1. McGraw-Hill/Irwin © The McGraw-Hill Companies, All Rights Reserved CHAPTER 9 Enabling the Organization—Decision Making

2. 9-2 LEARNING OUTCOMES 9.1 Define the systems that organizations use to make decisions and gain competitive advantages 9.2 Describe the three quantitative models typically used by decision support systems

3. 9-3 LEARNING OUTCOMES 9.3 Describe the relationship between digital dashboards and executive information systems 9.4 List and describe four types of artificial intelligence systems

4. 9-4 Business Intelligence Enablers Decision-enabling, problem-solving, and opportunity-seizing systems

5. 9-5 DECISION MAKING

6. 9-6 DECISION MAKING Model – a simplified representation or abstraction of reality Models can calculate risks, understand uncertainty, change variables, and manipulate time

7. 9-7 TRANSACTION PROCESSING SYSTEMS Moving up through the organizational pyramid users move from requiring transactional information to analytical information

8. 9-8 TRANSACTION PROCESSING SYSTEMS Transaction processing system - the basic business system that serves the operational level (analysts) in an organization 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

9. 9-9 DECISION SUPPORT SYSTEMS Decision support system (DSS) – models information to support managers and business professionals during the decision-making process Three quantitative models used by DSSs include: 1.Sensitivity analysis – the study of the impact that changes in one (or more) parts of the model have on other parts of the model 2.What-if analysis – checks the impact of a change in an assumption on the proposed solution 3.Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of output

10. 9-10 DECISION SUPPORT SYSTEMS What-if analysis

11. 9-11 DECISION SUPPORT SYSTEMS Goal-seeking analysis

12. 9-12 DECISION SUPPORT SYSTEMS Interaction between a TPS and a DSS

13. 9-13 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

14. 9-14 EXECUTIVE INFORMATION SYSTEMS Interaction between a TPS and an EIS

15. 9-15 EXECUTIVE INFORMATION SYSTEMS Digital dashboard – integrates information from multiple components and presents it in a unified display

16. 9-16 ARTIFICIAL INTELLIGENCE (AI) Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn Intelligent system – various commercial applications of artificial intelligence

17. 9-17 ARTIFICIAL INTELLIGENCE (AI) The ultimate goal of AI is the ability to build a system that can mimic human intelligence

18. 9-18 ARTIFICIAL INTELLIGENCE (AI) Four most common categories of AI include: 1.Expert system – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems 2.Neural Network – attempts to emulate the way the human brain works –Fuzzy logic – a mathematical method of handling imprecise or subjective information

19. 9-19 ARTIFICIAL INTELLIGENCE (AI) Four most common categories of AI include: 3.Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of- the-fittest process to generate increasingly better solutions to a problem 4.Intelligent agent – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users

20. 9-20 DATA MINING Data-mining software includes many forms of AI such as neural networks and expert systems