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Technology Roadmap Subsector Selection Study for the

development of the Aerospace Industry in Mexico and it’s

opportunities with Global Corporations, using Operational Research

Multi Criteria Decision Analysis Tools

von Raesfeld Porras, Bernardo

Cavendish School of Electronics and Computer Science, University of Westminster, United Kingdom

Bernardo.vonraesfeld@gmail.com, +52 (1) 2222994919

Abstract: Multi Attribute Utility Theory (MAUT) and Analytic Hierarchy Process (AHP), two well

known Operations Research’s Multi Criteria Decision Analysis (MCDA) tools, are used to solve a

strategic subsector selection problem aiming to strengthen and articulate the chain of value of the

Civil Aerospace Industry of Mexico, additionally looking for the possible common opportunities with

global companies, clusters and governments.

Keywords: Aerospace, Mexico, UK, Canada, Industrial Policy, Innovation, Operations Research,

MCDA, MCDM, MAUT, AHP

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ACKNOWLEGEMENTS

I wish to thank the following organizations for their support to this report:

Aerospace & Defence Knowledge Transfer Network (UK)

Aerospace Defence Security A|D|S (UK)

Aerospace Industries Association of Canada (Canada)

Catalyze Ltd (UK)

Mexican Consortium for Aerospace Education (Mexico)

Mexican Federation of the Aerospace Industry (Mexico)

Ministry of the Economy (Mexico)

National Council for Science and Technology (Mexico)

Northwest Aerospace Alliance (UK)

PROMEXICO Investment & Trade (Mexico)

University of Cambridge, Institute for Manufacturing (UK)

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INDEX OF CONTENTS

INDEX 3

OBJECTIVES AND GOALS 5

INTRODUCTION 5

DESCRIPTION OF THE PROBLEM 5

CHAPTER I : MAKING DECISIONS USING MULTI CRITERIA DECISION ANALYSIS TOOLS 7

Multi Criteria Decision Analysis (MCDA) 7

Different approaches of MCDA 7

The Analytic Hierarchy Process (AHP) methodology 8

Limitations 9

The Multi Attribute Utility Theory (MAUT) methodology 10

Limitations 11

CHAPTER II: MEXICO COUNTRY STRATEGY FORMULATION ON INDUSTRIAL POLICY 12

Technology and Strategy 12

Industrial Policy 13

Innovation & Technology policies as Industrial Policy within Knowledge Based Economy 13

TRM methodologies 15

Mexico Industrial Policy in the 1980’s and 1990’s 16

Recent Industrial Policy in Mexico formulation using TRM Methodologies 18

CHAPTER III : THE AEROSPACE SECTOR CHOICE OF MEXICO 21

Mexico Strategic Sectors selection 21

Aerospace Sector description 23

Subsectors and Niches within the Aerospace Sector 23

Aerospace Capabilities Maps description 25

CHAPTER IV : MEXICO AEROSPACE SUBSECTORS MCDA PRIORITISATION 26

Defining the Subsector Alternatives 26

Defining the Criteria 27

Generating the Operational Tables 30

Subsectors Market Share 30

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Mexico Development Presence per Subsectors 30

Relative Benchmark towards the Global Industry 31

Actor Objectives 31

Prioritisation according to the AHP Approach 32

Limitations 36

Prioritisation according to the MAUT Approach 37

Limitations 40

Combined differences in the outcome of the prioritisation 40

Validation 41

Cluster synergy opportunities for Mexico 41

CONCLUSIONS 43

REFERENCES 44

ATTACHMENTS 47

ATTACHMENT I: PROMEXICO Investment & Trade “Capabilities Matrix Comparison Mexico

UK” ProMexico Investment & Trade, Business Intelligence Unit, Mexico (2010) 47

ATTACHMENT III: Mexico National Level, HIVIEW3 Full report 49

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OBJECTIVES AND GOALS

In order to solve a selection problem of aerospace industry subsectors alternatives within which

Mexican Industrial Policy should invest its resources in, with the goal of taking maximum possible

advantage of the actual and prospective state of the industry worldwide, two well known Multi Criteria

Decision Methods1 are described, applied and it’s outcomes explained. Then additional analysis is

provided to identify alternatives suitable to offer concrete business opportunities with global

companies.

INTRODUCTION

The aeronautic industry in the world is within an intense process of reorganisation due to needs such

as maintain a high manufacturing competitiveness, generate new products with high operational

efficiency and reduce the overall operational cost alongside the life cycle of the product, while

keeping a strict compliance with regulatory aspects, mainly security related. Formerly the whole

manufacturing process concentrated only in the leading corporations of the industry, the trend now is

looking for subcontracting schemes in regions whose cost related competitiveness is higher than

those of countries such as the USA, UK, Germany, France or Canada.

The chain value associated to the design and manufacture of the aerospace industry is complex,

highly segmented in the structure of the productive chain as well in terms of market niches, which

implies a considerable level of difficulty for the entry of new corporations because of technical entry

barriers which enlarge the business cycle, but grant high economical benefit to the participants who

are able to find stability once linked within it.

The challenge for the Mexican industry in terms of manufacture is not only to increase the volume of

operations, but also to achieve recognition or quality certification from the source point, besides

promoting the integration of systems and identified in the sector are located within the service,

components and parts niches.

Mexico has established mechanisms for the development of advanced systems for the automotive

sector and because of its geographic location finds itself in a strategic position for the services area

due to the increase of air traffic in the central zone of the country. This situation presents the

opportunity to the country of taking advantage of the experiences in the development of effective

mechanisms to increase the supply of services and components for the aeronautic sector at both

national and international levels.

DESCRIPTION OF THE PROBLEM

The Industry, Academy and Government have developed recently relevant tools for achieving joint

planning within the realm of Industrial Policy. The Institute for Manufacturing of the University of

Cambridge has developed the Technology Road Map (TRM) Methodology (CAMBRIDGE, 2001),

proofed as successful for different uses within public and private sector. The Knowledge Transfer

Network Aerospace & Defence Group, a British public-private body, has applied the Technology Road

Map technique(KNOWLEDGE TRANSFER NETWORK, 2009) for similar purposes in the past years,

setting it as the standard nowadays for aerospace industrial policy formulation worldwide. Mexico has

1 Analytic Hierarchy Process (AHP) and Multi Attribute Utility Theory (MAUT)

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adopted this approach and in 2009 released the first version of the Mexican Aerospace Industry

Technology Road Map (MAITRM), publicly known as the “Plan de Vuelo Nacional

2009”(PROMEXICO, 2009), though it’s conclusions remained at a strategic level and did not go deep

into selecting within the operational alternatives. Thus there is a need in the Mexican Aerospace

Working Group, before releasing the second version of the MAITRM to count with a study that

weighting the evidence different subsectors yield, prioritizes them, thus actions can be considered by

the group for the short and long term. This document aims to contribute to that debate.

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CHAPTER I : MAKING DECISIONS USING MULTI CRITERIA DECISION ANALYSIS TOOLS

Multi Criteria Decision Analysis (MCDA)

Multi Criteria Decision Analysis (MCDA) also known practically as Multi Criteria Decision Making

(MCDM) deals with decisions involving the choice of a best alternative from several potential

candidates in a decision, subject to criteria or attributes that may be tangible or intangible (CHO,

2003)

Different approaches of MCDA Decision Theories; scale of measurement

Several methods have been proposed for solving Multi Criteria Decision Analysis (MCDA). One of the

main objections to these procedures is that depending on the techniques used, results might vary

while looking at the same problem. The industrial policy problem considered in this study consists of a

decision matrix input of N criteria weights of L alternatives on each criterion. Research has been done

about the comparative performance of some of these methods in a few, mostly field/practical, studies.

We can categorise the problems in two sets, for which different techniques have been developed. The

first kind are those when a feasible set of alternatives of a decision consists of a finite number of

elements that are explicitly known in the beginning of the solution process, these are called Multi

Criteria Evaluation Problems. These problems are often referred to also as Discrete Multi Criteria

Problems or Selection Problems(CHO, 2003).

The other kind is when the number of alternatives of a decision is considered to be uncountably

infinite, thus the alternatives are not specified in a direct manner, but are rather defined in terms of

decision variables as is usually done in single optimization problems like linear programming. This is

called Continuous Decision Problem in which the alternatives are only implicitly known. It is also

referred to as a Multi Criteria Design Problem or a Continuous Multi Criteria Problem. Listed ahead

are some decision techniques available in the literature(AKSOY, 1990)(BOOKER et al, 1985):

Multi Criteria Evaluation Methods: The outranking approach (Software ELECTRE) by Roy (ROY,

1980) and Roy and Vincke (ROY et al, 1981), other methods developed by some French – Belgian

school researchers are: ORESTE by Roubens (ROUBENS, 1982) and Pastijn and Leysen(PASTIJN

et al, 1989) PROMETHEE by Brans, Mareschal and Vincke (BRANS, 1984). Multi attribute utility

theory (MAUT) by Keeney and Raiffa (KEENEY, 1976). The analytic hierarchy process (AHP)

(software: Expert Choice) by Saaty (SAATY, 1990), the regime method by Hinloopen, Nijkamp and

Rietveld (HINLOOPEN et al, 1983), the convex cone approach by Korhonen, Wallenius and Zionts

(KORHONEN et al, 1984), the hierarchical interactive approach by Korhonen (KORHONEN, 1986),

the visual reference direction approach (software: VIMDA) by Korhonen (KORHONEN, 1988), the

aspiration-level interactive method (AIM) by Lofti, Stewart and Zoints (LOFTI et al, 1992), fuzzy set

theory (ZADEH, 1965), and Bayesian analysis(NEWMAN, 1971).

Multi Criteria Design Methods: Goal programming (GP) and data envelopment analysis (DEA) of

Charnes and Cooper and Charnes, Cooper and Rhodes, the method of Geoffrion, Dyer and Feinberg,

the method of Zionts and Wallenius, the reference point method of Wierzbicki, the reference direction

method of Korhonen and Laakso, Pareto race of Korhonen and Wallenius, interactive weighted

Tchebycheff procedure of Steuer and Choo.

