chapter 3

1

CHAPTER 3

Decision Support Systems:

An Overview

Fall, 2006 All Right Reserved, Yao Zhong, School of E&M, BUAA 3-2

Strategic planning is one of the most important tasks of modern management. It involves all functional areas in an organization and several relevant outside factors, a fact that complicates the planning process, especially in dealing with long-rum uncertainties. Thus, strategic planning is clearly not a structured decision situation, so it is potential candidate for DSS application.

The Gotass-Larsen Shipping Corp. (GLSC), subsidiary of International Utilities (IU), operates cargo ships all over the world. The company developed a comprehensive DSS for performing both short-and long-term planning. The system is composed of two major parts: data and models.The data include both external data (port or cannel characteristics, competitor’s activities, and fares) and

3.1 DSS configurations


internal data (existing plans, availability of resources, and individual ship’s characteristics). In addition, users can incorporate their own data or express their attitudes (for example, by adding their own risk assessments).The models include routine standard accounting and financial analysis model (such as cash flow computations and pro forma income and expenses) organized on a per ship, per voyage, per division, and company-wide basis. These models permit elaborate financial analyses. A simulation model is used to analyze short- and long-term plans and to evaluate the desirability of projects. In addition, the system interfaces with a commercially available application program for analyzing individual voyages.



A highly decentralized 15 month operational planning and control document is prepared within the framework of the long-term strategic plan. This document is used as the basis for detailed goal formation for the various ships and individual voyages. A detailed monitoring and control mechanism is also provided, including a regular variance report and diagnostic analysis. In addition, a detailed performance tracking report is executed (by voyage, ship, division, and entire corporation).

Once the assessment of the opportunity of individual projects (such as contracting a specific voyage) is examined, an aggregation is performed. The objective is to determine whether a series of individually profitable project



Adds to feasible and effective long-range plan. The DSS uses a simulation model that examines various configurations of projects in an attempt to fine-tune the aggregate plan. Specifically, when several projects are selected, resources might be insufficient for all projects. Therefore, modifications in scheduling and financial arrangements might be necessary. This fine-tuning provides a trial-and-error approach to feasibility testing and sensitivity analyses. The what-if capabilities of the DSS are especially important in this case because a trial-and-error approach to managing the organization would be disastrous. The strategic plan of GLSC is very detailed and accurate because of the contractual nature of the sales and some of the expenses. The model is geared to a traditional business policy structure, which helps in assessing the threats and risks in




The general operating environment and makes possible an examination of the impacts of new opportunity on existing plans.

This is an example of large-scale, strategic DSS. We refer to this vignette throughout this chapter.



Supports individuals and teams Used repeatedly and constantly Two major components: data and models It supports several interrelated decisions Web-based It uses both internal and external data Uses subjective, personal, and objective data Has a simulation model Used in public and private sectors Has what-if capabilities Uses quantitative and qualitative models


This vignette demonstrates some of the potential diversification of DSS. Decision support can be provided in many different configurations. These configurations depend on the nature of the management decision situation and the specific technologies used for support. These technologies are assembled from four basic components (each with several variations): data, models, knowledge, and user interface. Each of these components is managed by software that either is commercially available or must be programmed for the specific task. The manner in which these components are assembled defines their major capabilities and the nature of the support provided. For example, models are emphasized in a model-oriented DSS, as in the opening vignette. Such models can be customized with a modeling language (such as spreadsheet) or can be provided by standard algorithm-based tools such as linear



3.2 What is a DSS

programming. Similarly, in a data-oriented DSS, a database (or data warehouse) and its management play the major role.

DSS definitions

We have defined the DSS in chapter 1 like this:

Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer based support system for management decision makers who deal with semistructured problems (Keen and Morton,1987).

Why do we redefine it in this chapter?


Why do we redefine it in this chapter?

Keen and Morton’s definition is identified as a system intended to support managerial decision makers in semistructured decision situations. DSS were meant to be an adjunct to decision makers, to extend their capabilities but not to replace their judgment. It is a computer-based system.

Several other definitions appeared that caused considerable disagreement as to what really is a DSS.

