这是我在芝加哥大学华文课中的一个 mini project。希望与大家分享！照片是从网上下载的，也是关于我小时候住的Ghim Moh 熟食中心。
Screenshot of Arcstone's website
Original article by Terence Lee, taken from Tech In Asia
“Competition is for losers,” declared PayPal founder Peter Thiel in a Stanford lecture about startups. He elaborated on the theme in his book Zero to One, positing that startups should aim to monopolize a niche market first before expanding outwards.
Arcstone is following that prescription to a T. The Singapore-based startup is building a truly modular and customizable analytics platform for manufacturers. Think of it as a Google Analytics or Mixpanel for factories – but instead of tracking website visitors, Arcstone gives users an overview of what is happening on the production floor.
The company was founded by Willson Deng, an American software engineer who graduated with an industrial engineering degree from UC Berkeley and a master’s from INSEAD business school. Manufacturing bores most people, but he geeks out over it. He worked with Boeing in their manufacturing plant, and had stints at everything from smart meters to wineries to gourmet chocolates, all on the manufacturing side. He also worked as an engineer at Bioproduction, a startup that did production simulations for pharmaceutical companies.
“I’ve always found it fascinating: the idea of being able to walk into a factory and see everything in motion and see things moving along. It’s always been a fascination to me ever since I was young. If you watch How It’s Made on Discovery Channel, it’s been an inspiration for many of the production guys out there.”
Here’s a video of the show, zooming in on how drill bits are made. It actually has over 700,000 views, not bad considering it’s not a cat video: (See YouTube Video here).
The fact that many more people care about eating sausages than how they’re made is an advantage to Deng. His combination of domain expertise in manufacturing and engineering lets him spot opportunities others miss. Better, it allows him to act on the idea. Manufacturers won’t even listen to your pitch if you don’t have experience in production. That raises the barrier to entry for Arcstone’s competitors. Indeed, when Deng asked if I know of any other tech startup in the manufacturing space in Singapore, I drew a blank.
Building smart factories
Arcstone hasn’t raised any funding from investors, and it probably doesn’t need to do so to be a sustainable business (nonetheless it is raising a seed round to fund product development and sales). It has already secured six paying clients and generated six figures in revenue since it started in April 2013. Some customers include robotics and automation tech firm ABB, as well as Phoon Huat, a Singapore-based manufacturer of baking ingredients.
But what makes Arcstone special? While mom-and-pop stores have simple processes when making their goods, blind spots increase as they get bigger and production lines rise in complexity. Arcstone lowers the cost of monitoring manufacturing plants and increases the speed at which users can deal with changing situations.
For instance, it tells manufacturers which production line to fit in a rushed order, and where to pull people from to work on an urgent task. It alerts workers which line to move to without needing supervisors to shout orders, and the manufacturer’s clients check at which stage the production process is at. It automates scheduling of workers, notifies the maintenance crew when a machine breaks down, and sounds the alarms to supervisors just as things are getting delayed.
For many manufacturers today, error handling is often retroactive and resembles a firefighting situation. “You don’t hear about the news until the end of the day when people are clocking out and then they tell you something has gone wrong. And by then a lot of time has been wasted,” says Deng.
Arcstone isn’t the first company to focus on optimizing manufacturing processes, but Deng believes it has an edge. Some companies focus solely on optimization and analytics. This means they can’t prevent what’s called a “garbage-in, garbage-out” situation in which flawed data renders any analysis impossible.
The startup solves this by providing an end-to-end service: it takes care of data collection in addition to parsing the data. It hooks up to production floor machines to gather information from them, and combines that with data from kiosks (simple tablets) – keyed in manually by workers. The data involves manual processes that aren’t captured through any other means.
According to Deng, another problem with competing software is that they’re not built from the ground up to be flexible, because most features are hard-coded. That means the software needs to be reconfigured each time by a coder and analyst for every new client. That adds up to hefty consulting fees not just when a client acquires a new system, but over time whenever its requirements change because the system needs to be reconfigured again and again.
“[Our software] is modular in a way […] we don’t have to recode things. When we go from industry to industry we just flip those switches in the backend. Again, no code needs to apply, but an analyst we send out will just configure that and deploy it right away,” says Deng. That means customers can pick the features they need – perhaps they just want the data monitoring component – and just pay for those.
Productivity at any cost
Arcstone is in a good position right now, having made revenue with a small team of seven despite not securing any seed funding. But when it does raise money, it plans to invest the cash to develop ways to integrate its systems closer with machinery. That means going to original equipment manufacturers (OEMs) that build the production facilities and integrate directly with the machines, instead of going to factories and figuring out retroactively how the processes work and how to integrate the software.
