Chapter 6: Managerial Support Systems

Uses of data mining are:

Cross-selling: Identify products and services that will most appeal to existing customer segments and develop cross-sell and up-sell offers tailored to each segment Customer churn: Predict which customers are likely to leave your company and go to a competitor and target those customers at highest risk Customer retention: Identify customer characteristics associated with highest lifetime value and develop strategies to retain these customers over the long term Direct marketing: Identify which prospects should be included in a mailing list to obtain the highest response rate Fraud detection: Identify which transactions are most likely to be fraudulent based on purchase patterns and trends; identify insurance claims that are most likely to be fraudulent based on similar past claims Interactive marketing: Predict what each individual accessing a Web site is most likely interested in seeing Market basket analysis: Understand what products or services are commonly purchased together (e.g., beer and diapers) and develop appropriate marketing strategies Market segmentation: Segment existing customers and prospects into appropriate groups for promotional and evaluation purposes and determine how to approach each segment for maximum results Payment or default analysis: Identify specific patterns to predict when and why customers default on payments Trend analysis: Investigate the difference between an average purchase this month versus last month and prior months

Difference between OLAP and Data Mining

Difference between OLAP and data mining: OLAP has been described as human driven analysis, whereas data mining might be viewed as technique-driven.

Two examples of expert systems that are being used to assist in decision making

Expert system examples are: • MYCIN, which was developed at Stanford University: diagnose and prescribe treatment for meningitis and blood diseases • General Electric Co. created an expert system called CATS-1: to diagnose mechanical problems in diesel locomotives

Compare the raster-based and vector-based approaches to geographic information systems (GISs). What are the primary uses of each approach?

Geographic information system (GIS): names for a family of applications based on manipulation of relationships in space Raster-based GISs rely on dividing space into small, equal-sized cells arranged in a grid. In a GIS, these cells (rasters) can take on a range of values and are aware of their location relative to other cells. Vector-based GISs are widely used in public administration and utilities and, arguably, are the most common approach used in business. Vector systems associate features in the landscape with either a point, a line, or a polygon

What is the purpose of a group support system (GSS)?

Group Support System: A collaboration tool that is aimed at supporting meetings-is a specialized type of groupware called a group support system (GSS). GSSs are an important variant of DSSs in which the system is designed to support a group rather than an individual.

DSS Generator

IT is a software package that provides a set of capabilities to build a specific DSS quickly and easily

OLAP-Online Analytical processing

OLAP is essentially querying against a database, employing OLAP software that makes it easy to pose complex queries along multiple dimensions, such as time, organizational unit, and geography.

Two examples of the use of virtual reality in an organizational setting

The U.S. Army uses VR to train tank crews. Through multiple large video screens and sound, the soldiers are seemingly placed inside a tank rolling across the Iraqi desert, and they have to react as if they were in a real tank battle. In the field of medicine, medical students are learning through collaboration and trial-and-error on virtual cadavers

Expert Systems

The expert systems branch is concerned with building systems that incorporate the decision-making logic of a human expert.

Artificial Intelligence (AI)

The idea of artificial intelligence (AI), the study of how to make computers do things that are currently done better by people

How the 3 DSS components interact

The user interacts with the DSS through the dialog management component, identifying the particular model and data set to be used, and then the DSS presents the results to the user through this same dialog management component. The model management and data management components largely act behind the scenes, and they vary from relatively simple for a typical spreadsheet model to quite complex for a mathematical programming-based scheduling model.

Specific DSS

These are the actual applications that assist in the decision-making process.

Knowledge Management

a set of management practices that is practical and action oriented. KM involves the strategies and processes of identifying, creating, capturing, organizing, transferring, and leveraging knowledge to help individuals and firms compete

Why has EIS become part of business intelligence

an EIS is a hands-on tool that focuses, filters, and organizes an executive's information so he or she can make more effective use of it.

Characteristics of an Executive Information System

an executive information system (EIS) is a system that delivers online current information about business conditions in an aggregate form easily accessible to senior executives and other managers. EIS: designed to be used directly by these managers without the assistance of intermediaries. An EIS uses state-of-the-art graphics, communications, and data storage methods to provide the executive easy online access to current information about the status of the organization.

Knowledge management systems (KMSs)

are systems that enable individuals and organizations to enhance learning, improve performance, and, hopefully, produce long-term sustainable competitive advantage. KMS is a system for managing organizational knowledge.

Data mining

employs a variety of technologies (such as decision trees and neural networks) to search for, or "mine," "nuggets" of information from the vast quantities of data stored in an organization's data warehouse. Data mining, which is sometimes considered a subset of decision support systems, is especially useful when the organization has large volumes of transaction data in its warehouse.

Virtual reality (VR)

refers to the use of computer-based systems to create an environment that seems real to one or more senses (usually including sight) of the human user or users

Data warehousing

the idea of a company pulling data from its operational systems and putting the data in a separate data warehouse so that users may access and analyze the data without interfering with the operational systems.

Neural Networks

the study of how the human nervous system works, but which in fact uses statistical analysis to recognize patterns from vast amounts of information by a process of adaptive learning.

List at least two techniques (decision technologies) that are used in data mining

• Decision trees • Linear and Logistic regression • association rules for finding patterns of co-occurring events • clustering for market segmentation; rule induction, • the extraction of if-then rules based on statistical significance; • nearest neighbor-the classification of a record based on those most similar to it in the database • genetic algorithms

Describe two examples of specific DSSs that are being used to assist in decision making.

• Pro forma financial statement generator: Using a spreadsheet package such as Microsoft Excel, a manager builds a model to project the various elements of the organization or division financial statement into the future. • Police-beat allocation system used by a California city: This system enables a police officer to display a map outline and call up data by geographic zone, which shows police calls for service, types of service, and service times. • Interactive system for capacity planning and production scheduling in a large paper company: This system employs detailed historical data and forecasting and scheduling models to simulate overall performance of the company under differing planning assumptions.

Briefly describe the several areas of artificial intelligence (AI) research

• The work in natural languages, primarily in computer science departments in universities and in vendor laboratories, is aimed at producing systems that translate ordinary human instructions into a language that computers can understand and execute • Perceptive systems research involves creating machines possessing a visual and/or aural perceptual ability that affects their physical behavior. In other words, this research is aimed at creating robots that can "see" or "hear" and react to what they see or hear. • With genetic programming or evolutionary design, the problem is divided into multiple segments, and solutions to these segments are linked together in different ways to breed new "child" solutions. After many generations of breeding, genetic programming might produce results superior to anything devised by a human. • The expert systems branch is concerned with building systems that incorporate the decision-making logic of a human expert • A newer branch of AI is neural networks, which is named after the study of how the human nervous system works, but which in fact uses statistical analysis to recognize patterns from vast amounts of information by a process of adaptive learning.

Three primary components that make Decision Support System are

-Data management -Model Management -Dialog management

Uses of Neural Networks are

Credit rating and risk assessment for Share price forecast Insurance risk evaluation for Commodity price forecast Fraud detection for Economic indicator predictions Insider trading for detection Process control Direct mail profiling for Weather prediction Machinery defect diagnosis for Future drug performance Character recognition for Production requirements