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Steven Grant
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Process management consultant for start-up companies. Focused on establishing standardized operational processes and management disciplines to support rapidly growing small companies.
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  • Strategic Investing Themes: Autonomous Computing For Driver-Assisted Cars

    As noted in our earlier article, autonomous computing is a long term investing trend which will fundamentally change almost everything, nonetheless the question remains: Is there profit in this trend for the home investor? In a previous article the broad landscape of autonomous computing as it may impact various sectors was considered. In this follow-up article the application of autonomous computing for driver-assisted cars will be reviewed. Subsequent articles will continue peering down this ever-widening rabbit hole to identify participants from other industry categories where autonomous computing and autonomous tools are already widely deployed.

    First, a quick definitional review: autonomous computing encompasses a continuum of programmable tools which mimic and enhance human abilities to solve complex problems. Think of computers killing at Jeopardy, assisting in medical diagnoses, flying drones into war zones, and targeting ever more annoying ads at already annoyed online shoppers. The autonomous computing trend percolated over the past 10-20 years and will rapidly evolve over the next ten+ years until it becomes pervasive in everything from surgical instruments to excavators. Today's simple industrial robots will mature rapidly, drop their spot welders, and grab a seat in front of every imaginable complex problem solving and pattern recognition task.

    Numerous programs are underway in the automotive industry and nearly all transportation sectors will benefit from this technology. Google's (NASDAQ:GOOG) driverless-car is one of the most visible efforts, but there are R&D initiatives, prototypes and production implementations across a broad spectrum of manufacturers and designers.

    Despite impressive progress to date, it is difficult to understand the strategic rationale for Google's involvement from an investment perspective. Autonomous vehicles are an emerging multi-trillion dollar global business opportunity, but not an intuitively perfect match for Google's core competencies. The effort seems unlikely to increase Google's long term market value. In addition, there is tremendous competition from companies with greater domain expertise and more natural marketing channels: Ford, General Motors, Continental, BMW, Volkswagen, Audi, Saab and many others all have strong interests and investments in driver-assisted and driverless technologies dating back a decade or more. Perhaps Google is involved in this effort simply to accrue patents which can be used as trading currency in future negotiations with other industrial giants. Google could as easily, and with no less justification, get involved in drug development, chip manufacturing, advertising, operating systems, or mobile computing platforms-all big ideas requiring deep pockets and large quantities of smart people (okay, I still don't get Google's strategy with operating systems and mobile platforms either).

    Google essentially sells programmable structures today, which are one of the highest margin products because you're essentially selling an idea wrapped in a very small capital investment, much like pharma, which sells intellectual property, or Coke, which sells a syrup recipe, and ratings agencies, which sell imprimaturs-much like the church used to sell indulgences.

    As reported by the Detroit News, the head of Google's driverless car initiative confirmed their lack of interest in manufacturing cars. Anthony Levandowski stated: "We don't want to make cars. That's not our interest. All options are open. From giving the technology away to licensing it to working with (suppliers), working with (automakers) building a car with them - everything is open - and we're trying to figure out which paths make the most sense."

    According to an IEEE summary of Google's technology presentation the Google car does not depend on GPS and it does not typically drive a route cold. Detailed digitized maps are used since GPS is not nearly accurate enough and Google engineers drive a route several times to note specific details before the autonomous car is turned loose.

    USA Today reported the technology required for Google's automated Prius adds $150,000 to the car's cost. However, the same article noted the price of the 3D laser vision system (LIDAR) was dropping rapidly. USA Today noted, "German supplier Ibeo will supply lidar systems for an undisclosed automaker in 2014 for about $250 per vehicle." Let's see, $150,000 or $250, who might win this round?

    According to Ibeo the LIDAR system is accurate within 1.5" at just over 200 yards--in any weather. I will spare you the technical description of how LIDAR can work as well as RADAR in rain and snow; suffice it to say this is a useful breakthrough. Built into the system are automatic identification algorithms which can track up to 128 objects simultaneously and classify them as cars, bikes, pedestrians, etc. This allows manufacturers and system integrators to greatly reduce their programming time and bypass some thorny technical issues.

