Using the case below please help create a SWOT analysis. Internal and external environments of BMW entering into the world of autonomous automobiles.

The Ecosystem of Autonomous Driving Today

The idea of cars driving themselves has existed for a few decades, since the early days of Tsukuba Lab in Japan in 1977 and the European EUREKA Prometheus project in 1987. But only recently, with the advances in computer technology, has it become a reality. The 2004, 2005, and 2007 Urban Challenges conducted by the Defense Advanced Research Projects Agency (DARPA) in the U.S. yielded significant advances, with cars eventually completing a 132-mile course successfully as exemplified by the winner of the 2005 DARAP Urban Challenge: Stanford Universitys VW Touareg Stanley.

The domain of autonomous driving promises stunning prospects as well as some key uncertainties. It is at the intersection of large opportunity and the uncertainty of a number of future trends that could affect the domain to take a turn in one direction or another. According to Navigant Research, annual sales of autonomous vehicles could reach nearly 95 million by 2035. Morgan Stanley analysts also believe that self-driving cars will change the auto industry.

At the core of the self-driving car is state-of-the-art microprocessors, i.e., computer chips called Central Processing Units (CPU) or Graphical Processing Units (GPU). GPUs are CPUs that have special capabilities related to processing imagery or graphics. Two major players in the microprocessor technology market are working on the hardware for self-driving carsIntel, maker of CPUs and NVIDIA, maker of GPUs. Recently, through cooperation with these Silicon Valley stars, car manufacturers globally have obtained processing technology that powers critical components to allow them to build self-driving cars. Several companies and research centers are working on an even more powerful type of processorQuantum Computers that will be able to handle massive computational tasks in parallela quality essential for the artificial intelligence needed for autonomous driving. With Google recently joining the effort, the prospect of creating one (quantum computer?) becomes more realistic.

• Self-parking: A car with this feature can park itself without driver intervention. This is primarily a convenience feature for most drivers, but can also aid drivers that are physically impaired. It can help avoiding fender-bender accidents that may increase car insurance costs.

• Lane control: Helps the driver to steer though curving highway roads. This is mainly a security feature that helps drivers to avoid potentially dangerous accidents like the car driving into oncoming traffic or veering off the road.

• Speed control in heavy traffic: This feature goes a bit further by allowing the driver to let the car navigation system accelerate and slow down the vehicle when the car moves in a traffic jam. This adds the driver some relief to an otherwise tiring journey through tough traffic conditions.

• Fully automated car: The highest level of automation is achieved when the car can drive itself in any conditions, including driving through crossroads and crosswalks with or through traffic lights, making turns, changing lanes, keeping distance with other vehicles, and responding to any kind of emergency situations. In this case the driver inputs the destination into the navigation system and allows it to drive. This feature has been widely discussed as the future of mobility. Most drivers would spend their time being entertained, being social, or being productive in their cars.

Fully Automated Cars: The Competitive Landscape

While BMW and Audi have already presented prototypes of fully automated cars, other car manufacturers are developing and testing partial autonomy approaches. Toyota/Lexus are working on the concept of assisted driving. Tesla recently announced that it is already installing navigation hardware on its cars, although its system is not intended to take full control either, but rather provide assistance for the driver to improve safety. GM first invested $500M in ride-hailing company Lyft and then the two companies announced plans to test a fleet of autonomous Chevrolet Bolt electric taxis on the road within a year.

Other players are more skeptical: Volvos head of R&D, Peter Mertens, has been very direct in saying that the prospect of a driver reading a newspaper or answering e-mails while driving is a very, very long term vision. The carmaker is concentrated on safety instead, such as object avoidance and more traditional protection such as material strength. Yet, in a surprising twist, that same year, (which year?) Ubers Founder and Co-CEO Travis Kalanick, started to hire dozens of autonomous auto experts at leading technical institutions, and it was Volvo with its well-established reputation of making some of the safest automobiles on the road, that heeded the call to partner.

Along similar lines, Ford engineer Torsten Wey opined that he does not believe cars will ever be fully autonomous: I doubt we will ever get there, he said. According to Wey there are situations when the cars autopilot is not intelligent enough to make decisions. The human driver does not only consider behavior of his own car, but also takes into account behaviors of others. Experienced drivers can intuitively predict what other cars on the road will do and act accordingly, augmenting the measurable data of the moment with their own experience. For instance, when a driver sees a car in front of them slow down to turn into a restaurant parking lot, the driver can judge that the car will likely not stop right there in the middle of the lane, based on subtle contextual clues and a lifetime of learning. A computerized system, however, does not yet have that intuition and will not acquire it for a long time. Yet earlier, Ford tripled its autonomous vehicle development fleet and accelerated its on-road software and sensor testing.

Clearly, automakers are in an uncomfortable dance of cautioning expectations yet forging ahead full steam. But this diversity of signals, views and approaches between car makers is only the beginning of a complex picture: as a seasoned, technology-savvy strategist, Norbert Riedheim knows that competition may not only come from established players, but also from new entrants into a given market: BMW needs to anticipate.

