1 / 65

Business Driven Information Systems 2e

Business Driven Information Systems 2e. CHAPTER 2 STRATEGIC DECISION MAKING. Sear’s Catalog. Sears Roebuck changed the shape of an entire industry by being lucky enough to discover a huge untapped market that lay waiting to be discovered.

Télécharger la présentation

Business Driven Information Systems 2e

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.


Presentation Transcript

  1. Business Driven Information Systems 2e CHAPTER 2 STRATEGIC DECISION MAKING

  2. Sear’s Catalog • Sears Roebuck changed the shape of an entire industry by being lucky enough to discover a huge untapped market that lay waiting to be discovered. • In the 1880s about 65 percent of the population (58 million) lived in the rural areas. Richard Sears lived in North Redwood, Minnesota, where he was an agent at the Minneapolis and St. Louis railway station. Sears began trading products such as lumber, coal, and watches, when the trains would pass through. • Sears moved to Chicago in 1893 and partnered with Alvah C. Roebuck, and the Sears & Roebuck company was born. The company first published a 32 page catalog selling watches and jewelry. By 1895 the catalog was 532 pages long and included everything from fishing tackle to glassware. In 1893 sales reached $400,000 and by 1895 sales topped $750,000.

  3. Sear’s Catalog • Sears invented many new marketing campaigns and concepts that are still in use today, including a series of rewards (or loyalty programs) for customers who passed copies of the catalog on to friends and relatives. • Sears was one of the first companies to recognize the importance of building strong customer relationships. Sears’ loyalty program gave each customer 24 copies of the catalog to distribute, and the customer would generate points each time an order was placed from one of the catalogs by a new customer. • The Sears catalog became a marketing classic. It brought the world to the isolated farms and was a feast for the new consumers. The entire world was available through the Sears catalog, and it could be delivered to the remotest of doorsteps.

  4. What’s In A Name? A Lot! • Sunday, November 18, 1928, is a historic moment in time since it is the day that the premier of Steamboat Willie debuted, a cinematic epic of seven minutes in length. This was the first cartoon that synchronized sound and action. • Like all great inventions, Mickey Mouse began his life in a garage. • After going bankrupt with the failure of his Laugh O Gram Company, Walt Disney decided to rent a camera, assemble an animation stand, and set up a studio in his uncle’s garage. At the age of 21, Walt and his older brother Roy launched the Disney Company in 1923. • Their first few films failed and it wasn’t until 1928 when they released a seven minute film about a small mouse named Mickey. Disney never looked back. • The truth is Mickey Mouse began life as Mortimer Mouse. Walt Disney’s wife, Lilly, did not like the name and suggested Mickey instead. Walt Disney has often been heard to say “I hope we never lose sight of one fact – that this was all started by a mouse.” • Would Mortimer have been as successful as Mickey? Would Mortimer have been more successful than Mickey? How could Walt Disney have used technology to help support his all-important decision to name his primary character? There are many new technologies helping to drive decision support systems, however it is important to note that some decisions, such as the name of a mouse, are made by the most complex decision support system available, the human brain.

  5. The Harley-Davidson Mystique • They have been ranked 1st in Fortune’s 5 Most Admired Companies the motor vehicle industry, 2nd in ComputerWorld’s Top 100 Best Places to Work in IT and 1st in the Top 10 Sincerest Corporations in the Harris Interactive Report • HD’s technology budget is more than 2% of its revenue, far above the industry average. More than 50% of the budget is devoted to developing new technologies – information sharing, business intelligence and enhancing decision making. It has reduced operating costs by $40 million through using strategic information systems • Talon, it’s proprietary dealer management system handles inventory, vehicle registration, warranties and POS transactions for all dealerships. The system checks dealer inventory, generates parts orders and analyzes global organization information.

  6. The Harley-Davidson Mystique • HD uses software from Manugistics to enable the company to so business with suppliers in a collaborative, Web-based environment. It also has SCM software to manage material flows and improve collaboration with key suppliers. • They CRM to build relationships and loyalty with their customers and the Harley’s Owners Group (HOG – over 600,000 members) offers events and benefits to its members. • The corporate culture led to its winning the awards for best place to work and most admired company.

