chapter05.pdf

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Types of Information Systems

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Opening Case: Boeing

 Paperless design process using a

mainframe-based computer-aided design (CAD) tool

 All the design data stored in a database  Huge system, involving thousands of

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Product Design Systems

 CAD systems – accept coded

descriptions of components and

processes and graphically display the resulting product specifications

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Supply Chain Systems

 Determine material requirements  Generate new orders

 Send orders to suppliers  Obtain commitment dates

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 Material requirement planning (MRP)

 Integrate purchasing & production activities  Calculate a schedule based on the output

requirement

 Electronic data interchange (EDI)

 The electronic transfer of business data

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 Supply chain management (SCM)

 System that allows close coordination with

suppliers

 EDI and SCM are part of the general trend

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Manufacturing Systems

 Computer integrated manufacturing

(CIM)

 Computerized data collection + integrated

data flows between design, manufacturing, planning, and other business functions

 The use of CAD, CIM, and other

techniques lead to an

increase in the

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Sales & Marketing Systems

 Point-of-sale (POS) systems –

 Combined with customization techniques,

can be used in direct marketing

 Telemarketing

 Sales force automation (SFA)

 Customer relationship management

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Finance Systems

 Electronic funds transfer (EFT)

 Accounts between companies settled

electronically

 Electronic cash  Program trading

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Information System Categories That

Apply in Any Functional Area of Business

 Difficult to categorize

 Not mutually exclusive

 Features keep changing as the technology

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Office Automation Systems (OAS)

 Help people perform personal record

keeping, writing, and calculations efficiently

 Main types of tools include:

 Spreadsheet programs

 Text & image processing systems  Presentation packages

 Personal database systems and note-taking

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Communication Systems

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Teleconferencing

 The use of electronic transmission to permit

same-time different-place meetings

 Audio conferencing = a single telephone call

involving 3 or more people

 Audiographic conferencing = an extension of

audio conferencing, permitting the participants to see graphical material

 Videoconferencing = interactive meeting

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E-mail, Voice Mail, and Fax

 Issues:

 Social context

 Danger of misrepresentation  Power relationships

 Privacy & confidentiality  Electronic junk mail

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Instant Messaging and Chat

Rooms

 INSTANT MESSAGING = real time

exchange of messages

 CHAT ROOM = informal computer

conference

 Voice chat

 Both are part of the Internet culture, and

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Groupware

 Software and related procedures that help

teams work together by

sharing

information

controlling internal

workflows

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Intranets and Extranets

 INTRANETS:

 Private networks

 Use the same interface as the Web

 Accessible only to company employees  Examples of applications:

 Corporate news

 Employee manuals

 Corporate policies

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 EXTRANETS:

 Similar to intranets, but geared towards

customers

 Examples of applications:

 Detailed product descriptions

 FAQs

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Knowledge Management

 Systems designed to facilitate the sharing

of knowledge rather than information

 Tacit knowledge – understood & applied

unconsciously

 Explicit knowledge – formally articulated,

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Group Support Systems (GSS)

 Support communication by helping

facilitate meetings

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Transaction Processing

Systems

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 Transaction processing system (TPS) =

system that

collects and stores

control

decisions

 Transaction = an event that generates or

modifies data

 Some TPSs bypass clerks, and completely

automate transactions

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Batch vs. Real-time Processing

 Batch processing = transactions are

gathered and processed together later

 Inherent time delays that may cause

significant disadvantages

 Real-time processing = transactions are

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Management and Executive

Information Systems

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 Management information system (MIS) –

provides information for managing an organization

 Extract and summarize data from TPSs

 Allow managers to monitor & direct the

organization

 Provide accurate feedback

 Provide prespecified reports on a

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From MIS to EIS

 Executive information system (EIS) = a

highly interactive

flexible access

monitoring results and general business conditions

 Use both internal and competitive

information

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 Decision support system (DSS) – an

interactive information system that

provides information, models, and data

manipulation tools to help make decisions in semistructured and unstructured

situations

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Degree of problem structure

The Gorry and Scott Morton Grid

Management levels Structured Semistructured Unstructured Operational control Management control Strategic planning Accounts receivable Order entry Inventory control Budget analysis-- engineered costs Short-term forecasting Tanker fleet mix Warehouse and factory location Production scheduling Cash management COST systems Variance analysis-- overall budget Budget preparation Sales and production Mergers and acquisitions