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The second group is very large, and is almost impossible to list, but we have mentioned some of the

better known approaches developed for multiple objective linear programming. Regrettably, there are

several other methods in both categories that we know about but have not examined sufficiently

closely to list here.

The Analytic Hierarchy Process (AHP) methodology

The AHP is a basic approach to decision making. It is designed to cope with both the rational and the

intuitive to select the best from a number of alternatives evaluated with respect to several criteria. In

this process, the decision maker carries out simple pairwise comparison judgements which are then

used to develop overall priorities for ranking the alternatives. The AHP both allows for inconsistency in

the judgements and provides a means to improve consistency.

The simples form used to structure a decision problem is a hierarchy consisting of three levels: the

goal of the decision at the top level, followed by a second level consisting of the criteria by which the

alternatives, located in the third level, will be evaluated. Hierarchical decomposition of complex

system appears to be a basic device used by the human mind to cope with diversity. One organizes

the factors affecting the decision in gradual steps from the general, in the upper level of the hierarchy,

to the particular, in the lower levels. The purpose of the structure is to make it possible to judge the

importance of the elements in a given level with respect to some or all of the elements in the adjacent

level above. Once the structuring is completed, the AHP is surprisingly simple to apply.

The AHP is a general theory of measurement. It is used to derive the ratio scales from both discrete

and continuous paired comparisons in multilevel hierarchic structures. These comparisons may be

taken from actual measurements or from a fundamental scale that reflects the relative strength of

preferences and feelings. The AHP has a special concern with departure from consistency and the

measurement of this departure. It has found its widest applications in Multi Criteria Decision Analysis,

in planning and resource allocation, and in conflict resolution (SAATY, 1990) (SAATY, 1989). In its

general form, the AHP is a nonlinear framework for carrying out both deductive and inductive thinking

without use of the syllogism. This is made possible by taking several factors into consideration

simultaneously, allowing for dependence and for feedback, and making numerical tradeoffs to arrive

at a synthesis or conclusion.

For a long time people have been concerned with the measurement of both physical and

psychological events. By physical we mean the realm of what is fashionably known as the tangibles in

so far as they constitute some kind of objective reality outside the individual conducting the

measurement. By contrast, the psychological is the realm of the intangibles, comprising the subjective

ideas, feelings, and beliefs of the individual and of society as a whole. The question is whether there

is a coherent theory than can deal with both these worlds of reality without compromising either. The

AHP is a method that can be used to establish measures in both the physical and social domains.

In using the AHP to model a problem, one needs a hierarchic or a network structure to represent that

problem, as well as a pairwise comparisons to establish relations within the structure. In the discrete

case these comparisons lead to dominance matrices and in the continuous case to kernels of

Fredholm Operators (SAATY et al, 1993), from which the ratio scales are derived in the form of

principal eigenvectors, or eigenfunctions, as the case may be. These matrices, or kernels, are positive

and reciprocal, e.g. aij = 1/aji . In particular, special effort has been made to characterize these

matrices. Because of the need for a variety of judgements, there has also been considerable work

done to deal with the process of synthesizing group judgements.

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For completeness we mention that there are four axioms in the AHP. Briefly and informally they are

concerned with the reciprocal relation, comparison of homogeneous elements. Hierarchic and

systems dependence and with expectations about the validity of the rank and value of the outcome

and their dependence on the structure and its extension.

The steps followed by this methodology are (CHO et al, 2003):

1. Define the problem and structure of the hierarchy of that problem from the top goal through

intermediate levels of criteria, subcriteria, and actors to the lowest level of alternatives.

2. Construct a set of pairwise comparison matrices for each level in a hierarchy, and make all

the pairwise comparisons.

3. Hierarchic composition is used to weight the eigenvectors in a level by the eigenvector

weights of the corresponding criteria and the sum is then taken over all weighted eigenvector

entries in the next lower level of the hierarchy.

4. The consistency of the entire hierarchy is determined by multiplying each consistency index

by the priority of the corresponding criterion and adding. The result is then divided by the

same type of expression using the random consistency index corresponding to the dimension

of each matrix weighted by the priorities of the corresponding criterion.

Limitations

Several are the common areas of pitfalls in the application of the AHP identified by some authors

(PETERS et al, 2008):

Loss of cardinal information: If cardinal information for the alternatives is known exactly, utilizing the

relative measurement mode or the rating mode should be avoided since pair wise comparison

judgements as well as ratings are of ordinal nature and usage of such ordinal scales destroys

information on a finer grained cardinal scale (information loss pitfall). If the decision maker could be

characterized by linear preferences, direct measurement should be employed to avoid this pitfall,

since it does not cause loss of cardinal information. In the case of non-linear preferences, a cardinal

utility function should be modelled, because these functions also prevent loss of cardinal information.

But it is always problematic to ascertain utility functions empirically (PETERS et al, 2008).

Weighting of inputs and outputs: A decision maker has to bear in mind that in the standard separated

analysis, inputs and outputs are assumed to be of equal importance. Thus, the pitfall is to apply the

standard separated analysis, if inputs and outputs are not equally important (equal weight pitfall)

(PETERS et al, 2008).

Representation of quantities by priorities: In the case of normal goods, high input quantities are

represented by low priorities in the integrated analysis of inputs and outputs. In the separated analysis

of inputs and outputs the overall input priority is in the denominator of the efficiency ratio, while the

overall output priority is in the numerator. Thus, in the separated analysis high input quantities have to

be represented by high priorities so that the efficiency ratio is affected negatively by high input

quantities. The pitfall is to adopt the input priorities from the integrated analysis for the separated

analysis (representation pitfall) (PETERS et al, 2008).

Set of alternatives: In some cases a performance measurement or an efficiency analysis is carried out

recurrently. If the set of alternatives is changing but for some alternatives the input or output quantities

do not change between two efficiency analyses, the pitfall is to choose the relative measurement

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mode, since the pair wise comparison judgements have to be made again for all alternatives each

time the analysis is carried out (recurrent analysis pitfall) (PETERS et al, 2008).

Dependence and disjunction of criteria: An AHP decision hierarchy covers only vertical dependencies

between criteria and subcriteria as well as between (sub)criteria and alternatives. In practice, this

requirement is neglected regularly. In several real cases AHP hierarchies have horizontal

dependencies. The pitfall lies in neglecting horizontal dependencies when modelling a decision

problem (horizontal dependencies pitfall) (PETERS et al, 2008).

Building decision hierarchies: If the number of inputs and outputs is high (e.g. 10), a common pitfall is

neglecting to structure the inputs and outputs (structuring pitfall). Firstly, it is very time consuming to

fill out huge evaluation matrices. Secondly, the greater the dimension of an evaluation matrix is the

more difficult is it to judge the dominance of one element over another. This is crucial since the pair

wise comparison judgements are regularly made by practitioners who often feel over-challenged by

the AHP(PETERS et al, 2008).

The Multi Attribute Utility Theory (MAUT) methodology

MAUT, developed by Keeney and Raiffa (KEENEY et al, 1976), attempts to maximise a decision

maker’s utility or value (preference) represented by a function that maps an object measured on an

absolute scale into the decision maker’s utility or value relations. It is based on the following

fundamental axiom: any decision-maker attempts unconsciously to maximise a real valued function

u=u(x1,x2,...,xn) of the criteria x1, x2,...,xn. The role of the researcher is to try to estimate this function

by asking the decision-maker some well-chosen questions. It is assumed that utility functions are

monotonic and that, sometimes decision makers are risk averse. A utility function may be

monotonically increasing (that is, if xk is greater than xj, xk is always preferred to xj) of monotonically

decreasing (that is, if xk is less than xj, xk is always preferred to xj). In their book, Keeney and Raiffa

did not seem to think that weight could be applied to the criteria. Instead of speaking of the

importance of criteria, they considered scaling constants whose values they did not think could be

compared in the form of ratios(CHO et al, 2003).

Multiattribute value theory (MAVT) is a new version of multiattribute utility theory. Generically, it

assigns values from a ratio scale that fall in a range (for example, 0 to 100) to the criteria and similarly

assigns values to the alternatives from appropriate ranges chosen for each criterion. Unlike MAUT, it

has come to recognize that criteria weights are important in decision making. However, its alternatives

are still measured on interval scales(CHO et al, 2003).

In order to estimate a utility function, several points on the function curve are determined by the

decision maker.

The steps followed by this methodology are (CHO et al, 2003):

1. Identify the relevant criteria (attributes),

2. Assign quantifiable variables to each of the attributes and specify their restrictions,

3. Select and construct utility functions for the individual attributes,

4. Synthesize the individual utility functions into a single additive or multiplicative utility function,

5. Evaluate the alternatives using the function obtained 2 steps before, and then

6. Choose the alternative with the largest utility values.

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Limitations

This methodology aims to diminish the flaws coming from internal inconsistency measures in the

pairwise comparisons the AHP method used, while using similar approaches to issue weights out of

stated preferences. Though because of different consistency techniques designed to ensure more

congruence on the input preferences diminishing that risk, the problem yet remains.

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CHAPTER II: MEXICO COUNTRY STRATEGY FORMULATION ON INDUSTRIAL POLICY

Technology and Strategy

For as long as there has been technology (the application of science and innovation) and for as long

as there has been strategy (the use of forces and resources to achieve political goals) there has been

a relationship of sorts between these two preoccupations (CORNISH, 2010).

Recognizing that both concepts have historically evolved from military conceptions, necessities and

are thus closely linked to the latter topic, a standard account in that background of the relationship

between them might describe it in several categories, the first of which would include a series of

innovations (usually concerning weapons) which have proved to be tactically decisive (i.e. battle-

winning). The expectations that military innovation will confer a decisive advantage in battle has been

a particular feature of industrialized warfare. But the development and application of decisive

weapons long predates the Industrial Revolution, of course, and has taken a variety of paths. Military

history records weapon developments which have been remarkably simple, as well as others which

have been remarkably sophisticated. Some have been achieved in a singular moment of invention

while others have evolved through incremental improvement. The list of decisive battlefield weapons

would be lengthy and would almost certainly include the crossbow, the longbow and the bayonet, as

well as a series of developments in firearms: the flintlock, the breech-loader, the rifled bore, the pistol,

the machine gun and so forth. The second category would take account of tactically decisive

developments which have nothing directly to do with weaponry but have acted as a “force multiplier”

in battle: the stirrup, camouflage and battlefield radio communications come readily to mind. A third

category would include those moments at which innovation has had a decisive effect at the strategic

level (that is, the level at which the outcome of war itself is shaped, rather than battles won): radar,

the long-range bomber aircraft, the submarine, and the Colossus code-breaking computer used by

the British to decrypt German signal traffic during the Second World War. A fourth and final category

would encompass innovations which have had a paradigm-shifting of metastrategic effect in that they

have altered the very nature of war. The development and use of the atomic bomb in the 1940s

serves as the most obvious example of innovation on this level. More recently, some have argued that

warfare has been transformed fundamentally by developments in information and communication

technology, a phenomenon known since the early 1990’s, in the US and elsewhere as the “revolution

in military affairs” (CORNISH, 2010).