Working with OEMs also allows Arcstone to create a better way for machines to communicate with its system. Right now, different machines spit out data in different formats, some in a user-friendlier way than others. So instead of having to clean the data each time they’re collected, Arcstone and the OEMs can make sure the data comes out being machine-readable.
Given that Deng is an American, it’s refreshing though not surprising that he has chosen to focus on the Singapore and Southeast Asian markets for expansion. According to him, manufacturing plants in Asia are relatively less efficient compared to their European or American counterparts, and therefore in greater need for improvement. Besides, while China may be the world’s manufacturing hub, rising labor costs there has meant that a lot of facilities are shifting to Southeast Asia.
While Singapore is not known for low wages, it could be a lucrative market, given that it is a high-end manufacturing center. There’s now also a state-mandated push toward greater productivity and less reliance on cheap labor across all industries. The state is even giving money to companies to upgrade their systems in the name of productivity, and that’s an opportunity for the startup.
Arcstone is essentially enabling smaller manufacturers to invest in optimization systems by lowering costs. Its clients range from small and medium enterprises to multinational corporations. It also caters to firms that want to take advantage of globalization to win contracts outside local markets.
“If you look at one of the biggest reasons why the manufacturing plants around Southeast Asia, especially local ones that are being started, can’t get outside the local level is that there isn’t enough quality control and management on the products such that they can expand to the US and Europe where there are more stringent quality concerns,” says Deng.
“Yeah, it may be that labor is cheap, but if you don’t have a system like ours that is able to monitor, track, and provide the quality reference that’s needed, you can’t sell to the other markets. Even if you have low costs, you’re not solving everything.”
I'm proud to have worked with Willson and the team at Arcstone. My experience working there was phenomenal. I learned so much, and really admire the team for their dynamism and go-getter attitudes.
Written by Nouriel Roubini
Reposted from Project Syndicate
NEW YORK – Monetary policy has become increasingly unconventional in the last six years, with central banks implementing zero-interest-rate policies, quantitative easing, credit easing, forward guidance, and unlimited exchange-rate intervention. But now we have come to the most unconventional policy tool of them all: negative nominal interest rates.
Such rates currently prevail in the eurozone, Switzerland, Denmark, and Sweden. And it is not just short-term policy rates that are now negative in nominal terms: about $3 trillion of assets in Europe and Japan, at maturities as long as ten years (in the case of Swiss government bonds), now have negative interest rates.
At first blush, this seems absurd: Why would anyone want to lend money for a negative nominal return when they could simply hold on to the cash and at least not lose in nominal terms?
In fact, investors have long accepted real (inflation-adjusted) negative returns. When you hold a checking or current account in your bank at a zero interest rate – as most people do in advanced economies – the real return is negative (the nominal zero return minus inflation): a year from now, your cash balances buy you less goods than they do today. And if you consider the fees that many banks impose on these accounts, the effective nominal return was already negative even before central banks went for negative nominal rates.
In other words, negative nominal rates merely make your return more negative than it already was. Investors accept negative returns for the convenience of holding cash balances, so, in a sense, there is nothing new about negative nominal interest rates.
Moreover, if deflation were to become entrenched in the eurozone and other parts of the world, a negative nominal return could be associated with a positive real return. That has been the story for the last 20 years in Japan, owing to persistent deflation and near-zero interest rates on many assets.
One still might think that it makes sense to hold cash directly, rather than holding an asset with a negative return. But holding cash can be risky, as Greek savers, worried about the safety of their bank deposits, learned after stuffing it into their mattresses and walls: the number of armed home robberies rose sharply, and some cash was devoured by rodents. So, if you include the costs of holding cash safely – and include the benefits of check writing – it makes sense to accept a negative return.
Beyond retail savers, banks that are holding cash in excess of required reserves have no choice but to accept the negative interest rates that central banks impose; indeed, they could not hold, manage, and transfer those excess reserves if they were held as cash, rather than in a negative-yielding account with the central bank. Of course, this is true only so long as the nominal interest rate is not too negative; otherwise, switching to cash – despite the storage and safety costs – starts to make more sense.
But why would investors accept a negative nominal return for three, five, or even ten years? In Switzerland and Denmark, investors want exposure to a currency that is expected to appreciate in nominal terms. If you were holding Swiss franc assets at a negative nominal return right before its central bank abandoned its euro peg in mid-January, you could have made a 20% return overnight; a negative nominal return is a small price to pay for a large capital gain.
