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CHAPTER TWO

CHAPTER TWO

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CHAPTER TWO

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  1. CHAPTER TWO STRATEGIC DECISION MAKING and BUSINESS PROCESSES

  2. Introduction to Decision Making • How do we make decisions? • What methods do we use as a basis for the decisions we make? • How do we assess whether a decision was good or bad? • Could the decision have been improved? • Today, decisions are made using massive amounts of data and quantitative (statistical) analysis • Fact-based decision making

  3. Decision-making Steps • Problem identification • Gather facts to • Fully understand the problem • Who will fix it • What recourses are needed • Devise possible solutions • Evaluate and select • Implement

  4. Types of Decisions • Operational • Structured decisions • Software systems are making more and more of these • Managerial • Sem-istructureddecisions • Strategic • Unstructured decisions

  5. Measuring IT Performance (1) • IT capital expenditures can be huge • Software / hardware / training • 112 Million for Hershey’s ERP • Expenditures need to be measured to determine whether they are worthwhile • We measure efficiency and effectiveness • The two are interrelated

  6. Measuring IT Performance (2) • Efficiency metrics (The technology itself) • System uptime (availability) • Response time (time to render a Web page) • Transaction processing performance (database transactions per second) • Information accuracy

  7. Measuring IT Performance (3) • Effectiveness metrics (How well the technology works) • Customer satisfaction • Sales increases • Cost reductions • Use accounting and financial methods to assess • Cost/benefit analysis, NPV, ROI, cash flow • Usability • How many clicks to accomplish a task

  8. Measuring Success • Efficiency and effectiveness metrics • Critical success factors • These should be quantitative • Don’t create to many CSFs • Key performance indicators • How do we measure those CSFs • WHAT YOU MEASURE IS WHAT YOU GET

  9. IT Systems and Decision Making • Transaction processing systems • Management information systems • Decision support systems • Executive information systems • Supply chain systems

  10. Transaction Processing Systems (1) • POS transactions • All of those Amazon sales • Routine banking debit and credits • Every credit card swipe • VISA processes roughly 12,000,000,000 transactions / year (growing at 14% / year) http://investor.visa.com/phoenix.zhtml?c=215693&p=quarterlyearnings

  11. Transaction Processing Systems (2) • System runtime functions • Ensures data integrity, availability, security • System administration functions • Configuration and monitoring • Development functions • Development of custom business applications

  12. Transaction Processing Systems (3)

  13. Management Information Systems • The core role of IT in the 1970’s and early 1980’s • Aggregate transaction and produce summary reports • Accounting (AR / AP / Payroll / GL) • Inventory control • Financial statements • These are analytical systems

  14. MIS Critical Success Factors • Timeliness • Users get information when they need it • Accuracy • Automated and manual internal controls must ensure accuracy • Consistency • In data collection and processing

  15. Decision Support Systems (1) • Decision Support Systems (DSS) provide quantitative tools to help managers make better decisions • Executive Information Systems (EIS) are a specialized form of DSS • DSS and EIS systems often use data warehousing and data mining to find interesting nuggets • DSS and EIS systems often use artificial intelligence

  16. Decision Support Systems (2) • Quote from Frito-Lay President • Ten years ago, I could have told you how Doritos were selling west of the Mississippi • Today, I can tell you how well they are selling in California, in Orange County, in the town of Irvine, in the local Vons supermarket, in the special promotion, at the end of Aisle 4, on Thursdays

  17. Decision Support Systems (3) • What-If analysis • Sensitivity analysis • (Elasticity) • Goal-seeking • Optimization analysis • Excel’s Solver is a good example • Data mining may play a part • Market basket analysis (AM/PM example)

  18. Who Uses a DSS? • Insurance (Florida hurricanes) • Assess regions most prone to disaster and the probable risk • Predict types of structures most prone to hurricanes • Telecommunications • Examine call patterns of delinquent customers to predict which will become bad debts • Use neural networks to detect fraudulent calls and charges • Make sure to watch the FedEx custom critical video