It is useful to think of the technology-strategy relationship as having metamorphosed through several

stages. Thus, the industrial age is generally considered to have had a considerable effect on the

technology-strategy relationship, enabling technology to become more influential than ever before, but

the hierarchy in this relationship was always clear: technology (no matter how innovative or decisive)

served the higher politics of national strategy, and not vice versa. National strategy was not

something to be determined by technology, and was in any case too complex and refined to be

understood by mere technologists, engineers and inventors. By the end of the Second World War the

technology-strategy relationship was on the verge of another shift. It brought about a blurring of these

boundaries and altered the dynamic of the relationship. Technology should be understood as a

dynamic driver of strategy which must be kept under constant review; and, furthermore, that the

principles of defence policy could no longer determine technological developments; the two must now

interact.(CORNISH, 1996) The second half of the twentieth century saw further developments in the

technology-strategy relationship, with the most far-reaching implications for policy and strategy and

for the very idea of war. No longer the “subordinate” to strategy it had been for so long, technology

also escaped the bounds of the “partnership” with strategy envisaged by the British chiefs of staff to

become the “determinant” of strategy. With the advent of atomic, nuclear and thermonuclear

weaponry, with intercontinental-range ballistic missiles capable of ever-increasing accuracy, and with

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dramatic progress in communications and computing technology, war between the most

technologically advanced states (or the threat of it) promised unprecedented and unimaginable levels

of destruction (CORNISH, 2010).

In the final stage of the metamorphosis, occurring in the late twentieth and early twenty-first centuries,

technology might have broken altogether from its relationship with strategy. The strategy is committed

to achieve its objective, thus the challenge is adjusting it to meet that end in an environment

dominated by continuous and often quite radical technological and political change (BUZAN). But the

pace of technological change is now such that strategy can barely keep up, with the result that it

becomes ever more difficult to organize and rationalize fast-evolving technology within some kind of

strategic or security policy framework. Technology has also acquired its own momentum; there is no

longer any need for a national strategic stimulus to, or context for, technological innovation which is

now as likely, if not more likely, to be a response to commercial opportunity or international demand

as to national security. The image which best summarizes the contemporary relationship between

technology and strategy, therefore, should be that of technology racing ahead and national strategy

struggling to catch up. And as the relationship is loosened in this way, it ought not to be assumed that

in the race to catch up with technology, even if only periodically, governments will always be first

(CORNISH, 2010).

Industrial Policy

Few phrases elicit such strong reactions from economists, engineers and policymakers as industrial

policy. Though we put forward a definition of it as any type of selective government intervention or

policy that attempts to alter the structure of production in favour of sectors that are expected to offer

better prospects for economic growth in a way that would not occur in the absence of such

intervention in the market equilibrium (PACK et al, 2006)

Innovation & Technology policies as Industrial Policy within Knowledge Based Economy

We must begin considering the role of knowledge as the corner stone of all type of innovation,

knowledge and activities related to an organisation or community’s development. The World Bank in

their World Development Report 1999 has explained the previous in a remarkable way valid

nowadays:

“Knowledge is like light. Weightless and intangible, it can easily travel the world, enlightening the lives

of people everywhere. Yet billions of people still live in the darkness of poverty –unnecessarily... poor

countries –and poor people- differ from rich ones not only because they have less capital but because

they have less knowledge. Knowledge is often costly to create, and that is why much of it is created in

industrial countries. But developing countries can acquire knowledge overseas as well as create their

own at home

What should governments do?

When development is considered from the perspective of knowledge, three key insights emerge:

Because the market for knowledge often fails, there is a strong rational for public action. The

state is in a unique position to narrow knowledge gaps.

Information is the lifeblood of markets, yet markets on their own do not always provide

enough of it, because those who generate information cannot always appropriate the returns.

Public action is thus required to provide information to verify quality, monitor performance,

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and regulate transactions to provide the foundation for successful market-based

development.

No matter how successful a government may be in this endeavour, knowledge gaps and

information failures will remain.” (WORLD BANK, 1999).

Industrial policy is concerned with influencing the decisions of entrepreneurs. There are 2 ways in

which it can do this: by challenging and by supporting them (the establishment of framework

conditions, such as the protection of property rights and contract enforcement, is not discussed here)

(SCHMITZ, 2007).

Challenge: Enterprises are challenged when they have to meet parameters set by the market or the

government. Industrial policy can challenge enterprises in two ways: either by setting targets which

they have to meet in order to qualify for access to scarce resources (for example, credit, technology)

at a subsidised rate or to protected domestic markets; or by exposing them to foreign competition, for

example by lowering tariffs (SCHMITZ, 2007).

Support: Industrial policy can support enterprises in two ways: either by providing better and/or

cheaper access to credit or support services (for acquiring technological or marketing capabilities, for

example), or by protecting them from foreign competition by introducing tariffs or quantitative

restrictions (SCHMITZ, 2007).

Speeding up the transition process from emerging technologies to new industries is central to

successful economic growth, employment, competition and sustainability in economies. There are

three key mechanisms in speeding up the process. The first mechanism speaks to the

encouragement of partnership in the process of commercialization. Some of the issues of concern are

incentives and regulations provided to support innovation and cooperative R&D, partial public funding

of privately performed R&D, and the setup of effective public private partnership mechanisms. The

second mechanism in speeding up the transition process is related to the promotion of

entrepreneurship and venture initiatives in the innovation system. Some of the issues of concern in

the new economy are fostering the community dynamics of entrepreneurship, the effective support

mechanisms for private innovation, incentives for entrepreneurship in the transition, and the

international corporate entrepreneurship. The third mechanism addresses the sustainability of the

commercialization process and the creation of new firms. Some of the issues of concern are the

fostering R&D ventures and entrepreneurial capacity , establishing public support mechanisms

targeted for sources of innovation market failures, mobilizing public support to enhance the post entry

performance, and designing and effective policy for stimulating technology diffusion and development

of the industry sector. The three mechanisms are, however, neither isolated nor difficult to build up

once they are initiated properly, thus the government can build a statutory body to effectively address

the functions of the mechanisms as a whole (HUNG et al, 2006).

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The figure above offers a simple way of grouping industrial policies past and present by distinguishing

between high and low challenge and between high and low support. This terrain is characterised by

the constellation High Challenge + High Support, the significance of which becomes clear if we

contrast it with the other constellations. Low Challenge + High Support characterises the protectionist

policies for import substitution, adopted in many developing countries during the 1950-70s; and High

Challenge + Low Support characterises the policies of liberalisation, which many developing countries

adopted when the Washington Consensus dominated thinking on economic development. The central

message was: rely on the market and do not trust government. The combination of exposing

enterprises to competition from outside and establishing transparent rules on the inside was thought

to be the most promising way of achieving competitiveness and growth (SCHMITZ, 2007).

The disillusion with both the old protectionist and the subsequent Washington Consensus policies has

led to the search for strategies which accept the high challenge which comes from integration in the

world economy but seek to provide support for coping with it, for example tax incentives for

investment in training, low interest credit for developing new technology or subsidies for participation

in international trade fairs. This is referred to as “active industrial policy”. Such active policy can take

many forms but the common characteristic is the combination of high challenge and high support

(SCHMITZ, 2007).

The issue of foreign versus local investors is a big issue for many governments for both political and

technical reasons. Local enterprises often protest that the government pays too much attention to

attracting foreign enterprises and not enough to supporting local ones. In such situations it is

important to show the ability to differentiate and put forward a convincing argument as to why FDI is

particularly critical in certain sectors. Even where do not face such political pressure they need to

select. Attracting foreign investors is expensive. To make it viable, investment promotion agencies

need to target. It makes sense attracting Foreign Direct Investment (FDI) when technology and

market (national branding) gaps for an specific sector are wide (SCHMITZ, 2007).

When the marketing gap is narrow and the technology gap wide, attracting FDI may not be a priority.

Local firms can be targeted but they are likely to require assistance in negotiating licensing

agreements or joint venture contracts (SCHMITZ, 2007).

The co-existence of these gaps explains to a large extent why multinational enterprises have been

able to control large segments of international trade. Approximately one third of international trade

takes place within these firms (UNCTAD, 2005). Such intra firm transactions are particularly relevant

in high technology industries which are the most dynamic segment of world trade (UNIDO, 2004).

Insertion into the global networks of multinational corporations has been key to the export success of

most East Asian economies, in particular China (LALL et al, 2004). The subsidiaries of multinational

firms provide both the critical technology and the market access: local firms can become suppliers.

TRM methodologies

Technology is an important strategic asset for many organisations and certain economic sectors, and

there is an increasing need to include technological considerations in strategy and planning

processes. However establishing and communicating the linkages between technology, resources,

company and government objectives presents a continuing challenge for many organisations as well

as for industrial policy. These issues are becoming more important as the cost, complexity and rate of

technology increase, and markets and sources of technology globalise (UNIVERSITY OF

CAMBRIDGE, 2001).

16

A Technology Road Map (TRM), or route map, supports technology strategy and planning. These

maps can take various forms, but generally comprise a time-based chart, together with a number of

layers, which provide a means to link and other resources to future products, as well as to

business/policy objectives and milestones. The TRM is a high level planning tool that can be used to

support the development and implementation of strategy and plans, as well as communication for the

plan (UNIVERSITY OF CAMBRIDGE, 2001).