    Ibeo, a very small private company, and Valeo (OTCPK:VLEEY), a $4B market cap auto industry supplier, established a working agreement to jointly develop autonomous systems for the automobile industry. Valeo makes self-parking systems, exterior vision cameras, automated start-stop ignitions, lane change assistants, and automated wiper controls.

    One major reason the technology will come down in cost rapidly is because the technology can be installed and used incrementally by car manufacturers. Consider the following list of technology applications from Ibeo:

    • Autonomous Driving
    • Terrain Mapping
    • Object Tracking
    • Adaptive Cruise Control
    • Blind Spot Monitoring
    • Lane Keeping Assist
    • Traffic Sign Recognition
    • Pedestrian Detection
    • Automatic Emergency Braking
    • Collision Avoidance
    • Power Line Mapping
    • Wireless E-Stop
    • Lane Departure Warning
    • Forward Collision Warning
    • Pre-Crash Collision Mitigation
    • Headlight Control

    Many of these capabilities can be implemented, tested, and refined for simple, but productive applications, way in advance of installing or activating the refinements needed for fully autonomous vehicles. Collision avoidance, blind spot monitoring, lane keeping, headlight control, collision detection, self-parking, automatic braking, adaptive cruise control, and traffic sign recognition are all currently in, or announced, for production vehicles, many using the Ibeo or Valeo technologies. Not test vehicles or limited edition prototypes, but full production vehicles with option packages available to consumers. This incremental approach to the technology allows manufacturers to evaluate market acceptance of various features and build toward autonomous vehicles without requiring exorbitant all-or-nothing R&D expenditures. As the market will be driving acceptance and investment, it will move extremely rapidly-and without changes required in global highway infrastructure or design. Eventually infrastructure enhancements will come naturally, for example: sensors on Do Not Enter signs could automatically warn, or stop, a driver motoring up the off ramp rather than depending on sign recognition technology. At some point all vehicles will have communication protocols continuously making surrounding cars aware of their presence without the need for elaborate vision systems.

    Ultimately consumers will drive the pace of change for autonomous vehicles. Because of the pervasive usage of this technology throughout the military the basic R&D is largely complete. At the world congress for intelligent transport systems in Vienna there were over 600 exhibitors. The technology is already out there, simply waiting for the suppliers to adjust their pricing to a vastly bigger market and for the automobile manufacturers to undertake the last integration steps and recognize the marketing potential.

    Any parent who can afford a car for their teenager will break down the showroom doors to get a car which might help their child avoid a life-changing or life-threatening accident. Once this technology is understood it will be de rigueur with or without Federal or State mandates. Insurance rates will drop steadily as the technology proves itself and safe driver discounts will become much larger safe car discounts. Unlike a seat belt, which can be defeated any number of ways; a collision avoidance system will always be smarter than the dumb driver in front of you on the highway swerving into your lane.

    Disclosure: I have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

    Feb 11 11:03 PM | Link | Comment!
  • Strategic Investing Trends: Autonomous Computing

    Autonomous computing is a long term investing trend which will fundamentally change almost everything, nonetheless the question remains: Is there profit in this trend for the home investor? After all, flying machines also changed everything, and as Warren Buffett likes to joke investors would have been money ahead if someone had shot down the Wright Brother's first flight.

    First, a quick definition: autonomous computing encompasses a continuum of programmable tools which mimic and enhance our ability to solve complex problems. Think of a spreadsheet that solves the problem for you, after setting up the problem for you, after identifying the problem for you. Think of computers killing at Jeopardy, assisting in medical diagnoses, and targeting ever more annoying ads at already annoyed online shoppers. The autonomous computing trend will evolve over the next ten+ years until it becomes pervasive in everything from surgical instruments to automobiles to excavators. Today's simple industrial robots will mature, drop their spot welders, and grab a seat in front of every type of complex problem solving and pattern recognition task.

