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Semelhante a Team 4 Final Project Presetnation v4.0 - Copy
Semelhante a Team 4 Final Project Presetnation v4.0 - Copy (20)
Team 4 Final Project Presetnation v4.0 - Copy
- 2. Part 1 Use of Analytics in Retail Industry
- 3. Build Smarter Merchandising & Supply Networks • Localized Assortment • Increase the precision of customer segmentation • Select the right merchandise for each channel and fine-tune local assortment
- 4. • Inventory & Demand Replenishment • Build smarter supply chains and optimize merchandising across a multi-channel retail operation • Optimize inventory across multiple channels by using leading indicators such as customer sentiment and promotional buzz to anticipate future demand Build Smarter Merchandising & Supply Networks
- 5. Build Smarter Merchandising & Supply Networks • Dynamic Pricing • Predict optimal pricing to maintain a price leadership position • Real-time price comparisons with top competitors • synchronize price changes with demand and deliver real-time offers • Fleet & Logistics Optimization • Improve logistics by using real-time traffic, weather data and more to re-route shipments and avoid costly delays
- 6. Build Smarter Merchandising & Supply Networks • Space Optimization • Fine-tune store planograms by analyzing customer buying patterns and purchasing trends • Merchandise Selection • Identify emerging trends by analyzing 360-degree view of each individual
- 7. Deliver a Smarter Shopping Experience • Personalized Shopping Experience • Enrich the understanding of customers by integrating multichannel data to develop a 360-degree view of each individual • Predict consumer shopping behavior and offer relevant, enticing products to influence customers to expand their shopping list
- 8. Deliver a Smarter Shopping Experience • Marketing Optimization • Optimize customer interactions by knowing where a customer is and delivering relevant real-time offers based on that location
- 9. Drive Smarter Operations Realize a variety of operational goal • Improving labor utilization • Enhancing financial management Labor Optimization • Optimize staffing levels by predicting changes in customer demand • Better match employee skills with retail store needs and create the right incentives to drive strong sales performance Financial Management • Facilitate better-informed financial decision making by drawing on complete, trustworthy and timely data from a wide array of sources • Improve fraud detection by analyzing large volumes of transactions
- 10. Examples
- 11. Examples -Types of Analyses used Cluster Analysis and DecisionTrees Market Basket Analysis Procurement and Spend Analytics NewTechniques • E.g. Best Buy • E.g.Walmart • E.g.Walmart • Operational analytics • Text analytics
- 12. Examples 1. Cluster Analysis & DecisionTrees • Identify the most profitable customers • E.g. When analytics told Best Buy that 7% of its customers accounted for 43% of sales, the consumer electronics retailer reorganized its stores to address the needs of these high-value customers • Understand customer behavior • Fosters cross–sell and up–sell opportunities
- 13. Examples 2. Market Basket Analysis • Uncover hidden buying trends • Products display together to increase sales. • E.g. Walmart - exogenous demand models • Optimize pricing and discover up–selling and cross- selling opportunities. E.g. Staples
- 14. Examples 4. New techniques • Operational analytics – • Re–ordering to drive better inventory management • Instantly offering promotions to customers based on their purchases. • Text analytics – • Determine consumer trends and perceptions of their products and services • More quickly discover problems – comments on social media
- 15. Examples 3. Procurement and spend analytics • Data from suppliers • Identify savings across geographies, product categories, business units and procurement organizations • E.g. Walmart - inventory management system • Help better manage their stocks
- 18. Context of the Research • Stock market bubbles and economic meltdown resulted from: • Systematically misleading and overly optimistic research reports by stock market analysts. • Favorable analysis was traded for the promise of future investment banking business, • Analysts were commonly compensated for their role in garnering investment banking business for their firms. • Additionally, initial public offerings were allocated to corporate executives as: • A quid pro quo for personal favors or the promise to direct future business back to the manager of the IPO. • Auditors were supposed to be the watchdogs of the firms, but: • Incentives were skewed • Recent changes in business practice had made the consulting businesses of these firms more lucrative than the auditing function. • For example, Enron’s (now-defunct) auditor Arthur Andersen earned more money consulting for Enron than by auditing it; givenArthur Andersen’s incentive to protect its consulting profits.