3.2 What is a DSS


Little (1970) “model-based set of procedures for processing data and judgments to assist a manager in his decision making” Assumption: that the system is computer-based and extends the user’s problem-solving capabilities.

Alter (1980) Contrasts DSS with traditional EDP(electronic data processing) systems (Table 3.1)

3.2 What is a DSS


Use Passive

User Clerical

Goal (final) Mechanicalefficiency

TimeHorizon

Past

Objective(detail things)

Consistency

TABLE 3.1 DSS versus EDP.

Dimension DSS EDP

Flexibility

Effectiveness

Present and future

Active

Line and staff management


Moore and Chang (1980)

1. Extendible systems

2. Capable of supporting ad hoc data analysis and

decision modeling

3. Oriented toward future planning

4. Used at irregular, unplanned intervals

Bonczek et al. (1980) A computer-based system consisting of

1. A language system -- communication between the user and DSS components

2. A knowledge system

3. A problem-processing system--the link between the other two components


Keen (1980)

DSS apply “to situations where a ‘final’ system can be developed only through an adaptive process of learning and evolution”

Central Issue in DSSsupport and improvement of decision making

These definitions are compared and contrasted by examining the various concepts used to define DSS.


TABLE 3.2 Concepts Underlying DSS Definitions.

Source

DSS Defined in Terms of

Gorry and Scott Morton [1971]

Problem type, system function (support)

Little [1970] System function, interface characteristics

Alter [1980] Usage pattern, system objectives

Moore and Chang [1980] Usage pattern, system capabilities

Bonczek, et al. [1996] System components

Keen [1980] Development process


Working Definition of DSS A DSS is an interactive, flexible, and adaptable

CBIS, specially developed for supporting the solution of a non-structured management problem for improved decision making. It utilizes data, it provides easy user interface, and it allows for the decision maker’s own insights

DSS may utilize models, is built by an interactive process (frequently by end-users), supports all the phases of the decision making, and may include a knowledge component

3.2 What is a DSS


3.3 Characteristics and Capabilities of DSS

1. Provide support in semi-structured and unstructured situations, includes human judgment and computerized information

2. Support for various managerial levels (top to line manager)

3. Support to individuals and groups

4. Support to interdependent and/or sequential decisions

5. Support all phases of the decision-making process

6. Support a variety of decision-making processes and styles

(more)


7. Are adaptive

8. Have user friendly interfaces

9. Goal: improve effectiveness of decision making

10. The decision maker controls the decision-making process

11. End-users can build simple systems

12. Utilizes models for analysis

13. Provides access to a variety of data sources, formats, and types, ranging from geographic information systems to object-oriented ones.

Decision makers can make better, more consistent decisions in a timely manner.


3.4 DSS Components

1. Data Management Subsystem

Includes the database, which contains relevant data for the situation and is managed by software called DBMS.

2. Model Management Subsystem

A software package that includes financial, statistical, management science, or other quantitative models that provides the system’s analytical capabilities and appropriate software management. Modeling language for building custom models are also included. This is

often called a MBMS.


3. Knowledge-based (Management) SubsystemThis subsystem can support any of the other subsystems or act as an independent component. It provides intelligence to augment the decision maker’s own.

4. User Interface SubsystemThe user communicates with and commands the DSS through this subsystem.

5. The Useris considered to be part of system. Researchers assert that some of the unique contributions of DSS are derived from the intensive interaction between the computer and the decision makers.

3.4 DSS Components


Other Computer-based systems

Data Management

Model Management

Knowledge Management

User Interface

Manager (user)

Data: external and internal


3.5 The Data Management Subsystem

DSS database

A database is a collection of interrelated data organized to meet the need and structure of an organization and can be used by more than one person for more than one application. There are several possible configurations for a database. For lager DSS, the database is basically included in the data warehouse (next chapter). For some applications, a special database is constructed as needed. Several databases may be used in one DSS application, depending on the data sources. Data is extracted from internal and external data sources, as well as personal data belonging to one or more users. (Figure 3.3)


Internal Data Source

Finance Marketing Production Personnel OtherExternal Data

Source

Extraction

Decision Support database or

data warehouse

DBMS •Retrieval

•Inquiry

•Update

•Report

•Delete

Query Facility

Data Directory

Private, personal data

Interface Management

Model Management


Corporate data Warehouse

Organizational knowledge base


Internal data come mainly from the organization’s transaction processing system. Example are payroll monthly. Other internal data are machine maintenance scheduling, forecasts of future sales, cost of out-of-stock items, and future hiring plans. Some times internal data are made available through Web browser over an Internet, an internal Web-based system.

