The themes of technological innovation, entrepreneurship, and organizing

Innovation in Scenario Building: Methodological Advancements and a Foresight Study of the Automotive Industry in Brazil

Ariane Hin^a Schneider

Industry Federation of Parana, Brazil

Laila Del Bem Seleme

Industry Federation of Parana, Brazil

Felipe Fontes Rodrigues

Federal University of Parana, Brazil

Marilia de Souza

Industry Federation of Parana, Brazil

Helio Gomes de Carvalho

Federal Technological University of Parana, Brazil


Situated in Parana state in southern Brazil, the Metropolitan Region of Curitiba (MRC) is home to an automotive sector which plays a major role in the local and national economy. In order to expand the development ofthe automotive sector and to create new local and worldwide opportunities, the Federation of Industries of Parana (FIEP) developed and employed an innovative scenario building methodology to analyze the automotive industry’s potential for innovation and attendance of new market demands for 2020; which is Sector Foresight. Therefore, results allow the players to have a clearer managerial view of the industry’s possible future. This chapter seeks to publicize the experience as well as the results of this innovative project by focusing on the methodology and tools. Data sources included a review of the literature, document analysis, direct observation, semi-structured interviews and two rounds of ques­tionnaires. This experience contributed to innovate the organizational and methodological processes of FIEP, and to improve the perspective of innovation in the automotive sector through a new approach to scenario building. Results also shown this methodology can be applied to other industries infuture studies.

DOI: 10.4018/978-1-61350-165-8.ch017

Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.


The automotive industry plays an important role in the economies of over 40 countries, and is a benchmark for innovation and application in management and production technologies. Brazil is one of the world’s key automotive players and ranks fifth worldwide in vehicle assembly (OICA, 2010). Parana state is home to one of Brazil’s lead­ing auto industry clusters, with more than 6,000 related companies located in the Metropolitan Region of Curitiba (MRC).

Due to the industry’s importance to Parana, in 2008 and 2009 the Federation of Industries of Parana (FIEP) oversaw a foresight study conducted by its Industrial Development Observatory (ODI). It was designed to contribute to the MRC automo­tive industry by improving the sector’s growth and development, and create new opportunities worldwide. Scenario building was chosen as the key tool to assess the industry, while the project’s timeframe was set for the year 2020.

This chapter examines the innovative experi­ence and results of the project, specifically the methodology developed and the tools applied. The region’s automotive industry is relatively new compared to other Brazilian clusters, and as such it was somewhat unorganized, which hindered its view of the sector’s future. Accordingly, this project was driven in part to promote interaction among its key players and develop synergies within the sector.

The innovation of this experience is seen in the creation of a new organizational method (OECD, Oslo Manual, 2005): Sector Foresight. It was developed by FIEP to significantly improve its industrial relations through an enhanced view of the industries’ possible futures. This also contrib­uted to the acquisition of non-transactional assets, paramount to the automotive industry scenario building exercise. This methodology involves a process of assessing and analyzing opinions from the public and private sectors, universities and re­search centers, and was conducted in a structured, collaborative, coordinated and synergistic way. When making decisions about the automotive industry’s future as a whole, a systematic view of this environment is employed, as it considers the needs and wants of all participants.

This scenario building activity, based on the foresight methodology developed by FIEP, differs from other methods as it enables the par­ticipation of different players within an industry, thanks to FIEP’s impartiality in overseeing the entire process. It is regarded as an innovative methodology as it includes the perspectives and opinions of multiple stakeholders, not to the or­ganizations individually, but to the industry as a whole, leading to the collection and analysis of strategic information regarding the views of all participants. This new approach to scenario build­ing provides managers of different organizations with a forward-looking, systematic look at the need to innovate in technology, processes and products to meet future conditions. It can also contribute strategically to organizational planning. It not only provides significant improvements for scenario building, but is also an innovative new service provided to industry by FIEP.


Foresight is a methodology to collect and assess expert opinions about the future from the public and private sectors, universities and research centers, through a structured, interactive, participa­tive, coordinated and synergistic process (Godet, 2001). It is used to build strategic views that can spur competitiveness and the development of a country, territory, company or public institution and as shown below, an industrial sector or a productive chain.

The University ofManchester defines foresight as a process of anticipation that assesses expert opinion to set priorities regarding certain assump­tions about the future which are constrained by

the external environment. These assumptions are limited to the development of interfaces with customers, suppliers and regulatory bodies, al­lowing foresight to give meaning to the environ­ment by defining strategic views and reducing uncertainties.

For Coates (1985), foresight is a process for a deeper understanding of the factors that drive the design of the long-term future, and should be taken into account in policy, planning and decision making. Horton’s (1999) approach asserts that foresight is “a process of developing views on possible ways in which the future can be built: by understanding that present actions will contribute to the future’s best scenario”.

On the other hand, Hamel and Prahalad (1995) feel that foresight should reflect the idea that predicting the future must be predicated on a detailed perception of trends in lifestyle, technol­ogy, demography and geopolitics, but is equally based on imagination and prognosis. Similarly, Martin et al (1998) define foresight as a process for systematically analyzing the long-term future for science, technology, the economy and society. They add however, that the research should be able to identify fields of strategic study, develop­ment and emerging technologies that would most likely create the best economic and social benefits. Cristo (2000) takes foresight further, seeing it as the process of anticipating and exploring the opinion of experts from social networks in order to build strategic views.

According to Schwartz (2000), scenario build­ing is one of the activities that comprise the fore­sight methodology. Scenarios are tools for helping long-range views in a world of great uncertainty. Such tools can promote the recognition of change processes in the current environment. The author also says they can be seen as alternative stories or “tales” about the future. More than recognizing changes, they allow preemptive responses in order to fully adapt to them (Schwartz, 2000).

According to Godet (1987), scenarios are the consistent description of a future situation and leading events from the present onto that future.

From an industrial perspective, Porter (1989, p. 413) states that scenarios are:

“an internally consistent view of the future structure of an industry. They are based on a set of plausible assumptions regarding important uncertainties that could influence the industrial structure, taking into account their implications for the creation and support of competitive ad­vantage. A complete set of scenarios, and not the most probable one, is then used to design a competitive strategy”.

Scenario building involves a systematic pro­cedure to detect trends and identify the social forces that could alter them (Rattner, 1979). For this exercise one must define a timeframe, the key structures and parameters for the analysis, and the scenario building objectives.

According to Simpson (1992) and Schoemaker and Heijden (1992), the results from scenario building lead to a broader view of the external environment, thereby improving the decision­making process, as it enhances management’s perceptions and ensures faster decisions. Quinn and Mason (1994) complement this idea by stat­ing that the scenario building practice triggers strategic thought, which prepares one to face important changes.