- 19. Context of the Research • Dodd–FrankWall Street Reform and Consumer Protection Act • Signed into federal law by President Barack Obama on July 21, 2010 • Passed in response to the Great Recession • Significant changes to financial regulation in the United States • Addressed such areas as: • Wall Street transparency and accountability • Settlement supervision • Investor protection • Anti-predatory lending
- 20. Test & Learn 1. In at least 70% of the IPO violation cases, the plaintiffs have also nominated investment banks as defendants. 2. The average number of underwriters sued in an IPO related case has increased significantly in recent years (2012-2014) as compared to earlier years (2010-2011) due to “regulatory capture”. 3. The number of insider trading cases have increased significantly in recent years (2012-2014) as compared to earlier years (2010-2011).
- 21. Sample Size and Grouping • Sampling method • Simple random sample for each year from 2010 - 2014 • Sample size • 150 data points • i.e. 30 data points for each year from 2010 – 2014 • Data Grouping forAnalysis • 2010 + 2011 • 2012 + 2013 + 2014
- 22. Variables v1 false/misleading its business(prospect)/financial results/financial statements Inflate/overstate revenues/earnings/profit/assets/cash flows/operations failed to correct/disclose net income/revenue/assets/sales/negative trend failed to disclose proper losses/expenses (undisclosed/underestimate) misrepresent/omitting material information about its sales/earning/revenue inventories/ operations/financial results/performance (prospect)/customer/profitability/ company condition misrepresents/omitting material fact about its product( prospects/ strong demand) / quality control business (prospect)relation/billing practice/sales failed to disclose operational problems/financial /business condition/ customer service/product problem/division undisclosed adverse information/fail to disclose adverse material fact positive but false statement its product/business/earning growth/ financial results/(prospect) v2 Artificially inflate stock price, securities prices v3 The lawsuit mentioned that the firm had engaged in IPO /SEO issuance v4 Insider trading, stock sale by managers v5 SEC 1934 Sections 10(b) and rule 10b-5 v6 SEC 1933 Section 11 v7 GAAP; improper accounting v8 SEC 1933 Sections 12(2) and/or 15 v9 Investment bankers (underwriters, merger advisors) also sued in the same filing
- 23. Data Collection
- 24. 1st Test and Learn • There is systemic violation of SEC security laws, especially those pertaining to IPOs, where IPO underwriters collaborate with issuing companies to misrepresent the vital business information to potential buyers of company stock. • So, in more than 70% of IPO cases underwriters are nominated as defendants along with the issuing company.
- 25. HypothesisTesting of Population Proportion • H0: p ≤ 70% (Upper tail test for α=0.05) • Ha: p > 70% (Our claim) Population p 70% Total number of IPO/SEO Cases 80 Number of times the investment banks were sued 63 Sample p 78.75% Test Statistic, z 1.708 p-value 0.044 • So, we reject H0, because p-value < α. • This means that in more than 70% of IPO cases underwriters are nominated as defendants along with the issuing company.
- 26. Visual Insight into the Claim 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 2010 2011 2012 2013 2014 78.9% 88.9% 70.0% 75.0% 50.0% 21.1% 11.1% 30.0% 25.0% 50.0% Investment Banks Sued Investment Banks Not Sued
- 27. 2nd Test and Learn • Being sued in a securities case seldom has any impact on the underwriter's reputation and it doesn't modify their behavior.That's why we assume that with every passing year after recession higher percentage of underwriters get sued. • The average number of underwriters sued in an IPO related case has increased significantly in recent years (2012-2014) as compared to earlier years (2010-2011).
