1. A large stock of inventory, where decision support system applications can provide guidance on establishing supply chain movement that works for a business.
2. A semi tractor trailer truck pulls off the highway and begins driving up the road to a distribution center. As it passes the gate, scanners read the identification on the trailer and relay that information to the distribution ERP system. That trailer ID is instantly matched in the database to an advance shipping notification (ASN) and then to several purchase orders expected from a supplier.
The ERP system looks at the demand for those purchase orders and determines the optimal dock door for this delivery. A few seconds after passing the gate scanner, the truck is rolling and the driver is informed to take the trailer to the west side and dock number 47.
4 Type of Problem Context
As we known there are 4 type of problem context, which is Simple/Obvious, Complicated, Complex, and Chaos/Chaotic. In simple term, we can differentiate those 4 type of context which:
As we known there are 4 type of problem context, which is Simple/Obvious, Complicated, Complex, and Chaos/Chaotic. In simple term, we can differentiate those 4 type of context which:
Simple: easily knowable
Complicated: not simple, but knowable
Complex: not fully knowable, but reasonably predictable
Chaos: neither knowale nor praticable
Other things that can be differentiate is each problem has their own apporach for solving the problem. For Simple the approach is Sense-Categorize-Respond, for complicated is Sense-Analyze-Respond, for complex is Probe-Sense-Respond, and for Chaos is Act-Sense-Respond.
Complicated: not simple, but knowable
Complex: not fully knowable, but reasonably predictable
Chaos: neither knowale nor praticable
Other things that can be differentiate is each problem has their own apporach for solving the problem. For Simple the approach is Sense-Categorize-Respond, for complicated is Sense-Analyze-Respond, for complex is Probe-Sense-Respond, and for Chaos is Act-Sense-Respond.
Alibaba on managing big data analytics
Alibaba’s foray into the world of fund management—through a money-market fund-like investment product called Yu’E Bao, or leftover treasure—has so far been a huge success. The lingering question, however, is how exactly it works.
The question remains what alchemy Alibaba has used to get the best of both worlds. According to Zhou Xiaoming, vice general manager of Tianhong Asset Management, the fund company Alibaba engaged to run Yu’E Bao, the answer is the buzziest of tech buzz words right now: big data.One of Yu’E Bao’s appeals is that it gives customers the convenience of a demand deposit—they can withdraw their funds whenever they like—with returns similar to wealth-management products or other longer-term deposit options like CDs, which typically lock the funds for three to six months.
Sure, Alibaba can’t monitor offline activity such as whether a company is having trouble paying employees or has borrowed from someone else, but its data probably still gives a broadly representative picture of a borrower’s financial position. Lending credence to that, the company said that as of July, less than 2% of the loans it made were nonperforming. Nonperforming loans at China’s major banks are around 1%, but they typically lend to lower-risk, government-backed companies.
Despite the impressive statistic, big data might not be as useful for managing a pool of savings as it is for issuing loans to its customers.
The primary risk facing demand deposits like Yu’E Bao is one of liquidity. Because savers can take their money out at any time, banks need to keep enough cash on hand to manage withdrawals and have ways of tapping more cash on short notice if withdrawals suddenly exceed expectations. Demand deposits typically offer savers low returns because holding large amounts of cash and liquid assets isn’t particularly profitable for banks.
Alibaba believes it can use its big data to invest Yu’E Bao funds in assets with higher returns while still managing the liquidity risks. In other words, it believes it can predict when users will move money in and out of their Yu’E Bao accounts from their ordinary Alipay account used to buy goods on Alibaba’s sales platform. The movement of money between those accounts is influenced by whether a saver wants to buy something or is putting aside cash in anticipation of buying more in the future.
Mr. Zhou said that Alibaba’s data can signal when demand for cash will rise, allowing the fund to prepare accordingly. However, problems could arise if Alibaba’s data set isn’t as comprehensive as Mr. Zhou hopes.
China hasn’t experienced a meaningful slowdown for more than 20 years. That’s one of the reasons international credit-rating firms don’t rate China’s banks triple- or double-A – they don’t know how the banks would handle a recession. (Sure, China’s banks have been around for decades, but they’ve only operated as commercial banks, nominally independent from the government, for a decade or so.
The same goes for Alibaba’s big data. Alibaba came into being in 1999, so its numbers don’t go back far enough to offer any insight into how savers will handle their cash during an economic slowdown. Do they stop buying? Do they hoard cash? Do they pull their money from investments and put them in banks?
Yu’E Bao is investing in fairly liquid assets – interbank loans and deposits, government and corporate bonds – so even if savers suddenly demand more cash than expected, the fund should be able to sell assets relatively quickly if need be. However, if the data fails, it might not be able to sell at a price that returns all its customers their principal.
Alibaba has a clear interest in making Yu’E Bao work. But its big data might not be big enough to allay all concerns about the product.
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