One of these new entrants is Internet giant Google, which demonstrated its self-driving car in the summer of 2014. The technological program at the heart of the Google car is called Google Chauffeur. It is an example of a truly driverless car that can move itself in a targeted, pre-programmed fashion from point A to point B using advanced sensors that collect and interpret data from the environment. This is enabled by multiple Google technologies, including its Maps navigation technology. Google uses a Toyota-brand vehicle for testing its autonomous driving system, but it is not in a formal joint venture with the firm and could still choose any other automaker as a partner. Being cash-rich, the company could also develop its own car, as has been successfully demonstrated by Tesla.

Alternatively, much like Tesla, Google could cooperate with an established carmaker (in Teslas case it was a design collaboration with Lotus in the UK). Along those lines, the company announced its new self-driving technology development center in Novi, Michigan, in May 2016 and one of the first projects at the new facility will be the self-driving Chrysler Pacifica hybrid minivan, developed in-house.

But given its deep pockets, Google could conceivably also still buy an ailing carmaker, such as Saab, still struggling to recover after its purchase by National Electric Vehicles Sweden (NEVS), which is owned by Hong Kong-based energy company National Modern Energy Holdings. Or it could approach Volkswagen to take over the Seat or Skoda subsidiary, which seem to be duplicating each others offerings in the VW brands family.

To further complicate things, it is not just in the visible corners of the technology world that prominent companies like Google are working on autonomous automobiles and from which sudden advances could emerge. In start-ups, universities, and R&D centers around the world, leading technologists are working on pre-commercial solutions. In early 2013 there were multiple reports about companies and individuals who were working on an affordable self-driving feature. One of them is Professor Paul Newman from Oxford University who works on self-driving technology that utilizes cheap sensors. Also, Intel awarded the top prize in its Gordon E. Moore competition to a Romanian teenager for using artificial intelligence to create a viable model for a low-cost, self-driving car. One company took it a step further and designed a commercial self-driving accessory that can be installed on selected models of compatible cars with sensors mounted on the rooftop. It is a startup called Cruise, which emerged from a Silicon Valley incubator, Y-Combinator, and started accepting pre-orders for it assisted driving system in mid-2014. In March 2016, Cruise was acquired by GM, which appears to be interested in integrating the system into the design of its own cars.

Another critical element of autonomous drivingmapping and location servicesis also flourishing globally, especially in Europe. Nokia Corporations former mapping business, HEREbased in Berlinprovides an open platform for cloud-based maps. HERE is not only the main alternative to Google Maps, but also the market leader in built-in car navigation systems. According to Nokias website, four out of five cars in North America and Europe feature HERE integrated in-dash navigation. Not surprisingly, in August 2015 BMW, Audi, and Daimler announced their acquisition of HERE. These 3 automobile companies will be directly controlling an essential part of the autonomous automobiles value chainmapping and location serviceswhile securing the supply of critical geo-location data in their automobiles.

It would be wrong to limit the ecosystem view to traditional geographies, like Silicon Valley in the U.S., or other entrepreneurial hubs like Berlin in Europe and R&D labs in Japan that have been strong in automotive or IT innovation for decades. A look into the future of the automobile has to take into account developments in Asia. For instance, autonomous taxi startup nuTonomy announced a pilot in Singapore that it could become the first company to operate Level-4 driverless taxis commercially in a city. And, as mentioned, BMW selected Baidu as its partner in the Chinese market when, in the Fall of 2014, it needed a high-resolution GPS system to start testing in Shanghai and Beijing, two of the most demanding, densely populated, and vast automotive markets in the world. And now Baidu claims it is developing its own automated car, but unlike Google, it works on driver assistance and is not a fully self-driving car.

The Chinese market is already the largest and the fastest growing in the world, with 18 million cars sold in 2013, a compound annual growth rate (CAGR) between 2005 and 2012 of 18.1%, and an expected 6.3% average year-over-year growth through 2020 making it a tremendously important market for BMW.

Luckily, BMW made an early, courageous decision to enter the Chinese market, benefiting from the excellent relationships held by a former BMW board member and former government executive in charge of the companys government relations. The effort bore fruit: in 2013 BMW sold 390,713 cars in China, up 20% from a year earlier. This meant that China had officially overtaken the U.S. (375,782 cars sold) as the groups biggest market and had outpaced the overall companys market growth of 13.9 percent.

As Riedheim leans back in his sleek BMW carbon fiber chair, he wonders how this ecosystem might evolve and how should BMW position itself within it? What are some plausible, alternative futures? Having studied disruptive innovation and strategy throughout the years, Riedheim knows that big bets often dont pay off because too many variables in a market forecast change. So, understanding these alternative futures first will help him to craft a strategy that is robust against different market states.