Information systems for Managerial Decision
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Transcript of Information systems for Managerial Decision
Information systems forManagerial Decision
Chapter 9
Form of the Reports
Line chart
Bar chart
Pie chart
Image
Management tasks
Planning: goal seeking and strategy design
Organisation: develop organisational structures
Personnel: hiring , training , nominating
Management: motivation and communication
Guiding: evaluation of performance and draw conclusions
Management roles
The management has several roles to play: concerning persons
managecontact person
concerning informationcontroldistribute
concerning decisions takeproblem solvingdistribution of the resourcesnegotiate
Management levels
Strategic: strategic planning and management support for the direction committee
Tactic: tactic planning and support of the departments by the middle management
Operational: planning and support of the operations by the operations management
O’Brien p. 352
Information Requirements
Decision structure Information properties
Unstructured
Semistructured
Structured
Decisions
Information
Ad hocIrregularCompactNot frequentFuture OrientedExternalBroad
Pre-definedRegularDetailedFrequentHistoricalInternal
Strategic Management
Tactic Management
Operational Management
O’Brien p. 353
Model for the decision making process
Try to find and recognise situations that require a decision
Opportunities need to be identified and notified
Design and evaluate differences in behaviour
An information system has to contribute to create and evaluate opportunities
Decide on actions and control the implementation
The information system has to contribute to the decision making on the priorities of alternative decisions and has to provide feed-back for the execution
Researchactivities
Design activities
Choiceactivities
Decision making structure
Operations Management
Cash management
Credit management
Production scheduling
Daily work distribution
Stock management
Billing
Unstructured
Partially structured
Structured
Strategic Management
Planning new business
Company reorganisation
Production planningPlant locationCo-operation
Tactical Management
Work organisationPerformance
Analysis
Personnel evaluationBudgetingProject budgets
Program management
O’Brien p. 354
Decision Support Systems (DSS)
Computer supported Information systems designed to provide interactive and informative support for the managers during the decision making process.
DSS use :
analytical models
specialised databases
input and expertise of the person that has to take the decision
interactive , automated modelling process to support the usage of partially structured and unstructured decisions by individual managers
Ad-hoc quick response systems directed by mangers
Architecture
Hardware : workstation and communication system Software
DSS software packages (DSS generators)• database management• model base management• generate and manage dialogue windows
Datacompany databaseexternal databases personal databases for the manager
Models : libraries of mathematical models and analytical techniques
People
What is a decision support system?
More precise goal than a standard MIS system.The aim is to deliver capabilities and not only to provide information .
Database Model
DBMS MBMS
DGMSUSER
DBMS database management system
MBMS Model base management system
DGMS Dialogue generation and management software
Corporate MIS
TPS
Finance
Marketing
Production
Statistical model
Strategic plan
Operationalmodel
Flowchart analysis of investment decision
Portfoliodata
researchdata
Stockdata
Retrieve Portfolio
display performanceeach industry
pickstrategy
RetrieveResearchreports
Pickstock
graphstockperformance
A
A
No StockOK ?
Yes
projectFuturePerformance
StillOK ?
No
Yes
Purchase
Client
Decision process isfrozen as systemis developed
DSS approach to same problemSet of 4 capabilities
Representation
Operations
Memory aids
Control aids
Portfolio lists Graphs
Research reports
Simulationoutputs
Interface language
Listoperations
Graph operations
Reportoperations
Simulationoperations
Procedureoperations
Work spacerepresentations Storage Databases
Menus Trainingdocuments
A DSS is a decision-making scratch path , backed up by a database , that decision makers can use to support many decision making processes.
Differences
Dimension Microcomputing DSS MIS
Philosophy Provide computing Provide integrated Provide information power to end users tools, data models to end users and simple models and language to users
Objectives Increase productivity Directly impact key Enhance control of knowledge and decisions and enhance and monitoring office workers effectiveness of power of middle decision making managers
Systems Analysis Identify what software Establish what tools Identify information packages suitable for are used in the requirements task at hand decision process
Design Customise package Iterative process Deliver system to task never frozen based on frozen requirements
Three levels of DSS technology
Specific DSSsoftware to guide decision making ( spreadsheets , ... )
DSS generatorspackage of hardware and software , providing tools to build specific
DDS examples : IFPS ( Interactive Financial Planning System ) EIS ( Executive Information System )
DSS toolsbuilding blocks of generatorsspecial purpose languages ( APL )permit rapid development of applications , screens , menus , ...graphics routines , graphics hardware , supporting
telecommunications
Roles to play
Manager or end userresponsible for making key organisational decisionsa DSS must provide information on how things are going
The Intermediaryskilled staffer who helps to schedule manager’s or task force work
The DSS buildermust be familiar with the business problemmust have good understanding of how to make the technology work
The technical supportermember of the data processing groupdevelops and installs DSS generators and toolsDSS requires links to databases , graphic software , ...
