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CHAPTER 2

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  1. CHAPTER 2 STRATEGIC DECISION MAKING Opening Case Revving Up Sales at Harley-Davidson

  2. Richard Sears Decides to Sell Products Through a 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. Richard Sears Decides to Sell Products Through a 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. Overview • 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 • Verizon Communications CIO Shaygan Kheradpir 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

  22. 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

  23. 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

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

  25. 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

  26. 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

  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 • 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

  29. 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

  30. Traffic Light Expert System

  31. 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

  32. Loan Application Expert System

  33. 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

  34. 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). • 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

  35. 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”

  36. 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.

  37. 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.

  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. 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 • 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 take 2 months for an experienced designer, now 2 days and saves $1-$10 million each time it’s used

  41. Evolutionary Principles of Genetic Algorithms • Selection – or survival of the fittest or giving preference to better outcomes • Crossover – combining portion of good outcomes to create even better outcomes • Mutation – randomly trying combinations and evaluating the success of each

  42. 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)

  43. A really simple example • Suppose your “organisms” are 32-bit computer words • You want a string in which all the bits are ones • Here’s how you can do it: • Create 100 randomly generated computer words • Repeatedly do the following: • Count the 1 bits in each word • Exit if any of the words have all 32 bits set to 1 • Keep the ten words that have the most 1s (discard the rest) • From each word, generate 9 new words as follows: • Pick a random bit in the word and toggle (change) it • Note that this procedure does not guarantee that the next “generation” will have more 1 bits, but it’s likely

  44. Asexual vs. sexual reproduction • In the examples so far, • Each “organism” (or “solution”) had only one parent • Reproduction was asexual (without sex) • The only way to introduce variation was through mutation (random changes) • In sexual reproduction, • Each “organism” (or “solution”) has two parents • Assuming that each organism has just one chromosome, new offspring are produced by forming a new chromosome from parts of the chromosomes of each parent

  45. The really simple example again • Suppose your “organisms” are 32-bit computer words, and you want a string in which all the bits are ones • Here’s how you can do it: • Create 100 randomly generated computer words • Repeatedly do the following: • Count the 1 bits in each word • Exit if any of the words have all 32 bits set to 1 • Keep the ten words that have the most 1s (discard the rest) • From each word, generate 9 new words as follows: • Choose one of the other words • Take the first half of this word and combine it with the second half of the other word

  46. The example continued • Half from one, half from the other:0110 1001 0100 1110 1010 1101 1011 01011101 0100 0101 1010 1011 0100 1010 01010110 1001 0100 11101011 0100 1010 0101 • Or we might choose “genes” (bits) randomly:0110 1001 0100 1110 1010 1101 1011 01011101 0100 0101 1010 1011 0100 1010 01010100 0101 0100 101010101100 1011 0101 • Or we might consider a “gene” to be a larger unit:0110 1001 0100 1110 1010 1101 1011 01011101 0100 0101 1010 1011 0100 1010 01011101 1001 0101 101010101101 1010 0101

  47. Comparison of simple examples • In the simple example (trying to get all 1s): • The sexual (two-parent, no mutation) approach, if it succeeds, is likely to succeed much faster • Because up to half of the bits change each time, not just one bit • However, with no mutation, it may not succeed at all • By pure bad luck, maybe none of the first (randomly generated) words have (say) bit 17 set to 1 • Then there is no way a 1 could ever occur in this position • Another problem is lack of genetic diversity • Maybe some of the first generation did have bit 17 set to 1, but none of them were selected for the second generation • The best technique in general turns out to be sexual reproduction with a small probability of mutation

  48. Genetic Algorithm Applications • GE used them help optimize the design of a jet turbine aircraft engine • SCM software from i2 Technologies optimizes production-scheduling models incorporating hundreds of thousands of details about customer orders, material and resource availability, manufacturing and distribution capability and delivery dates

  49. Intelligent Agents • Intelligent agent – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users • Used for environmental scanning and competitive intelligence • An intelligent agent can learn the types of competitor information users want to track, continuously scan the Web for it, and alert users when a significant event occurs • software that assists you, or acts on your behalf, in performing repetitive computer-related tasks (e.g., paper clip in Word) • Buyer agents or shopping bots • User or personal agents • Monitoring-and surveillance agents • Data-mining agents

  50. Deep Space 1 Out of this World Agents Launched: Oct 24, 1998 Terminated: Dec. 18, 2001 Successfully tested 12 high-risk, advanced space technologies