555344S Johtamisen tietojrjestelmt 555344S Management Information Systems - PowerPoint PPT Presentation

Loading...

PPT – 555344S Johtamisen tietojrjestelmt 555344S Management Information Systems PowerPoint presentation | free to view - id: 1d3410-ZGYzN



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

555344S Johtamisen tietojrjestelmt 555344S Management Information Systems

Description:

SLAB. YARD. OPERAT. SHE- DULING. STRIP. ROLLING. CUTTING. PLATE. ROLLING ... operational managers and supervisors in daily activities or short-term decisions. ... – PowerPoint PPT presentation

Number of Views:66
Avg rating:3.0/5.0
Slides: 81
Provided by: pekka4
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: 555344S Johtamisen tietojrjestelmt 555344S Management Information Systems


1
555344S Johtamisen tietojärjestelmät 555344S
Management Information Systems
  • Luennot / lectures Pekka Kess
  • Oulun yliopisto / University of Oulu
  • Tuotantotalouden osasto /
  • Department of Industrial Engineering and
    Management
  • Kevät 2009 / Spring 2009

2
Course Description 4 cr.
  • Goal Opintojakson tavoitteena on antaa valmiudet
    yritysten informaatiojärjestelmien suunnittelu-,
    hankinta- ja kehittämistehtäviin. Tavoitteena on
    luoda kuva informaation merkityksestä ja sen
    hallinnasta toiminnan ohjauksessa kokonaisuutena.
  • Contents Pääsisältö rakentuu tietojärjestelmien
    hyödyntämiseen päätöksenteossa ja johtamisessa.
    Kurssilla käydään läpi seuraavia johtamisen
    tukijärjestelmiä Decision Support Systems (DSS),
    Group Support Systems (GSS) ja Executive
    Information Systems (EIS). Jaksolla perehdytään
    myös informaatioteknologian vaikutuksiin
    toiminnassa, jolloin tarkastellaan informaatio-
    ja kommunikaatioteknologian vaikutuksia mm.
    tuottavuuteen, taloudellisen kasvuun
  • Course lay-out
  • 8 luentokertaa 4.2, 11.2, 18.2, 25.2, 4.3,
    11.3, 18.3, 25.3. klo 8-10 SÄ118
  • Course acceptance 1. (suositeltava)
    Luentopäiväkirja artikkelikommentit 7/8, tai 2
    artikkelianalyysit tai 3. Kirjatentti tai 4. ??
  • Questions and comments pekka.kess_at_oulu.fi

3
(No Transcript)
4
Course Description 4 cr. - cont
  • Toteutus Kurssiin kuuluu luentojen lisäksi
  • pakolliset harjoitukset, jossa syvennytään
    erilaisiin
  • toiminnanohjaus- tai sähköistä kaupankäyntiä
  • tukeviin järjestelmiin. Suoritus loppukokeella.
  • Kurssikirjallisuus Tentittävä kirjallisuus
  • Laudon, K.C. Laudon, J.P. 2004. Management
    Information systems. Prentice Hall. 517 p.
  • Opetuskieli Englanti

5
Artikkelit
  • Larsen, T.J. Levine, L. (2005) Searching for
    management information systems coherence and
    change in the discipline. Info Systems J, 15, pp.
    357-381
  • Becker, J., Pfeifer, D., Janiesch, C. Seidel,
    S. (2006) proceedings of the 17th Conf on
    Information Systems., Acapulco, 4-6.8.2006, pp.
    3922-3933.
  • Choe, J. (2004) The consideration of cultural
    differences in the design of information systems.
    Information Management, 41, pp. 669-684.
  • Gunasekaran, A. Ngai, E.W.T. (2004) Information
    systems in supply chain integration and
    management. European Journal of Operations
    research, 159, pp. 269 295.
  • Tarokh, M.J. Soroor, J. (2006) Supply Chain
    Management Information Systems Critical Failure
    Factors. IEEE Xplore.
  • Tarafdar, M. Gordon, S.R. (2007) Understanding
    the influence of information systems competencies
    on process innovation A resource-based view.
    Journal of Strategic Information Systems, 16, pp.
    353-392.
  • Raymond, L Bergeron, F. (2007) project
    management information systems An empirical
    study of their impact on project managers and
    project success. International Journal of Project
    Management, 26, pp. 213-220.
  • Fenenga, C. de Jager, A. (2007) Cordfaid-IICD
    Health programme Uganda Health management
    information systems as tool for organisational
    development. EJISDC 31,3 pp 1-14.