External data may include industry data, marketing research data, census data, regional employment data, government regulations, tax rate schedules, or national economic data. Internet also is an important data sources.

Private data may include guidelines used by specific decision makers and assessment of specific data or situations.



Data Organization

Should a DSS have an independent database? It depends. In small ad hoc DSS, data can be entered directly into models sometimes extracted directly from larger database. The organization’s data warehouse is often used for building DSS applications. Some large DSS have their own fully integrated, multiple-source DSS databases. A separate DSS database need not be physically separate from the corporate database. They can be physically stored together for economic reasons.

A DSS database can also share a DBMS with other systems. A DSS database may include multimedia objects (such as pictures, maps, or sounds). An object-oriented database is found in some recent DSS.



Extraction

To create a DSS database, or a data warehouse, it is often necessary to capture data from several sources. This operation is called extraction. It is basically the importing of files, summarization, filtration, and condensation of data. Extraction also occurs when the user produces reports from the data in the DSS database. The extraction process is managed by a DBMS.



Database management system

The data base is created, accessed, and updated by a DBMS.

Most DSS are built with a standard commercial DBMS that provides capabilities such as those shown in the following list:


Captures/extracts data for inclusion in a DSS database

Updates (adds, deletes, edits, changes) data records and files

Interrelates data from different sources



Retrieves data from the database for queries and reports

Provides comprehensive data security (protection from unauthorized access, recovery capabilities, etc.)

Handles personal and unofficial data so that users can experiment with alternative solutions based on their own judgment

Performs complex data manipulation tasks based on queries

Tracks data use within the DSS

Manages data through a data dictionary


3.5 The Data Management Subsystem Data directory

Data directory is a catalog of all the data in the database. It contains the data definitions, and its main function is to answer questions about the availability of data items, their source, and their exact meaning. The directory especially appropriate for supporting the intelligence phase of the decision-making process by helping scan data and identify problem areas or opportunities. The directory, like any other catalog, supports the addition of new entries, deletion of entries, and retrieval of information on specific object.

Query facility In building and using DSS, it is often necessary to access, manipulate, and query the data. The Query facility performs all these tasks. It accepts requests for data from other DSS


3.5 The Data Management Subsystem Query facility

components, determine how these requests can be filled, formulates the detailed requests, and returns the results to the issuer of the request. The query facility includes a special query language. Important functions of a DSS query system are the selection and manipulation operations.


3.5 The Data Management Subsystem Data Management Issues

– Data warehouse – Data mining– Special independent DSS databases – Extraction of data from internal, external, and

private sources– Web browser data access – Web database servers– Multimedia databases– Special GSS databases (like Lotus Notes / Domino

Server)– Online Analytical Processing (OLAP)– Object-oriented databases– Commercial database management systems (DBMS)


3.6 The Model Management Subsystem

Analog of the database management subsystem(Figure on next slide )

Model base Model base management system Modeling language Model directory Model execution, integration, and command

processor



Models (Model Base)

•Stratigic, tactical,operational

•Statistical, Financial, marketing,MS, Accounting, engineering,etc.

•Model building blocks

Model Base Management

•Modeling commands: creation

•Maintenance:update

•DB interface

•Modeling language

Model

Directory

Model execution,

Integration, and command processor

Data Management

Interface Management.

Knowledge management


3.6 The Model Management Subsystem Model Base

A model base contains routine and special statistical, financial, forecasting, management science, and other quantitative models that provide the analysis capabilities in a DSS. The ability to invoke, run, change, combine, and inspect models is a key DSS capability that differentiates it from other CBIS. The models in the model base can be divided into four major categories: strategic, tactical, operational, and model-building blocks and routine.

