CHAPTER 9 -ENABLING THE ORGANIZATION - DECISION MAKING

CHAPTER 9: ENABLING THE ORGANIZATION -DECISION MAKING

 

DECISION MAKING

Reasons for growth of decision-making information systems.

• People need to analyze large amounts of information.
• People must make decisions quickly.
• People must apply sophisticated analysis techniques, such as modeling and forecasting, to make good decisions.
• People must protect the corporate asset of organizational information.

MODEL

-A simplified representation or abstraction of reality.

-IT system is an enterprise.

TRANSACTION PROCESSING SYSTEMS

• Moving up through the organizational pyramid users move from requiring transaction information to analytically information.

• 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. more to save the information.
• Online Analytically Processing (OLAP) - The manipulation of information to create business intelligence in support of strategic decision making. do the information for decision making. more to analysis.

 

DECISION SUPPORT SYSTEMS (DSS)

 

Models information to support managers and business professionals during the decision-making process (MANAGERS)

Describe the three quantitative models typical used by decision support systems:

• Three quantitative models used by DSS's include:

Sensitivity analysis. - A special case of what-if analysis, is the study of the impact on the other variables when one variables is changed repeatedly.

What-if analysis. - Checks the impacts of a change in a variable or assumption on that model.

Goal - seeking analysis. - Finds the inputs necessary to achieve goal such as a desired level of output.

 Interaction between a TPS and a DSS

EXECUTIVE INFORMATION SYSTEMS

A specialized DSS that supports senior level executives within the organization (EXECUTIVES)

•  Most EISs offering the following capabilities:

Consolidation - The aggregation of data from simple-\ roll-ups to complex groupings of interrelated information.

Drill-down - Enables users to view details, and details of details, of information.

Slice-and-dice -  The ability to look at information from different perspectives.

Interaction between a TPS and EIS 

•  Digital dashboard - integrates information from multiple components and present it in a unified display.

 ARTIFICAL INTELLIGENCE (AI)

The ultimate goal of AI is the ability to build a system that can mimic human intelligence.

List and describe four types of artificial intelligence systems:

❤Four most common categories of AI include;

Expert System – Computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems. Eg: Playing Chess.

Neural Network – Attempts to emulate the way the human brain works. Eg: Finance industry uses neural network to review loan applications and create patterns or profiles of applications that fall into two categories – approved or denied.

Fuzzy Logic – A mathematical method of handling imprecise or subjective information. Eg: Washing machines that determine by themselves how much water to use or how long to wash.

Genetic Algorithm – An artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem. Eg: Business executives use genetic algorithm to help them decide which combination of projects a firm should invest.

Intelligent Agent – Special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users which is Multi-agent systems and Agent-based modeling.Eg:  Shopping bot Software that will search several retailer’s websites and provide a comparison of each retailers’s offering including price and availability.

DATA MINING

Data-mining software 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.

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. Eg: Consumer goods by content, brand loyalty or similarity.

 

 

 

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. Eg: Maytag uses association detection to ensure that each generation of appliances is better than the previous generation.

 

STATISTICAL ANALYSIS

• Statistical Analysis – Performs such functions as information correlations, distributions, calculations, and variance analysis.
• Forecast – Predictions made on the basis of time-series information.
• Time-series Information – Time-stamped information collected at a particular frequency. Eg: Kraft uses statistical analysis to assure consistent flavor, color, aroma, texture, and appearance for all of its lines of foods.