Decision Support Systems
Author: Alan C. Dube
Date: 7/14/96
Decision Support Systems: Data Warehousing
This paper is part one of a five-topic series within the
subject area of decision support systems (DSSs). After defining
what a DSS is, the paper will provide an overview of data
warehousing, describing its importance to decision support
systems and reviewing current information on the topic. The
paper will close with some conclusions on data warehousing and
its use within the business environment.
Parker (1994) defines a DSS as a "system that provides tools
and capabilities to managers to help them satisfy their own
information needs" (p. 378). Turban (1995) expands on this
definition, defining a DSS as "a model based set of procedures
for processing data and judgments to assist a manager in his/her
decision making" (p. 82). Turban further notes that in order to
be successful, such a system must be simple, robust, easy to
control, adaptive, complete on important issues, and easy to
communicate with. Implied in this definition is the assumption
that the system is computer based and serves as an extension of
the user's problem solving capabilities.
One form of DSS that has been receiving much attention of
late is the "data warehouse." A data warehouse, like the term
"client/server computing," can have different definitions
according to the specific application involved. Robinson (1996)
describes a data warehouse as a system that simply takes
different databases and consolidates them into a single database
to meet the information needs of an organization. Radding (1995)
notes that the term is currently being used to describe a number
of different facilities and components:
* Giant physical database in the sky -- the actual physical database into which all the data for the data warehouse is gathered, along with the processing logic used to scrub, organize, and package the data for end-user access
* The logical data warehouse -- the business rules and processing logic required to preprocess the data, in addition to the information required to find and access the data, wherever it actually resides
* Data mart -- a subset of the enterprise data warehouse that performs the role of a departmental, regional, or functional data entity. An iterative process is used to build a series of data marts over time and link them via an enterprise logical data warehouse.
Whatever the definition, one thing remains clear: Data is the
lifeblood of many organizations, and the ability to tap into it
affects the way decisions are ultimately made by those
organizations.
Data warehouses are being used by corporate decision makers
to increase revenues and profits, improve customer services, and
identify new business opportunities. Investments in data
warehouses have grown at a phenomenal rate. A key reason for
this growth is the return-on-investment (ROI) potential of the
architecture. Coopers & Lybrand Consulting recently estimated
that companies can expect to generate a minimum of $6 and a
maximum of $400 for every dollar invested in data warehouses
(Tice, 1996).
Data warehouses are often built specifically for DSSs by
accessing a large data store (often created/updated from other
operational feeds) via the desktop, using a distributed,
client/server architecture. Cobb (1996) notes that a standard
DSS-based data warehouse has four architectural properties:
1. Data is organized by subject or entity (e.g., customer or part), not by application (e.g., stock or control).
2. Data is integrated in that it is held in a consistent form (e.g., in a sales application, a product may be identified and described by for data items)
3. Data is non-volatile. Unlike data within an operational database which is subject to frequent updates (e.g., movement of materials in and out of stock), data within a data warehouse is not updated -- it remains static.
4. A data warehouse is time variant. Whereas an operational database is updated to reflect current values, a data warehouse captures historical and trend-driven information.
The data warehouse must also scaleable, allowing organizations to
grow and reshape their warehouse as information needs change.
Because of its inherent client/server architecture and the
requirement to perform data queries on millions (and in some
cases billions) of records, data warehouses are often stored in a
relational database on one or more high-powered servers. A
flexible graphical user interface (GUI) application is often used
as a front-end to the data, with a production-quality relational
database management (RDBMS) system, such as Sybase or Oracle,
running on the back end. The RDBMS often resides on a high-end
server platform, such as a RISC architecture equipped with one or
more CPUs and disk subsystems to handle the processing load
(Elizer, 1996).
The true value of the data warehouse is its ability to
support ad hoc queries from users who may not know exactly what
specific data they are seeking. A typical DSS question might be
"How many males between the ages of 30 and 40 purchased blue
Fords in Florida in January 1995?" It is this type of capability
that makes the data warehouse an indispensable, if not required
DSS tool for corporations now and in the future.
As companies turn inward to analyze and dissect the wealth
of valuable customer information they already own, the can become
more focused and targeted in the marketing campaigns, inventory
control, sales promotions, product configurations, and customer
services. The challenge for corporations is to select and
implement the proper mix of technologies and tools that have
become available in order to achieve the ROI potential of the
data warehouse and its decision support systems.