  7. DECISION MAKING • Decision-enabling, problem-solving, and opportunity-seizing systems

  8. DECISION MAKING • Reasons for the growth of decision-making information systems • People need to analyze large amounts of information • People must make decisions quickly • People must apply sophisticated analysis techniques, such as modeling and forecasting, to make good decisions • People must protect the corporate asset of organizational information

  9. DECISION MAKING • Model – a simplified representation or abstraction of reality • IT systems in an enterprise

  10. TRANSACTION PROCESSING SYSTEMS • Moving up through the organizational pyramid users move from requiring transactional information to analytical information

  11. TRANSACTION PROCESSING SYSTEMS • Transaction processing system (TPS) - the basic business system that serves the operational level (analysts) in an organization • Payroll system • Accounts Payable system • Accounts Receivable system • Course registration system • Human resources systems • Online transaction processing (OLTP) – the capturing of transaction and event information using technology to (1) process the information according to defined business rules, (2) store the information, (3) update existing information to reflect the new information • Online analytical processing (OLAP) – the manipulation of information to create business intelligence in support of strategic decision making

  12. DECISION SUPPORT SYSTEMS • Decision support system (DSS) – models information to support managers and business professionals during the decision-making process • One national insurance company using a DSS discovered that only 3% of married male homeowners in their forties received more than one DUI. The company lowered rates for customers in this category, which increased its revenue while mitigating its risk. • Burlington Northern and Santa Fe Railroad (BNSF) regularly tests its railroad tracks. Each year hundreds of train derailments result from defective tracks. Using a DSS to schedule train track replacements helped BNSF decrease its rail-caused derailments by 33%

  13. DECISION SUPPORT SYSTEMS • Three quantitative models used by DSSs include: • Sensitivity analysis – the study of the impact that changes in one (or more) parts of the model have on other parts of the model • What-if analysis – checks the impact of a change in an assumption on the proposed solution • Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of output

  14. DECISION SUPPORT SYSTEMS • What-if analysis Excel’s Scenario Manager being used to determine what will happen to total sales as the price and quantity of units sold changes

  15. DECISION SUPPORT SYSTEMS • Goal-seeking analysis Excel’s Goal Seek tool being used to determine how much money a person can borrow with an interest rate of 5.5% and a monthly payment of $1,300

  16. Goal Seek Example

  17. DECISION SUPPORT SYSTEMS • Interaction between a TPS and a DSS

  18. EXECUTIVE INFORMATION SYSTEMS • Executive information system (EIS) – a specialized DSS that supports senior level executives within the organization • Most EISs offering the following capabilities: • Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information • Drill-down – enables users to get details, and details of details, of information • Slice-and-dice – looks at information from different perspectives

  19. EXECUTIVE INFORMATION SYSTEMS • Interaction between a TPS and an EIS

  20. Digital Dashboards • Integrates information from multiple components and presents it in a unified display • Executives can perform their own analysis, without inundating IT personnel with queries and request for reports, and quickly get results to respond to opportunities

  21. Digital Dashboards • DDs commonly use indicators to help executives quickly identify the status of key information or critical success factors • DDs help executives react to information as it becomes available and make decisions, solve problems and change strategies daily instead of monthly

  22. Digital Dashboards • Why, according to Nucleus Research, is there a direct correlation between use of digital dashboards and a company’s return on investment (ROI)? • Digital dashboards, whether basic or comprehensive, deliver results quickly • The quicker employees have information, the quicker they can respond to problems, threats, and opportunities • Verizon Communications CIO ShayganKheradpir tracks 100 plus major IT systems on a single screen called “The Wall of Shaygan” • Every 15 seconds a new set of charts communicating Verizon’s performance flashes onto a giant LCD screen in his officeand include 300 measures of business performance that fall into 3 categories – Market Pulse, Customer Service and Cost Driver • 400 managers at every level of Verizon have the same dashboard

  23. Artificial Intelligence • Intelligent system – various commercial applications of artificial intelligence • Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn • AI systems can learn or understand from experience, make sense of ambiguous or contradictory information and even use reasoning to solve problems and make decisions effectively