And yet negative bonds yields are also occurring in countries and regions where the currency is depreciating and likely to depreciate further, including Germany, other parts of the eurozone core, and Japan. So, why are investors holding such assets?
Many long-term investors, like insurance companies and pension funds, have no alternative, as they are required to hold safer bonds. Of course, negative returns make their balance sheets shakier: a defined-benefit pension plan needs positive returns to break even, and when most of its assets yield a negative nominal return, such results become increasingly difficult to achieve. But, given such investors' long-term liabilities (claims and benefits), their mandate is to invest mostly in bonds, which are less risky than stocks or other volatile assets. Even if their nominal returns are negative, they must defer to safety.
Moreover, in a “risk-off" environment, when investors are risk-averse or when equities and other risky assets are subject to market and/or credit uncertainty, it may be better to hold negative-yielding bonds than riskier and more volatile assets.
Over time, of course, negative nominal and real returns may lead savers to save less and spend more. And that is precisely the goal of negative interest rates: In a world where supply outstrips demand and too much saving chases too few productive investments, the equilibrium interest rate is low, if not negative. Indeed, if the advanced economies were to suffer from secular stagnation, a world with negative interest rates on both short- and long-term bonds could become the new normal.
To avoid that, central banks and fiscal authorities need to pursue policies to jump-start growth and induce positive inflation. Paradoxically, that implies a period of negative interest rates to induce savers to save less and spend more. But it also requires fiscal stimulus, especially public investment in productive infrastructure projects, which yield higher returns than the bonds used to finance them. The longer such policies are postponed, the longer we may inhabit the inverted world of negative nominal interest rates.
Read more at http://www.project-syndicate.org/commentary/negative-nominal-interest-rates-by-nouriel-roubini-2015-02#5Gm1QxZEoYMyLXD7.99
Re-posted from Project Syndicate. Written by Raghuram Rajan.
NEW DELHI – Two fundamental beliefs have driven economic policy around the world in recent years. The first is that the world suffers from a shortage of aggregate demand relative to supply; the second is that monetary and fiscal stimulus will close the gap.
Is it possible that the diagnosis is right, but that the remedy is wrong? That would explain why we have made little headway so far in restoring growth to pre-crisis levels. And it would also indicate that we must rethink our remedies.
High levels of involuntary unemployment throughout the advanced economies suggest that demand lags behind potential supply. While unemployment is significantly higher in sectors that were booming before the crisis, such as construction in the United States, it is more widespread, underpinning the view that greater demand is necessary to restore full employment.
Policymakers initially resorted to government spending and low interest rates to boost demand. As government debt has ballooned and policy interest rates have hit rock bottom, central banks have focused on increasingly innovative policy to boost demand. Yet growth continues to be painfully slow. Why?
What if the problem is the assumption that all demand is created equal? We know that pre-crisis demand was boosted by massive amounts of borrowing. When borrowing becomes easier, it is not the well-to-do, whose spending is not constrained by their incomes, who increase their consumption; rather, the increase comes from poorer and younger families whose needs and dreams far outpace their incomes. Their needs can be different from those of the rich.
Moreover, the goods that are easiest to buy are those that are easy to post as collateral – houses and cars, rather than perishables. And rising house prices in some regions make it easier to borrow even more to spend on other daily needs such as diapers and baby food.
The point is that debt-fueled demand emanates from particular households in particular regions for particular goods. While it catalyzes a more generalized demand – the elderly plumber who works longer hours in the boom spends more on his stamp collection – it is not unreasonable to believe that much of debt-fueled demand is more focused. So, as lending dries up, borrowing households can no longer spend, and demand for certain goods changes disproportionately, especially in areas that boomed earlier.
Of course, the effects spread through the economy – as demand for cars falls, demand for steel also falls, and steel workers are laid off. But unemployment is most pronounced in the construction and automobile sectors, or in regions where house prices rose particularly rapidly.
It is easy to see why a general stimulus to demand, such as a cut in payroll taxes, may be ineffective in restoring the economy to full employment. The general stimulus goes to everyone, not just the former borrowers. And everyone’s spending patterns differ – the older, wealthier household buys jewelry from Tiffany, rather than a car from General Motors. And even the former borrowers are unlikely to use their stimulus money to pay for more housing – they have soured on the dreams that housing held out.
Indeed, because the pattern of demand that is expressible has shifted with the change in access to borrowing, the pace at which the economy can grow without inflation may also fall. With too many construction workers and too few jewelers, greater demand may result in higher jewelry prices rather than more output.