Strategic Models: are used to support top management’s strategic planning responsibilities. Potential applications

include developing corporate objectives, planning for mergers and acquisitions, plant location selection,environmental impact analysis, and nonroutine capital budgeting.



Mostly external data are used. The GLSC opening vignette includes a long-range planning model.

Tactical Models: are used mainly by middle management to assist in allocating and controlling the organization’s resources. Examples of tactical models include labor requirement planning, sales promotion planning, plant layout determination, and routine capital budgeting. Tactical models are usually applicable only to an organizational subsystem such as the accounting department. Their time horizon varies from 1 month to less than 2 years. Some external data are needed, but the greatest requirements are for internal data. The GLSC vignette includes some tactical models for its 15 months plan.



Operational Models: are used to support the day-to-day working activities of the organization. Typical decisions are approving personal loans by a bank, production scheduling, inventory control, maintenance planning and scheduling, and quality control. Operational models support mainly manager’s decision making with a daily to monthly time horizon. These models normally use internal data.

The models in the model base can also be classified by functional areas ( such as financial models or production control models) or by discipline (such as statistical model, or management science allocation models). The number of models in a DSS can vary from a few to several hundred.


3.6 The Model Management Subsystem Modeling Language

Because DSS deal with semistructured or unstructured problems, it is often necessary to customize models. This can be done with high-level languages. Some examples of these are COBOL, with a spreadsheet or with other fourth-generation languages, and special modeling language such as IFPS/Plus.

The Model Base Management System (MBMS)The functions of the model base management system (MBMS) software are model creation using subroutine and other building blocks, generation of new routine and reports, model updating and changing, and model data manipulation. The MBMS is capable of interrelating models with the appropriate linkages through a database.


3.6 The Model Management Subsystem Major Functions of the MBMS

Creates models easily and quickly, either from scratch or from existing models or from the building blocks

Allows users to manipulate the models so they can conduct experiments and sensitivity analysis ranging from what-if to goal seeking.

Stores, retrieves, and manages a wide variety of different types of models in a logical and integrated manner

Accesses and integrates the model building blocks Catalogs and displays the directory of models for use

by several individuals in the organization Tracks model data and application use Interrelates models with appropriate linkages with the

database and integrates them within the DSS


3.6 The Model Management Subsystem Major Functions of the MBMS

Manages and maintains the model base with management functions analogous to database management: store, access, run, update link ,catalog, and query

Uses multiple models to support problem solving The Model DirectoryThe role of the model directory is similar to that of a database

directory. It is a catalog of all the models and other software in the model base. It contains the model definitions, and its main function is to answer questions about the availability and capability of the models.


3.6 The Model Management Subsystem Model Execution, Integration, and Command The following activities are usually controlled by model

management:– Model execution is the process of controlling the actual

running of the model.– Model integration means combining the operations of

several models when needed (such as directing the output of one model to be processed by another one).

– A model command processor is used to accept and interpret modeling instructions from the dialog component and to rout them to the MBMS, the model execution, or the integration functions.



Model Management Issues

– Model level: Strategic, managerial (tactical), and operational

– Modeling languages

– Lack of standard MBMS activities. WHY?

– Use of AI and fuzzy logic in MBMS


3.7 The Knowledge Based (Management) Subsystem

Provides expertise in solving complex unstructured and semi-structured problems

Expertise provided by an expert system or other intelligent system

Advanced DSS have a knowledge based (management) component that can provide the required expertise for solving some aspects of problem and providing knowledge.

Silverman [1995] suggested that: knowledge-based decision aids (support unaddressed problem with

mathematics) Intelligent decision modeling systems (build, apply and manage

libraries of models) Decision analytic expert systems (integrate theoretically rigorous

methods of uncertainty into the expert system knowledge bases)


3.7 The Knowledge Based (Management) Subsystem

Knowledge-based DSS can be called intelligent DSS, or DSS/ES, expert support system, or simply knowledge-based DSS.

