CHAPTER NINE : ENABLING THE ORGANIZATION - DECISION MAKING

DECISION MAKING

Reasons for Growth of Decision Making Information System

-          People need to analyze large amounts of information – Improvements in technology itself, innovations in communication, and globalization have resulted in a dramatic increase in the alternatives and dimensions people need to consider when making a decision or appraising an opportunity

-          People must make decisions quickly – Time is of the essence and people simply do not have time to sift through all the information manually

-          People must apply sophisticated analysis techniques, such as modeling and forecasting, to  make good decisions – Information systems substantially reduce the time required to perform these sophisticated analysis techniques

-          People must protect the corporate asset of organizational information – Information systems offer the security required to ensure organizational information remains safe.

Model – A simplified representation or abstraction of reality

IT systems in an enterprise

TRANSACTION PROCESSING SYSTEM

Moving up through the organizational pyramid users move from requiring transactional information to analytical information

Transaction processing system – the basic business system that serves the operational level (analysis) 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

Online analytical processing (OLAP) – the manipulation of information to create business intelligence in support of strategic decision making

DECISION SUPPORT SYSTEMS

Decision support system (DSS) – models information to support managers and business professionals during the decision-making process

Three quantitative models used by DSSs include;

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 outputs

What-if analysis

Goal-seeking analysis

EXECUTIVE INFORMATION SYSTEM

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 information

-          Slice-and-dice – looks at information from different perspectives

Interaction between a TPS and an EIS

Interaction between a TPS and a DSS

Digital dashboard – integrates information from multiple components and presents it in a united display

ARTIFICIAL INTELLIGENCE (AI)

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

Intelligent system – various commercial applications of artificial intelligence

Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn

- Four most common categories of AI include;

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

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

o   Fuzzy logic – a mathematical method of handling imprecise or subjective information

3.       Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem

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

DATA MINING

Data-mining software includes many forms of AI such as neutral networks and expert systems

I)                    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 depends on his system by segment customer info’s and identity behavioral traits.

II)                  Association Detection – reveals the degree to which variables are related and the nature and frequency of these relationship in the information.

Market basket analysis : analysis such items as Websites and the checkout scanner information to detect customers buying behavior  and predict future behavior by identifying affinities among the customers choices of products and services.

III)                Statistical Analysis – performs such functions as information correlations, distribution, calculation and variance analysis.

-          Forecast : predicts made on the basis time series information

-          Time series information : time stamped information collected at a particular frequency.