  24. Artificial Intelligence • The AI Robot Cleaner at Manchester Airport in England alerts passengers to security and nonsmoking rules while it scrubs up to 65,600 square feet of floor per day • SmartPump keeps drivers in their cars on cold, wet days • The SmartPump can service any automobile built after 1987 that has been fitted with a special gas cap and a windshield-mounted transponder that tells the robot where to insert the pump • The Miami Police Bomb squad’s AI robot that is used to locate and deactivate bombs

  25. Artificial Intelligence • The ultimate goal of AI is the ability to build a system that can mimic human intelligence

  26. Artificial Intelligence • RivalWatch (ql2.com) offers a strategic business information service using AI that enables organizations to track the product offerings, pricing policies, and promotions of online competitors • Clients can determine the competitors they want to watch and the specific information they wish to gather, ranging from products added, removed, or out of stock to price changes, coupons offered, and special shipping terms • RivalWatch allows its clients to check each competitor, category, and product either daily, weekly, monthly, or quarterly

  27. Artificial Intelligence • Four most common categories of AI include: • Expert system – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems • Human expertise is transferred to the expert system, and users can access the expert system for specific advice • Most expert systems contain information from many human experts and can therefore perform a better analysis than any single human • MYCIN - outperformed members of the Stanford medical school but not used because of ethical and legal issues related to the use of computers in medicine http://www.macs.hw.ac.uk/~alison/ai3notes/section2_5_5.htm

  28. Artificial Intelligence • Four most common categories of AI include: • Expert system – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems • Human expertise is transferred to the expert system, and users can access the expert system for specific advice • Most expert systems contain information from many human experts and can therefore perform a better analysis than any single human • MYCIN - outperformed members of the Stanford medical school but not used because of ethical and legal issues related to the use of computers in medicine http://www.macs.hw.ac.uk/~alison/ai3notes/section2_5_5.htm

  29. Artificial Intelligence • Countrywide Funding Corp uses an expert system to improve decisions about granting loans using a PC based system that makes preliminary creditworthiness decisions on loan requests • The systems has about 400 rules. It tested the system against an actual underwriter and refined the system until it agreed with the underwriter 95% of the time • All rejected loans are reviewed by an underwriter • An underwriter can now evaluate at least 16 loans per day as compared to 6 or 7 previously • The system is being used on their Web site to help customers who are inquiring is they qualify for a loan

  30. Artificial Intelligence • Galeria Kaufhof, a German superstore chain, uses a rule-based system to help inspect the quality of the 12,000 daily deliveries they receive of a wide range of goods • The system identifies high-risk deliveries (suppliers with poor delivery history, new products) for inspection and passes along the lower risk ones automatically • Successful expert systems deal with problems of classification in which there are relatively few alternative outcomes and in which the possible outcomes are all known in advance

  31. Traffic Light Expert System

  32. Traffic Light Expert System Is the light green (Yes/No)?No Is the light red (Yes/No)?No Is the light likely to change to red before you get through the intersection (Yes/No)?Why? Will only reach this point if light is yellow and then you’ll have two choices. Is the light likely to change to red before you get through the intersection (Yes/No)?No Conclusion: Go through the intersection

  33. Loan Application Expert System

  34. Artificial Intelligence • Neural Network – attempts to emulate the way the human brain works • Neural networks are most useful for decisions that involve patterns or image recognition • Used for solving complex, poorly understood problems for which large amounts of data have been collected • Typically used in the finance industry to discover credit card fraud by analyzing individual spending behavior • US Bancorp has cut credit card fraud by 70% using this technology

  35. Neural Networks • Neural nets consist of an input layer, output layer and one or mode hidden internal layers • Input and output layers are connected to the middle layers by “weights” of various strengths • Weights change as the net learns what is good and bad (e.g. credit card transaction) and stabilize after having been fed enough examples • Differs from expert system in that expert system follows rigid rules that don’t change. Neural net rules change based on experience/learning.

  36. Neurons and Synapses

  37. Neural Networks • A neural network is composed of several different elements. Neurons are the most basic unit and are interconnected. Each connection has a connection weight which may differ from other connections. • A neuron is a communication conduit that accepts input and produces output. The neuron receives its input either from other neurons or the user program. Similarly, the neuron sends its output to other neurons or the user program. • When a neuron produces output, that neuron is said to activate, or fire. A neuron will activate when the sum of its inputs satisfies the neuron’s activation function. The user decides what the trigger level will be.