Put differently, the bust that follows years of a debt-fueled boom leaves behind an economy that supplies too much of the wrong kind of good relative to the changed demand. Unlike a normal cyclical recession, in which demand falls across the board and recovery requires merely rehiring laid-off workers to resume their old jobs, economic recovery following a lending bust typically requires workers to move across industries and to new locations.
There is thus a subtle but important difference between my debt-driven demand view and the neo-Keynesian explanation that deleveraging (saving by chastened borrowers) or debt overhang (the inability of debt-laden borrowers to spend) is responsible for slow post-crisis growth. Both views accept that the central source of weak aggregate demand is the disappearance of demand from former borrowers. But they differ on solutions.
The neo-Keynesian economist wants to boost demand generally. But if we believe that debt-driven demand is different, demand stimulus will at best be a palliative. Writing down former borrowers’ debt may be slightly more effective in producing the old pattern of demand, but it will probably not restore it to the pre-crisis level. In any case, do we really want the former borrowers to borrow themselves into trouble again?
The only sustainable solution is to allow the supply side to adjust to more normal and sustainable sources of demand – to ease the way for construction workers and autoworkers to retrain for faster-growing industries. The worst thing that governments can do is to stand in the way by propping up unviable firms or by sustaining demand in unviable industries through easy credit.
Supply-side adjustments take time, and, after five years of recession, economies have made some headway. But continued misdiagnosis will have lasting effects. The advanced countries will spend decades working off high public-debt loads, while their central banks will have to unwind bloated balance sheets and back off from promises of support that markets have come to rely on.
Frighteningly, the new Japanese government is still trying to deal with the aftermath of the country’s two-decade-old property bust. One can only hope that it will not indulge in more of the kind of spending that already has proven so ineffective – and that has left Japan with the highest debt burden (around 230% of GDP) in the OECD. Unfortunately, history provides little cause for optimism.
Read more at http://www.project-syndicate.org/commentary/boosting-demand-impedes-recovery-by-raghuram-rajan#suvcs8VZkSvKBJRj.99
A Brief History of Quantitative Easing: 6 Years and 3.1 Trillion Dollars Later, How Has the Economy Performed?
In the fall of 2008, the US economy was on the brink of catastrophic financial collapse. With the burst of the subprime housing bubble, reputable financial institutions that held toxic assets were either acquired at a rock-bottom price or had gone into bankruptcy of one kind or another. Financial markets were sent into free-fall, as investors feared that the financial contagion of the collapsing housing market would spread. During a single day of trading on Sept. 29, 2008, $1.2 trillion was erased from the US stock market.
History Repeats Itself
Quantitative easing, or QE, in the US began against this backdrop. Economists who studied the Great Depression, like former Federal Reserve Chairman Ben Bernanke, believed that decisive intervention by the Fed was important for preventing the markets from descending into chaos. Beginning in November 2008, the Federal Reserve began purchasing large amounts of mortgage-backed securities and bank debt.
This morphine-like injection directly alleviated fear regarding the liquidity and value of these securities and also increased confidence regarding banks and other financial institutions that held large quantities of these toxic financial instruments. Instead of employing conventional monetary tools such as targeting interest rates, the extent of the crisis forced the Fed to venture into new territory with QE.
When the Federal Reserve announced the second round of QE in November 2010, the US economy was not directly threatened with financial collapse, but the economy was stagnating. This second round increased the value of reserves, in both Treasuries and other Fed-supported securities, held by corporations. The Fed hoped that increasing these reserves would stimulate spending and borrowing by corporations and consumers. Numerous economists and analysts opposed the second round of QE, citing the risks of an asset bubble or a currency war caused by a depreciating U.S. dollar.
In September 2012, the Federal Reserve began to publicly target employment rates along with its conventional inflationary targets. Then-chairman of the Fed Bernanke announced the third round of QE and promised to maintain these purchases while unemployment and inflation rates were above the threshold levels of 6.5 percent and 2.5 percent, respectively. This amount was eventually increased to $85 billion in December 2012.
The purpose of associating employment rate targets with QE is to provide forward guidance for the economy so that corporations and financial institutions are assured of continued monetary expansion and low interest rates.
The Jury is Still Out
6 years and $3.1 trillion later, how has the economy performed? On the employment front, it appears that the Federal Reserve has met its target. According to the Bureau of Labor Statistics, unemployment fell from 7.8 percent in September 2012 when QE3 was announced to 5.9 percent two years later. Inflation continues to be low and stable at 1.7 percent.