6
Aikataulusta
7
Introduction to MIS
  • Management information system
  • Management information

8
(No Transcript)
9
(No Transcript)
10
(No Transcript)
11
(No Transcript)
12
(No Transcript)
13
Ward Peppard, 2005
14
Maturity Levels in PCMM
15
Information systems of a steel mill
Business Planning and Quality control
SALES FORECASTING
eBUSINESS
COST CONTROL
CUSTOMER AND PRODUCT PROFIT
CAPACITY PLANNING
CUSTOMER DATA
DELIVERY REPORT
TRANSPORT COSTS
ORDER ENTRY
ORDER PROMISE
PRODUCT DATA
DELIVERY AND TRANSPORT
Production Planning and Production control
IRON MAKING
SCHEDULING
PRE- FABRI- CATION SYSTEM
SHIPING SCHEDULING
HEAT PLANNING
MATERIAL PLANNING
STRIP ROLLING PLANNING
PLATE ROLLING PLANNING
INVOICING
STEEL MAKING AND CONTI- NUOUS CASTING
SLAB PRO- DUCTION AND SLAB YARD OPERAT.
SHE- DULING.
PLATE ROLLING
Manufacturing Planning and Manufacturing control
STRIP ROLLING CUTTING.
PRODUCT QUALITY CONTROL.
COKING PLANT
PLATE PRE- FABRI- CATION
COILING
Process Control And Production lines
CUTTING
SINTERING PLANT
SLAB YARD STORAGE
STRIP ROLLING FINISHING
PLATE ROLLING
BLAST FURNACE
PACKING
MANAGEMENT ACCOUNTING FINANCIAL ACCOUNTING
HRM HEALTH CARE MATERIALS MANAGEMENT
MAINTENANCE MANAGEMENT SAFETY
Support systems
16
Managerial Decision Making
  • Decision making the process by which managers
    respond to opportunities and threats by analyzing
    options, and making decisions about goals and
    courses of action.
  • Decisions in response to opportunities managers
    respond to ways to improve organizational
    performance.
  • Decisions in response to threats occurs when
    managers are impacted by adverse events to the
    organization.

17
Types of Decision Making
  • Programmed Decisions routine, almost automatic
    process.
  • Managers have made decision many times before.
  • There are rules or guidelines to follow.
  • Example Deciding to reorder office supplies.
  • Non-programmed Decisions unusual situations that
    have not been often addressed.
  • No rules to follow since the decision is new.
  • These decisions are made based on information,
    and a mangers intuition, and judgment.
  • Example Should the firm invest in a new
    technology?

18
The Classical Model
  • Classical model of decision making a
    prescriptive model that tells how the decision
    should be made.
  • Assumes managers have access to all the
    information needed to reach a decision.
  • Managers can then make the optimum decision by
    easily ranking their own preferences among
    alternatives.
  • Unfortunately, mangers often do not have all (or
    even most) required information.

19
The Classical Model
List alternatives consequences
Assumes all information is available to
manager Assumes manager can process
information Assumes manager knows the best
future course of the organization
Rank each alternative from low to high
Select best alternative
20
The Administrative Model
  • Administrative Model of decision making
    Challenged the classical assumptions that
    managers have and process all the information.
  • As a result, decision making is risky.
  • Bounded rationality There is a large number of
    alternatives and information is vast so that
    managers cannot consider it all.
  • Decisions are limited by peoples cognitive
    abilities.
  • Incomplete information most managers do not see
    all alternatives and decide based on incomplete
    information.

21
Why Information is Incomplete
Uncertainty risk
Ambiguous Information
Incomplete Information
Time constraints information costs
22
Incomplete Information Factors
  • Incomplete information exists due to many issues
  • Risk managers know a given outcome can fail or
    succeed and probabilities can be assigned.
  • Uncertainty probabilities cannot be given for
    outcomes and the future is unknown.
  • Many decision outcomes are not known such as a
    new product introduction.
  • Ambiguous information information whose meaning
    is not clear.
  • Information can be interpreted in different ways.

23
Incomplete Information Factors
  • Time constraints and Information costs Managers
    do not have the time or money to search for all
    alternatives.
  • This leads the manager to again decide based on
    incomplete information.
  • Satisfying Managers explore a limited number of
    options and choose an acceptable decision rather
    than the optimum decision.
  • This is the response of managers when dealing
    with incomplete information.
  • Managers assume that the limited options they
    examine represent all options.

