t is specified as

where are dummy variables associated with each level of each attribute, is the fare to be paid to travel in a given mode of commute, are importance weights of the respective attribute levels, and is a random component that is unknown to the analyst but known to the respondent. For each factor, the number of dummy variables defined equals the number of levels in the factor minus 1. The dummy variables are

The utility maximizing respondent n chooses alternative i on occasion t with probability




References

Airbus (2006), Global market forecast 20042023, URL: http://www.airbus.com/.

Barnett F. W., Four steps to forecast total market demand, Harvard Business Review, 1988, 66(4), 28.

Boeing (2006), Current market outlook 2006, URL: http://www.boeing.com/commercial/cmo/pdf/cmo_06.pdf.

Bombardier (2006), Aerospace commercial aircraft market forecast 20062025, URL: http://www.bombardier.com.

Kane M. A. E. B., Airbus 3XX: Developing the worlds largest commercial jet, Harvard Business School Case No. 9-201-028, 2000.

Kuhfeld W. F., Marketing research methods in SAS experimental design, choice, conjoint, and graphical techniques, SAS Institute Inc., Cary, NC, USA. 2005.




Exhibit 1Available Demand Projections for Regional Jets

1. Airbus Global Market Forecast

According to a study by Kane (2000), Airbus predicts annual demand for new aircraft on each of the 10,000 passenger routes linking nearly 2000 airports based on an assumption that passenger and cargo growth will track GDP growth. For every airline on each route, Airbus estimates the aircraft requirements for different types of aircraft. The maximum feasible frequency limits for each route were estimated based on assumptions of airport capacity, airplane speed, distance, and other factors. It is assumed that all airlines will seek to maintain market share by adding capacity as demand increases, and by increasing aircraft size when it is not feasible to increase frequency. They then compared the number of aircraft required to existing supply taking retirements into account.

The latest market demand forecast available from Airbus is the Global Market Forecast 20042023 (Airbus 2006).Some salient features of Airbuss methodology and relevant observations that emerge from this document are:

Airbus divides the world aviation market into 140 distinct domestic, regional, and intercontinental passenger sub-markets such as Domestic Indian sub-continent, Intra Asia and Indian sub-continentUSA. For each of these markets, demand is forecast using the latest projections of economic growth, oil prices, and other relevant variables. Airbus uses econometric modeling techniques to forecast traffic for each individual flow based on a best fit of the different sets of economic and air transport variables (p. 32).

The projections also take into account any expected structural changes (such as deregulation) that could influence the future growth of a particular market. The growth of LCCs is one of the important developments considered in this context since LCCs stimulate traffic and create new routes (p. 32).

Airline sensitivity to oil price increases depends on (1) the revenue lost from lower demand, (2) the ability to impose a fuel surcharge on tickets, (3) the importance of fuel cost relative to other costs, (4) fuel hedging strategies, and (5) fuel efficiency of the fleet (p. 12). Rapid oil price increases trigger the acceleration of aircraft retirement (p. 14).

The propensity to travel in developing countries starts with domestic trips when incomes reach a particular threshold, and then graduates to international travel when a higher threshold is reached (p. 20). In China, income (measured by GDP per capita) and exports are the two primary drivers of demand growth (p. 19). Between 1980 and 1998, Chinese domestic air traffic grew at an annual rate of 16.5% fueled by an increase in disposable income (p. 20).

High-speed trains are important sources of competition to air traffic only when rail trip time is less than 3 hours (p. 21).

The bulk of air travel demand will be generated by mega-cities that will be the hub of economic activities (p. 27).

In mature markets, air travel is driven by ticket price and consumer confidence in addition to economic growth (p. 29).

Airbus uses the actual fleet replacement plans of each individual airline to predict induction of new aircraft. If such plans are not available, historical behavior of that airline or airlines in that region is used to estimate replacement behavior (p. 38).

The emerging low-cost airlines of Asia will drive single-aisle fleet expansion in the region (p. 44).

Based on their analysis, Airbus estimated that world air passenger traffic would increase at an average annual rate of 5.3% per year between 2004 and 2023, and that this would result in a growth of 4.5% seats per year. Airbus predicted a compound annual growth rate of 4.0% for passenger traffic in the Domestic Indian sub-continent sub-market, and 3.0% for passenger traffic in the Intra Indian sub-continent sub-market.

2. Boeing Current Market Outlook

According to Kane (2000), Boeing forecasts economic growth in 12 regions. Based on economic growth estimates, it then forecasts regional traffic flows in 51 intra- and inter-regional markets.




The latest market demand forecast available from Boeing is the Global Market Outlook 2006 (Boeing 2006). Some salient features of Boeings methodology and relevant observations that emerge from this document are:

Boeing predicts an annual world economic growth rate of 3.1%, and a passenger travel growth rate of 4.9% between 2006 and 2025 (p. 4).