The first formal academic mention recorded in literature by the developers and owners of the TRM

Copyrights, Cambridge University’s Institute for Manufacturing (IfM), of the term Road Map was in

1945, and has increased constantly it’s presence through the 1990-2010 term where mentions of it in

the last few years have exceeded the number of 2,200 per year (PROBERT, 2010).

While the TRM approach has been used successfully in a number of companies (for example

Motorola, Lucent Technologies, Philips and ABB) as well as governments (UK Bureau of Innovation

and Skills’ Knowledge Transfer Network, and the US State of Massachusetts) the implementation of

the approach is challenging. In particular, initiating the process and keeping it “alive” on an ongoing

basis can be difficult, especially for smaller companies and governments. One of roadmapping’s

strengths is flexibility, enabling it to be applied in various contexts. However, this also represents a

challenge, in that the particular format of the roadmap needs to be defined as part of the process

(MASS INSIGHT, 2004).

Mexico Industrial Policy in the 1980’s and 1990’s

In the middle of the decade of the 1980’s the Mexican Federal Government embraced an industrial

policy approach regretted by some as the abdication of the government to regional development

planning and defined by others as the change from a regional policy with a planified vision to another

with a non interventionist approach. It is argued also budget constraints as the cause. Though

afterwards in the 1990’s the government issued policies aimed to promote the competitiveness of the

small and medium enterprises in regional or local surroundings, although this actions proofed being

insufficient (TAMAYO, 2002).

The official Industrial Policy of Mexico was to favour “Maquila”, which is a name given to importing

unfinished products to assemble them and ship them back out of the country, allowing low costs in

labour to work as driving force. The concept worked but lacked of a sense of motivating at the same

time the addition of value and of national content, and even worse, gave incentives to the existing

formal industry installed in the country to see more profit in converting and integrating themselves to

the maquila business structure, which did not require much skilled labour, than to structure a more

complex integrated operation. Inertial evidences of this can be observed even nowadays by

examining the industrial sectors yet registering before the Mexican Customs their products under the

special Maquila Presidential Decree issued at the time to grant special treatments. We can observe a

group of big exports with low degree of national content in them. This is the case for sectors such as

medical devices, electrical, and electronical equipment; in a mid level the case for sectors such as

autoparts, plastic, house appliances, metal-mechanic, and aerospace; and the other extreme with

lower value exports and high national content sectors as processed foods, agriculture, automotive,

passenger and cargo trucks.

17

Research explaining the factors that promoted the rise of this policy shows the next as some of the

reasons (PROMEXICO, 2010b):

There was a need for providing low skilled jobs to a large number of former agriculture migrant

workers who used to work in the United States on a seasonal basis under the umbrella of a binational

government working visas agreement signed by Mexico and the USA that was called off at that time

leaving the Mexican government with a considerable working force unoccupied and looking for jobs.

Also while a democratization process was doing it’s way in Mexico after more than half a century of a

single party rule, and therefore diversity in opinions and reconfiguration of political and economical

power was taking place making deep changes more difficult by the larger number of stake holders

included to agree on the national decision making for industrial policy, a large number of the peer

countries to Mexico, such as China, Singapore, Korea and India, many of them part of the today

called BRIC’s entered a process of political authoritarian re-enforcement that eased the way for

profound industrial policy definition and implementation processes that are paying them hansome

dividends nowadays. The larger exception to this argument might be the case of Brazil that without

departing from a democratic rule achieved sound industrial policy accomplishments. (BREMER, 2010)

As the OECD states correctly in his most recent report on Perpectives on Global Develompment

2010 (OECD, 2010) while talking about the decade of the 1990’s which recalls as featured by a “four

speed world” with countries respectively grouped in four groups: developed; convergent; struggling

their way out; and into poverty, comes back discussing the developments during the decade of the

2000’s and makes the point Mexico remains in the same group: those struggling their way out of

poverty.

18

The description of the circumstances done is not that mistaken. “At a first sight the policy to have

achieved much in terms of diversifying Mexico’s exports structure and rising its level of technological

sophistication through the promotion of its maquila industry: the share of trade in GDP has doubled

over the past 20 years, with the share of manufacturing rising from 20% to about 85%. The country

has an increasing export specialisation in sectors of products integrated in global value chains. But

most of this is based on imported inputs whic are re-exported with low levels of value-added and little

use of local inputs. Mexico’s trade performance can be attributed more to comparatively low labour

costs that to high and rising productivity for innovative capacity. In fact the MVA as a share of GDP in

Mexico has fallen since the 1990’s, and its overall growth performance has been poor. What lies

behind this disappointing performance is open to dispute, but it has been blamed on a slow

“maquilización” of the Mexican economy, whereby domestic industry has copied the maquila model

and has been “hollowed out” by a rising share of imported intermediaries, with a subsequent collapse

of the export multiplier” (OECD, 2010) (MOLD et al, 2006)(PALMA, 2005).

Recent Industrial Policy in Mexico formulation using TRM Methodologies

In year 2001, the Mexican Ministry of the Economy recognized (SECRETARÍA DE ECONOMÍA, 2001)

that although the outstanding macroeconomic environment the country created and benefited

resulting from NAFTA increased exports and strict fiscal discipline (and all the other Washington

Consensus abiding set of policies) over the last few years, that had not been sufficient for enterprises

to develop in a competitive, dynamic and sustained manner over time, for there were additional

factors that had not been given sufficient attention in shaping an environment favouring the adequate

performance of firms.

These factors which called for an immediate active intervention of the State included those pointed

out by the private sector: High costs associated with normativity and over-regulation; lack of formation

and development of entrepreneurial skills; limitations in the training and development of human

resources; few information systems, lack market knowledge and commercialization problems; lack of

linkage with development and technology innovation instruments; and difficult access to opportune,

adequate financing schemes with competitive conditions. The vast majority of them linked to a broad

understanding of a much needed industrial policy encompassing and integrating knowledge,

innovation, skills and a joint effort by the public, private and academic sectors.

At the peak of this factors, researchers came up with recommendations (SOLLEIRO et al, 2005)

jointly with the Ministry of the Economy and the National Council for Science and Technology stating

that “it is clear that competitiveness in the Mexican industry is still low and that the competitive

environment is not sufficiently favourable. It is therefore urgent to make a national effort to improve

innovation management as a condition of sovereignty , to construct the country’s future, the

competitiveness of its enterprises and the welfare of it’s society. To this end it is necessary to adopt a

science, technology and innovation policy that will include at least the following elements:

Increase in a sustained way investment for science, technology and innovation.

Break with the linear conception of the innovation system prevailing in the institutional

apparatus of the country, given that it constitutes and obstacle to the formation of networks

and interinstitutional articulations that favour the flow and adoption of technologies in the

productive sector that strengthen its sector competitiveness.

Expand and modernize the human resources training system for science, technology and

innovation.

19

Modernize and consolidate institutions, regulations and management programs in science,

technology and innovation and the mechanisms for their articulation with other areas of the

economy.

To popularize the concept of a society of knowledge.

Intensify international cooperation through commercial and non-commercial mechanisms.

Incorporate science, technology and innovation matters in the relations between Mexico and

the United States.

Promote alliances between governments and entrepreneurial organizations to generate

technologies.

Promote a network of technical service suppliers to support innovation.

Identify specific development mechanisms aimed at decreasing the regional disparities in the

country. Mexico must learn to take advantage of its diversity and adopt political approaches

leading to the capitalization of the wealth of its different localities (RUIZ et al, 1997) and, at

the same time respond to a completely heterogeneous composition with respect to its factor

endowment, human capital, socio-economic context and environment for competitiveness

(REINOSO, 1995).

If intervention is not planned at a regional level, the probability of the economic and social

disparity between the different states of the country becoming more accentuated is very high

and its consequences would be very serious.”

The most powerful asset Mexico has now at the international geostrategic table is the enormous size

of his Science and Technology Pool of Young Talent, recalled as the largest held by a single nation in

Europe, North America and Latin America (BUSINESS WEEK, 2006), which is a result of it’s

demographic bonus and will start declining in some few years, thus the challenge for the country to

make the most of it and provide opportunities for it now.

Business Process Outsourcing, and even Knowledge Process Outsourcing services are a great area

for development of this talent, but one of the key issues yet to go ahead in the road for upgrading to a

global class the role of this pool of talent, is achieving the gradual transfer of high level decisions, like

research and development and corporate strategy2 ones into Mexico, with or of, Mexican Intellectual

Property. One good example of this can be shown with the Honeywell Aerospace facility located in

the Mexican State of Chihuahua within the Northwest Region, where their entire structures lacks of

expats and is entirely source with Mexican talent, where a team of twenty Mexican engineers are

responsible for the global design capabilities of the company in a certain area. Yet big challenges are

faced for retaining such a talent once educated, Mexico has a ratio of people residing outside the

country of one out of fifteen students reaching their undergraduate degree, one out of eight of the

their students reaching a Master’s degree, and one out of five of the ones achieving a Doctoral

degree3.

Recently, since 2007, ProMexico Business Intelligence Unit’s Executive Direction for Prospective and

Strategy (national authority for issuing international business promotion policy), together with the

Ministry of the Economy’s Direction General for Heavy & High Technology Industries (national

authority for issuing industrial policy), and National Council for Science and Technology’s Deputy

Direction for Innovation Businesses (national authority for issuing science, technology and innovation

policies) have extensively promoted awareness (seminars, workshops, trainings) about TRM

methodologies among the different private (national and foreign), academic and regional governments

2 Those related to broad market or innovation lines decisions

3 Words given by Mexican Ambassador Carlos García de Alba with occasion of the installation of the

Mexican government sponsored Mexican Talent Network

20

stake holders in the economy and pushed forward their implementation in selected sectors suitable to

provide large benefits to the country aiming to tackle almost all of the points mentioned in the previous

paragraph.

The latest version of the Technology Road Map for Mexico’s Aerospace Sector can be found in

PROMEXICO’S website (PROMEXICO, 2009).