24
Decision Making Steps
Recognize need for a decision
Frame the problem
Generate assess alternatives
Choose among alternatives
Implement chosen alternative
Learn from feedback
25
Decision Making Steps
  • 1. Recognize need for a decision Managers must
    first realize that a decision must be made.
  • Sparked by an event such as environment changes.
  • 2. Generate alternatives managers must develop
    feasible alternative courses of action.
  • If good alternatives are missed, the resulting
    decision is poor.
  • It is hard to develop creative alternatives, so
    managers need to look for new ideas.
  • 3. Evaluate alternatives what are the advantages
    and disadvantages of each alternative?
  • Managers should specify criteria, then evaluate.

26
Decision Making Steps
  • 4. Choose among alternatives managers rank
    alternatives and decide.
  • When ranking, all information needs to be
    considered.
  • 5. Implement choose alternative managers must
    now carry out the alternative.
  • Often a decision is made and not implemented.
  • 6. Learn from feedback managers should consider
    what went right and wrong with the decision and
    learn for the future.
  • Without feedback, managers never learn from
    experience and make the same mistake over.

27
Evaluating Alternatives
Is the possible course of action
Legal?
Ethical
Economical?
Practical?
28
Evaluating Alternatives
  • Is it legal? Managers must first be sure that an
    alternative is legal both in this country and
    abroad for exports.
  • Is it ethical? The alternative must be ethical
    and not hurt stakeholders unnecessarily.
  • Is it economically feasible? Can our
    organizations performance goals sustain this
    alternative?
  • Is it practical? Does the management have the
    capabilities and resources to do it?

29
Cognitive Biases
  • Suggests decision makers use heuristics to deal
    with bounded rationality.
  • A heuristic is a rule of thumb to deal with
    complex situations.
  • If the heuristic is wrong, however, then poor
    decisions result from its use.
  • Systematic errors can result from use of an
    incorrect heuristic.
  • These errors will appear over and over since the
    rule used to make decision is flawed.

30
Types of Cognitive Biases
Prior Hypothesis
Representativeness
Cognitive Biases
Illusion of Control
Escalating Commitment
31
Types of Cognitive Biases
  • Prior hypothesis bias manager allows strong
    prior beliefs about a relationship between
    variables and makes decisions based on these
    beliefs even when evidence shows they are wrong.
  • Representativeness decision maker incorrectly
    generalizes a decision from a small sample or one
    incident.
  • Illusion of control manager over-estimates their
    ability to control events.
  • Escalating commitment manager has already
    committed considerable resource to project and
    then commits more even after feedback indicates
    problems.

32
Group Decision Making
  • Many decisions are made in a group setting.
  • Groups tend to reduce cognitive biases and can
    call on combined skills, and abilities.
  • There are some disadvantages with groups
  • Group think biased decision making resulting
    from group members striving for agreement.
  • Usually occurs when group members rally around a
    central mangers idea (CEO), and become blindly
    committed without considering alternatives.
  • The group tends to convince each member that the
    idea must go forward.

33
Improved Group Decision Making
  • Devils Advocacy one member of the group acts as
    the devils advocate and critiques the way the
    group identified alternatives.
  • Points out problems with the alternative
    selection.
  • Dialectical inquiry two different groups are
    assigned to the problem and each group evaluates
    the other groups alternatives.
  • Top managers then hear each group present their
    alternatives and each group can critique the
    other.
  • Promote diversity by increasing the diversity in
    a group, a wider set of alternatives may be
    considered.

34
Devils Advocacy v. Dialectic Inquiry
Devils Advocacy
Dialectic Inquiry
Alter. 1
Alter. 2
Presentation of alternative
Critique of alternative
Debate the two alternatives
Reassess alternative accept, modify, reject
Reassess alternatives accept 1 or 2, combine
35
Decision Support in Business
  • Information Needs of Decision Makers

Information Characteristics
Decision Structure
Ad Hoc Unscheduled Summarized Infrequent Forward
Looking External Wide Scope
Strategic Management Executives Directors
Unstructured
Tactical Management Business Unit Management
Self-Directed Teams
Information
Decisions
Semi-Structured
Prespecified Scheduled Detailed Frequent Historica
l Internal Narrow Focus
Operational Management Operating Management
Self-Directed Teams
Structured
36
DSS Overview
  • DSS definition/description
  • DSS characteristics and capabilities
  • DSS components, the roles they play, and how they
    integrate
  • DSS hardware and software platforms
  • DSS classifications
  • Conduct of the class from now onward