Liberalization is an important driver of air traffic growth and can stimulate market growth of 12% to 35% (p. 7).

Similar to the Airbus approach, Boeing studies travel flows at a detailed level of individual routes and then aggregates these into the main travel regions of the world. Travel growth rates are influenced by factors in the countries at each end of the flow (p. 8).

The size of each economy and its growth rate has the largest effect on the growth of passenger travel in each market. The rate of change of airfares will affect travel growth. In many markets, the biggest influence on growth rates is the degree to which governments open market access to airlines based either within their own territory or outside (i.e., the degree of liberalization in each countrys markets). Other important factors to consider include the propensity for people of a given culture to travel, and the outlook for change in any government-imposed restrictions on travel (p. 9).

The size of airplane used in any given region depends on traffic volumes, distances in key markets, competitive strategies, availability of alternate forms of transport, and market access. Boeing argues that in a deregulated market, airlines will tend to use smaller aircraft and provide more frequent service (p. 10).

Air transport accounts for about 8% of the world aggregate GDP. The break-up of this 8% is leisure travel 3%, visiting family and friends 2%, business travel 2%, and air transportation of cargo 1% (p. 16).

For the Southwest (SW) Asia region (that includes India), Boeing predicts a GDP growth rate of 5.4% and an air passenger traffic growth rate of 7.1%. Boeings estimates incorporate recent developments such as the spurt in the number of airlines in India. Based on these projections, Boeing visualizes a demand for only 70 regional jets in SW Asia in the next 20 years.

Boeing demand projection for regional jets.

Regional jets20062025 3450 (World)

580 (Asia-Pacific)70 (SW Asia, including India)

Source: Boeing Current Market Outlook 2006. http://www.boeing.com/commercial/cmo/pdf/CMO_06.pdf

3. Embraer demand projections for regional jets

6190 seats 91120 seats20062015 1300 1550

20062025 2950 3450

Source: Embraer predicts 7,950-jet market for 30- to 120-seaters, Aviation International News, November 2006. Downloaded from http://www.ainonline.com/Issues/10_06/10_06_embraer_80.htm on November 2, 2006.

All the above demand figures are for the whole world.

Of its total projection of demand for 7,950 aircraft in the 30120 seats category over the next 20 years, Embraer visualizes the major markets as North America and the Caribbean (53%), Europe (18%), Russia and Eastern Europe (7%), and China (7%).




4. Bombardier demand projection for regional jets

6099 seats

20052025 4100 (world) 435 (Asia-Pacific excluding China)

Source: Bombardier Aerospace Commercial Aircraft Market Forecast 20062025 (Bombardier 2006)

Exhibit 1aDomestic traffic (passenger movements) at top 46 airports in India

(20052006 and 20062007)

Airport 200607 200506International AirportsMumbai 14902373 11682444Delhi 13790078 10468028Chennai 6078196 4173345Bangalore 6863965 4792051Kolkata 5187867 3664548Hyderabad 4535519 2994021Ahmedabad 2085875 1438969Goa 1808447 1267864Trivandrum 595041 322597Calicut 233277 191506Guwahati 1073869 724001Amritsar 107918 77974Srinagar 690384 457000Jaipur 595386 394452Nagpur 583208 351236Total 59131403 43000036Cochin International Airport Ltd.Cochin (CIAL) 1134604 731762Custom AirportsPune 1527938 905291Coimbatore 852239 559133Lucknow 482537 430993Mangalore 449696 304824Varanasi 317418 232365Patna 311171 218824Tiruchirappalli 54366 29556Gaya 0 71Total 3995365 2681057Domestic AirportsVadodara 404242 360489Jammu 473764 306385Indore 358496 272484Visakhapatnam 330894 239979Agartala 324664 236970Bhubaneswar 350128 220084Udaipur 236502 210030Bagdogra 259058 207587Port Blair 504064 204375




Exhibit 1a (Continued)

Airport 200607 200506Madurai 265406 178824Bhopal 169131 147268Rajkot 160711 139982Aurangabad 170498 137388Raipur 246038 135320Imphal 214689 134701Chandigarh 154705 130723Leh 140609 122401Juhu 137205 106973Dibrugarh 127485 104125Jodhpur 107733 101765Ranchi 145575 93508Silchar 116590 81967Total 5398187 3873328Other Airports 953707 696048Grand Total 70613266 50982231

Source: Airports Authority of India

The following table compares the passenger movement statistics

200506 (Crore)

200607 (Crore)

200708 (Crore)

Domestic air traffic (passenger movements) at top 46 airports in IndiaSource: Airports Authority of India

5.09 7.06 8.7

Railway traffic (passengers originating) in IndiaSource: Data as per Central Statistical Organization (CSO), Compiled by Indiastat. Note: 1 Crore = 10 million

