Review of Enterprise Information Systems

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Review of Enterprise Information Systems
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GROWTH OF DECISION MAKING SYSTEMS

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

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Types of Information

• Transactional Information
• All data contained in a single unit of work
• Supports daily operational tasks
• Analytical information
• All organizational information
• Supports managerial analysis and decision making

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Organizational Need for Information

• Different levels of management have different
  information needs

Executives
Strategic Mgt.
Managers
Tactical Mgt.
Analysts
Operational Mgt.
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Strategic Management

• Responsibilities
• Long-range strategic planning
• Planning and organizing
• Employment levels

• Information Needs
• Summarized historical data
• Summarized present data
• Internal and external data sources

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Tactical Management (Middle Mgt.)

• Responsibilities
• Short-term tactical decisions
• Accomplish overall goals set by strategic planners

• Information Needs
• Uses mostly internal data
• Needs detailed past and present information

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Operational Management

• Responsibilities
• Day to day operations
• Implements tactical decisions

• Information Needs
• Needs detailed current information from internal
  sources

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TRANSACTION PROCESSING SYSTEMS

• Transaction processing system (TPS) - the basic
  business information system that serves the
  operational level (analysts) in an organization

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Transaction Processing

• major business processes
• provide the mission-critical activities when
  company produces a product or provides a service
• transaction may generate additional transactions
• large volume and repetitive transactions

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TPS Characteristics

• Keeps track of current and potential resources
• Large amounts of data are processed
• Sources of datainternal
• output for an internal audience
• High level of accuracy, data integrity, and
  security is needed

• processes information on a regular basis
• Large storage (database) capacity is required
• High processing speed is needed due to the high
  volume
• High processing reliability is required
• Must have ability to query

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TPS Features

• Rapid response
• Turnaround time must be seconds or less
• Reliability
• Any disruption may stop a business
• Inflexibility
• Every transaction processed exactly the same way
• Controlled processing
• Must support basic function of the organization

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TRANSACTION PROCESSING SYSTEMS

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

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Typical TPS Tasksfrom all functional areas

• Order Processing
• General ledger activities
• Accounts payable and receivable
• Inventory and shipping
• Payroll
• Required reportsIRS, Sales tax, etc.

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Management Information System (Information
Reporting System)

• Used by managers to
• identify problems
• Solve problems
• Make decisions
• Are things working well?

• Status of the organization
• Compares output with previous periods
• Produces periodic reports
• Scheduled reports
• Demand reports
• Exception reports
• Information produced is decision-oriented

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MIS plays key role in middle management functions

• Planningfacilitates analysis by providing key
  information
• Staffingidentify, recruit, train, and retrain
  personnel

• Directingfacilitates communications in and
  outside organization
• Controllingprovides performance feedback

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Criteria for Effective MIS

• Provide high quality information
• Responsive to managers inquiries
• Provide exception reporting
• Flexible
• Accepted by the user

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MIS uses only TPS data
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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
• Sensitivity analysis the study of the impact
  that changes in one (or more) parts of the model
  have on other parts of the model
• What-if analysis checks the impact of a change
  in an assumption on the proposed solution
• Goal-seeking analysis finds the inputs
  necessary to achieve a goal such as a desired
  level of output

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DECISION SUPPORT SYSTEMS

• Interaction between a TPS and a DSS

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Typical DSS Applications

• General Accidental Insurancecustomer buying
  patterns and fraud detection
• Bank of Americacustomer profiles
• Frito-Layprice, advertising, and promotion
  selection

• Burlington Coat Factorystore location and
  inventory mix
• United Airlinesflight scheduling, passenger
  demand forecast

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Who are our most frequent customers?
Customer Data Warehouse
Use statistical analysis to identify the top 25
of frequent shoppers sorted by sales volume.
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Do customers live close to retail outlets?
Customer Data Warehouse
Establish correlation between storelocation,
customer address and sales frequency.
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How can we re-segment inactive customers?
Customer Data Warehouse
Establish past buying patterns and then create
marketing plan to reach inactive customers.
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Group Decision Support

• Interactive computer-based system to facilitate
  solutions of unstructured problems by a group

35-70 of time spent in meetings of
meetings length of meetings of attendees
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Session Planning
Session Manager
Organizational Memory
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EXECUTIVE INFORMATION SYSTEMS