- 28. Time-Series Analysis y = -0.0718x + 145.15 R² = 0.6217 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% 2008 2010 2012 2014 2016 2018 Percentage of Investment Banks Sued in IPO Cases H0: β1 = 0 Ha: β1 ≠ 0 F-value: 4.929 P-value: 0.113 > α = 0.05 We do not have enough evidence to reject H0. So, regression is not significant.
- 29. Compare-Means Analysis • H0: μ2012-2014 - μ2010-2011 ≤ 0 • Ha: μ2012-2014 - μ2010-2011 > 0 (Our claim) • Since p-value > α, we do not have enough evidence to reject H0. • Hence, the average number of IPO cases in which the plaintiffs have named investment banks as defendants are significantly less or same in 2012-2014 as compared to similar cases in 2010-2011. 2010-2011 2012-2014 Mean 0.839 0.65 Variance 0.005 0.018 Observations 2 3 Pooled Variance 0.013 Hypothesized Mean Difference 0 df 3 t Stat 1.796 P(T<=t) one-tail 0.085 P-value > α (0.05) t Critical one-tail 2.353
- 30. 3rdTest and Learn • The number of insider trading cases have increased significantly in recent years (2012-2014) as compared to earlier years (2010-2011).
- 31. Time-Series Analysis H0: β1 = 0 Ha: β1 ≠ 0 F-value: 14.90 P-value: 0.03 < α = 0.05 We can reject H0. So, regression is significant.y = -6.3x + 12698 R² = 0.8324 0 5 10 15 20 25 30 35 40 2008 2010 2012 2014 2016 Number of InsiderTrading Cases
- 32. Compare-Means Analysis • H0: μ2012-2014 - μ2010-2011 ≤ 0 • Ha: μ2012-2014 - μ2010-2011 > 0 (Our claim) • Since p-value > α, we do not have enough evidence to reject H0. • Hence, the average number of insider trading cases are significantly less or same in 2012-2014 as compared to similar cases in 2010-2011. 2010-2011 2012-2014 Mean 0.839 0.65 Variance 0.005 0.018 Observations 2 3 Pooled Variance 0.013 Hypothesized Mean Difference 0 df 3 t Stat 1.796 P(T<=t) one-tail 0.085 P-value > α (0.05) t Critical one-tail 2.353
- 33. Managerial Implications – 1st Test and Learn • Negative underwriter image and reputation Security mispricing • Managers avoid underwriters recently accused of mispricing. Underwriter selection • Information asymmetry is created by the issuing firm and the underwriter, which is illegal. Overpricing of securities at IPO • banks intentionally under-price securities to gain larger profits at IPO Under pricing of securities
- 34. Managerial Implications – 2nd Test and Learn • Test and learn and regression shows that in the later years (2011-2014) the average number of IPO cases suing investment banks are almost same or less. • This means that there are signs of behavior modification and once sued, a bank generally avoids getting sued again. • Also, monetary penalties help in curbing securities violations e.g. • Morgan Stanley paid $5 Million Fine Over Facebook IPO in 2012 • Citigroup was slapped with a $2 million fine in 2012 • Citigroup fined $15M by FINRA for mishandling of non-public information in two IPO roadshows
- 35. Managerial Implications – 3rd Test and Learn • Issuing firms, their auditors, and underwriters can avoid insider trading through: • Stringent data security • Regular inside audits of data systems • Monitoring data transfers through flash drive and emails • Keep track of lost or stolen devices • Deter any unauthorized access to the company’s data • Very recent popular insider trading scandal was in 2013 at KPMG, causing it to resign as auditor at two companies (Herbalife Ltd. and Skechers USA)
- 36. Part 3 The Design of an Analytics Organization in a Retail Industry
- 37. Analytics Organization Right Product in the Right Place at the RightTime for the Right Price Inventory planning Accurate, available data Supply-chain speed Forecasting
