2. Enabling Organization-Decision Making
CHAPTER 9
Enabling Organization-Decision Making

3. Learning Outcomes
Define the four systems organizations use to make decisions and gain competitive advantages
Describe the three quantitative models typically used by decision support systems
Describe the relationship between digital dashboards and executive information systems
List and describe three types of artificial intelligence systems
Describe three types of data-mining analysis capabilities

5. 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

6. Decision Support System (DSS)
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

7. What-If Analysis

8. Goal Seeking Analysis

9. Executive Information System (EIS)
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

10. Digital dashboard – integrates information from multiple
components and present it in a unified display

11. Artificial Intelligence (AI)
Intelligent systems – various commercial applications of 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

12. AI Components
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
Most expert systems contain information from many
human experts and can therefore perform a better
analysis than any single human

13. Data Mining
Data-mining software typically includes many forms of AI such as neural networks and expert systems
Common forms of data-mining analysis capabilities include
Cluster analysis
Association detection
Statistical analysis

14. 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

15. Some Examples of Cluster Analysis
Consumer goods by content, brand loyalty or similarity
Product market typology for tailoring sales strategies
Retail store layouts and sales performances
Corporate decision strategies using social preferences
Control, communication, and distribution of organizations
Industry processes, products, and materials
Design of assembly line control functions
Character recognition logic in OCR readers
Data base relationships in management information systems

16. 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

17. 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

18. Finding 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

19. 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