37
Working Definition of DSS
  • A DSS is an interactive, flexible, and adaptable
    CBIS, specifically developed for supporting the
    solution of a non-structured management problem
    for improved decision-making. It utilizes data,
    it provides easy-to-use user interface, and it
    allows for the decision makers own insights
  • A DSS may utilize models, is built by an
    interactive process (frequently by end-users),
    supports all of the phases of decision-making,
    and may include a knowledge management component
  • Central Issue in DSS is
  • Support for and improvement in decision-making

38
Working Definition of DSS
  • A DSS is
  • Flexible
  • Adaptive
  • Interactive
  • GUI-based
  • Iterative and
  • Employs modeling.

39
DSS Description
  • DSS application
  • A DSS program built for a specific purpose
    (e.g., a scheduling system for a specific
    company)
  • Business intelligence (BI)
  • A conceptual framework for decision support. It
    combines architecture, databases (or data
    warehouses), analytical tools, and applications

40
DSS Description
  • Business analytics
  • The application of models directly to business
    data. Business analytics involves using DSS
    tools, especially models, in assisting decision
    makers. It is essentially OLAP/DSS. See also
    business intelligence (BI).

41
DSS Description
  • Predictive analytics
  • A business analytical approach toward
    forecasting (e.g., demand, problems,
    opportunities) that is used instead of simply
    reporting data as they occur

42
DSS Description
  • A DSS supports all phases of the decision-making
    process and may include a knowledge component
  • A DSS can be used by a single user on a PC or can
    be Web-based for use by many people at several
    locations

43
DSS Characteristics and Capabilities
44
DSS Characteristics and Capabilities
  • Support for decision makers, mainly in
    semi-structured and unstructured situations, by
    bringing together human judgment and computerized
    information
  • Support for all managerial levels, ranging from
    top executives to line managers
  • Support for individuals as well as groups

45
DSS Characteristics and Capabilities
  • Support for interdependent and/or sequential
    decisions
  • Support in all phases of the decision-making
    process
  • Support for a variety of decision-making
    processes and styles
  • DSS are flexible, so users can add, delete,
    combine, change, or rearrange basic elements DSS
    can be readily modified to solve other, similar
    problems

46
DSS Characteristics and Capabilities
  • User-friendliness, strong graphical capabilities,
    and a natural language interactive humanmachine
    interface can greatly increase the effectiveness
    of DSS
  • Improved effectiveness of decision making
  • The decision maker has complete control over all
    steps of the decision-making process in solving a
    problem
  • End users are able to develop and modify simple
    systems by themselves

47
DSS Characteristics and Capabilities
  • Models are generally utilized to analyze
    decision-making situations
  • Access is provided to a variety of data sources,
    formats, and types
  • Can be employed as a standalone tool used by an
    individual decision maker in one location or
    distributed throughout an organization and in
    several organizations along the supply chain
  • Can be integrated with other DSS and/or
    applications, and it can be distributed
    internally and externally, using networking and
    Web technologies

48
Architecture and Components of DSS
49
Components of DSS
  • (1) Data management system (DMS)
  • Software for establishing, updating, and
    querying (e.g., managing) a database
  • Data warehouse
  • A physical repository where relational data are
    organized to provide clean, enterprise-wide data
    in a standardized format
  • Database
  • The organizing of files into related units that
    are then viewed as a single storage concept. The
    data in the database are generally made available
    to a wide range of users

50
Components of DSS
  • (2) Model management subsystem (MMS)
  • Model base management system (MBMS)
  • Software for establishing, updating, combining,
    and so on (e.g., managing) a DSS model base
  • (3) User interface subsystem (UIS)
  • The component of a computer system that allows
    bidirectional communication between the system
    and its user

51
Components of DSS
  • (4) Knowledge-based management subsystem
    (KMS)
  • The knowledge-based management subsystem can
    support any of the other subsystems or act as an
    independent component
  • Organizational knowledge base
  • An organizations knowledge repository
  • (5) User

52
Data Management Subsystem (DMS)
  • The data management subsystem is composed of
  • DSS database contains and interrelates data
    from different sources to aid the decision-making
    process
  • DBMS software that controls and retrieves data
    from the database for queries and reports
  • Data directory catalogs and manages all data
    through a data dictionary, provides logical views
    of both internal and external data
  • Query facility helps perform complex data
    manipulation tasks on the database based on
    queries