The techniques for prospective scenario building date from the twentieth century and have subsequently been refined. The methods are many - (Godet, 1993, 2000a, 2000b; Porter, 1992; Schoemaker, 1995; Ringland, 1998; Wilson, 1998; and Schwartz, 2000): Logical-Intuitive; the GBN Model; Schoemaker; Michel Godet; Arthur D. Little; the Mitchell Method, Tydeman and Georgiades; Michel Porter’s Competitive Strate­gies; Impact and TrendsAnalysis; Comprehensive Situation Mapping, Future Mapping, Crossed Impacts and Grumbach.

Of this sample of 12 methods only three are described in this study, as they are basis to the methods used by FIEP’s ODI/PR for this case study: Michel Godet’s Method (1993), Global Business Network - GBN (Schwartz, 2000) and Grumbach’s (1997) Methodology. All use fore­sight as a theoretical basis. However, each has distinctive features which can help meet specific demands of varying situations. All three methods are also variable-based, taking into account the behavior of the players and the consistency of the multiple scenarios that can emerge. These results are used in strategy building in response to the object of study. Nevertheless, the number ofvariables and their possible outcomes can make the process lengthy and time consuming, requiring know-how and adaptability from the researchers. The three methods are briefly described below.

Michel Godet’s Methodology

Godet’s (1993) scenario building methodology begins with a diagnosis ofthe external environment to further support strategic decision making. The author believe s that every action in the present will reflect on the future scenario, making it possible to predict possible scenarios and take actions to achieve desirable results. This anticipation to action helps deal with the growing effects of un­certainties and interdependency, plus changes in certain areas and inertia in others. The objectives ofthe scenario building under this perspective are:

• Building possible scenarios through the evolution of the system under study by tak­ing into account the most likely outcomes of the key variables, and then assessing the variables’ hypotheses.

Scenario building, under Godet’s methodol­ogy, begins with defining the scope of study, and then listing the relevant variables and players. A structural analysis is conducted to identify the influence/dependence of each variable between them. Games are then used to identify the strength ofthe relations among the players, as well as their objectives, strategies, behavior, uncertain elements and trends. All of these can cause ruptures in the scenario’s environment. Thus, the analyses help define the futures’ main drivers, allowing further hypotheses to be made that will be used later on, in the “morphological analysis”.

Accordingly, Godet’s Methodology consists of eight stages:

1. Definition of the scope and the environment

2. Retrospective analysis of the environment and current situation

3. Structural analysis of the system and environment

4. Selection of futures’ drivers

5. Design of alternative scenarios

6. Consistency tests

7. Establishment of polices and strategies

8. Strategic monitoring

Identifying key variables through a global analysis that can establish relationships among the variables related to the object of study;

Identifying, from these key variables, the main players, their strategies and the means they have to reach their goals. The analysis of these elements, as well as the evolution of the relationships among play­ers, can provide indicators towards pos­sible scenarios; and

Global Link Network Methodology (GBN): Peter Schwartz (1988)

The Global Link Network Methodology, known as GBN, was created by Peter Schwartz, who sees scenarios as an instrument of long-range strategic planning that considers macro-environmental uncertainties. These qualitative or quantitative uncertainties require that multiple future scenarios be generated during the planning process. This method tries to identify those issues with the most effect on decision making, based on what he calls the “strategic urgency” in a given system.

These issues can be accessed through inter­views, panels or expert discussions that deepen the understanding of a subject. After defining the most relevant aspects of a given system, one should identify the driving forces with the most influence on the macro-environment. From these driving forces one can identify the critical uncertainties that will be the required inputs for scenario building.

This scenario building methodology is unique as it begins with the micro-environment of the system and grows towards the outside, or the ex­ternal macro-environment. The goal is to highlight the most likely significant changes in the future of such a system. Therefore, the system should be analyzed from the inside-out, focusing on the most significant differences that can occur in its future scenarios. It can be considered a learning process of a specific system’s nature as it allows the players involved to have a shared view of the possible outcomes. It also enables managers to learn how to cope with the unexpected.

The GBN methodology features eight steps:

1. Identification of the focal issue or decision

2. Identification of key factors (micro-environment)

3. Identification of driving forces (macro-environment)

4. Ranking of pre-determined uncertainties

5. Selection of scenario logics

6. Description of the scenarios

7. Selection of key indicators and signalers

8. Analysis of the implications and options

Grumbach’s Methodology

The initial version of Grumbach’s (1997) meth­odology was a tool for making and analyzing prospective scenarios. However, it evolved into a future view-based strategic planning process anchored in prospective scenarios. The method uses an open system approach, meaning that the object of analysis can either influence or be influenced by its environment. From this point of view, it seeks to model the key strategic questions and the players’ behavior, and only then build the prospective scenarios. For Grumbach, one should ideally build four alternative scenarios: “the more likely”, “the ideal”, “the optimistic” and “the pessimistic”.

Grumbach’s method relies on various tools to complete its scenarios: brainstorming, Delphi rounds, cross-impact analysis, Bayes’ theorem, Monte Carlo Simulation and game theory. Its use is made simpler through the development of two software programs: Puma, a system for strategic planning and prospective scenarios; and Lince, a future management and simulation system. The method consists of three stages:

1. Understanding the problem

2. Assessing the alternatives

3. Evaluating and interpreting the alternatives for decision making


The automotive industry is a global economic powerhouse. Vehicle assembly alone generates over US$3 trillion in revenue worldwide. Auto manufacturing plants operate in more than 40 countries, including six South American coun­tries, of which Brazil is the leader. The industry consumes massive amounts of raw materials and has invented production systems from Ford’s traditional mass-volume assembly line to today’s advanced approaches such as the Toyota Produc­tion System and Lean Manufacturing, to name a few. The plants have become increasingly effec­tive and are the de facto creators of today’s global supply-chains.

Over 7 0 million vehicles are produced globally each year, and this number has grown despite the 2008 economic crisis (OICA, 2010). The tradi­tional industrial leaders have played little part in this growth, as new markets have emerged in the past decade, namely China, India and South Korea. These countries already represent well over 20% of worldwide production, while South America represents about 5% (3.5 million vehicles).

Production on this scale requires a massive labor force: the automotive industry accounts for eight million direct jobs, which represent 5% of total world employment in manufacturing (ILO, 2008). It is also estimated that every direct job accounts for five other indirect jobs worldwide. Among manufacturers, the auto industry is the Research & Development leader, investing 4% of its revenue into R&D. However, the nature of R&D has changed. It has gradually been passed down from the automakers to Tier 1 auto parts suppliers, mainly due to the latter’s thriving specialization in high technology parts and high value-added assembly sets (London School of Economics, 2006).