Data mining application can be one of them.

Knowledge worker

I –access services

Authentication; Translation and transformation for diverse applications and appliances (e.g., browser, PIM, file

system, PDA, mobile phone


II – Personalization service

Personalized knowledge portals; profiling; push-service; process-; project-; or role-oriented knowledge portals

IV –Integration service

Taxonomy, knowledge structure, ontology; multi-dimensional metadata (tagging); directory services; synchronization services.

Discovery

Search, mining, knowledge maps, navigation, visualization

Publication

Formats, structuring, contextualization, workflow, o-authoring

Collaboration

Skill/expertise mgmt, community space, experience mgmt, awareness mgmt.

Learning

Authoring, course mgmt, tutoring, learning paths, examinations

III – Knowledge service

V –Infrastructure service

Intranet infrastructure service; groupware services; extract; transformation; loading; inspection service.


(1)

(1) Intranet/extranet, Messages, contents of CMS, e-learning platforms

(2) DMS documents, files from office information systems,

(3) Data from RDMS, TPS, data warehouse,

(4) Personal information management data

(5) Content from Internet, WWW, newsgroups

(6) Data from external online database

(2) (3) (4) (5) (6)VI- data and knowledge source


3.8 The User Interface (Dialog) Subsystem

Includes all communication between a user and the MSS

Graphical user interfaces (GUI) Voice recognition and speech synthesis possible To most users, the user interface is the system Management of the User Interface Subsystem


Management of the User Interface Subsystem

This subsystem is managed by software called the user interface management system(UIMS)

UIMS capabilities:

– Provides graphical user interface

– Accommodates the user with a variety of input devices

– Presents data with a variety of formats and output devices

– Gives users help capabilities, prompting, diagnostic and suggestion routines, or any other flexible support

– Provides interactions with the database and the model base

– Stores input and output data



UIMS capabilities:– Provides color graphics, three-dimensional graphics, and

data plotting– Has windows to allow multiple functions to be displayed

concurrently– Can support communication among and between users and

building of MSS– Provides training by example (guiding user through the

input and modeling process)– Provides flexibility and adaptiveness so the MSS can

accommodate different problems and technologies– Interacts in multiple, different dialog styles– Captures, stores, and analyzes dialog usage to improve the

dialog system



The User Interface Process

Figure 3.5 shows the process for an MSS. The user interacts with the computer via an action language processed via the UIMS. In advanced system the user interface component includes a natural language processor or may use standard objects (such as poll-down menu and buttons) through a graphical user interface (GUI). The UIMS provides the capabilities listed in DSS in Focus 3.5 and enables the user to interact with the model management and data management subsystems.



Data Management and

DBMS

User Interface Management Sys. UIMS


Model Management and

MBMS

Natural Language Processor.

Action

Language

Display

Language

User

Printers, Plotters

Terminal


3.9 The User The person faced with the decision that the MSS is

designed to support is called the user, the manager, or the decision maker

Two board classes:– Managers – Staff specialists: e.g. Financial analysts, production

planners, etc. Intermediaries: the connectors between manager and

DSS1. Staff assistant2. Expert tool user3. Business (system) analyst4. GDSS Facilitator

In detail,


3.9 The User

1. Staff assistant: has specialized knowledge about management problems and some experience with decision support technology

2. Expert tool user: is skilled in the application of one or more types of specialized problem-solving tools.

3. Business (system) analyst: has a general knowledge of the application area, a formal business administrator education (not computer science), and considerable sill in DSS construction tools.

4. GDSS Facilitator: controls and coordinates the software of GDSS.


3.10 DSS Hardware Evolved with computer hardware and software technologies

Major Hardware Options – Mainframe– Workstation– Personal computer– Web server system

• Internet• Intranets• Extranets


3.11 Distinguishing DSS from Management Science and MIS

MIS can be viewed as an IS infrastructure that can generate standard and exception reports and summaries, provide answers to queries, and help in monitoring and tracking. It is usually organized along functional areas. Thus, there are marketing MIS, accounting MIS, and so on. A DSS, on the other hand, is basically a problem-solving tool and it is often used to address ad doc and unexpected problems. MIS is usually developed by the IS department because of its permanent infrastructure nature. DSS is usually and end-user tool; it can provide decision support within a short time. An MIS can provide quick decision support only to situations for which the models and software were prewritten.