    Some examples of autonomous computing have been under development for over 10 years and are already being deployed. Audi was granted a license to operate autonomous cars in Nevada in January of 2013, Google received a similar license in May of 2012 and Continental, an automobile supplier, in December, 2012. Trivial, though useful, current examples of autonomous cars include self-parking cars for nervous urban drivers. More profound autonomous vehicle technology may largely eliminate the number of "texting" drivers who end up in the wrong lane or upside down in a ditch. What parent wouldn't want to buy an autonomous vehicle as their teenager's first car with the accelerator set to legal speed limit?

    Caterpillar's MineStar™ system is developing an integrated set of mining tools which includes autonomous mine vehicle tracking. Cat is deploying this technology with Fortescue and began experimenting with autonomous vehicles in the mid-1990's. Rio Tinto is piloting Komatsu's FrontRunner® technology for semi-autonomous explosives loading at their "Mine of the Future" test site. So eventually we'll have autonomous mining systems barking directions at autonomous excavators fighting with an intelligent drilling machine over who gets the parking spot closest to the office. All complex, dangerous, and lucrative physical processes will be automated along these lines. Do we really need people to fly cargo planes, drill for oil, process sewage, mine diamonds, route electrical power or match ask and bid prices?

    It becomes pretty obvious that control centers, probably thousands of miles away, will soon monitor/control (or already do control) physical systems of highly automated, autonomous machines carrying oil around the horn, producing earnings reports, or replacing kidneys in Shanghai.

    Autonomous computing is unlikely to replace human intelligence in the near term, but these tools will greatly extend human capabilities while also pushing technological capabilities forward. The limitation at this point is that all these autonomous systems are using primarily conventional technology (with some specialized chips) with highly unconventional (and grossly expensive) algorithms. Our brains, as slow as they are, are still more advanced general purpose pattern recognition engines than our current chip technology can duplicate cost-effectively. Still, it is only a matter of time.

    The practical deployment of autonomous computing from the R&D labs into production environments over the next ten years will produce winners and losers, just as the deployment of Arpanet and its evolution into the Internet produced winners and losers. Big winners (think Google, Facebook, Twitter, maybe Amazon) and big losers (every company I invested in from 1997 to 2000).

    For autonomous computing to be effective the capabilities of current pattern recognition technologies need to break through current performance barriers. Pattern recognition systems will evolve rapidly in response to the demands of autonomous computing and extend the capabilities of current vision systems, natural language recognition engines, unstructured data analyzers and contextual parsers.

    The technologies used to develop these pattern recognition breakthroughs will enable correlative breakthroughs in expert systems, artificial intelligence systems, neural net training systems and similar tools required for autonomous systems to react to and learn from unexpected changes in their physical or quantitative environment.

    Cloud computing will be pushed, during the near term, because these applications will be incredibly compelling, but sophisticated (read expensive) programmers will be required to create and maintain the early versions of these tools. Eventually most of the programming will be largely initiated and extended by the tools themselves.

    There are a few industries where it seems obvious autonomous machines will introduce disruptive changes.

    Medicine: automated diagnostics, automated surgery, drug interactions, knowledge management, drug development, prototyping and evaluation, remote collaboration.

    Architecture: automated design, automated construction, automated building management.

    Finance/Accounting: purchasing, invoice processing, pricing, reporting, variance analysis, risk management.

    Marketing/Advertising: ever more obnoxious and intrusive individual and life cycle targeting.

    Energy: grid management, monitoring, exploration, drilling, refining, pricing, hedging, arbitrage.

    Machinery (Autos, Ships, Trains, Foundries, Factories, etc): Inventory management, order processing, machine tool management, automated design, global resource routing.