  38. The Layers of a Neural Network

  39. Neural Networks Can… • Learn and adjust to new circumstances on their own • Take part in massive parallel processing • Function without complete information • Cope with huge volumes of information • Analyze nonlinear relationships

  40. Artificial Intelligence • Fuzzy logic – a mathematical method of handling imprecise or subjective information • Values for ambiguous information range between 0 and 1. A washing machine continues to wash until the clothes are clean. How do you define clean? Analyze financial information that has a subjective value (goodwill). • Fuzzy logic and NNs are often combined to express complicated and subjective concepts • In Japan, the subway system uses fuzzy logic controls to accelerate so smoothly that standing passengers need not hold on • A system has been developed to detect possible fraud in medical claims submitted by healthcare providers

  41. Artificial Intelligence • Fuzzy logic can be used in a computer program to automatically control room temperature • Cool is between 50-70 degress, although 60-67 is most clearly cool. Cool is overlapped by cold and norm. • Thus a rule might be “if the temperature is cool or cold and the humidity is low while the outdoor wind is high and the outdoor temperature is low, raise the heat and humidity in the room”

  42. ***Genetic Algorithms • Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem • Essentially an optimizing system, it finds the combination of inputs that give the best outputs

  43. ***Genetic Algorithms • Take thousands or even millions of possible solutions, combine and recombine them until the optimal solution is found • Example: Create a portfolio of 20 stocks with growth rate of 7.5% • Pick a large group of stocks, combine them into groups of 20 at a time and see how each group performed based on historic information • 30 stocks  30 million combinations, 40 stocks  137 billion possibilities of 20 • US West uses this technique to determine the optimal configuration of fiber-optic cable in a network that may include as many as 100,000 connection points • Used to take 2 months for an experienced designer, now 2 days and saves $1-$10 million each time it’s used

  44. Evolutionary Principles of Genetic Algorithms • According to evolutionary theory, Mother Nature is often confronted with the problem of evolving species to fit their environment. • Selection – good specimens reproduce more often, weak specimens less often or not at all • Crossover – combining portion of good outcomes to create even better outcomes • Mutation – occasionally, a child is born with a new characteristic

  45. The basic genetic algorithm • Start with a large “population” of randomly generated “attempted solutions” to a problem • Repeatedly do the following: • Evaluate each of the attempted solutions • Keep a subset of these solutions (the “best” ones) • Use these solutions to generate a new population • Quit when you have a satisfactory solution (or you run out of time)

  46. Genetic Algorithms • Consider the problem of finding a "best" stock portfolio. The steps of the genetic algorithm are listed below: • Step 1 From the list of NASDAQ stocks, generate several portfolios of randomly chosen stocks. • Step 2 Evaluate each of the portfolios, assigning it a score. • Step 3 If a portal has a high score, make several "clones" (copies) of it. If it has a low score, get rid of it. • Step 4 Recombine the portfolios. Choose several pairs of portfolios and mix and match their stocks. • Step 5 Mutate a few of the portfolios by randomly adding or removing a small number of stocks. • Repeat steps 2-5 above many, many times to weed out weak portfolios and find the "best" portfolios.

  47. Genetic Algorithms • First, we randomly generate several portfolios: • Next, we assign a score to each portfolio using such criteria as: • Best average monthly percentage price gain (for low risk, high gain portfolios) • Best average annual percentage gain • Best annual market capitalization gain

  48. Genetic Algorithms • After scoring, we perform selection on the portfolios, deciding how many "child" portfolios that portfolio will "create" based on its score. • Since Portfolio 4 had a high score (90), it will "reproduce" twice (have two children or clones) in the next round. Since potrfolio 1 had the lowest score, it will not appear in the next round.

  49. Genetic Algorithms • For the Recombine phase, we swap two stocks between Portfolio 2 and Portfolio 4 (stock G for stock Q and stock H for stock T).

  50. Genetic Algorithms • Then, we mutate one of the stocks in one of the portfolios (stock T is randomly replaced with stock Z) • Then, we repeat the above process of scoring, selection, recombination, and mutation many thousands (or millions) of times until we are left with a set of optimum portfolios.

More Related