Because of its unstructured nature, DSS is usually developed by a prototype approach. MIS, on the other hand, is often developed by a structured methodology such as the system development life cycle (SDLC)

A DSS can evolves as the decision maker learn more about the problem. Often managers cannot specify in advance what they want from computer programmers and model builders. Many computerized applications are developed in a way that requires detailed specifications to be formalized in advance. This requirement is not reasonable in many semistructured and unstructured decision-making tasks.



The Major characteristics of MIS, MS, and DSS: MIS:

The main impact has been on structured tasks, where standard operating procedures, decision rules, and information flows can be reliably redefined.

The main payoff has been in improving efficiency by reducing costs, turnaround time, and so on, and by replacing clerical personnel.

The relevance for manager’s decision making has mainly been indirect ( for example, by report and access the data)

MS/OR The impact has mostly been on structured problems

(rather than tasks), where the objective, data, and constraints can be pre-specified.



The payoff has been in generating better solutions for given types of problems.

The relevance for manager’ has been the provision of detailed recommendations and new methods for handling complex problems.

DSS the impact is on decisions in which there is sufficient

structure for computer and analytic aids to be of value but where the manager’s judgment is essential.

The payoff is in extending the range and capability of manager’s decision processes to help them improve their effectiveness



The relevance for manager is the creation of a supportive tool, under their own control, that dose not attempt to automate the decision process, predefine objectives, or impose solutions.

Example, Marketing DSS.


Standard Statistical Models

Regresssion analysis

Factor analysis

Cluster analysis

Discriminant analysis

Standard MS Models

Linear Programming

Markov analysis

Decision table

Inventory

User Interface

Marketing data

Sales Reports

Market reports

Government reports

…

Marketing model

Media Mix

Site Location

Advertising budget

Product Design

….

Marketing recommendations & evaluation

database

User


3.12 DSS Classifications

Alter’s Output Classification (1980)

Degree of action implication of system outputs (supporting decision) (Table 3.4)


OrientationCategoryType of

Operation Type of Task User Usage pattern Time FrameData File drawer Access data items Operational Nonmanagerial Simple inquires Irregular

system line personnelData analysis Ad hoc analysis Operational, Staff analysis or Manipulation and Irregularsystems of data files analysis managerial line display of data or periodical

personnelData or Analysis Ad hoc analysis Aanlysis, Staff analyst Programming spe- PeriodicModel information involving multiple Planning cial reports, deve-

systems databases and loping smallsmall models models

Models Accounting Standard calcula- Planning Staff analyst or Input estimate of PeriodicModels tions that esti- Budgeting manager activity: receive or irregular

mate future estimated monet-results on the ary results as basis of account- outputing definitions

Representation Estimating Planning Staff analyst Input possible PeriodicModels consequences of Budgeting decision; receive or irregular

particular actions estimated resultsas output

Optimization Calculation an Planning, Staff analyst input constraints PeriodicModels optimal solution Resource and objectives; or irregular

to a combina- allocation receive answertorial problem

Suggestion Performing calcu- Operational Nonmanagerial Input a structured daily or periodicModels lation that gener- line personnel decription of the

ate a suggested decision situationdecision receive a suggested

decsion as output


3.12 DSS Classifications Holsapple and Whinston’s Classification [1996]

1. Text-oriented DSSInformation (including data and knowledge) is often stored in a textual format and must be accessed by the decision makers. The amount of information to be searched by the decision makers is exponentially growing. Therefore, it is necessary to represent and process text documents and fragments effectively and efficiently.A text-oriented DSS supports a decision maker by electronically keeping track of texually represented information that could have a bearing on decisions. It allows documents to be electronically created, revised, and viewed as needed. Information technologies such as document imaging, hypertext, and intelligent agents can be incorporated into the text-oriented DSS application. New Web-based systems are revolutionizing the development and deployment of text-oriented DSS.