The market became relatively saturated by the early 2000s, after a period of high growth in the industry’s traditional markets: Japan and the developed countries in Europe and NorthAmerica. This saturation is thought to be mostly due to the vehicle/population ratio as well as negative demo­graphic trends. The industry’s current focus has shifted to a restructuring of production capacity, parts replacement and new markets, in the face of sharp increases in prices of raw materials, and a rapid rise of new players in Asia.

Among the markets that present the greatest potential are the so-called BRICs: Brazil, Russia, India and China. These emerging economies have a low vehicle/population ratio, vast territorial di­mension and significant increases in purchasing power. These factors led to an increase in demand for personal and cargo mobility (PWC, 2006). An underdeveloped multimodal transport infrastruc­ture actually favored road transportation, which is more flexible and adaptable to these countries’ rapid economic growth.

This recent growth cycle of the BRIC coun­tries attracted new assembly plants which can supply domestic and neighboring markets. These countries are primarily seen as the answer to con­tinued growth in the industry. After all, they not only boast growing demand, but also offer lower production costs, mostly favorable exchange rates and heavy government incentives. Recent invest­ments made by auto makers and parts suppliers back up this trend. To exploit these opportunities, North American, European and Asian countries have invested in emerging economies, which are estimated to generate up to half the growth in worldwide production, and which are responsible for around 80% of the industry’s growth in sales.

On the other hand, added pressure is being placed on the mature markets, as there has been an increase in product life-cycle. This is the result of higher value-added technology and superior production assembly. It also allows for longer ownership and a higher average age of the fleet. A lower repair frequency in the European Union has been reported, while at the same time the average maintenance value remains the same.

Even more pressure can be felt from the supply and demand side. High price sensitivity among consumers, rising production and raw material costs and the emergence of new competitors are already a reality. China and India have started up exporting programs, mainly in light commercial and low cost vehicles, and plan to export up to one million vehicles per year (including the new ultra-low-cost segment). The auto parts segment has also become a key factor in emerging coun­tries, and will become even more so. Although it represents only 5% of the auto makers’ revenues, it generates up to 50% of their profits.

Apart from some regional manufacturers (Tata, Mahindra & Mahindra in India, FAW and Chery in China, and GAZ in Russia), the bulk of the world’s domestic car and heavy vehicle markets is distributed among the maj orAmerican, European, Japanese and most recently, Korean multination­als. Nevertheless, a lower market concentration can be found in the auto parts business. The 30 largest companies in Europe are responsible for providing almost all the parts and assemblies for the manufacturers which operate in the European Union (London School of Economics, 2006).

Despite its contribution to economic develop­ment and mobility, the industry faces challenges that directly affect its sustainability. Internal com­bustion engines and tailpipe emissions are major public opinion topics, despite being responsible for only a small amount of total emissions from human activities (VDA, 2007). Heavy metals and other materials are also pollutants and a fun­damental part of today’s automotive technology. The automotive industry infrastructure also causes environmental impacts, while the use of cars has a strong social impact. The industry’s response to these findings has been a crusade to produce safer, quieter vehicles with embedded clean technologies and a comprehensive assessment of the product’s life cycle. (WBCSD, 2004). This position demands that automakers - in fact, the industry as a whole - to organize itself around a supply chain that is more sustainable and governed by best social, labor and environmental practices.

In view of the global warming phenomenon, the industry has made it a top priority to signifi­cantly reduce its emissions (ACEA, 2007). This commitment has become a reality for automaker associations such as ACEA (EuropeanAutomobile ManufacturersAssociation), JAMA (JapaneseAu - tomobile Manufacturers Association) and KAMA (KoreanAutomobile ManufacturersAssociation). These groups have all adopted greenhouse gas emissions caps. Automakers and suppliers seek to help reduce CO2 emissions, mostly through better designs, lighter materials, alternative fuels and more fuel-efficient engines. They have also invested heavily in emission control technologies.

According to the “Well-to-wheel” report, edit­ed by the European Union Commission, switching to alternative-foel sources, either renewable or less polluting, could bring huge reductions in emis­sions. However, the high costs in R&D, the need for synergies among different companies along the supply chain and the many energy-intensive technologies can offset such savings significantly.

The automotive industry has been faced with numerous challenges, whether threats or oppor­tunities. Diverse strategies are well underway, taking into account the specific conditions of each regional market. Likewise, comparative advantages and disadvantages of each locale have preeminent importance regarding the role they will play in the industry’s future.

On this basis, following is a look at the au­tomotive industry in Brazil, showing its current role in the domestic economy and the challenges it faces, which underscored the efforts taken to research possible futures for the industry in Brazil and the state of Parana.

The Automotive Industry in Brazil

Vehicle production in Brazil began in the late 1950s with the establishment of Volkswagen, Toyota and Ford plants (the latter also produced light trucks) in the town of Sao Bernardo do Campo, in Sao Paulo state. Scania and Mercedes-Benz set up truck and bus plants in Sao Caetano do Sul in Sao Paulo, where General Motors later established a diversified plant. The city of Sao Paulo also re­ceived a Ford truck plant shortly thereafter. Later investments by Fiat in the state of Minas Gerais and others in the states of Rio de Janeiro, Parana and Rio Grande do Sul comprised the basis of the automotive industry in Brazil until the 1990s.

With the opening of the Brazilian economy to foreign markets and the concurrent economic stabilization that took place in the 1990s, the industry received a push with the retooling of existing plants and the building of new plants by Renault, Peugeot-Citroen, Nissan and Mitsubishi, to name a few (BNDES, 1999).

In 2006 the automotive industry in Brazil turned 50 years old, with over 24 automakers, over 500 parts suppliers and an endless number of service providers (ANFAVEA 2007). Over the previous ten years domestic production and sales of vehicles had increased significantly.

The Brazilian auto market is characterized by the production of small and medium-sized vehicles. In 2005, 55% of sales came from cars with engines no larger than 1000cc. Such vehicles have government incentives - such as tax waivers, first introduced in the 1990s - to encourage the market for these so called “popular” cars. High demand and competition push the need for cost- cutting, large-scale production and low margins.

A unique feature of the Brazilian market is the predominance of flex-fuel vehicles (which run on gas, ethanol or any mix of both), whose technol­ogy has helped reduce the country’s dependence on foreign oil. Successive hikes in the price of oil worldwide have led several markets to further research the Brazilian experience and assess the potential benefits of flex-fuel technology. In fact, automakers and parts suppliers of flex-fuel technologies hope to exploit this interest through exports or technology licensing.

By the early 2000s the domestic market had trouble meeting the industry’s initial forecasts of high-volume growth, and so shifted its focus to exports to better utilize excess capacity. Ex­change rate policy changes and a strengthening local currency, which occurred by mid-decade, showed up the weaknesses of this strategy. As in other countries, Brazil couldn’t sustain its role in the international market until its domestic market boosted demand. It wasn’t until the late 2000s that the market flexed its muscles and began its long awaited growth path. Other automakers took notice and competitors have now arrived to challenge the established players, through direct investments in new plants and via favorable exchange-led imports.