Decision Support Systems: Tools and Software
This paper is the second in a five-topic series within the
subject area of decision support systems. This paper will
provide an overview of some of the tools and software now in use
for decision support, describing their importance and relevance
to decision support systems and reviewing current information on
their various applications. The paper will close with some
conclusions on the DSS tools and software.
Lobely (1996) observes that there are various levels of
decision software, from the overly simplistic spreadsheet
"templates" that are sufficient for basic decisions to more
sophisticated tools for handling complex decisions involving
hundreds of factors and countless options. As the use of
decision support systems have increased, a plethora of companies
have come to market with a variety of tools and software to meet
the growing demand. The software offerings range from solutions
designed to integrate, or "plug-in," to existing decision support
system infrastructures to those that offer complete soup-to-nuts
solutions for business.
One such plug-in tool is GQL from Andyne Computing Ltd.
Andyne recently debuted a new version its reporting and analysis
tool that supports object linking and embedding (OLE) and the
Windows 95 and NT operating systems. GQL 4.0 builds on GQL's
ability to pull information from virtually any relational
database and build reports, charts, and programs that help
managers make decisions. GQL's main strength is its ease of use,
using a friendly GUI and a data modeling and drill-down approach
to support both structured and ad hoc analysis of data (Willet,
1996).
A more complete and complex DSS tool is Comshare's Commander
Decision product: a new object-oriented, 32-bit client/server
application built using Microsoft Visual C++ and foundation class
libraries. Commander Decision incorporates a Visual Basic
compatible scripting language and can run on the Windows 3.1, 95,
and NT platforms. Commander Decision incorporates Arbor
Software's Essbase multi-dimensional OLAP (on-line analytical
processing) database for its back-end storage engine. The front-
end for the product, known as the "Decision Desktop," provides
end-user tools "out of the box" as well as allowing developers to
produce highly functional information applications (Newing,
1996). Commander Decision provides traditional EIS (executive
information system) style graphical presentation, utilizing color-
coded exception reporting and ad hoc queries and calculations.
It also reports data geographically through the use of an
integrated mapping system.
The "Decision Access" module is the server software which
sits alongside the Essbase server and deals with Decision
Desktop's information requests, returning the results when the
operation is complete. The Decision Desktop client and the
Decision Access server work together to support ad hoc
calculations. Since Essbase requires all calculations to be pre-
defined, carried out in advance, and stored in the database, the
database can quickly grow to an enormous size. The calculation
facility in Decision Access gives organizations the option
whether to pre-calculate data using Essbase or calculate it
dynamically using Decision Access. The latter reduces the size
of the Essbase database and minimizes network traffic.
As mentioned in the previous paper, data warehousing has
taken center stage as a valuable and effective decision support
system architecture for business. The ability to dig for data
within the data warehouse and make sense of it, known as "data
mining," is the main purpose for Pilot Software's Pilot Discovery
Server. The server is aimed specifically at sales and marketing
users. It is touted as being able to analyze a relational
database and identify groups of customers who have similar
characteristics and make predictions for those groups. The
marketer can generate models and interpret results without the
need to have the information systems department extract the data
from the warehouse and then have a statistician produce the model
for the marketer (Gaudin, 1996).
Many RDBMS vendors are coming to market with decision
support add-ins for their traditional product lines to support
data warehousing applications. Informix and Oracle recently
released their Universal Server products, and Sybase followed
shortly thereafter with its IQ system, a relational database
designed specifically for data warehousing decision support that
supposedly is 5 to 500 times faster than competitive products.
Sybase uses a bit-wise technology in IQ, storing information in
compressed column format -- thus resulting in faster query
response and more database flexibility (Gaudin, 1996).
IBM is in a race with all the other RDBMS vendors to add
object and multimedia support to their DB2 relational database.
IBM is also leading the efforts to integrate data warehousing and
decision support systems with the World Wide Web by announcing
the availability of object extensions to DB2 that will support
unstructured data (e.g., audio and video objects) and HTML
(hypertext markup language) pages (Vadlamudi & DeMocker, 1996).