2. Database-oriented DSSDatabase plays a major role in the DSS structure. Rather than

being treated as streams of text, data are organized in highly structured format (relational or objective-oriented). The early generations of database-oriented DSS used mainly the relational database configuration. The information handled by relational databases tends to be voluminous, descriptive, and rigidly structured. Database-oriented DSS features strong report generation and query capability

3. Spreadsheet-oriented DSSA spreadsheet is a modeling language that allows the user to write models to execute DSS analysis. These not only create, view, and modify procedural knowledge, but also



instruct the system to execute their self-contained instructions. Spreadsheets are widely used in end-user developed DSS. The most popular end-user tools for developing DSS are Microsoft Excel and Lotus 1-2-3, both of which are spreadsheets.

Because package such Excel can include a rudimentary DBMS, or can readily interface with one, such as Access, they can handle some properties of a database-oriented DSS, especially the manipulation of descriptive knowledge. Some spreadsheet development tools include what-if analysis and goal-seeking capabilities and they are revisted in Chapter 5.



4. Solver-oriented DSSA solver is an algorithm or procedure written as a computer program for performing certain computation for solving a particular problem type. Examples of a solver can be an economic order quantity procedure for calculating an optimal ordering quantity or a linear regression routine for calculating trend.

5. Rule-oriented DSSThe knowledge component of DSS described earlier includes both procedural and inferential (reasoning) rules, often an expert system. These rules can be qualitative or quantitative. This application of artificial intelligence is describes in Chapter 6.



6. Compound DSS A compound DSS is a hybrid system that includes two or

more of the five basic structured described above. A compound DSS can be built by using a set of independent DSS, each specializing in one area. A compound DSS can also build as a single, tightly integrated DSS.


3.13 Other Classifications

Institutional DSS vs. Ad Hoc DSS

Institutional DSS deals with decisions of a recurring nature

Example, a portfolio management system, GLSC vignette. Ad Hoc DSS deals with specific problems that

are usually neither anticipated nor recurring.

Often involving strategic planning and sometimes management control problems.


3.13 Other Classifications Degree of nonprocedurality (Bonczek et al., 1980)

BASIC and COBOL language called procedure language, most non-procedure language is used in DSS building. This non-procedure language is four generation language (4GL)

Personal, group, and organizational support (Hackathorn and Keen, 1981)

Individual versus group support systems (GSS) Custom-made versus ready-made systems

Ready-made systems: Some organizations such as school , hospital, banks etc. have similar problems to be solved. Building a DSS can be used (mirror modification) in several organizations. Such DSS called ready-made systems.


Individual Assignment 1. How to distinguish DSS from MS and MIS? 2. According to Alter [1980], how to Classify DSS?. 3. What are the relationships and distinguishes between

Alter’s [1980] classification and Holsapple and Whinston’s [1996] classification about DSS?

4. What components does a DSS have? Briefly describing functions of each component in DSS.

What are the model-oriented DSS and data-oriented DSS?

Group Assignment (option)Design a DSS framework for the case of this chapter open vignette, that is Gotaas-Larsen Shipping Gorp.


Individual Assignment 3. What are the relationships and distinguishes between Alter’s

[1980] classification and Holsapple and Whinston’s [1996] classification about DSS?

Distinguish: Alter’s classification is based the “degree of action implication of system output”, or system output can directly support (or determine the decision). H&P classification is based on the applications or processing objectives.

Relationships: According to Alter’s classification, there are seven categories: the first two types are data oriented, performing data retrieval or analysis, which is correspond to the H&P’s Text- and Database-oriented DSS; the third deals with data and models (H&P:database, Spreadsheet). The reminders are model oriented, providing either simulation capabilities, optimization, or computations that suggest an answer (solver- and rule-oriented DSS). Not every DSS fits neatly into a single classification system. Some have equally strong data and modeling orientation

chapter 3

Documents

dss application

dss configurationsadds

comprehensive dss

longterm planning

financial analysis model

individual voyages

simulation model

strategic plan of glsc