2676.43 2669.48 2535.81




Exhibit 2RTA-70 prototype and specification




Exhibit 2 (Continued)




Exhibit 3Rajamundry




Exhibit 4 Attributes and levels

Exhibit 5Linear factor design for travel modes

Travel Modes Air Rail-2TAC

Factors Time Fare Accessibility Availability Time Fare Accessibility Availability1 1500 Current Next day 15 1100 Current Next day

1.5 2200 Improved One week 1400 Improved One week2900

No. of Levels 2 3 2 2 1 2 2 2

Travel Modes Rail-3TAC Bus Garuda

Factors Time Fare Accessibility Availability Time Fare Accessibility Availability15 740 Current Next day 15 700 Current Next day

1040 Improved One week 1000 Improved One week

No. of Levels 1 2 2 2 1 2 2 2

Travel Modes Bus-Volvo

Factors Time Fare Accessibility Availability15 1000 Current Next day

1300 Improved One week

No. of Levels 1 2 2 2

Mode Time(hours)Fare(Rs.) Accessibility/connectivity Availability

Air 1,1.5 2900,2200,1500 Current, Improved Next day, One weekRail-2TAC 15 1400,1100 Current, Improved

Next day, One week

Rail-3TAC 15 740,1040 Current, Improved

Next day, One week

Bus-Garuda 15 700,1000 Current, Improved

Next day, One week

Bus-Volvo 15 1000,1300 Current, Improved

Next day, One week




Exhibit 6Conjoint choice analysis using SAS

Steps for choice-based conjoint using SAS 9.1 (Kuhfeld 2005)

Design the Choice Experimento Identify the attributes and the levelso Run %MktRuns to suggest the number of runs for orthogonal and balanced designso Run %MktExe to suggest efficient designso Run %MktEval to evaluate the design generatedo Run %MktKey and %MktRoll to create the choice design

Administer the experiment by asking the respondents to fill the choice cards

Analysis of the datao Run %MktMerge to merge the collected response data with the design.o Run TRANSREG procedure to code the dummy variableso Run PHREG procedure to do the multinomial logit on the collected data.




Exhibit 7SAS code for Phase I design

libname data ;

/* suggests the number of choice sets */ %mktruns( 3 2**15); /* look for entry with violations = 0 */ /* generate Linear Design with 24 choice sets */ %mktex( 3 2**15, n=24, seed=17)

title2 Examine Correlations and Frequencies; %mkteval; title2 Examine Design; proc print data=randomized; run; data key;

length mode $ 15; input (Mode Time Fare Accessibility Availability) ($);

datalines; Air Rail-2TAC . Rail-3TAC . Bus-Garuda . Bus-Volvo . None . . . .

; run;

title2 Create Choice Design from Linear Design; %mktroll( design=randomized, key=key, alt=mode, out=data.phase1_design ) proc print; id set; by set; run; run; title2 Evaluate Design; %choiceff(model=class(Mode Time Fare Accessibility Availability), /* model, expand to dummy vars */ nalts=6, /* number of alternatives */ nsets=24, /* number of choice sets */ beta=zero, /* assumed beta vector, Ho: b=0 */ intiter=0, /* no internal iterations just */ /* evaluate the input design */ data=data.phase1_design, /* the input design to evaluate */ init=data.phase1_design(keep=set))/* choice set number from design*/

data data.phase1_design; set data.phase1_design; length time_text Fare_text $20; length acc_text avail_text $100; label time_text= "Travel Time"; label Fare_Text= "Ticket Price Per Head"; label acc_text= "How easily you can get to the Airport/Station/Bus Stand"; label avail_text= "Advance Booking";




label mode_text ="Travel Mode"; label fare_num= "Fare"; if mode='None' then mode_text='None of the Above'; else mode_text=mode; fare_num=0; if mode="Air" then do;

if time = 1 then time_text = "1.5Hrs"; else if time=2 then time_text= "1 Hrs";

if Fare=1 then fare_text= "Rs. 2900/-" ; else if Fare=2 then fare_text="Rs. 2200/-"; else if Fare=3 then fare_text="Rs. 1500/-";

if Fare=1 then fare_num= 2900 ; else if Fare=2 then fare_num=2200; else if Fare=3 then fare_num=1500;

end; if mode not in ('None','Air') then time_text="15 Hrs.";

If mode='Rail-2TAC' then do;

if Fare=1 then fare_text= "Rs. 1400/-"; else if Fare=2 then fare_text="Rs. 1100/-";

if Fare=1 then fare_num= 1400; else if Fare=2 then fare_num=1100;

end; If mode='Rail-3TAC' then do;



end; If mode='Bus-Garuda' then do;



end;