New product planning

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Levels of Alter’s DSSs

 Level of problem-solving support from lowest

to highest

 Retrieval of information elements (sales figure)

 Retrieval of information files (monthly payroll report)  Creation of reports from multiple files (reports from an

income statement and an analysis of product sales)

 Estimation of decision consequences (enter a price to

the price model to see the effect on net profit)

 Propose decisions (enter data that describes a plant

and its equipment, and then a linear programming model determines the most efficient layout)

 Make decisions (a computer model to determine the

insurance premium)

Little

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Importance of Alter’s Study

 Supports concept of developing systems

that address particular decisions

 Makes clear that DSSs need not be

33 Retrieve information elements Analyze entire files Prepare reports from multiple files Estimate decision consequen-ces Propose decisions Degree of problem solving support Degree of

complexity of the problem-solving system

Little Muc

h

Alter’s DSS Types

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Three DSS Objectives

1. Assist in solving semistructured

problems

2. Support, not replace, the manager 3. Contribute to decision effectiveness,

rather than efficiency

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Simulation and Optimization – the

Model-Oriented Approach to DSS

 Simulation model – calculates the

simulated outcome of tentative decisions and assumptions

 Optimization model – determine optimal

decisions based on criteria supplied by the user, mathematical search techniques,

and constraints

 Both are used iteratively by asking what-if

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OLAP and Data Mining – the

Data-Oriented Approach to DSS

 Online analytical processing (OLAP) –

the use of data analysis tools to explore large databases of transaction data

 Motivation: intense data analyses slows

down transaction processing

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 Data mining = the use of analysis tools to

find patterns in large transaction databases

 Potential problem: many patterns are

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Appeal of Expert Systems

 Computer program that codes the

knowledge of human experts in the form of heuristics

 Two distinctions from DSS

 1. Has potential to extend manager‟s

problem-solving ability

 2. Ability to explain how solution was

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Expert System Model

 User interface

 Allows user to interact with system

 Knowledge base

 Houses accumulated knowledge

 Inference engine

 Provides reasoning

 Interprets knowledge base

 Development engine

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User Interface

 User enters:

 Instructions  Information

 Expert system provides:

 Solutions

 Explanations of

 Questions

 Problem solutions

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 Questions

 the manager may desire explanations while the

expert system performs its reasoning. Perhaps the expert system will prompt the manager to enter some information. The manager asks why the information is needed, and the expert

system provides an explanation.

 Problem solutions

 the manager can ask for an explanation how

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Knowledge Base



Description of problem domain



Rules

 Knowledge representation technique  „IF:THEN‟ logic

 Networks of rules

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Evidence Conclusion

Conclusion

Evidence Evidence Evidence Evidence

Evidence Evidence Evidence Conclusion

A Rule Set That

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Inference Engine

 Performs reasoning by using the contents

of knowledge base in a particular sequence

 Two basic approaches to using rules

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Forward Reasoning

(Forward Chaining)

 Rule is evaluated as:

 (1) true, (2) false, (3) unknown

 Rule evaluation is an iterative process  When no more rules can fire, the

reasoning process stops even if a goal has not been reached

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 To solve the fruit classification problem,

we start with an initial set of data

gathered from observing a piece of fruit and progress towards the classification.

 Example 1

 A client to verify a meal.

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Forward Reasoning Example

 Consider the following fact base

 Diameter = 1 inch Shape = round Seedcount = 1 Color =

red

 in cycle 1, inference process finds two rules apply, 3 and 4  Rule 3 : IF Shape = round and Diameter < 4 inches THEN

Fruitclass = tree

 Rule 4 : IF Seedcount = 1 THEN Seedclass = stonefruit  both these rules would derive new facts, so we pick the

lowest numbered and execute it, deriving Fruitclass = tree

 In each execution cycle, one rule is selected for executing

and its specified conclusion is derived and added to the database.