21

CHAPTER III : THE AEROSPACE SECTOR CHOICE OF MEXICO

Mexico Strategic Sectors selection

The Ministry of the Economy, ProMexico and The Boston Consulting Group released in 2010 a study

(THE BOSTON CONSULTING GROUP et al, 2010) aiming to diagnose the advantages and

limitations of Mexico for attracting FDI, which additionally provided a selection study of the sectors the

country should devote it’s efforts to. Broadly speaking the results were positive since Mexico presents

important competitive advantages in relation with other countries in aspects such as corporate taxes,

transportation times to main global consumption centres, leasing cost of industrial terrains among

others. Although important issues remain behind such as electric energy, cost of telecommunications

(including phone and internet), the state of the communications infrastructure and the education

quality.

The research covered 41 sectors of the Mexican economy throughout approximately 50 indicators per

sector. This quantitative analysis was complemented with 57 interviews with outstanding decision

makers (businessmen, Mexican and foreign executives, academics, business union leaders, etc). The

sectoral selection study quantitative model was created using as inputs the mentioned interviews,

other existing similar studies for other countries and sector specific data for Mexico creating two basic

key dimensions: Mexico’s relative competitiveness in the face of different world economies, and the

potential impact pushed forward by the development of each sector for the country. Thus this model

created four regions:

The resulting sectors prioritised were: I Priority Sectors with high impact in the medium and long term:

manufacturing of electrical and electronic equipments; manufacturing of transport equipment;

manufacturing of machinery and equipment; food industry; temporary accommodation services;

business support services; and health services. II Low competitiveness but high impact: housing; and

commerce. III Bets for the future, sectors with high competitiveness but lower scale of impact:

architectural, engineering and design services; research and development services; software

development; and media development (mainly digital contents). IV Low competitiveness and low

relevance: electrical generation; educational services; financial services; telecommunications; a

“logistics platform” where issues have been grouped such as infrastructures, real-estate services, and

storage services.

The document states about the transportation equipment sector that “for Mexico, this sector is an

important exports platform to the US, due to its wide availability of skilled labour, a very well

22

developed chain of value and an important internal market. This industry represents 3.4% of the GDP

and employs more than 900 thousand people. The jobs range the 6th best paid ones of the studied

sectors. Mexico exports more than 40 billion USD a year, and for the US, Mexico is the 3rd

largest

source of transportation equipment imports” (THE BOSTON CONSULTING GROUP et al, 2010).

Mr. Finbarr Livesey, at the Institute for Manufacturing of the University of Cambridge developed

further this concept for the development of sector strategies, classifying the sectors with regards to

their national comparative maturity degree with the global development in that sector. This analysis

also results useful to determine the adequate heading of the TRM, and the sector policies attached to

their competitive development as applied in the next graph for the case of Mexico.

The specific location where the Aerospace Sector is located within this graph, know as Area 2,

indicates the Emerging Industry Protection Strategy, where countries attempt to enter an existing

global industry and protect while they build up within the learning curve and costs attached to that

industry. In this region the strategy is focused in the insertion of the country’s innovation capabilities

within the international innovation ones and the attraction of strategic projects. In this zone the

mechanisms for compensation, technology transfer and technological assimilation, play a key role for

the competitive and protected maturing of the sector. This incubation process of sectors is

determinant for the future development of the sector, that may well head towards the simple

“maquila”, or towards the added value.

23

Aerospace Sector description

The definitions about the sector vary widely. We put forward the following: Aerospace industry refers

to the high technology industry that researches, designs, manufactures, operates, and maintains

vehicles moving through air and space.

Subsectors and Niches within the Aerospace Sector

Aerospace is a very diverse field, with a multitude of commercial, industrial and military applications.

Just as a token of the complexity of it we can look at how the US Bureau of Labor Statistics defines

individually the aerospace manufacturing activity as the one that produces "aircraft, guided missiles,

space vehicles, aircraft engines, propulsion units, and related parts"

As it can be observed below in the process diagram for the F35 Lightning II Assembly, the different

integration of large quantities of parts, components, processes create a diverse chain of value

(NORTHROP GRUMAN, 2010).

24

As well the criteria used in each aerospace industrial cluster to identify the different elements of the

sector tend to vary widely4, thus after examining different kinds of them, ProMexico Business

Intelligence Unit decided to assume as the standard classification of the subsectors, with the

respective breakdown of niches in each case the following:

a. Aeroengines

1. Aeroengine Fabrication Forming

2. Aeroengine Sub Assemblies

3. Aeroengine Components

b. Structures

4. Aircraft Construction Assemblies

5. Aerosctructures Fabrication Forming

6. Aerostructures Sub Assemblies

7. Aerostructure Componetns

8. Composite Structures

9. Composite Materials

4 we can see this comparing the Aerospace Capabilities Maps of Montreal (Canada), North West

England, West Midlands (England) that we discuss next.

25

c. Interiors

10. Aircraft Interiors Equipment Furnishings

11. Fasteners / Hardware Stockists

12. Galley Equipment

13. Technical Textiles

d. Avionics

14. Avionics

15. Pilot Navaids

16. Electrical / Electronic Systems

e. Maintenance, Repair & Overhauling (MRO) & Airport Planning Services

17. Maintenance, Repair & Overhauling

18. Airfield Equipment

19. Ground Support

f. Military

20. Missile Related Products

g. Manufacturing Services

21. Machinery Manufacturer

22. Castings

23. Forgings

24. Tooling

25. Treatment / Process

26. Raw Material Supply

h. Services

27. Research / Development

28. Design Developments

29. Testing / Certification

30. Human Resources / Manpower

31. Training / Skills

32. Consultancy

33. Finance

34. Computer Systems Software

35. Logistics

36. Aviation Services

Thus the set of 36 niches, which in each case group the business portfolio for each product or

service, represent the finite number of alternatives the industry can develop forward.

Aerospace Capabilities Maps description

The standard organisational method the aerospace industrial clusters use in order to keep a track of

the size, quality, and other internal mechanics of their individual company and group configurations in

regards to the industrial processes linked to the sector that take place there is through matrixes called

Capabilities Maps.

The Joint Aerospace Capabilities Maps of the different Mexican and British Clusters can be found

within the attachments of this document actualized by PROMEXICO (PROMEXICO, 2010) after

relevant research done before (LICONA, 2009). This study abides to these Capabilities Definitions set

by the Mexican Government.

26

CHAPTER IV : MEXICO AEROSPACE SUBSECTORS MCDA PRIORITISATION

Defining the Subsector Alternatives

The available data collected from Promexico and the Mexican Aerospace Working Group, as well as

from information cooperation exchange with ADS (Aerospace, Defence and Security), the Aerospace

& Defence Knowledge Transfer Network, the North West Aerospace Alliance (NWAA), and the

Aerospace Industries Association of Canada (AIAC), yielded the Aerospace Capabilities Comparison

Matrix of Mexico, the UK and Canada5 (cfr attachment I).

6 After detailed review to match the common

grounds among them, a final set of subsector alternatives was defined as follows:

5 The matrix for Canada has been extracted directly from the Aerospace Industries Association of

Canada, and is not included within the Attachment I because of it is coming from a different source

than those stated there, but can be consulted at http://www.aiac.ca/canadas-aerospace-

industry/canadian-aerospace-products-and-services/ 6 The Mexican Aerospace Map is updated to January 2010 and includes the States of Aguascalientes,

Baja California, Chihuahua, Coahuila, Distrito Federal, Estado de México, Guerrero, Jalisco, Nuevo

León, Puebla, Querétaro, San Luis Potosí, Sonora, Tamaulipas, Yucatán. The Canadian Aerospace

Map is updated to October 2010, and includes all of Canada with it’s largest concentration in the

Province of Quebec. The British Aerospace Map is updated to 2009, and includes the Northwest and

West Midlands Regions.

27

It can be argued that the proposed set of alternatives is not purely neither a niche nor a subsector set7

but more likely a mixed one, constituted mostly with formal subsector classified units. This has been

done so because in this way they match better the concept of “business units” intended for the study,

as well as fit better within the format configuration of the available data to compare them.

Knowing there is a finite and known number of alternatives keeps us in track with our assumptions for

using Multi Criteria Evaluation Methods such as AHP and MAUT.

7 This can be appreciated by considering the subsector Engines as a single unit, and on the other

hand dividing the subsector Structures in OEM and Tiers.

28

Additionally, in order to analyze the independent behaviour of the largest geographic concentrations

of companies within each subsector in Mexico, we have divided them in three regions according to

the State where they are based:

Region State

CENTER & SOUTH Aguascalientes

CENTER & SOUTH Distrito Federal

CENTER & SOUTH Estado de México

CENTER & SOUTH Guerrero

CENTER & SOUTH Jalisco

CENTER & SOUTH Puebla

CENTER & SOUTH Querétaro

CENTER & SOUTH San Luis Potosí

CENTER & SOUTH Yucatán

NORTHWEST Baja California

NORTHWEST Chihuahua

NORTHWEST Sonora

NORTHEAST Coahuila

NORTHEAST Nuevo León

NORTHEAST Tamaulipas

Defining the Criteria

MCDA methodologies call for defining a set of criteria constraining the possible finite number of

alternatives in order to achieve and objective. The Criteria proposed was divided in four groups:

Group 1: Global Subsectors Market Share

The measure of how much each subsector accounts for within the global industry is relevant since

wealth creation is a common criteria for any of the stake holders within the Mexican Aerospace

Group.

Group 2: Mexico Development Strength / Weakness Presence per Subsector

Stating the level of presence each subsector has at present time within Mexico yields a realistic

measure of what is the current installed capacity of each subsector.

Group 3: Mexico Relative Opportunity/Threat Benchmark towards the Industry per Subsector

Identifying the relative gaps existing among the state of development of each subsector in Mexico

with reference to the existing standards in the British and Canadian aerospace industries.

