User:Peng.po/sandbox

Bryan (Po-Yu) Peng, ENGW 3304, Dr. Amy Carleton

Week 2: Article Review

Leveraged Buyout (LBO)

I reviewed the Leveraged buyout article on Wikipedia. Parts of the article were relevant, while other parts did not fit into the overall scope. The article had several sections, including 1) Characteristics, 2) History of Private Equity/LBOs, 3) Management buyouts, 4) Secondary/tertiary buyouts, and 5) Failures.

I liked the first section, "Characteristics," because it did a good job of breaking down why companies pursue LBOs, how the process works (qualitatively), and the kind of financing used in LBOs. It can be assumed that the article was written by a knowledgeable individual, as the subject of LBOs is quite technical and requires a deep level of finance knowledge. It is a well-written and neutral article, and the article does not portray any opinions toward one side. While I did like the inclusion of the section regarding "history of private equity/LBOs," it felt like some of the sections were just mushed together and not cohesively written. Though it followed a timeline and was chronologically ordered, it was hard to follow. I would recommend discussing the major players in each era, specific LBOs, and then whether or not they succeeded (in that order).

One viewpoint that is underrepresented is the "math" (quantitative) behind LBOs. I would have liked to see a discussion on how profits from LBOs are generated, and how private equity firms pursue exit strategies within a few years that allow them to "cash out" and profit off the LBO. The math behind it is likely too dense for Wikipedia, but I believe that it is important to include it as it is the component that most people (including myself) struggle with when calculating the proceeds from an LBO. Furthermore, the "secondary/tertiary buyouts" and "failures" sections should have been expanded, and I did not particularly understand what the contributor was trying to illustrate with those sections.

The citations generally work and the links back up the claims made in the article, although a lot of the content in the article was based on historical events, so not much could be misstated there. Furthermore, there are few places in the article where an opinion could be inserted; therefore, while most facts are correct (by nature of LBOs being a quantitative topic), not every fact is backed up by a reliable source. Most of the information likely comes from online sites such as Investopedia or finance textbooks; while these may be neutral sources, relying on online, non-scholarly journals may lead to false facts being stated, so contributors must be careful when relying on those sites. Information is not out of date, but as discussed above, certain content can be added. Next, there is limited dialogue within the "Talk" page; however, one individual brought up that the issue of "general utility" and social problems (that stem from LBOs) should be discussed in the article. The article is rated as B-class and of "mid-importance" to WikiProject Business and WikiProject Finance. It is within the scope of WikiProject Private Equity but has not been rated by that project.

Week 3: Citations

As part of the short WikiEd assignments, I made two edits, one each to the following articles: Carmel-by-the-Sea, California and Thousand Oaks, California.

• Carmel by the Sea: First sentence under the "History" section, Citation #14
• Thousand Oaks: Last citation in first paragraph in the "Climate" section, Citation #96

This week's assignment satisfies the "Add to Article" part of the rubric, as I chose to do the second method. I can provide proof of citations if needed.

Week 4: Possible Topics for Unit 2

1. Automotive industry in the United States - There is a lot of discussion of content without citation/appropriate sources, as well as lack of content given the extensive history of the auto industry in the U.S.
2. Electric vehicles - Very extensive article with lots of info, there is discussion on separating it into 2 different articles or cutting down on content to make it easier to read
3. Shared mobility - Lack of discussion; there isn't much content on future usage given autonomous vehicles or expansion of Uber/Lyft to capture the AV potential
4. Autonomous car - Some discussion on over/underrepresentation of information, including discussions on automation and the lack of citations
5. Effects of the car on society - Needs new format, photos, and lack of opinion

Given that my literature review/interests touch upon most of these topics, I would likely choose to improve some, if not all, of these articles in order to fully utilize what I have already researched.

Week 5: Intro to Unit 2

I have selected the "Automotive industry" article to improve.

Contributions to selected article ("Automotive industry"): I plan on adding a section on the future of cars and the current research/innovation into developing technology. I will discuss the technology surrounding the autonomous car, both the advantages and disadvantages of autonomous technology, and use statistics that have been gathered from different sources. I chose this article to update, because many other articles around this space have already been updated, but this one has not been.