 In cycle 4 we find all the applicable rules have been

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Reverse Reasoning Steps

(Backward Chaining)

¶ Divide problem into subproblems · Try to solve one subproblem

¸ Then try another

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Backward reasoning

 starts with a hypothesis of what the fruit is

and we see if the characteristics of the actual fruit support this hypothesis. The system

requests input only when it is needed.

 in goal-driven reasoning, a goal is selected

and the system seeks to verify its validity by finding supporting evidence (backward)

 Example

 A client order meal A

 He can have sausage, egg, toast and tea

 A doctor makes a diagnosis

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Forward Versus Reverse Reasoning

 Reverse reasoning is faster than forward

reasoning

 Reverse reasoning works best under

certain conditions

 Multiple goal variables  Many rules

 All or most rules do not have to be examined

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Development Engine

 Programming languages

 Lisp

 Prolog

 Expert system shells

 Ready made processor that can be tailored to a particular problem domain

 Case-based reasoning (CBR)

 Commonly used in helpdesk expert systems

 Use historical data to identify problems and recommending solutions

 Decision tree

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Expert System Advantages

 For managers

 Consider more alternatives  Apply high level of logic

 Have more time to evaluate decision rules  Consistent logic

 For the firm

 Better performance from management

team

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Expert System Disadvantages

 Can‟t handle inconsistent knowledge

 In business world, few things hold the true

all the times.

 Can‟t apply judgment or intuition

 Those are important to semistructured or

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Keys to Successful ES Development

 Coordinate ES development with strategic

planning

 Clearly define problem to be solved and

understand problem domain

 Pay particular attention to ethical and legal

feasibility of proposed system

 Understand users‟ concerns and expectations

concerning system

 Employ management techniques designed to

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Figure 5.12

 Expert system logic combines:

 Forward chaining – starts with the data and tries to

draw conclusions from it

 Backward chaining – starts with a tentative

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Neural Networks

 Mathematical model of the human brain

 Simulates the way neurons interact to process

data and learn from experience

 Bottom-up approach to modeling human

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Neural Networks

 An information system that

recognizes

objects and patterns

examples that have been used to train it

 Each training example is described in terms

of a set of characteristics and a result

 While it is being trained, the neural network

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Current Methodology

 Mathematical models don‟t duplicate

human brains, but exhibit similar abilities

 Complex networks  Repetitious training

 ANS “learns” by example

 The training consists of many repetitions of inputs that express a variety of relationships. By progressively

refining the weights of the system nodes (the simulated neurons), the ANS “discovers” the

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 Expert systems – explicitly represent

expert knowledge as rules

 Neural networks – used for tasks that

have no predefined formulas or procedures

 Especially applicable in situations where:

 A large database of examples is available

 There are no known rules for recognizing the

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Fuzzy Logic

 A form of reasoning that makes it possible

to combine imprecise conditions stated in a form similar to the types of descriptive categories people use

 Attempts to avoid the artificial cutoffs

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Case-based Reasoning (CBR)

 A decision support method based on the

idea of finding PAST CASES most similar

to the current situation

 Maintain a history of past cases

 Operate based on data, not on rules

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Intelligent Agents

 An

autonomous, goal-directed

computerized process that can perform background work

 Similar to the shopbots that search the

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Internet-based Software Agents

Software Robots or Softbots

Major Categories

 E-mail agents (mailbots)

 Web browsing assisting agents

 Frequently asked questions (FAQ) agents

 Intelligent search (or Indexing) agents

 _{Internet softbot for finding information}

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Enterprise Systems

 Enterprise systems = firm-wide systems

that serve as a common information

infrastructure for basic business processes

 Enterprise resource planning (ERP) systems

 Controversial

 Expensive and very difficult to implement

 Business processes may have to be modified

Enterprise Information Systems

Objectives

 Understand limitations of Legacy

systems

 Describe enterprise systems &

Enterprise resources planning (ERP) systems

 Explain the value chain concept

 Show how an enterprise system

supports the organization’s value chain

 Illustrate value of systems

integration

 Enumerate the pros and cons of

implementing enterprise systems

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ERP Brings Integration

Purchasing

Fin. & Acct.

Inventory Mkt. & Sales

Manf.

H/R

Shared data

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Enterprise Resource Planning (ERP)

systems

 ERP systems are software packages that

can be used for the core systems

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73 Basic SAP Modules INCLUDING CENTRALIZED DATABASE Empty boxes represent third-party add-on modules

Most ERP are based on

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Major ERP Modules

 Sales and Distribution

 Based on SAP (best selling ERP)  Materials Management

 Financial Accounting

ERP Marketplace

 Oracle - www.oracle.com

 Peoplesoft -

www.peoplesoft.com

> J.D. Edwards - www.jdedwards.com

 SAP - www.sap.com

 SSA Global - www.baan.com  Microsoft

 Industry-Specific

 SYSPRO: Small manufacturers  Banner: Universities