53
Data Management Subsystem
54
Data Management Subsystem
  • Key data management subsystem issues
  • Data quality
  • Data integration
  • Scalability
  • Data security

55
Data Management Subsystem features and
capabilities
  • Extraction of data from internal (transaction
    processing systems), external (government
    agencies, trade associations, market research
    firms, forecasting firms), and private (to the
    decision-maker) sources
  • Data warehouse
  • Data mining (knowledge discovery)
  • Web browser data access
  • Web database servers
  • Multimedia databases
  • Special GSS databases (like Lotus Notes/Domino
    Server)
  • Multi-dimensional databases
  • Online analytical processing (OLAP)
  • Object-oriented databases

56
The Model Management Subsystem (MMS)
57
The Model Management Subsystem
  • Analogous to the data management subsystem
  • Model base contains a model library which
    stores different classes of models based on
    criteria such as decision types, user types, etc.
  • Model base management system (MBMS) software to
    help create models, data manipulation in models,
    update models, and create new routines in models.
    The modeling language for model building, could
    be text-based or graphical
  • Model directory contains catalog of models, and
    model definitions

58
The Model Management Subsystem
  • Model execution controls running of models
  • Model integration combines operations of
    several models
  • Command processor is used to accept and
    interpret modeling instructions from the user
    interface component and route them to the MBMS,
    model execution, or integration functions
  • There is a lack of standard in MMS activities
    compared with DMS activities. Why?
  • Use of Artificial Intelligence (AI) and fuzzy
    logic in MMS is quite prevalent in building
    standards for MMS.

59
Model Examples in Model Base
  • Strategic models support top managements
    strategic (long-term) planning decisions. E.g.,
    at the University level major campus expansion,
    affiliation with other universities, development
    of a new school or college
  • Tactical models support mainly middle
    management in resource allocation and control.
    E.g., at the School level development of a new
    course, opening a new department, marketing plans
    for the fiscal year
  • Operational models support operational managers
    and supervisors in daily activities or short-term
    decisions. E.g., at the School or Departmental
    level course scheduling for a semester,
    specific admission decisions on MBA applicants
  • Analytical models are used to perform analysis
    of data

60
The Model Management Subsystem
  • Model building blocks and routines
  • Model building blocks
  • Preprogrammed software elements that can be used
    to build computerized models. For example, a
    random-number generator can be employed in the
    construction of a simulation model
  • Modeling tools

61
User Interface (or Dialog) Subsystem (UIS)
  • Covers all aspects of communication between the
    user and the DSS
  • It is the interface to the user and consists of a
    GUI that is typically displayed via a Web browser
  • Includes factors that deal with ease of use,
    accessibility, and human-machine interactions
    (which incorporate such things as Cognitive
    Style, Decision Style, and Display Preferences)
  • To most users, the user interface is the system

62
User Interface (Dialog) Subsystem
  • UIS manages the cognitive styles and decision
    styles of managers which include their abilities
    and preferences for ways at arriving decisions
  • Cognitive style is the subjective process
    through which people perceive, organize, and
    change information during the decision-making
    process
  • Decision style is the manner in which decision
    makers think and react to problems
  • Intelligent DSS have natural language processing
    capabilities in the UIS

63
User Interface (Dialog) Subsystem
64
User Interface (Dialog) Subsystem
  • User interface
  • Therefore, is the component of a DSS that allows
    bidirectional communication between the system
    and its user.
  • User interface management system (UIMS)
  • The DSS component that handles all two-way
    interactions between the user and/or system
    components and the system

65
User Interface (Dialog) Subsystem
  • The user interface process
  • Object
  • A person, place, or thing about which
    information is collected, processed, or stored
  • Graphical user interface (GUI)
  • An interactive, user-friendly interface in
    which, by using icons and similar objects, the
    user can control communication with a computer

66
User Interface (Dialog) Subsystem
  • DSS user interface access is provided through Web
    browsers including
  • Voice input and output (speech recognition)
  • Display panel
  • Direct sensing devices (tactile and gesture
    interface)
  • Natural language processor (text parsing, speech
    processing)

67
User Interface (Dialog) Subsystem
  • DSS developments
  • Parallel processing hardware and software
    technologies have made major inroads in solving
    the scalability issue
  • Web-based DSS have made it easier and less costly
    to make decision-relevant information and
    model-driven DSS available to users in
    geographically distributed locations, especially
    through mobile devices

