CHAPTER 9

Enabling Organization-Decision Making

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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

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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

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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: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

What-If Analysis

Goal Seeking Analysis

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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

Digital dashboard – integrates information from multiplecomponents and present it in a unified display

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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

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AI Components

1. Expert systems – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems

2. Neural Networks – attempts to emulate the way the human brain works

3. Intelligent agents – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users

Most expert systems contain information from manyhuman experts and can therefore perform a betteranalysis than any single human

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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

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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

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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

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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

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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

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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

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Case Questions

1. Summarize why decision making has improved at Best Buy with the implementation of a data warehouse

2. Determine what types of information might be presented to a Best Buy marketing executive through a digital dashboard

3. Evaluate how Best Buy could use the information in the data warehouse for sales forecasting