Possible sources that will be used for this assignment:

Currently all sources used will be from McKinsey as I continue to work on finding additional sources with different perspectives. However, these topics provide an outline of what I would like to cover in this assignment.

• How shared mobility will change the automotive industry
• Self-driving technology
• Cracks in the ridesharing market
• Dynamics in the global electric vehicle market
• How carmarkers can compete for the connected consumer
• Autonomous-driving disruption: technology use and opportunities

What is missing in the current article?

There is very little information regarding the "automotive revolution," which refers to how there is massive investments into researching autonomous technology and the potential impacts from it. Furthermore, electric vehicles continue to inject its way into the market and there are massive consequences from this. Lastly, there needs to be specific content regarding current technology surrounding cars.

Weeks 6-10: Final Draft of Improved Article

Note to Dr. Carleton: I have selected the "Automotive industry" article to improve. This is the final version that I had posted to mainspace (before it was edited).

Trends transforming the automotive industry

The automotive industry is currently seeing changes in the following areas: autonomous vehicles, shared mobility, connectivity, and electrification (including electric vehicles). For example, BlackRock believes that the two main changes in the near future are increases in usage of advanced driver assistance systems (ADAS) in new models and in adoption of electric vehicles.

Significant growth in adoption over recent years

• Autonomous car: In 2016, only 1% of all vehicles sold had very basic levels of autonomous-driving technology. In 2017, Eight of the top ten original equipment manufacturers have stated that they will implement autonomous technology at deep levels by 2025.
• Shared mobility: By mid-2017, ridesharing start-ups had generated investments of up to $47 billion. However, there is room to grow, as only 1% of all vehicle miles traveled stem from shared mobility.
• Connectivity: Only 12% of cars today come with connectivity; however, most premium cars have seen an increase in infotainment systems, which allow data to be sent to third parties and for cars to receive significant improvement in connectivity.
• Electrification: The automotive industry believes that half of all new vehicle models by 2021 will have electrified-vehicle powertrains (xEVs). Less than 5% of all vehicles sold in 2016 had xEV components. However, the main inhibitor in progress is that the cost of lithium-ion batteries must be reduced by 25-40%. It is still too expensive for mass-market adoption, but parity in cost of ownership between internal combustion engine (ICE) vehicles and electric vehicles is likely in the near term.

Scenarios of technology implementation

According to McKinsey, there are four main scenarios with different levels of technology integration that result in different revenue growth for traditional sources by 2030. Each scenario examines the growth in four areas: autonomous driving, shared mobility, connectivity, and electrification.

1. Stalled development: There will still be standard growth in industry revenue of about 2.5-3.0% annually, but the four technologies listed above will have minimal impact on the industry.
2. Gradual evolution of traditional mobility: All four areas will impact the industry, but not by a lot.
3. Disruption to personal mobility: All four areas will impact the industry more than the previous two scenarios, and industry revenue may increase between 4-5% per year.
4. City-driven acceleration: This scenario will lead to 5-6% growth annually in industry revenue, and all four technologies will impact the industry significantly.

While the above discusses how the technologies help generate traditional revenues for the industry, it does not discuss the revenue the technologies can generate by themselves. The potential for growth varies widely. For example, there were almost no revenues in autonomous driving in 2016, but by 2030, there could be up to 99% variance in growth across estimates. The same is true for shared mobility and sales for fuel-cell electric vehicles and battery EVs. These two rapidly-growing technologies could see variances of 80-90% and see increases in revenue of 35 times and 29 times their current revenues, respectively.

Autonomous vehicles

One of the major developments in the automotive industry revolves around the autonomous car. An autonomous vehicle is able to successfully navigate without direct human intervention. There is significant investment by various companies into autonomous technology. For example, since 2009, Alphabet's Waymo has invested over $1.1 billion into autonomous development. However, Level 5 (full) automation has not yet been reached. Level 5 automation would only occur when a car does not even require a human to be inside the vehicle, and can move from start to finish without any human assistance. Currently, Waymo is utilizing a fleet of Chrysler Pacifica Hybrids to test automation.