- 38. Analytics Organization • Marketing: • Sales Forecasting, Advertising, Promotions, Pricing, Consumer Insights • Finance: • Reporting, Profitability, Pricing, Marketing Support, etc. • Supply Chain: • Sourcing & Procurement – PurchasingAgreements with vendors, inventory planning, order forecasting • Distribution & Logistics –Warehousing &Transportation • Demand Planning – Forecast Accuracy,TrendAnalysis, Statistical Modeling
- 39. Analytics Organization Description Avg. Cost/ Employee # of Employees Total Amount Labor Costs: • Marketing $100k 1k $100M • Finance $100k 1k $100M • SupplyChain $100k 1k $100M Total Labor Costs: $300M Hardware & Software Costs: • Laptops, Monitors, etc. $2k 3k $6M • Server $1k 3k $3M • Microsoft Sharepoint $100 3k $300k • Statistical Software (SPSS) $8k 3k $24M • Other/Miscellaneous $16.7M Total Hardware & Software Costs: $50M Total Costs: $350M
- 41. References • Fisher, M., Raman, A., & McClelland, A. (2000). Rocket Science Retailing is Almost Here. Are you Ready? 115-124. • Forbes (2014).Morgan Stanley Hit With $5 Million Fine Over Facebook IPO - Forbes. [ONLINE] Available at:http://www.forbes.com/sites/steveschaefer/2012/12/17/morgan-stanley-hit-with-5-million-fine-over-facebook-ipo-by- massachusetts/. [Accessed 02 December 2014]. • IBM (2014).Big Data in Retail - Examples in Action (n.d.). Retrieved November 28, 2014, from http://www.slideshare.net/IBMBDA/big-data-in-retail-examples-in-action?related=2 • IBM (2014). Capitalizing on the power of big data for retail. [ONLINE] Available at: http://www-01.ibm.com/common/ssi/cgi- bin/ssialias?subtype=WH&infotype=SA&appname=SWGE_IM_DM_USEN&htmlfid=IMW14679USEN&attachment=IMW1467 9USEN.PDF. [Accessed 23 October 2014]. • IBM (2014).Harness the Power of Data for Improved Business Outcomes in Retail . [ONLINE] Available at: https://www- 950.ibm.com/events/wwe/grp/grp006.nsf/vLookupPDFs/Session%203%20-%20Selling_Big_Data_in_Retail%20- %20N.Katsan%20/$file/Session%203%20-%20Selling_Big_Data_in_Retail%20-%20N.Katsan%20.pdf • Marks, G. (2013, April 29). Do You Replace Your Server Or Go To The Cloud? The Answer May Surprise You. Retrieved November 21, 2014, from http://www.forbes.com/sites/quickerbettertech/2013/04/29/do-you-replace-your-server-or-go-to- the-cloud-the-answer-may-surprise-you/ • NASDAQ.com. 2014. Citigroup (C) Fined $15M by FINRA for Negligence - Analyst Blog - NASDAQ.com. [ONLINE] Available at:http://www.nasdaq.com/article/citigroup-c-fined-15m-by-finra-for-negligence-analyst-blog-cm417412. [Accessed 02 December 2014]. • Passport Advantage Express. (n.d.). Retrieved November 21, 2014, from https://www- 112.ibm.com/software/howtobuy/buyingtools/paexpress/Express?P0=E1&part_number=D0EJNLL,D0EEELL,D0EJJLL,D0ED 4LL&catalogLocale=en_US&locale=en_US&country=USA&PT=html • (n.d.). Retrieved November 21, 2014, from www.dell.com • State of the Industry Research Series : The Future of Retail Analytics. (2013, January 1). Retrieved November 21, 2014, from http://www.sas.com/content/dam/SAS/en_us/doc/research2/ekn-report-future-retail-analytics-106717.pdf
Notas do Editor
- Our idea behind the test and learn was that when US economy went into recession after subprime mortgage crisis in 2007-2008, lawmakers proposed stringent laws to govern the stock markets. For instance – Dodd Frank legislation was passed in 2010 to protect consumers from predatory lending and to monitor the behavior of stock markets. But with the passage of time the SEC and wall street become really friendly and it results in “regulatory capture” in which SEC starts seeing things from the perspective of investment banks, whom it is supposed to keep and eye on. SEC doesn’t act as a watchdog anymore and the financial institutes find a way to work around regulations, which should lead to increasing number of security violations as the time passes by. So, for this research we collected secondary data from the Stanford Law school’s website that records all security related cases in the US courts.