The main foreign markets for Brazil’s automo­tive industry are Latin American countries with similar features to Brazil (high concentration in small hatchbacks), and lower income countries such as Mexico, China, India, Russia and South Africa.

One of the challenges of operating in Brazil is the higher-than-expected production cost, seen mostly in its cumbersome and complex tax system. Even among other developing countries, Brazil’s taxation is a heavy burden. The value-chain is strained by fiscal regulations, outdated laws and union issues. In fact, tax and labor concerns might jeopardize future foreign direct investments by automakers. On top of that, the fragile and under­sized logistics infrastructure only helps increase the cost of operating in Brazil.

Although the country posts moderate growth when compared to other developing economies, the automotive industry has a more positive out­look for the short and medium-term. Downturns in the global economy have had limited effect on Brazil’s automotive industry, which has managed to retain an export base and meet domestic demand.

It is not widely known that Brazil andArgentina combined represent 92% of Latin American pro­duction. Brazil’s automotive market alone (around two million vehicles) is at least four times that of Argentina and dwarfs those of Venezuela, Chile, Colombia, Paraguay and Uruguay, which have a combined market of less than 600,000 vehicles.

This brief background on the automotive industry in Brazil and around the world clearly shows that it plays a key role in the economies where it operates. There are many challenges to be tackled, particularly related to uncertainties over factors such as competitiveness, alterna­tive fuels, new production models, technologies, markets, governments and a qualified labor force. Through understanding and assessing these vari­ables, specific actions can be directed towards a thorough development of the industry and the communities affected by it. Foresight studies such as scenario building become essential for collective long-range thinking. The industry can also undertake collective actions leading to the desired outcomes it seeks.


Based on its goals, this research is descriptive by nature (Vergara, 2005), as it describes the various stages and tools employed during the scenario building exercise for the MRC automotive indus­try. It is also a qualitative case study, and according to Yin (2001), was designed to comprehensively and empirically approach a contemporary phe­nomenon in its real-life environment. To achieve this, multiple sources of empirical evidence were required not only from a review of the literature, but also through questionnaires, semi-structured interviews, direct observation and analysis of the FIEP document database.

The literature review was designed to identify the leading scholars and practitioners of scenario building, such as Michel Godet (1987), Grumbach (1997) and Peter Schwartz (2000). Primary data collection was conducted through semi-structured interviews applied to the FIEP scenario building project personnel. The aim of the interviews was to establish the scenario building practice at FIEP. At the same time, direct observation was used to obtain more detailed information regarding the respondents’ experience with the automotive industry. Secondary data was obtained through documentation assessment, which included meth­odology files, notes, videos, project reports and minutes of meetings. These documents demon­strate the procedures and results ofthe project. The interviews were submitted for content analysis, along with the secondary data. The goal was to approximate Bardin’s (1977) regularity identifi­cation and develop a model that could be used in further practice and research.


The Federation of Industries of Parana (FIEP) “produces research and reports on economic is­sues, suggests and discusses strategies related to its many industrial sectors, defends the interests ofthe industry and provides tools for the develop­ment ofbusiness associations, fosters international partnerships, and eases the access to credit lines and innovation” (SISTEMA FIEP, 2010).

The help meet these goals, the FIEP System started a project called Rede de Competencias (Competences Network), which sought to mobilize the automotive industry’s knowledge base to gen­erate growth within the industry and consequently for Brazil. This project was carried out by the National Industry Confederation (CNI) - which coordinates all state Industry Federations - and is supported by the federal government through the Studies and Projects Funding Agency (FINEP).

One of the innovative mechanisms created by the Competences Network is the Industrial De­velopment Observatory think tanks (ODIs). Six Brazilian states have implemented local ODIs: Bahia, Minas Gerais, Parana, Pernambuco, Rio Grande do Sul and Santa Catarina. The central goal of the ODIs is to create strategic knowledge regarding the economy, technology and societal changes and trends which occur globally, nation­ally and locally. It anticipates the impacts of these changes and trends on numerous industries, and seeks to identify and support possible pathways for innovation and sustainable industrial devel­opment.

Each ODI selected an industry which was economically important and of local strategic interest. These were then the subject ofa foresight pilot project. The FIEP System, through the ODI/ PR, chose Parana’s automotive industry because it has national and regional importance, it employs over 51 thousand workers in Parana (Brazil, 2006), and is a regional benchmark in technology and innovation.

Through its ODI think-tank, FIEP led the prospective scenario project from 2008 to 2009, with the goal of further developing the state’s automotive industry and bringing in new oppor­tunities worldwide by the year 2020. To build the scenarios, certain steps had to be taken: defining
the scope of the project; establishing the Strategy Board and determining its responsibilities; diag­nosis and trend analysis; and lastly, the scenarios themselves. As this was a pilot project, these stages were defined during the working process, and were continuously tested and reassessed until the final format was determined.

As part of defining the scope, the key problem and main goals were also set. This stage also fine - tuned the range of the project (industry sector vs. production chain), geographical area (the MRC) and the project timeframe: the year 2020. Choos­ing the automotive sector posed the challenge as foresight studies mostly relate either to individual organizations or territories. Adding to the chal­lenge the main goal was defined as “to contribute to strengthening the automotive industry in the MRC and fostering new opportunities worldwide under the scope of 2020”.

The Strategy Board was made up of key play­ers that could benefit from the development of the project, namely senior managers and decision­makers from leading companies and organiza­tions linked to the industry. These individuals are the ones that could contribute the most to the improvement of the industry if they chose to be part of a foresight study.

Diagnosis and trend analysis began with gather­ing the main reports and market studies published by think-tanks, associations and consulting firms around the world. The diagnosis was divided into global and local market structures, trends and industry core competences. From the diagnosis the main variables of the system were extracted and cross-impacted into a structural analysis that allowed for the key variables at stake to be iden­tified, which were used throughout the project.

For the scenario building, each at stake variable resulted in hypotheses or possible outcomes. Based on combinations ofhypotheses, possible scenarios were built considering the pre-set time frame.

Following is a description of the main steps and sub-steps that will result in scenario building for the MRC automotive industry.

Defining the Scope of the Project

According to Godet’s methodology (2000b), the first and one ofthe most important steps is to define the scope, as it provides the basis from which the theme/problem ofthe study can be determined, as well as the main goals. It also leads to decisions over using the industrial sector or its production chain, the geographical area and the time frame.