Features of an autonomous driving system

An autonomous driving system combines many different facets as it aims to remove the need for any human intervention. Elements include actuation, cloud, perception analysis, drive control, decision making, localization and mapping, analytics, operating system, hardware, and sensors. The integration of advanced driver-assistance systems is extremely important as self-driving cars continue to be developed. ADAS include sensors, mapping, and processors. Sensors are storage points that receive external data, such as the distance between the car itself and other cars, as well as distance to the edges of traffic lanes. Mapping allows the vehicle to process geological and infrastructure information, which is needed for a car to navigate safely to its destination. Lastly, processors are the mechanisms with which a vehicle makes a decision; for example, processors allow a self-driving car to decide whether to change its speed or traffic lanes in any given scenario.

Current issues

Currently, the industry is seeking solutions to three big issues: perception, mapping, and localization. Each solution has two popular options. To develop perception, engineers are using either radar, sonar, and cameras or lidar augmentation. The combination of radar, sonar, and cameras requires less processing power but does not assess the vehicle's surroundings with significant detail. lidar augmentation is more useful in urban environments, as they contain more traffic; however, lidar uses more processing and data computation. For mapping, autonomous vehicle engineers are looking at either very detailed, high-definition maps or feature mapping. Granular maps with excruciating detail will involve three-dimensional pictures with 360-degree information, capturing any and every piece of information around it in order to build a highly detailed data environment for the vehicle to operate in. Feature mapping will only look at certain features on a given road, such as lane markings, signs, and traffic lights. Localization is needed so that a vehicle can know exactly where it is at any given time. The first option is HD mapping, which allows the car to perceive its location based on existing, high-detailed maps. The second option is to rely on GPS, vehicle's sensors, and onboard cameras to put together the vehicle's location. The first approach is safer and has a higher confidence interval for accuracy, but it will be difficult to have HD mapping in all areas, especially ones that are rural or hard to map.

The adaptation of ADAS to different navigation scenarios is also likely to be challenging. Given that weather is such a critical variable when driving, engineers will need to determine how to apply ADAS in such scenarios. Furthermore, unusual scenarios, such as a four-way stop sign, roundabouts, or random objects on the road, will likely be tricky to engineer. For example, in a constantly busy four-way intersection which all have stop signs, a vehicle could just end up sitting there for several minutes until it is clear, which would significantly delay the driving time from start to finish. On March 18, 2018, a Uber self-driving vehicle struck a 49-year woman in Tempe, Arizona, causing her death. Since the incident, Uber has placed all autonomous car testing on hold.

Shared mobility and purpose-built vehicles

Shared mobility, or the concept of purpose-built, rather than private, individually-owned, vehicles serving the needs of different individuals at once, is beginning to gain traction in the industry. The rising popularity of ride-sharing applications, such as UberPOOL and Lyft Line, illustrates this phenomenon. In December 2013, Uber and Lyft combined for only 30 million vehicle miles traveled (VMT) monthly, but by December 2016, they had reached over 500 million VMT per month. Despite this, ridesharing comprised of only 1% of all VMT traveled in 2016 and has room to grow. Meanwhile, purpose-built vehicles will take shared mobility to a whole new level. McKinsey's recent research shows that improved interior design in purpose-built vehicles may improve rider experiences. Different vehicle layouts could be used for different riders, such as shoppers, families, commuters, or friends.

Shoppers

75% of all ridesharing passengers travel with personal possessions, which necessitate the need for storage. Shoppers carry significant amounts of bags and it will be important for such purpose-built vehicles to include lots of interior space. Foldable seats could allow for even very large bags to fit inside the vehicle instead of having to use the trunk. There would be fewer seats, perhaps just one or two for shoppers. Construction of specific bag holders could be necessary for fragile items. Furthermore, as deliveries (such as UberEATS) for perishable items become increasingly popular, waterproof containers may be necessary in order prevent spillage or any damage to the interior of a vehicle.