If mode='Bus-Volvo' then do;



end; if Accessibility= 1 then acc_text = "At Current Level"; else if Accessibility= 2 then acc_text = "Improvement in connectivity"; else if Accessibility= . then acc_text = " "; if availability= 1 then Avail_text = "Next Day Availability"; else if availability= 2 then Avail_text = "Book One week in Advance"; else if availability= . then Avail_text = " "; run; ods html ;

options nodate pageno=1 linesize=80 pagesize=60 ; footnote 'IIM Bangalore'; title1 Travel Choice;

Title3 'How would you choose to travel?'; proc print data=data.phase1_design noobs label; by set; pageby set; var mode_text time_text Fare_text acc_text avail_text; label set="Choose one among the following options/ Choice #";

run; ods html close;




Exhibit 8Design summary

N Design Reference24 2 ** 16 3 ** 1 Orthogonal Array36 2 ** 27 3 ** 1 Orthogonal Array36 2 ** 20 3 ** 2 Orthogonal Array36 2 ** 18 3 ** 1 6 ** 1 Orthogonal Array48 2 ** 40 3 ** 1 Orthogonal Array48 2 ** 37 3 ** 1 4 ** 1 Orthogonal Array48 2 ** 34 3 ** 1 4 ** 2 Orthogonal Array48 2 ** 33 3 ** 1 8 ** 1 Orthogonal Array48 2 ** 31 3 ** 1 4 ** 3 Orthogonal Array48 2 ** 28 3 ** 1 4 ** 4 Orthogonal Array48 2 ** 25 3 ** 1 4 ** 5 Orthogonal Array48 2 ** 22 3 ** 1 4 ** 6 Orthogonal Array48 2 ** 19 3 ** 1 4 ** 7 Orthogonal Array48 2 ** 16 3 ** 1 4 ** 8 Orthogonal Array

Saturated = 18Full Factorial = 93,304

No. ofLevels Frequency

2 153 1

Some Reasonable Design Sizes Violations

Cannot Be Divided by

24 * 036 * 048 * 020 16 3 628 16 3 632 16 3 640 16 3 644 16 3 618 105 430 105 4




Exhibit 9Sample conjoint card

Mode Travel Time (hours)Fare (Rs.)

How easily you can get to the Airport/Station/Bus Stand Advance Booking

Air 1.5 2900 Improved connectivity Next day availability

Rail-2TAC 15 1100 Improved connectivity Book one week in advance

Rail-3TAC 15 1040 At current level Book one week in advance

Bus-Garuda 15 700 Improved connectivity Book one week inadvance

Bus-Volvo 15. 1300 Improved connectivity Next day availabilityNone of the

Above




Exhibit 10SAS code for Phase I analysis

libname data ; /* Analysis */

/* create sas dataset from spreadsheet */ proc import datafile='\phase1_data.xls'

out=data.phase1_data replace; run; title2 Merge Data and Design; %mktmerge(design=data.phase1_design, /* input design */ data=data.phase1_data, /* input data set */ out=data.phase1_data, /* output data set with design and data */ nsets=24, /* number of choice sets */ nalts=6, /* number of alternatives */ setvars=r1-r24) title2 Code the Independent Variables;

proc transreg design norestoremissing nozeroconstant data=data.phase1_data; model class(mode time_text acc_text avail_text /zero='None' ) identity(fare_num); id subject set c ; output out=data.phase1_coded(drop=_type_ _name_ intercept) lprefix=0; run; %phchoice( on ) title2 Multinomial Logit Discrete Choice Model; proc phreg data=data.phase1_coded brief ; /*&_trgind*/ model c*c(2) =&_trgind / ties=breslow; strata subject set; run; %phchoice( off )




Exhibit 11Model statistics

Summary of Subjects, Sets, and Chosen andNot Chosen Alternatives

Pattern Number ofChoices

Number ofAlternatives

ChosenAlternatives

Not Chosen

1 720 6 1 5

Model Fit StatisticsCriterion Without

CovariatesWith

Covariates2 LOG L 2580.134 1816.769AIC 2580.134 1836.769SBC 2580.134 1882.562

Testing Global Null Hypothesis: BETA=0Test Chi-Square DF Pr > ChiSqLikelihood Ratio 763.3642 10


Exhibit 12Utilities associated with travel mode

ModeTravel Time

(hours)

Fare(Rs.) Connectivity Advance Booking

Utility

Air 1.5 2900Improved

connectivity Next day availability0.00117*2900

= 3.393

Rail-2TAC 15 1100 Improved connectivity Next day availability0.00117*1100

= 1.287

Rail-3TAC 15 740 Improved connectivity Next day availability0.00117*740

= 0.8658

Bus-Garuda 15 700 Improved connectivity Next day availability0.00117*700

= 0.819

Bus-Volvo 15 1000 Improved connectivity Next day availability0.00117*1000

= 1.17None of the

Above0



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estimating demand for a new regional transport aircraft (a)

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