The choice for the automotive industry as the object of research arose from the economic findings of its importance to the state of Parana. Upon analyzing the state’s industrial base by manufacturing value, the five main industrial sec­tors represented 53.5% of total industry in 1996, rising to 63.1% by 2005, namely: food processing, oil refining, and automotive assembly, equipment manufacturing and chemical products. (Instituto Paranaense de Desenvolvimento Economico e Social [IPARDES], 2005).

Automotive assembly (division 34 of Brazil’s national economic activity classification system - CNAE) rose 320% in monetary terms from 1996 to 2005. This same growth is also seen in the number of businesses, which increased from 373 to 462 in the same period (Brazil, 2006). The automotive industry was also a leader (together with the food industry) in job creation during those same years. However, only the automotive industry increased in each of manufacturing added value, job creation and number of businesses. Also, the state’s largest automakers and related firms (Volkswagen, Renault/Nissan, Volvo and Bosch) generate 20% of the state’s international trade (Brazil, 2008).

The auto industry’s manufacturing chain covers everything from raw materials to the most com­plex electronics components, as well as sales and maintenance-related services. Considering the size and complexity of the manufacturing chain, the study was limited to the sectors shown in Table 1.


1st Level (TIER 1)1

2nd Level (TIER 2)

3rd Level (TIER 3)

• Passenger cars and light commercial vehicles

• Trucks and buses

• System Suppliers

• Auto parts

• Auto parts

• Tires

• Auto bodies

• Forged metal

• Cast metal

• Die cast

• Plastic

• Rubber parts

• Glass

• Non-metallic parts

An assessment of economic fundamentals revealed that most of the automotive industries

in Parana are located in the MRC, thus setting the study’s geographical scope.

Discussions among FIEP management resulted in the year 2020 timeframe and the drafting of the research problem. The former was due to the lengthy maturation process of the automo­tive business, while the latter was defined by the Strategy Board as2:

“To contribute to strengthening the automotive industry in the MRC andfostering new opportuni­ties worldwide under the scope of2020”.

Establishment of the Strategy Board

The main actors interested in the development of a foresight study for the MRC’s automotive industry were identified in the second stage, referred to as the Establishment and Modus Operandi of the Strategy Board. The main activities the Strategy Board had to develop were to define the scope, direct activities, articulate resources, and monitor the work and its results.

Following the methodology assumptions (GODET, 2000a), the FIEP System considered an approximation to the MRC automotive industries very important; so that all decisions related to the project could be made together.

A survey was first taken of both the companies and experts in the automotive industry located in the MRC. Sources used were: the FIEP industrial database, news bulletins, newspapers, associa­tions, unions and the “Lattes research platform” of Brazil’s National Council of Research, the

CNPq, company sites, amongst others. Selection criteria were selected from this survey in order to establish the “Strategy Board.”

The search for strategic actors for the industry was handled respecting the industry’s delimita­tions, by selecting 3 representatives from each segment selected, such as automakers, 1st level companies (system suppliers), 2nd level (auto parts, pneumatics and auto bodies) and 3rd level (basic parts). The actors were first categorized according to their work category: associations and unions, governments, industries and research. They were also classified according to their position in the company: management positions, strategic deci­sion making jobs, operational positions and sales. The search for products and services was both efficient and useful and was complemented with customer, competitor and supplier research. Bits of information such as ongoing or concluded proj ects and scientific publications or opinions, related to the subjects ofthe industry, were added especially for the actors connected to research centers. In short, the criteria used for choosing strategic actors were: the company’s size, number of employees, its capital source (national/multinational), and the company’s position in the chain as well as its field of operation.

After the strategic companies were selected, their CEOs were invited to be a part of the Board. This process was done through a formal docu­ment signed by the FIEP System’s president. The companies were then contacted by telephone so that each one’s importance in the project was reaffirmed as well as making sure that the com­pany was interested in taking part of the project. Those who showed interest in taking part in the project were visited by a technical team from FIEP which was able to explain the project and talk about the expectations regarding each company’s participation.

The first official Board meeting took place in August 2008, at the International Innovation Cen­ter ofthe Federation of Industries of Parana. In this meeting the members ’ participation was confirmed during the building of the Strategy Board and a bimonthly agenda of meetings was accepted, in order to put into action the activities proposed. In agreement with the chosen methodology, all the stages of the foresight scenario building were directed and validated by the Strategy Board so that the project’s results had credibility.

The initial idea was to have the participation of approximately 10 companies. Currently the Board companies are: Bosch, Brose, Denso, Fiat Powertrain, Hubner, Igasa, Johnson & Controls, Perkins, Renault, Treves, Volvo and Volkswagen. There were a total of 12 important companies from the MRC automotive industry, as well as a development and innovation agency.

Diagnosis and Trend Analysis

An information survey regarding the automotive industry was conducted in this stage of Diagnosis and Trend Analysis. The survey conducted this diagnosis on a global and local level, running trends analysis and sought out current compe­tences identified as necessary for the industry’s development. Later came the structural analysis, which allows for the identification of variables considered relevant for the next stage ofthe project.

The structural analysis technique was used to do a survey regarding the structuring variables for the automotive industry. According to Godet (2004), this is a tool used for diagnosing the influence and dependence relations between the variables of a system. The variables were classified according to their dimension (global, regional and local) and subject (environment, economics, energy, MRC, government, automotive industry, infrastructure, market, automotive products, technology and so­ciety.) From a list of 250 variables, the research team consolidated them into 46, which are: global economic growth, automobiles’ life cycle, pro­ductive systems, mobility services, interest rates, international commerce policies, tax legislations, alternative fuels and public transport.

The 46 variables chosen were organized in a matrix. The objective was to establish the influ­ence and dependence relations of a certain variable over another. From this variables confrontation, a matrix was obtained: the Cross-Impact Matrix (Figure 1), in which the placement ofthe variables of the system is determined. This procedure was done with all 46 variables which are considered the most important for the automotive industry.

With this matrix established, a “plan of influ­ences” (Figure 2) was built which allowed for better visualization of the variable’s role in the system. The spatial character of the plan of influ­ences is fundamental for understanding the system under study and is of great value in defining future scenarios.

The variables of this influence plan are clas­sified into influence variables, variables at stake, dependable variables, excluded variables and borderline variables, according to Table 2.

Figure 1. Example of a cross-impact matrix


In flu en ce of

Variable A







Variable С








Variable E

Variable A


Variable В

Variable С

Variable D

Variable E

The 46 variables from the automotive industry were placed on this influence plan, as shown in Figure 3. The most relevant variables in this plan were selected to define the scenarios. Thus this allowed a synthetic analysis and at the same time allowed a deep systems analysis. This selection was made in a Scenario Building Workshop where the variables were shown to a Technical Board consisting of 32 participants, such as senior en­gineers, analyst researchers and other automotive professionals. This Technical Board analyzed the variables shown and was able to incorporate, transfer or exclude the variables from quadrants, always focusing on the variables at stake. This process was conducted with the consensus of each sub-group of participants. They elected a “spokes­person” to communicate their decision to the spokespersons that represented the other groups. Consequently the spokespersons from all groups were able to reach a general consensus among them and then share their common decision with everyone.