- Other scandals of that period included systematically misleading and overly optimistic research reports put out by stock market analysts. (Their favorable analysis was traded for the promise of future investment banking business, and analysts were commonly compensated not for their accuracy or insight, but for their role in garnering investment banking business for their firms.) Additionally, initial public offerings were allocated to corporate executives as a quid pro quo for personal favors or the promise to direct future business back to the manager of the IPO. What about the auditors who were supposed to be the watchdogs of the firms? Here too, incentives were skewed. Recent changes in business practice had made the consulting businesses of these firms more lucrative than the auditing function. For example, Enron’s (now-defunct) auditor Arthur Andersen earned more money consulting for Enron than by auditing it; given Arthur Andersen’s incentive to protect its consulting profits, we should not be surprised that it, and other auditors, were overly lenient in their auditing work.
- So, with the backdrop of this context we developed these three test and learn in which we tested our assumption that the trend of security violations is growing. We will come back to these during our hypothesis testing.
- So, we collected 150 data points, 30 for each year from 2010-2014 through simple random sampling, and then grouped them together to perform statistical analysis such as compare means.
- We extracted nine variables from the case files.
- Data was extracted from case files and each of these files was at least 30 pages long and it took approximately 10 minutes per file to extract the required variables. So, for 150 data points it was approximately 1500 minutes of work i.e. around 20 to 25 hours.
- Before this project, I used to think that mispricing an IPO only has financial and reputational repercussions for stock issuers as we have studied in the financial management course. But now I realize that since underwriters play a significant role in IPO, so, they are also seen with suspicion when it comes to mispricing the IPO stock. So, what happens later is that stock buyers file a class action lawsuit against the issuing company and the investment bank to cover their losses.
- So, lets put the hypothesis to test. We have put our claim as alternative hypothesis. The hypothesis testing suggests that we have enough evidence to reject the null hypothesis, which means that our assumption was right.
- This can also be shown visually in this chart, where you can see that in prior 4 years at least 70% of the IPO cases also nominated investment banks as defendants.
- The 2nd test and learn is more about the Regulatory capture, where SEC becomes friendly and easy with the banks and gets used to their mode of thinking. As a result investment banks don’t care if they get sued once in a while as it may not adversely affect their reputation. So this can be interpreted from our assumption that:
- Lets put the assumption to test through time-series analysis, which is basically regression using the year as an independent variable. In this case the regression didn’t come out to be significant despite the good coefficient of determination (r-square). So, we need another way to test our hypothesis.
- So, then we used the compare means in which we grouped the data in terms of years 2010-2011 and 2012-2014. We stated our claim as alternative hypothesis and the hyp. Testing suggests that: This means that the number of cases in the recent years are significantly less or same as in the previous years when the Dodd-frank legislation was passed and to some extent it can be corroborated through a declining trend as shown in the time-series analysis.
- Again due to regulatory capture we assume that the number of insider cases must increase in the recent years, as the financial institutes and their staff might assume that that can get away with their securities violations in the recovering market.
- So, in this case the regression is significant along with a strong coefficient of determination, and this shows that number of insider trading cases are decreasing and it is good news for the stock market in terms of fairness and transparency.
- We went a step ahead and did the compare-means analysis of the grouped data and again found out that in the time period from 2012-2014 the average number of insider trading cases are less or equal to those in 2010-2011.