Families

McKinsey's research showed that parents or babysitters were hesitant to use ridesharing services, due to the confusion regarding available car seats, safety, and cleanliness. This creates an opportunity for ridesharing to capture a segment that is currently not using shared vehicles as significantly as other demographic segments. A very important feature is built-in car seats and child booster seats. These seats should be in between two normal seats for parents, family members, or guardians, in order to provide a comfortable ride for all members of the family.

Commuters

13 percent of underserved United States vehicle miles traveled belong to commuters. These commuters likely use shared vehicles because they do not want to cram into public transportation. Therefore, it is important to create space in between commuters, as currently, normal vehicles would have strangers sitting next to one another at very early or late hours, causing unnecessary conflicts and fuss. McKinsey believes that "pods," or adjustable/movable seats (that also are safe for travel in a moving vehicle) and potential sliding doors within each pod would be popular among commuters. Built-in WiFi and conference call functionalities, as well as the ability to control lighting and sound would allow for work to and from work. Many tech companies in the Silicon Valley currently utilize buses that have plugs and WiFi to allow employees to work while commuting.

Friends

It may be difficult for friend groups that socialize into the late hours to find vehicles that could bring everyone home at the same time. Shared vehicles could allow for seats where passengers face one another, instead of right next to each other and in front/behind others. This allows for increased socializing to/from events, such as sporting events or concerts.

Integration of software and electronics into the automobile

Currently, software comprises of 10 percent of overall vehicle content; by 2030, this percentage is expected to grow to 30 percent. In 2010, the number of source lines of code (SLOC) in an average vehicle was 10 million SLOC; by 2016, the number of SLOC had grown to 150 million lines. The addition of so many lines have added confusion to vehicles, leading to vehicle recalls; therefore, software companies and other technology start-ups are moving up the value chain as tier-one suppliers in order to develop operating systems that integrate their technology into the car of the future. Broadly, examples of software innovation include connectivity, autonomous driving, mobility, and electrification. In the coming decade, software and electronics will serve as differentiation between vehicles, rather than hardware (which was the case in the past). Software developers will look to include intelligent safety features, advanced driver assistance systems, connectivity solutions, and much more. The vehicle will become a service-oriented architecture (SOA), which refer to software design that is linked via application components and through a network.

Layers of an automobile

• Existing layers: Currently, the vehicle itself serves as the foundation, with limited additions. These limited additions include sensors, actuators, and power components that are closely added on due to safety considerations. The traditional electronic hardware, other human-machine interfaces, such as touchscreen navigation or the ability to play music/radio while driving, as well as some Bluetooth connectivity.
• Modified layers: There will be a significant increase in applications, including safety features, infotainment features (referred to as plug and play), and other middleware/operating systems that will differentiate between cars.
• New layers: The biggest addition to cars in the future should be its cloud platform and artificial intelligence/advanced analytics. The cloud platform will combine in-vehicle data with external data, while AI will allow for real-time decisions and, in the future, autonomous driving. New sensors and applications will add significant amounts of data, which companies need to manage effectively because the main concern of driving is still safety. Cybersecurity will also need to increase as attacks need to be anticipated/detected and avoided.

Sensor function ratings

McKinsey believes that the next two to three generations of new vehicles will yield a significant number of new sensors. This will be done for research purposes, in an effort to ensure safety redundancies occur. As results come back, the next batch of new vehicles will likely see less sensors, as the additions will be more specific in order to lower the cost for consumers. Introduction of lidar technology will be necessary for object identification and localization purposes, but a mix of radar and camera will be more likely in the next decade. According to research, Level 4 automation (Level 5 is full automation) necessitates 4-5 lidar sensors to guarantee visibility and passenger safety. While usage of lidar is useful, it is not perfect for all issues. In fact, the only mix of sensors that results in good function ratings for the many issues surrounding vehicle automation is one consisting of radar and camera.

The usage of lidar and camera will also result in improved functionality, but it is expensive and will take time to be produced and placed into mass-market cars. On the other hand, ultrasonic sensors is cost-friendly but does not cover visibility into far distances nor lane tracking. The usage and number of sensors will likely depend on regulation, the connectivity between sensors, and the technical ability of sensors. Examples of use cases for sensors include motion sensors for heart rate or drowsiness of the driver. In order to lower cost, engineers would like to lower the number of sensors, but this will take time because advanced machine learning is necessary in order for sensors to continually improve in its functionality.