According to Godet’s “prospective” methodol­ogy (GODET; 1993, 2001), the “variables at stake”, that are the simultaneously very influential and dependable variables, should be given higher priority than the other ones. These variables

Figure 2. Types of variables of the plan of influ­ences


influence variables 1

Variables at stake 2

Border variables


Excluded variables


Dependent variables



have an “ambiguous” behavior and can interfere in all the other system variables. They are “at stake” because their system structure positioning is uncertain, as well as, the position that they can assume. Consequently, the scenarios can be built according to the different positions that these variables at stake can assume.

The experts raised likely hypothesis for each variable after the consensus was made of which variables at stake would be suitable for the prospec­tive scenarios in the industry. These hypotheses represent the likely states that the variables would assume within the prospective scenario target. The results were that 24 variables and their hypotheses were chosen. These variables and their respective hypothesis were used as a basis for the scenario building. An example of a chosen variable was “alternative fuels” and their hypotheses were: an increase in regulations and a reduction of alterna­tive fuels available; and also a reduction in the regulations as well as an increase in the different types of alternative fuels offered.

Scenario Building

Once the step of choosing variables and their respective probable hypothesis was concluded, the specialist’s query process and the variables probability were started. The hypotheses were presented as semi-structured questionnaires con­ducted through interviews with members of the Strategy Board ofthe MRC’sAutomotive Industry. The hypotheses that best represented the opinions of the strategic MRC’s board members, regarding the most propitious events, were consolidated in a scenario referred to as a “desirable scenario”. The hypotheses that represented the “trend” behavior, that is, the most likely to occur, were consolidated in the “most likely scenario”.

Table 2. Classification of variables in the influence plan




Influence Variables

• They are very influential and not very dependable variables.

• They are variables that explain the system’s dynamics due to their high impact capacity over it.

Variables at stake

• They are simultaneously very influential and dependable variables.

• They have an unstable nature and are capable of producing great changes in the system’s dynamics

• They are generally considered as challenges and must be constantly controlled.

Dependable Vari­ables

• They are not very influential but are very dependable variables.

• They have little impact or no impact at all on the system, but they are part of the results of its dynamics.

• Their development is explained by the variables performance in quadrants 1 and 2.

Excluded Variables

• They are not very influential and not very dependable variables.

• They are independent factors of the system having only a few connections with it.

• Their importance lies in the fact that they can constitute system trends, ie. Factors that have little or no influence nowadays; however, they can represent elements that will have impact on the subject/problem dynamics in the foresight study.

Borderline Vari­ables

• They are moderately influential and dependable variables

• A priori, these variables may not indicate anything in a consistent way as its location is not well defined

• Variables on this specific area deserve attention in the way of research efforts in order to better understand their performance.

Source: SENAI (2010)

The hypothesis combinations, that seemed the most likely/desirable for the foresight study, triggered the scenario building - Probable and Desirable - that aimed at contributing to the MRC’s Automotive Industry consolidation for
the development of new opportunities worldwide around 2020.

This stage had 17 experts participating from the MRC industry, all of which were members of the Strategy Board. They were interviewed using a prospective questionnaire. 10 majorthemes were touched on, as follows: (1) economics, (2) energy, (3) government, (4) automotive industry, (5) in­frastructure, (6) market, (7) automotive products, (8) MRC (9) social and (10) technological. In the end, 24 variables and 83 possible hypotheses were considered. The interviews contributed to variable filtering and this resulted in 23 variables that were considered to have the most impact on the MRC’s industry, as can be observed on Table 3.

The Strategy Board considered it paramount to conduct a broader survey in order to obtain more details about the hypotheses and even create their probabilities. A Delphi survey was con­ducted online especially for this study. Structured questionnaires were sent to experts countrywide, both upstream and downstream along the automo­tive supply chain; 125 questionnaires were filled out.

The expert’s survey was done in two rounds via the internet. The first round was open for 47 days. After analyzing the data from the first round, a reasonable 12 question consensus approach was reached. The remaining 11 questions that presented either irregular central tendencies or where there was no consensus among the respondents were re-submitted to the 2nd round with the same 125 experts that had already answered the first round questions. The 2nd round was conducted a month after the consolidation of the first round results. This was available on the internet for 13 days, resulting in 68 valid responses. In the second round, relative consensus or central tendencies were reached, allowing for a more robust analysis of the experts’ opinions regarding the automotive industry scenarios for the MRC.

A morphological analysis tool was used after obtaining the hypothesis probability and was able to analyze and consolidate the most representative prospective scenarios - Probable and Desirable.

According to Godet (2004), the morphological analysis term comes from “morphology” which means the study of forms. It is a tool that can be

used to build scenarios from the parts that com­pose it. In a “prospective” study this tool aims at providing a systematic scanning ofpossible futures within a given foresight timeframe, through a set of combinations from different scenarios prepared for each variable at stake.

The paths for the two main scenarios were: the desirable scenario, or what was referred to as the “Sustainable Future”, and the most likely scenario, that was referred to as the “Future Contingency”. Those trajectories were a good exercise on how changes and ruptures could happen in the near future and therefore, be anticipated. Thus the industry or chain could be better prepared for challenges and opportunities from the competitive environment in which they are located.

Regarding achieved results, the study suggests one “likely scenario” for the MRC automotive industry that includes Brazil’s economic growth through trade agreements and lower trade barri­ers in the external market. It also facilitates the Brazilian companies’ access to supply chains and distribution channels abroad, and includes at least some improvements in logistics infrastructure and the maintenance ofthe Brazilian automotive indus­try as a key industry in Brazil’s trade balance. The scenario shows the growth of renewable fuels in the energy matrix as well as new business models such as rental services. It is believed that in this study the industry would specialize in technology according to its core competences; MRC is known as an important local production cluster.

