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Measures
Targets
Initiatives
Profitable Growth
Return on Invested Capital
Return on Equity
Only accept strong NPV projects
15% ROIC
20% ROE
Simplify the organization structure
Provide an open environment for idea generation and brainstorming
Industry leading innovation
Update product upgrade cycle. Refresh or introduce a product at least once every two years.
Highest Quality products and services
Higher Gross Margin
Invest heavily in R and D with excess Free Cash Flow
Establish strong customer and brand loyalty
Adopt the net promoter score and customer satisfaction rating survey
4% Market Share growth per year
Rewards programs and partnerships with other service providers
Establish a well recognized brand
Strong brand recognition
Become the number 1 or number 2 rated brand in each product category
Invests heavily in marketing and advertising
Question 2
A major retailer such as Wal-Mart would be best served by using a transactional database. Wal-Mart unlike many other retailers has a strong competitive advantage relative to peers in the industry. It competes primarily based on price and value. As a low cost producer, Wal-Mart has enduring advantage that resonates with both...
Many competitors, particularly in retail do not have this advantage and are thus forced to compete based on other metrics such as brand, style, or luxury. Due in part to the fact that Wal-Mart is a low cost producer, it will sell large quantitates of merchandise. The last fiscal year alone saw Wal-Mart sell nearly $500 Billion worth of merchandise. Although it had strong sales, Wal-Mart had a Net Operating Profit after tax (NOPAT) of roughly $18 Billion. Over the last 10 years, Wal-Mart has average a Net Operating Profit Before Tax margin of roughly 6%.
Due to these low margins, the company must have a strong understanding of transactions that are occurring. A transactional database, combined with Wal-Mart's just in time inventory management system, the company is able to quickly shift product assortment. Due to the fact that Wal-Mart has such a large volume of transactions that occur in varying regions throughout the world, a transactional database is required.
If a retailer does decide to use both systems, it should do so under varying circumstances. A data warehouse would be best suited for a retailer who has many disparate parts. A classic example would be Amazon who must operate a vast online data system while attempting to establish is brick and mortar footprint. The decision as to where a particular warehouse or distribution center will go is critical. To arrive at the correct decision, management must use a wide…
I would use linear regression as it allows a practitioner to see clusters of date scattered around a particular area. Although many problems can persist with linear regression, I believe it provides the best means of explaining the overall relationship between loans and default risk. The practitioner must first eliminate non-stationary variables in addition to co dependence. Variable that depend on the proceeding variable can cause problems and errors in the overall regression analysis. However, solutions such as use of the adjusted R squared metric, the Dickey-Fuller test and others can help eliminate these concerns. Regressions, through the use of the R squared metric can help an analyst better determine what percentage of the loan defaults can be explained by variables such as income, debt, or other variables. Regressions are also flexible allowing for multiple variables to be used in an explanatory fashion.
The data mining items I would need to conduct a regression analysis are varied. For example, I would need variables relating to debt levels on and individual basis. I would also need income, education, and demographic information. For example, homes in the New York will be more expensive than homes in North Dakota. As a result, a loan will be much higher in New York. With the higher loan amount, the possibility of default and capital loss is also higher. With a higher default risk, the bank will demand higher collateral, more money down, etc. The bank must be sure that the collateral backing the loan is appropriately priced given the market conditions that are prevailing and will prevail in the future.
In 2008, regression analysis failed at financial institutions because they failed to see or account for "tail risk." These risks are those that are three standard deviations away from the mean. Their regressions didn't take these occurrences into account because they were very rare, or had never happened before. By omitting these risks from the regression analysis, the outputs were in error. In particularly, a wave of massive loan defaults occurred that nearly crippled the United States financial system. Due to these occurrences, regressions must take into all the variables, no matter how farfetched or rare they may be.
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