BUSINESS DRIVEN TECHNOLOGY UNIT 3: Enhancing Business Decisions OPENING CASE

1. BUSINESS DRIVEN TECHNOLOGY UNIT 3: Enhancing Business Decisions OPENING CASE Revving Up Sales at Harley-Davidson

2. Unit Three • The chapters in this unit include: • Chapter Nine – Enabling the Organization – Decision Making • Chapter Ten – Extending the Organization – Supply Chain Management • Chapter Eleven – Building a Customer-centric Organization – Customer Relationship Management • Chapter Twelve – Integrating the Organization from End to End – Enterprise Resource Planning

3. Unit Three • Decision-enabling, problem-solving, and opportunity-seizing systems

4. BUSINESS DRIVEN TECHNOLOGY Chapter Nine: Enabling the Organization – Decision Making

5. LEARNING OUTCOMES 9.1 Define the four systems organizations use to make decisions and gain competitive advantages 9.2 Describe the three quantitative models typically used by decision support systems 9.3 Describe the relationship between digital dashboards and executive information systems

6. LEARNING OUTCOMES 9.4 List and describe three types of artificial intelligence systems 9.5 Describe three types of data-mining analysis capabilities

7. CHAPTER NINE OVERVIEW • The amount of information people must understand to make decisions, solve problems, and find opportunities is growing exponentially

8. CHAPTER NINE OVERVIEW • Model – a simplified representation or abstraction of reality • The following systems use models to support decision making, problem solving, and opportunity capturing: • Decision support systems (DSS) • Executive information systems (EIS) • Artificial intelligence (AI) • Data mining

9. DECISION SUPPORT SYSTEMS Start

10. DECISION SUPPORT SYSTEMS • Decision support system (DSS) – models information to support managers and business professionals during the decision-making process • Three quantitative models typically used by DSSs: • 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

11. DECISION SUPPORT SYSTEMS • What-if Analysis

12. DECISION SUPPORT SYSTEMS • Goal-seeking analysis

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. EXECUTIVE INFORMATION SYSTEMS • Digital dashboard – integrates information from multiple components and present it in a unified display

15. ARTIFICAL INTELLIGENCE (AI) • Intelligent systems – various commercial applications of artificial intelligence • Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn and typically can: • Learn or understand from experience • Make sense of ambiguous or contradictory information • Use reasoning to solve problems and make decisions

16. ARTIFICAL INTELLIGENCE (AI) • The ultimate goal of AI is the ability to build a system that can mimic human intelligence

17. ARTIFICAL INTELLIGENCE (AI) • The three most common categories of AI include: • Expert systems – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems • Neural Networks – attempts to emulate the way the human brain works • Intelligent agents – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users

18. DATA MINING • Data-mining software typically includes many forms of AI such as neural networks and expert systems

19. DATA MINING • Common forms of data-mining analysis capabilities include • Cluster analysis • Association detection • Statistical analysis

20. Cluster Analysis • Cluster analysis – a technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible • CRM systems depend on cluster analysis to segment customer information and identify behavioral traits

21. Association Detection • Association detection – reveals the degree to which variables are related and the nature and frequency of these relationships in the information • Market basket analysis – analyzes such items as Web sites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services

22. Statistical Analysis • Statistical analysis – performs such functions as information correlations, distributions, calculations, and variance analysis • Forecasts – predictions made on the basis of time-series information • Time-series information – time-stamped information collected at a particular frequency 9-22

23. OPENING CASE STUDY QUESTIONSRevving Up Sales at Harley-Davidson • Explain how Talon helps Harley-Davidson employees improve their decision-making capabilities and highlights potential business opportunities • Assess the business impact Harley-Davidson could gain by using executive information systems • Determine how Harley-Davidson can benefit from using artificial intelligence to support its business operations

24. CHAPTER NINE CASEFinding the Best Buy • Best Buy has annual revenues of over $1 billion and employs over 10,000 people • The company uses data-mining to: • Simplify information • Consolidate information • Enhance infrastructure operations • Reduce complexity • Increase performance • Streamline business processes

25. CHAPTER NINE CASE QUESTIONS • Summarize why decision making has improved at Best Buy with the implementation of a data warehouse • Determine what types of information might be presented to a Best Buy marketing executive through a digital dashboard • Evaluate how Best Buy could use the information in the data warehouse for sales forecasting