Table 3. Themes and variables



1. Positive trend in Exports


2. Increase in GNP per capita

3. Economic importance of the automotive indus­try in the RMC


4. Brazil’s transport matrix


5. Brazil’s tax load


6. Industrial policy

7. Probability of reduction in barriers of entry

8. Trends in the supply chain


9. Industry growth strategies


10. Vehicle Production share

11. Positioning of newcomers



12. Improvement of Brazil’s transport infrastruc­ture


13. Cost/benefit ratio of vehicle ownership in Brazil

14. Vehicle density in Brazil

15. New vehicle sales in Brazil


16. Average fleet age in Brazil


17. Relative production costs of MRC compared to other clusters in Brazil

18. Qualified workforce in MRC


19. Probability of alternative transport use among vehicle owners

20. Shift in employers/employees Union relations

21. Productivity gains in the automotive industry



22. New productive arrangements in the automo­tive industry

23. Location and intensity of R&D activities in the automotive industry

The “desirable” scenario or the “Sustainable future” includes the MRC as an important world - class production center as it becomes an agent of technology transformation, not only for the industry but also the State. The scenario suggests a lower logistics cost in Brazil due to better use of the multi-modal system as well as a more impor­tant role in Brazil’s exports. Higher investments in education would reflect in a better qualified workforce, trained in universities and technical colleges that are well integrated into the industry. The scenario boils down to a new automotive industry, based on a greater use of public trans­portation and alternative means of transportation for vehicle owners and are directly linked to the use of alternative fuels and fleet renewal.


Based on the previously stated experience of industrial scenario building and in rigorous ac­cordance to the literature - highlighting Godet (1993), Grumbach (1997) and Schwartz (2000) - the research team at FIEP’s Industrial Develop­ment Observatory (ODI/PR) was able to develop a stage-based framework comprising the neces­sary steps for such an exercise, as well as the best fit tools for its application in industrial scenario building (Table 4). The main stages can be summa­rized as follows: scope definition; establishment of Strategy Board; diagnosis and trend analysis; and scenario building per se.

Scope definition was considered the paramount stage since it defines the problem of research and its goals. It also sets the geographic area, the ap­proach (industry/sector vis-a-vis production chain), and the project’s timeframe.

The establishment of the Strategy Board is the stage in which the researchers identify the key players that would be most interested in the development of this type of foresight study. Among its most important activities, one would highlight assisting in scope definition, guidance for the activities according to its relevance to the industry and the validation of the research’s work and results.






• Definition of topic or problem for foresight study

• Assignment of chain or industry

• Geographic delineation

• Definition of foresight timeframe

• Definition of objectives

• Assessment of Stakeholders

• Perceived ideas workshop

• Change, rupture and inertia workshop

• Retrospective and prospective questionnaires



• Assignment of Strategy Board members

• Appointment of attributions of the Strategy Board

• Operation and procedures of the Strategy Board

• Perceived ideas workshop

• Change, rupture and inertia workshop Retro­spective and prospective questionnaires

diagnosis/ trend analysis

• Global and Industry-specific diagnosis

• Trend analysis

• Assessment and analysis of key players in the industry

• Structural analysis and Stakeholder’s game

• Research and Assessment of industry’s indicators

• Retrospective and prospective questionnaires

• Perceived ideas workshop

• Change, rupture and inertia workshop

• SWOT analysis

• Structural analysis

scenario building

• Identification of uncertainties

• Formulation of hypotheses

• Hypotheses presumption

• Scenario assembly

• Retrospective and prospective questionnaires


• Hypotheses probability presumption

• Morphologic analysis

The project, as it was developed by the ODI/ PR, included a great amount of work on diagnosis and trend analysis. It enables researchers to fully understand the object of study as well as gather the upmost critical information regarding the industry in reference. From this stand point, both global and local diagnosis and trend analysis take place as
well as identifying the core competences necessary for the industry’s development. Moreover, this is when the key variables are first gathered that will be applied throughout the research.

Finally, the “scenario-building” stage consists of the identification of possible hypotheses for each variable “at stake” within the system. The likely combinations of hypotheses are, thus, the basis for the possible scenarios considering the given timeframe.

The applicable tools for the foresight studies should be those that contribute the most to gather­ing and analyzing strategic information during the multiple stages of prospective scenario building. They should also assure relevant results not only for the industry but also for the organizations individually. These tools are:

• Retrospective and prospective question­naires: They help the research team obtain information from the players about chang­es, ruptures, inertia related to past, pres­ent and future that are related to a given research topic.

Change, rupture and inertia workshops:

Group dynamics aimed at the acknowledg­ing perceptions, behavior and mental rep­resentations that experts have in regards to the theme and research problem. Through this exercise, one can identify the changes foreseen, desired or feared by the players during a given timeframe. It can trigger the formulation of alternative responses to the changes previously identified.

Perceived ideas workshops: It aims at identifying the experts conceived ideas or behavior that has an impact on the dynam­ics of the theme or given research problem. As such ideas are taken for granted; they have a powerful impact on the player’s be­havior in a given system.

Structural analysis: Technique which structures the collective thinking about the variables of a foresight study, thus reduc­ing its complexity. It offers one the possi­bility of describing any given system with the help of a matrix and a chart that estab­lishes the relationships of all its constituent elements.

Stakeholder’s game: As is the case with the structural analysis, the stakeholder’s game aims to reduce the complexity of the competitive movements of a given system’s players. Through systematically gathering information on the strategies of stakeholders, one can position the players according to their interests in the system. SWOT analysis: This well known tech­nique helps the industry to take advantage of the opportunities and avoid external threats. It also explores the strengths and weaknesses of the players helping them to make the best of both. Its application makes it possible to obtain and process relevant information for scenario building as well as to plan strategies for the players involved.

Morphologic analysis: This tool provides a systematic exploration of possible fu­tures within a given timeframe, through the likely combinations between hypoth­eses variables.

Hypotheses’ probability presumption:

Aims to identify the set of hypotheses most likely to occur in order to put together the possible prospective scenarios. It comple­ments morphological analysis as it quan­tifies the probability of the occurrence of each set of hypothesis.

Delphi: Based on the elaboration, submis­sion and resubmission of a given question­naire to a group of experts, thus allowing them to acknowledge the group’s collec­tive opinion about a research problem. The consecutive rounds of responses also pro­mote reflection on the individual respon­dents as they have access to the group’s general trends. As such, this technique verifies the degree of convergence/diver­gence of knowledge about a given set of hypotheses so they can be applied in the scenario building.

It is important to note that the stages and their tools are non exclusives as in some cases; they can be re-arranged or even excluded from the foresight research. The combination of stages, sub­stages and their tools will depend on the theme, time available and resources, personnel, level of understanding of the tools, budgeting and other factors peculiar to the effort of foresight research.


Scenario building based on “prospective” helps players in acquiring relevant knowledge, compe­tences and tools that allow them to raise the player’s foresight skills, which in turn fosters short-term innovation capability as well as increases com­petitive performance while adding value to the processes, products and services in the long run.

Attesting to the innovation in the modus - operandi of scenario-building at FIEP, Table 5 gives one a comparison of methods anchored in the “prospective” for scenario-building. The last column is focuses on outlining the elements exclusive to FIEP’s model.

FIEP’s model is the result of the significant improvement of other methods and is currently offered as a new service for the Parana State In­dustrial base.