• Executive information system (EIS) an
  information system
• to facilitate and support decision making by
  senior executives
• With easy access to internal and external
  information
• To meet strategic goals

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Differences with other types of information
systems (F. Kelly)

• Specifically tailored to executives information
  needs
• Access data about specific issues and problems as
  well as aggregate
• Provides extensive on-line analysis tools
• Accesses a broad range of internal and external
  data
• Simple graphic user interface
• Presents information in a graphical format

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EIS 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

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EIS Example

• Verizon CIOtracks 100 IT systems on a single
  screen
• New set of charts every 15 second
• 300 measures of digital performance

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Digital Dashboards

• Digital dashboard integrates information from
  multiple components and presents it in a unified
  display

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Artificial Intelligence (AI)

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

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Types of Artificial Intelligence

• Game playing programming computers to play games
  such as chess, checkers, poker with the ability
  to learn from opponents moves
• Expert systems programming computers to make
  decisions in real-life situations
• Natural language programming computers to
  understand human languages

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Types of Artificial Intelligence

• Neural networks systems that attempt to simulate
  intelligence by reproducing the connections made
  in animal brains
• Roboticssystems that see and hear and react
  to sensory stimuli

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Expert Systems

• Primary goal is to make expertise available to
  decision makers and technicians who need answers
  quickly when a human expert is not available
• Computers loaded with in-depth knowledge can
  assist with situation assessment and long-range
  planning

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How do expert systems work?

• An expert identifies rulestruths that have been
  learned from experience
• This expertise is obtained from human expert and
  programmed as a set of rules knowledge base
• Computer uses the rules to come to a decision
  based on a set of criteria

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Examples of Expert Systems

• Medical diagnosis
• Insurance underwriting
• Loan risk
• Disaster preparedness
• Automotive diagnosis
• Crop and soil analysis

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• Expert system is reactive
• Data mining is proactive

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Data Mining

• Data-mining systems sift instantly through
  information to uncover patterns and relationships
• Data-mining systems include many forms of AI such
  as neural networks and expert systems

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What is Knowledge Management?

• Process of accumulating and creating knowledge
  efficiently, managing a knowledge base, and
  facilitating the sharing of knowledge
• Extrinsic capitalbooks, databases, manuals, etc.
• Intrinsic capitalknowledge about the business
  that employees have gained through experience

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What is Knowledge Management?

• Information systems are accessible to all who
  need it
• In a form that is useful
• That will cut the time needed to find information
• To allow better decisions

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Facts, images, or sounds that may not be
organized or pertinent
Data
Data that has been organized and is pertinent for
a particular use
Information
Knowledge
Combination of instincts, ideas, rules, and
procedures that guide decisions
Learning environment that encourages risk
taking Identifies information crucial to
long-term success Creates cross-functional experts
Knowledge Sharing
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Data
Traditional computer systems programmed to follow
specific steps
Information
Knowledge
KM recognizes new patterns in data Adapts to new
concepts Assesses true importance of
information Exposes untapped sources of revenue
and savings
Knowledge Sharing
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Knowledge-based Organization (Drucker)

• Creates knowledge sharing
• Specialists who direct and discipline their own
  performance through organizational feedback

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Knowledge Management Activities

• Knowledge identificationwhat knowledge is
  critical to the organizations decision-making
  capabilities?
• Knowledge discovery and analysisproper knowledge
  must be found, analyzed, and put in proper
  context
• Establish the organizations knowledge basewhat
  are the best practices?
• Knowledge distribution and use

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Corporate Portal

• Wide variety of structured and unstructured data
  sources through single, personalized Web-based
  interface
• Uses internets, intranets, extranets

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Key is Knowledge Discovery

• Identification of novel and useful patterns in
  data
• OLAPOnline Analytical Processing
• DSS Modeling using spreadsheets and graphics
• EX Sales data aggregated by region, product
  type and sales channel
• Data mining
• Finding predictive information in large databases
• Artificial neural networks
• Computer learns by examples
• Discovers new classes, patterns, and
  relationships in data sites

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Artificial Neural Network Examples

• Handwriting recognition
• Speech recognition
• Future stock prices
• Assessing credit risks
• Optimizing cargo routing
• Supply chains

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What Does Knowledge Management Signal?

• Organization is managed so employees can apply
  the knowledgefreedom to use available knowledge
• Information from experts is captured
• Sharing of information is encouragedopen
  environment
• The company values intellectual capital