- Mispricing will lead to a negative image for the investment bank, which is likely to be reflected in the underwriter’s reputation. Managers should decide which investment banks to choose before issuing an IPO. Several researches suggested previous background of underwriters mispricing has been an indication for the companies while choosing a investment bank. On the contrary Investment bank owners should take this information into account. When valuing the bank’s share value and we thus expect excessive under pricing to negatively impact underwriter market value. We observe that banks that underwrite an excessively underpriced IPO do indeed experience a significant loss in their market value. Moreover as far as the management of issuing firm is concerned if they want to overprice their share they have to create information asymmetry in the market. For that they need to provide more incentives to underwriters.
- The investment bank owners believe that the loss in underwriter market value of the bank due to displeased investors will be smaller than the loss due to displeased issuers. The logic behind that is quite reasonable. They create syndication specially the issuing companies and investment bankers. The issuing companies are paying more money to the investment bankers for pricing an IPO than we as an individual investor in the stock market do. As long as the investment banks are making money through the syndication their shareholders (shareholders for investment banks) will be happy as well. Our results also suggest their reputations are seldom affected being sued. This is an ethical issue in general from the investment bank’s management perspective. Forbes. 2014.Morgan Stanley Hit With $5 Million Fine Over Facebook IPO - Forbes. [ONLINE] Available at:http://www.forbes.com/sites/steveschaefer/2012/12/17/morgan-stanley-hit-with-5-million-fine-over-facebook-ipo-by-massachusetts/. [Accessed 02 December 2014]. NASDAQ.com. 2014. Citigroup (C) Fined $15M by FINRA for Negligence - Analyst Blog - NASDAQ.com. [ONLINE] Available at:http://www.nasdaq.com/article/citigroup-c-fined-15m-by-finra-for-negligence-analyst-blog-cm417412. [Accessed 02 December 2014].
- Managers of different institutional investors are involved in this kind of fraudulent activities along with some audit firm’s employees. From the Audit firm’s management perspective, they should put more emphasis on data theft and breach. They should have high security for insider data of their clients and ensure that there won’t be data breach (we can use some of the ways we discussed in the presentation on how to reduce data breach).
- - In a Harvard Business Review Article called “Rocket Science Retailing is Almost Here. Are you Ready?” It talks about having the Right Product, in the Right Place, at the Right Time, for the Right Price. - It also mentions how Rocket Science Retailing involves a marriage between left-brain (scientific) and right-brain (intuitive). - A retailer will need IT that has enough power to capture, store, and analyze data. - Forecasting can be done by updating predictions based on early sales data, tracking the accuracy of their forecasts, and getting product testing right. - With Supply Chain, the retailer often has to commit with a vendor on what they will be ordering and how much months in advance. If the lead times can be reduced, they can be better about matching supply with demand. - For Inventory Planning, they also have to decide when and how much to order. This is slightly different than forecasting because the planner may decide to stock more or less than predicted demand. If stockouts and lost sales are tracked, this could be a good way to improve accuracy.
- Business Analytics is the art, science, and philosophy of using insights to improve decision making. Business Analytic software is used to help gain these insights. The analytics organization that we would put together would be centralized at Headquarters in Minniapolis, MN and there will be no outsourcing. The Marketing group would help with sales forecasting, advertising, promotions, pricing, and consumer insights. With pricing, they can help determine what the everyday price should be or what and when bundling or basket pricing should be. For consumer insights, it is very important to understand customer wants, needs, preferences, and behaviors. The finance group would help support marketing and other functions with analysis on profitability and pricing. They would assist with financial reporting, annual operating plans, financial projections, and yearly budgets. The supply chain group would include sourcing and procurement to secure purchasing agreements with vendor. They would also be responsible for inventory planning and order forecasting. Distribution and logistics would focus on warehousing and transportation. And Demand Planning would use statistical modeling to assist with trend analysis and forecast accuracy.
- In 2013, Target’s revenues were approximately $73 Billion. For the budget, I figured if we have about 1,000 employees from each analytic function, that would mean on average, each employee helps to manage about $73 Million worth of business. There are going to be some salaries above $100k and some below, but on average I would estimate salaries to be around $100k. All of these costs come out to be about 0.5% of revenues.