The Toolsmithdevelops new technology , new softwareworks often for private vendors
Type of Analytical Model
What-if analysis
Sensitivity analysis
Goal-seeking analysis
Optimise
Examine how changes in selected variables influence other variablese.g.: what is the impact on sales if we spent 10% less on publicity?
Examine how repeated changes in a variable can influence other variables
e.g.: Lower the budget for publicity several years with € 5.000 to discover the relationship between publicity budget and sales
Modify selected variables until a specific variable reaches a pre-defined value
e.g.: Increase the publicity budget until sales reaches € 10M
Determine optimal value for variables
Executive information systems (EIS)
Information systems where the characteristics of modern information reporting systems are combined with characteristics of DSS’s .
Provide direct and easy access to information on CSF’s.Factors for good EIS: Involvement and support of top-management Knowledge of information sources Concentrate on crucial factors Response times Insight in the level of computer knowledge of managers Learning time for the development team Flexibility Ongoing support
Artificial Intelligence
Characteristics of intelligent behaviour think and logical reasoningproblem solving via logical reasoning learn and getting insight based on experiencegather knowledge and apply thiscreativity and imaginationhandle complex and chaotic situations react successfully on new situationsestimate the relative importance of different factors ability to work with ambiguous or incorrect information
AI tries to build computer systems that show this type of behaviour
Artificial Intelligence Family Tree
NaturalLanguage
Expert systems Robotics
Intelligent machines:AI hardware
PerceptiveSystems(vision, hearing)
Human and Artificial Intelligence
Successful AI systems are neither artificial nor intelligent
based on : human expertise
knowledge
selected reasoning patterns
act like textbooks
cannot learn without being rewritten
existing systems extend the powers of experts
they do not substitute experts
they have no common sense
Knowledge-based Expert Systems
An expert system is a knowledge-intensive program that normally requires human expertise.
An expert system can assist decision-making by asking relevant questions and explaining the reasons for adopting certain actions.
Characteristics:they perform some of the problem-solving work of humansthey use knowledge in the form of rules or framesthey interact with humansthey can consider multiple hypotheses simultaneously
Today’s expert systems are quite narrow , they do not think , do not resort to reasoning , do not draw analogies , lack common sense .
Three levels assistant
helps doing routine analysis
Colleagueuser discusses the problem until a joint decision is reached
when system is wrong , user adds additional information
Complete expert automatonmakes the decisions for the user without questions
operates remotely beyond human intervention
not yet applied in practical areas
Components of an expert system
Development team Expert(s)
Knowledge Engineers
Development Interface
Production RulesSemantic netsFrames
User Interfaces
Users
suggestedsolutions
Questions
Answers data
Commands
Corpus ofKnowledge
Shell orDevelopmentenvironment
Expert systems vs. Decision Support Systems
Support of human decision maker
the man or the system
man inquiries system
individuals , groups and
Numeric
complex, broad
ad hoc, uniquefactsno
limited
Copy or replace human advisor
the system
system inquiries man
individuals and groups
symbolic
limited , specialised
repeatingprocedures and factsyes , limited
yes
Goals
Who makes decisions or recommendations?
Direction of the inquiry
Type of support
type of data manipulation
Characteristics of the problem domain
Type of problems
Database contents
Deduction capacity
Explain capacity
DSS ES
ES applications Decision making management
evaluate performances, insurance's , ... Diagnostics / problem solving
help desk, error detection in software, ... Maintenance / planning
maintenance planning , production planning , training , ... Intelligent text / documentation
regulations , security standards , taxation , ... Design / configuration
feasibility studies , assembly schema’s, ... Selection / classification
material selection, information classification, person identification Process management / Steering
machine steering, production control, stock management, ...