  19. Who Uses a DSS? • Credit cards • Examine transactions and compare against known spending patterns • Use known customers to predict credit worthiness of new accounts • Airlines • Fares change every hour or so • Maximize revenue per seat mile • Think of the scheduling problem during a snowstorm

  20. Executive Information Systems • These are a specialized form of DSS • Goals • Consolidation of information • Drill-down • Slice and dice • Many EIS systems rely on digital dashboards

  21. View of IT

  22. Artificial Intelligence (Introduction) • Designed to leverage human capabilities rather than replace them • Goals • Develop machines that think • We are trying to mimic human intelligence • There philosophical and moral debates about AI

  23. Artificial Intelligence (The Turing Test) • A human interviewer and computer interact • The test is passed if: • The computer did not know if it was interacting with a person • The person did not know if it was interacting with a computer • No machine has ever passed the Turing test

  24. AI (Case Studies) • Authorizing financial transactions • AMEX fraud detection • Configure hardware and software • Dell and others • Problem diagnosis

  25. Applications of AI • Decision management • Diagnostic • Design • Product or process selection • Process control

  26. Domains of AI • Expert systems and knowledge-based systems • Neural networks • Fuzzy logic • Soft computing • Neural networks • Generic Algorithms • Robotics • Natural Interfaces

  27. Expert Systems • The machine is acts as the expert • They are knowledge-based information systems • Types of knowledge bases • Case based • Frame based • Object bases • Rule based • Pharmacologic interaction • Medical diagnosis

  28. AI (Neural Networks) • Try to mimic the operation of the human brain • Software that learns • Handwriting recognition • Medical diagnosis • Pattern recognition • Sports betting systems

  29. AI (Fuzzy Logic) • Deals with uncertainty • Near, far, similar to • Example • Auto-focus cameras

  30. AI (Genetic Algorithms and Intelligent agents) • Genetic algorithms • Conceptually similar to evolution and genetic mutation • Intelligent agents • Outlook detects spam and deletes it • The Roomba vacuum (http://store.irobot.com/corp/index.jsp)

  31. AI (Intelligent Agents) • Software surrogate for an end user • Uses built in rules to make decisions for an end user • Adaptive testing • Outlook to delete junk e-mail • User interface agents • Help users run software

  32. Data Mining • Use data mining to sift through information to uncover hidden patterns • More later

  33. Business Processes • A standardized set of activities that accomplish a specific task • Business processes are typically connected together • A process should be stable (have few, if any, exceptions)

  34. Business Process (Order to Cash) • Issue a sales quotation (sales) • Receive a purchase order (sales) • Issue a sales order (sales) • Ship goods (warehouse) • Issue an invoice (accounting) • Receive payment (accounting

  35. Optimizing Business Processes • Improving a business process can • Speed the checkout process (automated check stands) • Reduce cost (online banking and other transactions) • Production and manufacturing optimization • Business processes exist in every functional area of a business

  36. Categorizing Business Processes • Customer facing processes • Seen by the customer • Your Web site • Business facing processes • Seen by the business • Human resource systems

  37. Enhancing Business Processes • Business Process Improvement • Business Process Reengineering • Business Process Modeling • A graphical description of a business process

  38. Business Process Improvement • Make incremental improvements on existing business processes • Take advantage of new technologies • Simple automation tasks • Process improvement can be continuous or apply to a discrete processes

  39. Business Process Reengineering • Redesign workflow and existing business processes • Reengineering is a sliding scale • From a simple change to a process • To a complete overhaul of the way a company does business • This can carry a high risk of failure

  40. Deciding What to Reengineer • Analyze the costs and benefits of the project using financial and accounting methods • Perform risk assessment

  41. Modeling (General) • A model is a simplified, often pictorial, representation of reality • We can model many things • Architectural plans and drawing • 3-dimensional electronic models • Models of business processes

  42. Business Process Modeling • Models can be used reverse engineer as system (as-is process) • Models can be used to design new processes and workflows (to-be process) • Several diagramming tools are used to model systems • Flowcharts / UML diagrams / Use case diagrams / etc…

  43. Flowchart of a Business Process

  44. Business Process Management • We take a proactive and enterprise-wide approach to • Understanding processes • Optimizing them • Integrating processes across functional business units