29

Group 4: Mexican Stakeholders Objectives

The actors involved in this sector are gathered in the Mexican Aerospace Working Group, and include

the Industry, the Government and the Academia.8 Since the TRM methodology, and this study is a

contribution as a part of it, calls for identifying the drivers pursued by each of these actors, we come

up with the following additional breakdown of objectives per actor:9

Industry Drivers

o Short Term Benefits

Economic & Competitiveness indexes

Skills indexes

o Long Term Opportunities

Research & Innovation indexes

Supply Chain Integration

Government Drivers

o Short term Impact

Economic, Environmental and Social indexes

o Long term Industrial Policy opportunities

Cluster/ Competitiveness Pole Integration

Academia Drivers

o Exploit current Knowledge

Technology Transfer

IP Generation

o Prospective Analysis & Innovation Monitoring

R&D Networks

Generating the Operational Tables

Subsectors Market Share

Analyzing the Global Aerospace behaviour (DATAMONITOR, 2009) (DELOITTE, 2009), an option in

this study was done to focus on the civil industry classification for transparency reasons, since their

internal dynamics and numbers are more easily accessible. Thus tracing back the civil sector market

8 The Government section is integrated by the national level issuers of the International Business

Promotion Policy (PROMEXICO Investment & Trade), the Economical & Industrial Policy (Ministry of

the Economy –Secretaría de Economía-), the Innovation, Science & Technology Policy (National

Council for Science and Technology –CONACYT-), and the regional governments (embodied and

represented by the Mexican Association of Economical Development Ministers –AMSDE-). The

Industry section is represented by the existing companies settled in Mexico of national and foreign

ownership, that are linked due to their industrial activity, processes, and product codes to the

aerospace sector. They are embodied and represented by the Mexican Federation of the Aerospace

Industry (FEMIA). The Academia is represented by the existing higher education institutions,

universities and centres that include within their taught courses or research areas the aerospace

sector. They are embodied and represented by the Mexican Aerospace Educational Consortium

(COMEA). 9 The drivers are a result from workshops held by the Mexican Aerospace Working Group within the

works developed for issuing the MAITRM using the TRM methodology.

30

share and with the help of further research (ADS et al, 2006) yielded the approximate10

market share

value per subsector, which were then stated as follows:

Mexico Development Presence per Subsectors

The presence of development each subsector holds in Mexico was calculated as the proportion that

the number of companies in each of them represent with respect to the total11

, yielding the following

metrics:

10

The difference between the stated total value for the Global Civil Aerospace Market Value in 2006 is

due to the calculation assumptions done by Datamonitor and ADS/SBAC/Roland Berger, and while

we were unable to match them, it was dismissed as irrelevant due to their relative low proportion

within the total value, and that we are more interested in the percentages that in the units themselves. 11

Other measures such as revenue per company per subsector per product would have been much

more precise and representative but information to that level of detail, fetched in the brief time allowed

for this report, was not available. Though this is not perfect, it allows some light on the internal

dynamics and behaviour or the subsectors within Mexico. Subsector internal homogeneity in scope,

organisational and quality standards is assumed since most of these companies are fairly young (no

more than 10 years old, and a third of them only 2 year old) being mostly world class established

foreign subsidiaries settling in Mexico around the aerospace chain of value.

31

Relative Benchmark towards the Global Industry (Distance)

The benchmark was calculated taking as main reference the average proportions the global

aerospace industry structure12

has, and the relative distances each subsector in Mexico has towards

them, either above interpreted then as with an ease to develop since barriers for entry have already

been passed, or either below evidencing that larger investment or resources may yet be needed to

develop those subsectors to a global standard. Those gaps were then processed and rescaled to

reflect the proportions they maintained among themselves and towards the global industry, yielding:

Actor Objectives

The tables for this criteria are developed differently for each MCDA methodology, and shown further.

12

Calculated taking as universe the available information for the Mexico, Canada, UK ecosystem.

32

Prioritisation according to the AHP Approach

Values for the Actor Objectives Criteria

The tables for this criteria are entirely developed using the pairwise comparisons method the AHP

methodology calls for. They were proposed as an hypothesis for this report and validated through the

expert opinion of the people involved in the workshops developed by the Mexican Aerospace Working

Group that addressed this issue.

When pairwise comparisons are done, the weights of attributes yield a consistency measure (CM),

which indicates the consistency of the decision maker. The smaller the consistency measure is the

more consistent are the pairwise comparisons and vice versa. This is our measure for verification.

Criteria Groups 1 -Global Subsectors Market Share- and 4 -Mexican Stakeholders Objectives- will

weight 30% each towards the final decision, and Criteria Groups 2 -Mexico Development Strength /

Weakness Presence per Subsector- and 3 -Mexico Relative Opportunity/Threat Benchmark towards

the Industry per Subsector- will weight 20%, in order to favour market and actors relevance in the

model.

We assign the obtained values to the three first groups of criteria in a direct way in order to maximize

the use of hard data as input that helps the output to be more robust, transparent and traceable. We

will add up the fourth group criteria -Actor Objectives- with the inputs stated in the next tables to gain

the benefits of applying AHP methodology within the decision model.

33

In all 7 cases the Consistency Measure (CM) is not significant and keeps a value below 0.000 thus

the consistency of the entire Criteria Group 4 is verified.

The latter data assembled together shows this content and configuration:

34

The software tool we use to apply this AHP approach is WEB-HIPRE, a free web based tool, once the

model is configured and operationalized with the data it looks like this:

The prioritised output of the model shows a preference for the Engine Subsector with 23%, followed

by Electrical and fuel Systems with 16.5%, then Outsourced Aerostructures with 14.3% almost

35

matched by Manufacture and Airframe Assembly by OEMs with 14.3%; next Airframe MRO witn

12.8%, and Avionics and Sensors with 6.6%; Interiors with 6.6%; and Landing Gear, Wheels and

Breaks with 6%13

as can be seen in the following graphs and tables:

There is a clear case for the engine, electrical and Fuel Systems Subsector, the entire Structures

classification enclosing OEMs and Tiers, and for the development of airframe MRO.

Applying the same criteria and using the proper data sets for each case we found the following results

for the individual regions of Mexico:

13

The breakdown of contributions per criteria for Interiors subsector not shown in the images is:

0.005; 0.011; 0.010 and 0.040 accordingly with Market Share; Presence; Actor Objectives; and

Distance Criteria.

36

Limitations

The results offered by this method are bounded by the simplicity of the model as well as the scarcity

of the information available, and results must be also interpreted with caution since the decomposing

of the Structures subsector in OEMs and Tiers allows a deeper understanding of their internal

dynamics with comparison with the rest of the proposed alternatives, but should be considered

together, at least from the Market Share point of view, to reflect the importance of it.

The main limitation this method evidences is that the logic with which the pairwise comparison matrix

works does not force to have a high degree of consistency, and thus flaws coming from an

inadequate input can easily tamper with the results, in this case the consistency reached in all the

pairwise comparisons was close to zero, which is regarded as the highest possible value.

37

Prioritisation according to the MAUT Approach

Having the same common MCDA assumptions as in the AHP case, the same datasets for Criteria

Group 1 -Global Subsectors Market Share-; Criteria Groups 2 -Mexico Development Strength /

Weakness Presence per Subsector- and Criteria Group 3 -Mexico Relative Opportunity/Threat

Benchmark towards the Industry per Subsector- are thus also used with fixed values.

Criteria Group 4 -Mexican Stakeholders Objectives- is also operationalized using the Measuring

Attractiveness by a Categorical Based Evaluation Technique (MACBETH) built into Hiview3, the

MAUT software tool in use, to incorporate the qualitative judgement of measurement of the

differences between alternatives used before. The validation was successfully certified by the

Consistency Index calculated by the MACBETH tool built in within the Hiview3.

The input configuration and values remain the same:

38

On the visual interface of Hiview3, the model looks like this:

The prioritised output of the model shows a preference for the Engine Subsector with 25.1%, followed

by Outsourced Aerostructures with 20.9%, then Manufacture and Airframe Assembly by OEMs with

16.8%; next Electrical and fuel Systems with 16.2%; then Airframe MRO with 10.5%, then Avionics

and Sensors with 6.3%and Landing Gear, Wheels and Breaks with 2.1% matched with Interiors with

2.1% (cfr Attachment II).

39

The results shown above issued by the software are in diverse units and need been transformed, thus

assembled together they yield:

and arranged by regions we have the table below

40

Limitations

The data sets of the criteria groups 1, 2 and 3 are exactly the same used in the AHP exercise, though

the one for group 4, which is intended to hold the concept of judgements and expert opinions fed as

preference of choices among options was slightly different since MACBETH forces the judgements to

be more consistent than the simple pairwise comparison method does.

The resulting preferences ranking the industry, government and academia drivers directly toward the

alternatives did not change neither, but it was in the case of the weight among the preferences of

these actors where the system provided additional weights (as shown in the contribution tables

results) that were slightly different, and thus lead to the differences.

Combined differences in the outcome of the prioritisation

The analysis of the outputs of both methods yields the following point differences between them per

national and regional levels.

We can observe the main discrepancies are in the ranking of the Subsector Outsource of

Aerostructures to Tiers with 6.6%, and that itself that is contributed largely by the Northeast Region

evaluation with 7.2%.

If we compare the final ranking priority given to the subsectors choice by the two methods we find

interesting results.

41

Both methods agree entirely on ranking as the most important option attending the Engines

Subsector, while also do the same in the third position for the Subsector of Manufacture of

Aerostructures by Primes (OEM’s), in the fifth position for the Airfrae MRO Subsector and the sixth

place for the Avionics and Sensors Subsector.

Despite registering the highest individual points ranking difference, the Subsector of Outsourcing

aerostructures to Tiers, does not end up in a very different overall ranking in both methods, showing a

fair agreement as the second position since the agreement is complete for another Subsector as the

third position. The same fair agreement can be called on the Landing Gear, Wheels and Brakes

Subsector as the eighth position.

The case of the Electric and Fuel Systems Subsector is perhaps more difficult to solve since the final

ranking distance is wider and it’s meaning in real life is relevant since a relevant proportion of the

actual installed capacity has gone that direction, therefore requiring additional consensus being done

in judgements from the stakeholders to grant it a final rank properly. The same case can be said

about the Interior Subsector, though it’s relevance and position is much smaller.

Validation

The process of validation of this report has been done by having it being revised by the leadership of

the Mexican Aerospace Working Group, though it yet requires additional consultations and feedback

from their constituent stake holders, and the time allowed for issuing this report has come soon before

the official scheduled meetings for this purpose take place within the framework of the Mexican

Aerospace Technology Road Map Revision Process 2011.