68
User Interface (Dialog) Subsystem
  • DSS developments
  • Artificial intelligence continues to make inroads
    in improving DSS
  • Faster, intelligent search engines
  • Intelligent agents promise to improve the
    interface in areas such as direct natural
    language processing and creating facial gestures
  • The development of ready-made (or
    near-ready-made) DSS solutions for specific
    market segments has been increasing

69
User Interface (Dialog) Subsystem
  • DSS developments
  • DSS is becoming more embedded in or linked to
    most EIS
  • GSS improvements support collaboration at the
    enterprise level
  • Different types of DSS components are being
    integrated more frequently

70
Knowledge-Based Management Subsystem (KMS)
  • Advanced DSS are equipped with a component
    called a knowledge-based management subsystem
    that can supply the required expertise for
    solving some aspects of the problem and provide
    knowledge that can enhance the operation of other
    DSS components

71
Knowledge-Based Management Subsystem
  • KMS is the intelligence component incorporated
    into every subsystem of a DSS thus, leading to
    intelligent DSS
  • Expert system or other intelligent systems
    provide the required expertise
  • Provides expertise for solving some or many
    aspects of complex unstructured and
    semi-structured problems
  • Provides knowledge that can enhance the
    operations of each subsystem of a DSS
  • All advanced DSS have KMS

72
The User
  • The person faced with a decision that an MSS is
    designed to support is called the user, the
    manager, or the decision maker
  • MSS has two broad classes of users managers
    (users or decision-makers) and intermediaries (
    designated staffs)
  • Staff specialists use the MSS much more
    frequently than managers and tend to be trained
    in detail-oriented system and are willing to use
    more complex system
  • Staff specialists are often intermediaries
    between managers and the MSS

73
The User
  • Intermediary
  • A person who uses a computer to fulfill requests
    made by other people (e.g., a financial analyst
    who uses a computer to answer questions for top
    management)
  • Staff assistant
  • An individual who acts as an assistant to a
    manager. Have specialized knowledge about
    management problems and experience with MSS
    technology

74
The User
  • Expert tool user
  • A person who is technically skilled in the
    application of one or more types of specialized
    problem-solving tools
  • Business (system) analysts
  • An individual whose job is to analyze business
    processes and the support they receive (or need)
    from information technology. For example, MBA MIS
    graduates.
  • Facilitators (in a GSS)
  • A person who plans, organizes, and
    electronically controls a group in a
    collaborative computing environment

75
DSS Hardware Software
  • Hardware Software affect the functionality and
    usability of the MSS
  • The choice of hardware can be made before,
    during, or after the design of the MSS software
  • Major hardware options
  • Organizations servers
  • Mainframe computers with legacy DBMS,
  • Workstations
  • Personal computers
  • Client/server systems

76
DSS Hardware Software
  • Portability has become critical for deploying
    decision-making capability in the field,
    especially for salespersons and technicians
  • The power and capabilities of the World Wide Web
    have a dramatic impact on DSS
  • Communication and collaboration
  • Download DSS software
  • Use DSS applications provided by the company
  • Buy online from application service providers
    (ASPs)

77
DSS Classifications
  • AIS SIGDSS classification for DSS
  • Communications-driven and group DSS (GSS)
  • Data-driven DSS
  • Document-driven DSS
  • Knowledge-driven DSS, data mining, and management
    ES applications
  • Model-driven DSS
  • Compound DSS (a hybrid of two or more above)

78
DSS Classifications
  • Holsapple and Whinstons classification
  • Text-oriented DSS hypertext, WWW (documents and
    applications using http), electronic document
    management
  • Database-oriented DSS OLAP, Data mining
  • Spreadsheet-oriented DSS Financial planning
    packages such as IFPS
  • Solver-oriented DSS large scale mathematical
    programming based solvers written in programming
    languages such as C
  • Rule-oriented DSS qualitative and quantitative
    rules with inference capabilities
  • Compound DSS a hybrid of two or more above

79
DSS Classifications
  • Other DSS classifications
  • Institutional DSS
  • A DSS that is a permanent fixture in an
    organization and has continuing financial
    support. It deals with decisions of a recurring
    nature
  • Ad hoc DSS
  • A DSS that deals with specific problems that are
    usually neither anticipated nor recurring

80
DSS Classifications
  • Other DSS classifications
  • Personal support
  • Group support
  • Organizational support
  • Individual vs. Group support system (GSS)
  • Information systems, specifically DSS, that
    support the collaborative work of groups
  • Custom-made systems versus ready-made systems
    (such as many BI systems)
About PowerShow.com