While unemployment and inflation rates are low, GDP growth continues to be sluggish. The World Bank reported that the U.S. economy grew by 1.9 percent in 2013, as compared to 2.8 percent in 2012 and 1.8 percent in 2011. With labor force participation rates at a 35-year low, however, experts have expressed skepticism about the apparent improvement in the labor market.
In addition, evidence of an asset bubble is mounting, albeit in the stock market instead of the housing market. Stock indices reached all-time record highs in November 2014. In fact, the Price-Equity ratio of the S&P 500 has increased by more than 46 percent since October 2011.
As investment manager Warren Buffett warned, the consequences of QE, particularly inflation and asset bubbles, may take decades to play out. In addition, it is nearly impossible to answer whether the American economy would have been better off without quantitative easing. Furthermore, because QE is an unconventional and relatively untried policy tool, there are few case studies or benchmarks to which to compare the effects of the Fed’s QE policy.
When will the moon be clear and bright?
With a cup of wine in my hand, I ask the clear sky.
In the heavens on this night,
I wonder what season it would be?
I'd like to ride the wind to fly home.
Yet I fear the crystal and jade mansions
are much too high and cold for me.
Dancing with my moonlit shadow,
It does not seem like the human world.
The moon rounds the red mansion,
Stoops to silk-pad doors,
Shines upon the sleepless,
Bearing no grudge,
Why does the moon tend to be full when people are apart?
People experience sorrow, joy, separation and reunion,
The moon may be dim or bright, round or crescent shaped,
This imperfection has been going on since the beginning of time.
May we all be blessed with longevity,
Though thousands of miles apart, we are still able to share the beauty of the moon together.
We live in boxes. We sleep in a box shaped bedroom, on a box shaped bed, and wake up via an alarm from a box shaped clock. We enter our box shaped toilet to wash up in a box shaped shower stall before eating our breakfast from a cereal box. After working in a box shaped cubicle and eating from a lunch box, we return home in our box shaped vehicles. We watch our favourite programmes from a TV box set or from a box shaped computer. We sleep on our same old box shaped bed and the cycle repeats.
But the attention that we should pay is not so much towards the boxes we live in, but the restrictive boxes that take away our full purpose in Christ, which LIVES in us. These boxes, in the shape of strongholds and dominating attitudes and mindsets, prevent us from having life-changing breakthroughs.
What are these boxes? Christ addresses them and Satan attacks using them when Jesus was tested in the desert.
1.The box of immediate fulfillment.
And after fasting forty days and forty nights, he was hungry. And the tempter came and said to him, “If you are the Son of God, command these stones to become loaves of bread.” But he answered, “It is written, “‘Man shall not live by bread alone, but by every word that comes from the mouth of God.’” (Matthew 4:2-4 ESV)
Eating is important to sustain our bodies. But should bodily desires dominate our priorities so much so that they must be fulfilled immediately? It is true that many, including myself, have lost the will or are 'incapable' even to wait or delay fulfilling our hunger or thirst, resulting in many problems and sins such as pornography. The perceived need to immediately fulfill our own needs, especially bodily ones, drags us away from God's purpose and into the box that satan has set for us.
From Kaiser Fung, Harvard Business Review Blog
In their best-selling 2013 book Big Data: A Revolution That Will Transform How We Live, Work and Think, authors Viktor Mayer-Schönberger and Kenneth Cukier selected Google Flu Trends (GFT) as the lede of chapter one. They explained how Google’s algorithm mined five years of web logs, containing hundreds of billions of searches, and created a predictive model utilizing 45 search terms that “proved to be a more useful and timely indicator [of flu] than government statistics with their natural reporting lags.”
Unfortunately, no. The first sign of trouble emerged in 2009, shortly after GFT launched, when it completely missed the swine flu pandemic. Last year, Nature reported that Flu Trends overestimated by 50% the peak Christmas season flu of 2012. Last week came the most damning evaluation yet. In Science, a team of Harvard-affiliated researchers published their findings that GFT has over-estimated the prevalence of flu for 100 out of the last 108 weeks; it’s been wrong since August 2011. The Science article further points out that a simplistic forecasting model—a model as basic as one that predicts the temperature by looking at recent-past temperatures—would have forecasted flu better than GFT.
In short, you wouldn’t have needed big data at all to do better than Google Flu Trends. Ouch.
In fact, GFT’s poor track record is hardly a secret to big data and GFT followers like me, and it points to a little bit of a big problem in the big data business that many of us have been discussing: Data validity is being consistently overstated. As the Harvard researchers warn: “The core challenge is that most big data that have received popular attention are not the output of instruments designed to produce valid and reliable data amenable for scientific analysis.”