Implications

• Separation of process of creating new vehicles versus creating new vehicle functions: It may be important to create and implement new features on vehicles without consideration of how vehicles, in general, are being created (in terms of new models. It is more important that companies develop a process in which to create new software or updating existing units. Companies may also have to shift to a "horizontal" setup of different software applications., instead of a dependency and "vertical" structure that it is currently in.
• Illustration of the value add for new software in the car: Consumers may be hesitant to add so many new features without understanding the helpfulness and implications that arise from their addition. Original equipment manufacturers (OEM) must demonstrate the ability to control information as well as showing how they can help the consumer via the addition of new sensors.
• Defined price tag for software: Most software installed in cars come with the purchase of the vehicle itself, which means OEMs do not need to value the software. However, as software applications continue to grow, it will be important for companies to understand how to evaluate the worth of their applications. Furthermore, this allows companies to monetize these automotive features and remain competitive. Business models need to be developed so that companies can capture market share.

Review Comments

*To class: Please feel free to leave comments in this section*

Jared

• Everything is relevant and provides new information that's relevant to the modern age.
• Article is completely unbiased, doesn't seem to be any opinionated text
• No citations as of yet, but information seems like it's coming from reputable sources
• Great relevant article, just needs citations to back it up. Good luck! - JaredR95

Rachel

• Is everything in the article relevant to the article topic? Is there anything that distracted you?

Everything in the article is relevant to the article topic. I liked how you included updated information regarding the current state of the automotive industry.

• Is the article neutral? Are there any claims, or frames, that appear heavily biased toward a particular position?

The article is neutral without any biases toward a particular position.

• Are there viewpoints that are overrepresented, or underrepresented?

No, different viewpoints are represented equally. In particular, the discussion about the different vehicle layouts for different audiences showcases the fair representation in this article.

• Check the citations. Do the links work? Does the source support the claims in the article?

No citations yet.

• Is each fact supported by an appropriate, reliable reference? Where does the information come from? Are these neutral sources? If biased, is that bias noted?

No citations yet.

• Is any information out of date? Is anything missing that should be added? The information seems up to date.

-Rachelhannahlee

Sagar

• Everything is relevant and provides new information that's relevant to the modern age.
• Overall article is unbiased, doesn't seem to be any opinionated text except, where you say "lithium-ion batteries must be reduced by 25-40%" seems to be your personal view because you didn't mentioned where that percent number came from. Or you can rephrase it and also remove "must be".
• No citations as of yet, but information seems like it's coming from McKinsey as mentioned by you, which is a credible source. However, a scholarly source is even more credible because it is reviewed and verified by an independent expert.
• Great article with a lot of impact on the original page with new information, just take care of the citations.

Dickson

• I see that you are very enthusiastic about your subject! Due to the complexity, there are some minor formatting issues (inconsistent section style like with underlining) and grammar issues (Eight->eight, etc.) that I'm sure you or another future editor will pick up, as well as topics that maybe should be linked to another article (i.e. LIDAR).
• Relevance - Your headings are all relevant to the present and future of the auto industry.
• Neutrality - Yes, the content is very neutral and not opinionated.
• Over/underrepresentation - The original Wikipedia article is actually underrepresented in future trends, which your content will be helpful. You may have too much detail that may be unnecessary or summarized, such as in "Shared mobility," or that may be better in its own article.
• Citations - While this is a draft, I assume that you just haven't added them in yet.
• Reference quality - Same issue with citations, although referenced studies like with McKinsey seem reputable.
• Outdated/missing - This information is very comprehensive (sometimes too much), modern, and relevant.
• Overall, your content is very good, but I would work on removing or summarizing rather than adding. Although the detail is good, it is sometimes far too much and more similar to a report than an encyclopedia entry. However, it may be too big of a task for yourself, hence the existence of the Wikipedia community. Good work! - Dickson He