MGT300 - CHAPTER 9

                              ENABLING THE ORGANIZATION - DECISION MAKING

 Decision Making

  1. Reason for the growth of decision-making information systems:
  • People need to analyze large amount of information 
  • People must make decisions quickly
  • People must apply sophisticated analysis technique, such as modeling and forecasting , to make good decisions
  • People must protect the corporate asset of organizational information



Transaction Processing System
  • Transaction processing system - the basic business system that servers the operational level (analyst) 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 System
  • Models information to support managers and business professionals during the decision-making process
  • Three qualitative model 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 input necessary to achieve a goal such as desired level of output 
Executive Information System
  • A specialized DSS that support senior level executives within the organization
  • Most EISSs offering the following capabilities:
  1. Consolidation - involves gregation of information and features simple roll-ups to complex grouping of interrelated information
  2. Drill-down - enables users to get details of information
  3. Slice and dice - looks at information from different perspectives


Artificial Intelligence (AI)
  • Intelligent system - various commercial applications of artificial intelligence
  • Artificial intelligence - simulates human intelligence such as the ability to reason and learn
          *Advantages - can check info on competitor
  • The ultimate goal of AI is the ability t built a system that can mimic human intelligence 
  • 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
            → Neutral network - attempts to emulate the way the human brain works. Eg : finance industry uses neutral network to review loan application and create patterns or profile of applications that fall into  two categories - approved or denied
                    ⇨Fuzzy logic - a mathematical method of handling imprecise or subjective information. Eg : washing machine that determine by themselves how much water to use or how long to wash.
  • Genetic algorithm - an artificial intelligence system that mimics the evolutionary ,survival of the -fittest process to generate increasingly better solutions to a problem.
          *Eg: business executives use generic algorithm to help them decide which combination of projects a firm should invest.
  • Intelligent agent - a special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users
          *Multi-agent system
          *Agent-based modelling  
          *Eg : shopping bot - software that will search several retailer's websites  and provide a comparison of each retailer's offering including prive and availability


    Data mining
  • Common forms of data mining analysis capabilities include :
         *Cluster analysis
         *Association detection 
         *Statistical 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 system depend on cluster analysis to segment customer information and identify behavioral traits
          *Eg: consumer goods by content, brand loyalty or similarity
  • 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 check out scanner information to detect customer's buying behavior and predict future behavior by identifying affinities among customers' choice of products and services.
            *Eg: Maytag uses association detection to ensure that each generation of appliances is better that the previous generation
  • Statistical analysis - perform such functions as information correlations, distribution, calculations. and variance analysis
          *Forecast - prediction 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
  
                    

Comments

Post a Comment

Popular posts from this blog

MGT300 - CHAPTER 15

Image
                              Outsourcing in the 21 st Century Outsourcing Projects Ø   Insourcing (in-house development) – a common approach using the professional expertise within an organization to develop and maintain the organization’s information technology system Ø   Outsourcing – an arrangement by which one organization provides a service or services for another organization that choose not to perform them-in-house Ø   Factors driving outsourcing growth include : * Core competencies – many companies have recently begun to consider outsourcing as a means to fuel revenue growth rather just a cost-cutting measure * Financial saving – it is typically cheaper to hire workers in China and India than similar workers in the United States * Rapid growth – an organization is able to acquire best-p...
Read more

MGT300-CHAPTER 8

Image
                                  ACCESSING ORGANIZATIONAL INFORMATION - DATA WAREHOUSE  History of Data Warehousing In the 1990's executives became less concerned with the day-to-day business operatios and with more concerned with overall business functions. The data warehouse provided the ability to support decision making without disrupting the day-to-day operations because :           - Operational information is mainly current -does not include the history for better decision making           - Issue of quality information           - Without information history, it is difficult to say how and why things change over time Data Warehouse Fundamentals Data Warehouse - a logical collection of information - gathered from many different operational databases - that support business analysis activities and dec...
Read more

MGT 300 - CHAPTER 12

Image
Integrating the organization from end-to-end – Enterprise Resource Planning (ERP) Ø   Integrating SCM, CRM and ERP *SCM,CRM and ERP are the backbone of e-business *Integration of this applications is the key to success for many companies *Integration allows the unlocking of information to make it available to any user, anywhere, anytime   Ø   Integration tools *Many companies purchase modules from an ERP vendor ,and a CRM  vendor and must integrate the different modules together - Middleware : several different types of software which sits in the middle of and provide connectivity between two or more software applications - Enterprise application integration (EAI ) : packages together commonly used functionality which reduced the time necessarily to develop solutions that integrate applications from multiples vendors Ø   ERP  system must integrate various ...
Read more