Cluster synergy opportunities for Mexico

In addition to the ranking, a list is provided of the companies that might best suit the possible

complementarities arising from this selection:

Interpreting the results, the following synergy strategies per region are recommended

42

The above strategy abides to the rational stated by the prioritisation of subsectors per region and per

number of companies, which largely gives us a broad two polar configuration for the country on the

Engine Subsector chain of value in one part and on the other the entire Structures Subsector chain of

value as anchors with the rest of the Subsectors connected to them following in the order stated by

the models, the first one primarily developing in the Northwest Region of the country and the second

one in the Central and South Regions of the Country, while the Northeast region holds a mixed

configuration of the two mentioned.

In the case of the Aerostructures Subsector there is a logic also for having the Outsourcing of

Aerostructures (Tiers) nearby the north border with the United States since at present the largest

market opportunities for these would be in that market, while the OEM’s, like Bombardier has already

done, are less dependant on that issue since their aim is integrating all their final assembly process

within the country, and having their final products taking off from Mexico for delivery around the globe.

It is very important to state that this opportunities stated above show mainly the scope for global

leading corporations involved in the specific subsectors and the purpose of naming them is more on

the interest of them to clearly locate conceptually and geographically the potential synergies they

could benefit from if doing business in Mexico, knowing already that moving into the country may yield

them an overall average 18.2% Cost Advantage against leading aerospace countries (KPMG, 2010),

disclosed as 13.7% in regards to Manufacturing costs, 38.6% in Corporate & IT services costs, and

38.9% in Research & Development costs. Yet location of their operations in the proposed locations

could further more the advantages.

On the Mexican Corporate Sector part, this strategy as any other in industrial policy calls for a strong

effort on Mexican companies to innovate, expand and develop a strong local base of support around

the different chains of value the different subsectors offer. No wonder the largest effort in this part has

been done by large Mexican companies already holding a strong position in the automotive sector,

where Mexico is yet regarded as one of the top 6 global producers, transferring gradually capabilities

towards the aerospace sector. Example of this are the Electrical Systems Subsector, in a far second

place the Outsourced parts (Tiers) one, and the Interiors Subsector.

43

CONCLUSIONS

These report reaches three different conclusions.

The first one is that the combined outcome of the prioritisation of Aerospace Industrial Policy

Subsector Alternatives at a national level for Mexico shows a strong case for setting as first priority for

a long term aerospace sustainable and defendable global position the choice for the Engines

Subsector involving the design, fabrication forming, subassemblies and components, supply and

aftermarket support of propulsion engines and associated control systems and fuel pumps in strong

partnership with similar oriented clusters with whom to share research, financing, and risk. As a

second priority in the long term the preference shown is for the Structures Subsector, combining

OEM’s and Tiers yields important benefits and must be advanced broader and faster, perhaps with

special interest in the higher aggregated value niches of research, design and manufacture of

composite materials. Next to these the model shows a strong moment for the Electrical and Fuel

Systems Subsector due to the strong presence of companies already working in this field not only in

relation to the size of the Mexican Industry but also in relation with the average configuration of the

Global Aerospace Structure, which is also a result of the migration of Mexican capabilities from other

sectors where Mexico has developed a strong competitiveness such as automotive and electronics,

though it would be important to deepen within this Subsector in the niche of Fuel Systems which

together with the engine design might proof to be also a high value concentration field for sustainable

leadership based on knowledge and innovation.

The second is that the regional configuration of these priorities shows a rationale for finding better

suited to host the Engines Subsector the Northwest Region of Mexico due to the number of existing

companies in the field operating, and the supporting chains of value already in place and number

supporting it, while the Central and South Regions, farther from the border with the USA, seem best

suited for end products not requiring low cost transportation to and from that country, such as the

Aerospace Structure Subsector OEM’s, which is the case of the business plan logic behind the

existing Bombardier facility in Querétaro, having the Northeast Region as a relevant global player for

the Outsourcing of Aerostructures Subsector (Tiers) serving the Mexican and American markets.

The third one has to do with showing that there is a case for mathematical tools, in this case Multi

Criteria Decision Analysis, as valid support systems to advice relevant decisions in industrial policy.

The acquisition of the data, integration, transformation, processing, the conceptual design, selection

and operationalization of the criteria as well might always be perfectible, so does the selection of the

proper MCDA techniques, but allowing stake holders to discuss the possible final choices on the

grounds of having transparency and accountability on the contributions made by each criteria and the

factors bounding them is a valuable effort attempted which must be carried forward with improved

accuracy and detail.

44

REFERENCES

ADS; ROLAND BERGER, “Introduction to the UK civil aerospace industry” Aerospace Defence and

Security / Society of British Aerospace Companies, Roland Berger Strategy Consultants (2007)

AKSOY, Y. “Interactive Multiple Objective Decision Making: A bibliography (1965-1988)”,

Management Research News 13, 1-8 (1990).

BOOKER, J.M.; BRYSON, M.C., “Decision Analysis in Project Management: An overview”, IEEE

Transactions on Engineering Management 32 (1), 3-9 (1985).

BRANS, J.P.; MARESCHAL, B.; VINCKE, P., “PROMETHEE: A new family of outranking methods in

multicriteria analysis”, Operational Research’84, IFORS Elsevier, North Holland, Amsterdam (1984)

BREMMER, I “The End of the Free Market: Who Wins the War Between States and Corporations?”,

Penguin Group (2010)

BUSINESS WEEK “Mexico: Pumping Out Engineers”, Business Week Magazine, May (2006)

BUZAN “An introduction to Strategic Studies”, p 289

CHO, Keun Tae, “Multicriteria Decision Methods: An Attempt to Evaluate and Unify”, Mathematical

and Computer Modelling 37 (2003) 1099-1119.

CORNISH, P. “Technology, Strategy and Counterterrorism” International Affairs Volume 86, Number

3, pp 875-888 (2010)

CORNISH, P. “British military planning for the defence of Germany, 1945-50”, Macmillan, p 105,

(1996)

DATAMONITOR, “Global Aerospace & Defense Industry Profile” Datamonitor plc. Reference Code

0199-1002 (2009)

DELOITTE, “2009 Global Aerospace & Defense Industry Performance Wrap-up. A study of the 2009

financial performance of 91 global A&D companies” Deloitte LLP (2009)

HINLOOPEN, E.; NIJKAMP, P.; RIETVELD, P., “The regime method: A new multicriteria technique”

Topics in Essays and Surveys on Multiple Criteria Decision Making, (Edited by P. Hansen) pp. 146-

155, Springer Verlag (1983).

HUNG, S.; CHU, Y. “Stimulating new industries from emerging technologies: challenges for the public

sector”, Technovation 26, pp 104-110 (2006)

KEENEY, R. L.; RAIFFA, H., “Decisions with Multiple Objectives: Preference and Value Tradeoffs”,

John Wiley, New York (1976)

KNOWLEDGE TRANSFER NETWORK Aerospace & Defence Group, “Autonomous Systems,

Engines, Equipment, & Rotorcraft Technology Roadmap, 2009”, KTN A&D Group, 2009

KORHONEN, P. “A hierarchical interactive method for ranking alternatives with multiple qualitative

criteria” European Journal of Operational Research 24, 265-276 (1986).

KORHONEN, P. “A visual reference direction approach to solving discrete multiple criteria problems”,

European Journal of Operational Research 34, 152-159 (1988)

KORHONEN, P.; WALLENIUS, J.; ZIONTS, S., “Solving the discrete multiple criteria problem using

convex cones”, Management Science 30, 1336-1345 (1984).

KPMG “Competitive Alternatives. KPMG’s Guide to International Business Location”, KPMG LLP,

March (2010)

LALL, S.; ALBALADEJO, M.; ZHANG, J. “Mapping Fragmentation: Electronics and Automobiles in

East Asia and Latin America” Oxford Development Studies 32, 3 pp 407-432 (2004)

LICONA, O. “Technological Regimes. Mexico and the UK Aerospace Industry”, The University of

Manchester, Manchester Business School (2009)

LOFTI, V.; STEWART, T.; ZIONTS, S., “An aspiration level interactive method for multiple criteria

deicision making”, Computers and Operations Research 19, (1992).

MASS INSIGHT; BATTELLE “The Massachusetts TRM and Strategic Alliances Study. Choosing to

Lead: The Race for National R&D Leadership & New Economy Jobs”, Mass Insight Corporation,

Boston, Massachusetts (2004)

45

MOLD, A.; ROZO, C. “Liberalisation, Growth and Welfare – The “Maquiliación” of the Mexican

Economy” Trade, Growth and Poverty in Developing Countries, London, Chapter 12 (2006)

NEWMAN, J. W., “Management Applications of Decision Theory”, Harper & Row (1971).

NORTHROP GRUMAN “CTOL SWBS Manufacturing Sequence Flow”, Flight Global (2010)

OECD, “Perspectives on Global Development, 2010” OECD, p130 (2010)

PACK, H.; SAGGI, K “Is There a Case for Industrial Policy? A Critical Survey”, The International Bank

for Reconstruction and Development / Wold Bank, Oxford University Press (2006)

PALMA, J. “The Seven Main “Stylized Facts” of the Mexican Economy Since Trade Liberalization and

NAFTA”, Industrial and Corporate Change, pp 941-991 (2005)

PASTIJN, H.; LEYSEN, J., “Constructing an outranking relation with ORESTE”, Mathematical

Computer Modelling 12 (10/11), 1255-1268 (1989)

PETERS, M.; ZELEWSKI, S. “Pitfalls in the application of analytic hierarchy process to performance

measurement”, Management Decision Volume 46, Issue 7, pp 1039-1051 (2008)

PROMEXICO Investment & Trade “Capabilities Matrix Comparison Mexico UK” ProMexico

Investment & Trade, Business Intelligence Unit, Mexico (2010)

PROMEXICO Investment & Trade, “Plan de Vuelo Nacional 2009”, Business Intelligence Unit, (2009)

PROMEXICO Investment & Trade “Propuesta para la integración de cadenas productivas para las

empresas maquiladoras en México” ProMexico Investment & Trade, Business Intelligence Unit,

Mexico (2010b)

REINOSO, E. “La competitividad de los estados mexicanos” Instituto Tecnológico y de Estudios

Superiores de Monterrey, México (1995)

ROUBENS, M. “Preference relations on actions and criteria in multicriteria decision making”,

European Journal of Operational Research 10, 51-55 (1982).