Some testimonies of key stakeholders explic­itly state innovations that were triggered in the industry. According to Mr. Yoshio Kawakami, President ofVolvo Construction Equipment Brazil:







Problem definition





Historical analysis





Description of present situation





Variable Identification





Stakeholder’s Identification





Consistency check





Multiple-variable hardship





Expert consultancy





Competitor behavior monitoring





Qualitative and quantitative variables





Detailed technical presentation





Cross-impact analysis





Delphi method





Probabilistic hierarchy





Managers’ mental map





Multiple exploratory scenarios





Strategy Board’s involvement with multiple chain competitors





Source: adapted from Marcial (1999) and the authors

“The research FIEP conducted via ODI/PR was surprising due to its ability to mobilize and coor­dinate strategic actions. The exercise significantly added value to the entire local automotive industry as it led to the establishment of the automotive engineering graduate program in Curitiba. This opportunity is beneficial to Volvo Brazil not only because it is increasing the professional growth of

its engineers, but also because it provides the same opportunity for many of its suppliers in special­ized product development engineering services.”

Mr. Alain Tissier of Renault Brazil’s executive management adds:

“Such collective work could have never been done by my company’s strategic planning team, mostly due to the heavy workload needed and for not being able to mobilize that many key players [...] there is no such service in the market. Sce­nario building is important for defining courses of action, and FIEP proved it has the know-how to pull it off. The result is that we now have input for our strategic planning.

We dominate the market, but need more informa­tion from the external environment, which FIEP was able to deliver through its scenarios. A good example is the variable that proxies hybrid-electric vehicle production for the year 2020 which will total 5% of the vehicle production. This is the sort of information that I cannot find nor buy in the market and was only possible through FIEP’s work [... ] it is an important parameterfor defining new products by Renault.”

As shown above, the stakeholder involved in the exercise is able to take advantage of the op­portunities and benefits ofthe structural tendency analysis, better understand the industry and the individual organizations and assess the market’s future needs. Furthermore, the collective is able to anticipate the industry’s possible opportunities and threats, thus enabling faster and more robust responses to environmental changes. Another benefit would also be in the development of raising and managing key information for the development of new products, services, concepts and business models.


This foresight study can be seen as a methodol­ogy for building the future, which contributes to the conception and implementation of new economic development and innovation strategies for a country, region, organization, or as shown in this chapter, an industry or supply chain. Based on articulated and cooperative efforts, this kind of research encourages entrepreneurs to think about the relevance of future studies, prospective views, of medium and long-term planning as well as col­lective action, with the aim of achieving innovation and sustainable competitive advantage for firms, industries, local and national economic sectors.

With the goal of strengthening the MRC’s automotive industry, the Federation of Industries of Parana (FIEP), through ODI/PR, led a sector foresight project, a truly innovative service, aimed at adding to the industry’s development and creat­ing new opportunities in the worldwide arena. The project took over two years of research with over 200 experts directly involved, including scholars, researchers, businessmen, managers and govern­ment agents. This initiative was a pioneer effort to bring together an industry known for its indi­viduality and independence, focusing on collective thinking as a means for joint competitive gains.

This exercise allowed for the comprehensive consolidation of the necessary methodological stages and sub-stages for a sector foresight study. In fact, it resulted in an innovative methodology for scenario building, as it requires an industry view as well as cooperative involvement of the industry’s players both in the process and re­sults. Aside from that, tools that were suggested throughout literature were tested in the different study phases and were confirmed according to the particular industrial focus analysis. In short,

FIEP’s methodology consisted of 4 stages, 18 sub-stages in total and 9 different tools that could be replicated in studies for different industries.

This methodological advancement works not only as means of raising the awareness of a given industry’s competitive global, regional and local environment, but also spurs the individual and group innovative responses towards critical uncertainties. Scenario building under the stra­tegic “prospective” school developed by Michel Godet’s seminal papers (1993) was traditionally used by either enterprises or territories. By add­ing stakeholder’s governance as a neutral partner to legitimate scenario building for stakeholders and multiple research techniques, the foresight methodology was greatly inhanced allowing an entire industry to conduct its common strategic thinking in an innovative and collaborative way.

This research contributed by proposing a methodological framework for “sector foresight”, which can hereafter be applied to other local indus­tries as well as to other auto industries located in other regions and nations. The exercise promoted communication between the MRC’s automotive industry players, enabling collective thinking about the industry’s future as well as the building of the “most likely” and “desired” scenarios. The delivery of those consolidated scenarios is the main product offered to the industry, allowing the players to have a global view ofthe industry’s impacting variables and critical uncertainties. In turn, they can follow their own paths which best suit their desired future.

Furthermore, the appropriation of this con­tent allows for a clearer managerial view of the industry’s possible future. This overview allows technologies, products, processes and enterprises to be anticipated in a way that meets the neces­sary responses to future demands shown in the scenarios. Thus, the scenarios become the local managers’ decision making support platform as it offers premiere strategic information for all players in the industry.

The local automotive industry benefits from the communication channels now established through FIEP’s ODI/PR. The exchange and shar­ing of strategic information fosters innovation and innovative responses as it triggers anticipative intelligence and research as well as development engagement for the sector.

As FIEP developed the most needed resources for this connection - know-how, credibility, trust, legitimacy and impartiality - an innovative ser­vice became available for the first time in Parana State’s industry. This service can significantly contribute to the enhancement of the industry’s competitiveness - not only in products, but also services and processes. This new model that FIEP has developed has supported the industry by embedding this futuristic exercise in the player’s thinking patterns, an essential step for industrial innovation processes.

The next stage of this research would be to monitor the variables at stake and the players’ repositioning, as they respond to the different scenarios or not. Those responses to variable shifts, competitive policies and institutionalized actions that interfere in the industry would ultimately result in feedback on how the players apply the knowledge that was transmitted by each strategic scenario. This is not only collectively, but also as to foster innovation in their companies and so continuing the scenario building exercise.

The themes of technological innovation, entrepreneurship, and organizing

About the Contributors

Farley S. Nobre (PhD, MSc, BSc) is Professor at the School of Management of Federal University of Parana, Brazil. His research interests include organizations, knowledge management systems, innova­tion and sustainability. …

The Roles of Cognitive Machines in Customer — Centric Organizations: Towards Innovations in Computational Organizational Management Networks

Farley Simon Nobre Federal University of Parana, Brazil ABSTRACT This chapter proposes innovative features of future industrial organizations in order to provide them with the capabilities to manage high levels …

Tools That Drive Innovation: The Role of Information Systems in Innovative Organizations

Jason G. Caudill Carson-Newman College, USA ABSTRACT The purpose of this chapter is to examine computer technology as a tool to support innovation and innovative processes. The primary problem that …

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