    Brokerage and Capital Management: Investing could become more about identifying strategic trends and less about data analysis. Most of the data analysis or underlying regression algorithms will be massaged by these autonomous computing engines and the human analyst will be left to identify broad themes which are populated with appropriate stocks by the non-human research engine. The quants would be recognized as a rather primitive attempt to apply linear algorithms to an environment of fundamentally hyper-causal linkages. As central banks introduced hyper-causal effects on world economies in the past few years the quants have suffered predictably. The good news is that the stock market, once autonomous market-makers are involved, could decide to return to rational analysis and longer term investing as short term price movements are seen to be largely random-unless inside information is obtained-and the underlying capabilities and decisions within companies could actually drive long term performance. The markets themselves will become entirely emptied of people shepherding day-to-day trades and the current cumbersome system of bond trading will be pulled into the electronic trading system currently used for equities.

    Given that autonomous computing will drive widespread and comprehensive change across many sectors we should begin the process of identifying winners and losers, placing our bets and assessing our investments against this strategic backdrop. Future articles will explore individual programs by specific companies in an effort to find suitable investments.

    Disclosure: I am long CAT.

    Jan 29 10:18 AM | Link | Comment!
  • China Yuchai International (CYD)
    China Yuchai International (NYSE:CYD)
    China Yuchai International’s (CYD) share price increased from under 20 to just over 30 in 2010. EPS increased substantially over the past five years while sales increased just over 20% per year. The growth was not entirely even--2007 showed outsized improvements, 2005 produced some negative numbers. 
    Free cash flow analysis (NYSE:FCF) suggests a fair value today of over a $100 a share if revenue can be expected to increase by at least 10% a year over the next five years. Some analysts project earnings for 2010 of $2.76 and $3.54 for 2011, a 28% increase year-over-year. Others suggest the $3.54 number will be largely achieved in 2010.   Even so, in fairness, there is very little analyst coverage of this stock, which may help explain the potential opportunity in share price. There may be room for some additional appreciation if institutional holders or other buyers confirm the FCF projection of fair value. 
    At $30.70 a share, the TTM P/E is 6.99 versus an average P/E for CYD’s industry group over 27. Assuming earnings of $2.76, the industry P/E suggests a market price just above $74 in 2010 and $95+ by the end of 2011.
    One of the negative factors for CYD, mentioned by the analysts, is that the current quarter EPS growth is less than the prior quarter growth. On the other hand, the prior quarter grew over 1,000% and the current quarter only grew 250%. It’s the right problem to have. 
    CYD trades below book value and appears to be a reasonable target for additional analysis by value investors. CYD is a relatively small company with $1.9B in sales and a market cap of $1.1B. It pays a $.25 dividend with a payout ratio of 10.3% so there is room for considerable future dividend growth. The current ratio is 7.9x and there is little usage of debt despite robust growth.
    The question for CYD becomes more one of competitive positioning rather than value. Their valuation is rock solid, so far, though RINO has taught everyone that Chinese small caps require more than the ordinary level of scrutiny. Their strategy of continuing to move upstream and develop larger trucks with more insourced components and hybrid diesel/electric motors appears to be working. CYD will eventually compete against major international players aggressively pursuing the burgeoning Chinese construction and transportation market. Despite the central government’s efforts to cool down the Chinese economy, there is every sign near term growth will remain strong and the transformation of this largely agricultural economy into an industrial power will require massive amounts of material, money, and trucks. The story for CYD is powerful, if they have the competitive muscle to continue taking share once they are large enough to compete with the major players. At present they represent themselves as owning the largest market share in China. CYD has established joint ventures with Caterpillar and others, which should create both a learning opportunity and the potential opening for future acquisitions.
    From a value investor perspective the increase in stock price throughout 2010 gives some reason to consider CYD carefully before jumping in at a peak, however, unless they stumble, the valuation upside appears interesting.       
    Disclosure: Long CYD.
    Jan 10 1:20 AM | Link | Comment!
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