ROY, B. “How outranking relation helps Multiple Criteria Decision Making”, Topics in Multiple Criteria

Decision Making, pp 179-201, University of South Carolina Press.

RUIZ, C.; DUSSEL, E.; PIORE, M. “Lo territorial como estrategia de cambio. Pensar globalmente y

actuar regionalmente” Universidad Nacional Autónoma de México, Fundación Friedrich Ebert,

Editorial Jus, México pp 433-463 (1997)

SAATY, Thomas L. “The Analytic Hierarchy Process”, paperback edition, RWS Publications,

Pittsburgh, PA. First appeared 1982, McGraw Hill, New York, 1990

SAATY, Thomas L.; ALEXANDER, J. “Conflict Resolution”, Praeger, New York, 1989

SAATY, Thomas L.; VARGAS, L.G. “A Model of Neural Impulse Firing and Synthesis”, Journal of

Mathematical Psychology 37 (1993) 200-219.

SAATY, Thomas L.; VARGAS, Luis G, “Models, methods, concepts & applications of the analytic

hierarchy process”, Boston ; London : Kluwer Academic Publishers, ISBN0792372670 (2001)

SAATY, Thomas L., “Analytic hierarchy process : planning, priority setting, resource allocation”,

Pittsburgh, Pa. : RWS Publications , ISBN096203172

SCHMITZ, H. “Reducing Complexity in the Industrial Policy Debate”, Development Policy Review

25(4), pp 417-428 (2007)

SECRETARÍA DE ECONOMÍA “Programa Nacional de Desarrollo Empresarial 2001-2006” Secretaría

de Economía, México (2001)

SOLLEIRO, J. L.; CASTAÑON, R. “Competitiveness and innovations systems: the challenge’s for

Mexico’s insertion in the global context” Technovation 25, pp 1059-1070 (2005)

TAMAYO, R. “Los nexos teóricos de la política de desarrollo industrial regional en México:

desconcentración, laissez-faire y crecimiento local endógeno” Centro de Investigación y Docencia

Económica: Gestión y Política Pública XI, 1, pp 115-156 (2002)

THE BOSTON CONSULTING GROUP; MINISTRY OF THE ECONOMY; PROMEXICO “Diagnóstico

de ventajas y limitantes para la atracción de Inversión Extranjera Directa (IED) a México” The Boston

Consulting Group, Boston (2010)

UNCTAD, “World Investment Report 2005”, New York and Geneva: United Nations (2005)

46

UNIDO, “Industrial Development Report 2004: Industrialization, Environment and the Millenium

Development Goals in Sub-Saharan Africa”, Vienna UNIDO (2004)

University of Cambridge, “T-Plan: Fast Start to Technology Roadmapping – planning your route to

success”, University of Cambridge, Institute for Manufacturing, 2001

WORLD BANK “World Development Report 1998/1999” World Bank, Washington (1999)

ZADEH, L. A.; “Fuzzy sets”, Information and Control 8 (3), 338-353 (1965)

ZANAKIS, Stelios H., SOLOMON, Anthony, WISHART Nicole, DUBLISH Sandipa, “Multi-attribute

decision making: A simulation comparison of select methods”, European Journal of Operational

Research 107 (1998) 507-529.

47

ATTACHMENTS

ATTACHMENT I: PROMEXICO Investment & Trade “Capabilities Matrix Comparison Mexico UK”

ProMexico Investment & Trade, Business Intelligence Unit, Mexico (2010)

48

49

ATTACHMENT II: Mexico National Level, HIVIEW3 Full report

TRM Aerospace

File: C:\Users\Bernardo\Downloads\101005 maut model v40.hv3

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options

-------

Short Name: Engines

Long Name: Engines Subsector

Description: Engines, including ERO

Short Name: Manufacture

Long Name: Manufacture and Airframe Assembly

Description: Manufacture and Airframe Assembly by Airframe primes. OEM Companies

Short Name: Outsource

Long Name: Outsource Aerosctructures

Description: Tier 1, and Tier 2 companies

Short Name: Airframe MRO

Long Name: Airframe MRO

Description: Airframe Maintenance, Repair and Overhauling

Short Name: Avionics

Long Name: Avionics and Sensors

Description: Avionics and Sensors

Short Name: Landing Gear

Long Name: Landing Gear

Description: Landing Gear, Wheels and Brakes

Short Name: Electrical/Fuel

Long Name: Electrical and Fuel Systems

Description: Electrical and Fuel System Companies

Short Name: Interiors

Long Name: Interiors

Description:

-----

nodes

-----

Short Name: Actor Objectives

Long Name: Actor Objectives

50

Description:

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criteria

--------

Short Name: Market Sha

Long Name: Subsector Market Share

Description:

Scale Type: Fixed

Fixed Upper: 120.00

Fixed Upper: 0.00

Units: Billion USD

Currency:

Value Function: Linear

Short Name: Presence

Long Name: Subsector Presence

Description:

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Fixed Upper: 274.00

Fixed Upper: 0.00

Units: Data

Currency:

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Long Name: Subsector Distance

Description:

Scale Type: Fixed

Fixed Upper: 1965.00

Fixed Upper: 0.00

Units: Data

Currency:

Value Function: Linear

Short Name: Ind Short Obj

Long Name: Ind Short Obj

Description:

Scale Type: Relative

Fixed Upper: 20.00

Fixed Upper: 5.00

Units: Judgement

Currency:

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Short Name: Ind Long Obj

Long Name: Ind Long Obj

Description:

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51

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Fixed Upper: 5.00

Units: Judgement

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Units: Judgement

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Long Name: Acad

Description:

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Long Name: Academia Analysis

Description:

Scale Type: Relative

Fixed Upper: 20.00

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Units: Judgement

Currency:

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---------------

scores

---------------

52

Short Name Option Score Weighted Score

Market Sha Engines 30.83 9.25

Market Sha Manufacture 22.50 6.75

Market Sha Outsource 15.83 4.75

Market Sha Airframe MRO 17.50 5.25

Market Sha Avionics 6.67 2.00

Market Sha Landing Gear 2.50 0.75

Market Sha Electrical/Fuel 2.50 0.75

Market Sha Interiors 1.67 0.50

Presence Engines 27.37 5.47

Presence Manufacture 1.82 0.36

Presence Outsource 21.17 4.23

Presence Airframe MRO 10.22 2.04

Presence Avionics 4.01 0.80

Presence Landing Gear 2.19 0.44

Presence Electrical/Fuel 27.74 5.55

Presence Interiors 5.47 1.09

Distance Engines 14.20 2.84

Distance Manufacture 12.37 2.47

Distance Outsource 4.43 0.89

Distance Airframe MRO 10.79 2.16

Distance Avionics 7.63 1.53

Distance Landing Gear 12.67 2.53

Distance Electrical/Fuel 24.73 4.95

Distance Interiors 13.33 2.67

Ind Short Obj Engines 100.00 4.28

Ind Short Obj Manufacture 100.00 4.28

Ind Short Obj Outsource 100.00 4.28

Ind Short Obj Airframe MRO 0.00 0.00

Ind Short Obj Avionics 0.00 0.00

Ind Short Obj Landing Gear 0.00 0.00

Ind Short Obj Electrical/Fuel 100.00 4.28

Ind Short Obj Interiors 0.00 0.00

Ind Long Obj Engines 100.00 7.71

Ind Long Obj Manufacture 100.00 7.71

Ind Long Obj Outsource 100.00 7.71

Ind Long Obj Airframe MRO 0.00 0.00

Ind Long Obj Avionics 0.00 0.00

Ind Long Obj Landing Gear 0.00 0.00

Ind Long Obj Electrical/Fuel 100.00 7.71

Ind Long Obj Interiors 0.00 0.00

Gov Short Imp Engines 100.00 1.71

Gov Short Imp Manufacture 100.00 1.71

Gov Short Imp Outsource 100.00 1.71

Gov Short Imp Airframe MRO 100.00 1.71

53

Gov Short Imp Avionics 0.00 0.00

Gov Short Imp Landing Gear 0.00 0.00

Gov Short Imp Electrical/Fuel 0.00 0.00

Gov Short Imp Interiors 0.00 0.00

Gov Long Imp Engines 100.00 8.59

Gov Long Imp Manufacture 100.00 8.59

Gov Long Imp Outsource 100.00 8.59

Gov Long Imp Airframe MRO 100.00 8.59

Gov Long Imp Avionics 0.00 0.00

Gov Long Imp Landing Gear 0.00 0.00

Gov Long Imp Electrical/Fuel 0.00 0.00

Gov Long Imp Interiors 0.00 0.00

Academia Current Engines 100.00 1.71

Academia Current Manufacture 0.00 0.00

Academia Current Outsource 100.00 1.71

Academia Current Airframe MRO 0.00 0.00

Academia Current Avionics 100.00 1.71

Academia Current Landing Gear 0.00 0.00

Academia Current Electrical/Fuel 100.00 1.71

Academia Current Interiors 0.00 0.00

Academia Analysis Engines 100.00 6.00

Academia Analysis Manufacture 0.00 0.00

Academia Analysis Outsource 100.00 6.00

Academia Analysis Airframe MRO 0.00 0.00

Academia Analysis Avionics 100.00 6.00

Academia Analysis Landing Gear 0.00 0.00

Academia Analysis Electrical/Fuel 100.00 6.00

Academia Analysis Interiors 0.00 0.00

--------

weights

--------

Short Name: Market Sha

Long Name: Subsector Market Share

Weight: 30Short Name: Presence

Long Name: Subsector Presence

Weight: 20Short Name: Distance

Long Name: Subsector Distance

Weight: 20Short Name: Ind Short Obj

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Weight: 180Short Name: Ind Long Obj

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Long Name: Gov Short Imp

Weight: 72Short Name: Gov Long Imp

Long Name: Gov Long Imp

54

Weight: 361Short Name: Academia Current

Long Name: Acad

Weight: 72Short Name: Academia Analysis

Long Name: Academia Analysis

Weight: 252

55

56

57

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