In doing this, IBM recognizes that the Web is well suited for
data warehouses -- mainly because a Web browser is an inexpensive
and easily maintained way to open up decision support databases
to a large number of users.
It is clear that the information systems industry is
responding to the demand for both simple and more sophisticated
decision support systems. Companies and vendors alike realize
the current and future value of DSSs in shaping business
decisions and affecting corporate profitability. However, much
like client/server technology, decision support systems software
and tools are still in their infancy and need to be clearly
understood by corporations before costly investments are made in
search of enhanced efficiency and profitability. Most
importantly, decision support system tools should be selected and
implemented with a clear purpose, application, and solution in
mind. The technology should not be implemented as a solution in
search of a problem.
Decision Support Systems: Group DSSs
This paper is the third in a five-topic series within the
subject area of decision support systems. This paper will
provide an overview of group decision support systems and their
applications, defining the different types of group DSSs and
discussing their importance to decision support. The paper will
close with some conclusions on group DSSs and their use as an
effective tool for group business decisions.
Laudon & Laudon (1994) define a group decision support
system (GDSS) as an interactive computer-based system used to
facilitate the solution to unstructured problems by a set of
decision makers working together as a group. Sage (1991) and
Silver (1991) are more comprehensive in their classification of a
GDSS: A collection of information technologies, such as
electronic mail, electronic meeting and discussion systems,
decision rooms, and teleconferencing, that provide decision-
making support to groups.
GDSSs are typically comprised of the following components:
* Technological - in terms of computer hardware and software, and communication equipment
* Environmental - in terms of the people involved, their locations in time and space, and familiarity with the task at hand
* Process - variables that are comprised of the conventions used to support task performance, and enable the other components of decision making to function appropriately (Sage, 1991).
Thus, GDSSs support groups of people as they interact "any place,
any time," whether or not they are within arms length of each
other or geographically dispersed -- and whether or not they
communicate synchronously or asynchronously.
The number and types of GDSSs in use today by businesses are
relatively extensive. Johansen (1988) has identified several
GDSSs that are commonly used for group decision making and input:
* Project Management. This represents software that is receptive to presentation team input over time and has capabilities to organize and structure tasks associated with the group. The output is often a GANTT or PERT chart used for project management.
* Calendar Management. Individuals in a group need to coordinate times with one another for meetings and conferences. Software is used to set personal schedules and resolve time conflicts within a work group.
* Group Authoring. This allows members of a group to suggest changes to a stored in the system, without changing the original. Revisions are managed by a central author, and the group can view the different revisions suggested. The goal is to encourage and streamline the group writing process.
* Computer-supported Audio or Video Conferences. Standard audio and video teleconferencing systems that allow multiple participants to hear/view the interaction of others within the group and have input to a group decision process. This also includes one-to-one and one-to-many audio and video systems.
* Computer-supported Spontaneous Interaction. These systems encourage impromptu interaction on an unscheduled basis in an informal setting. Bulletin board and discussion databases such as Lotus Notes or Collabra Share often serve as the vehicle for such interaction. Electronic mail is also used to trigger and thread spontaneous discussions.
The final GDSS identified by Johansen, computer-supported
electronic meeting systems, is perhaps the most visible and
traditional GDSS now in use by corporations. Electronic meeting
systems, through the use of dedicated meeting rooms, board rooms,
and teleconferencing facilities, allow individual members of a
group to work electronically with one another to reach a group
decision or consensus. The members of the group may be in the
same room, or electronically dispersed over wide geographic
areas.
Typically members in an electronic meeting sit at individual
workstations or locations and contribute individually to the
discussion. Often a central, large monitor screen is used to
display and collect and document individual inputs to the central
task or issue. A meeting facilitator will monitor and guide the
discussion using the central monitor station as a reference
(Niederman, Beise, & Beranek, 1996). The facilitator has great
influence over the success of the medium due to the element of
control he/she is allowed, and thus must have a clear
understanding of group-meeting issues and the technology used to
advance the group decision-making process.
Another form of electronic meeting systems is the use of a
"smart" whiteboard or shared drawing space. In this environment,
the facilitator and group participants document ideas on a
central location (the whiteboard), using special marking devices.
The end results of the discussion can then be electronically
captured for distribution to the group members or throughout the
company for further discussion and enhancement. Whiteboards may
also be electronically linked on a central monitor: here two or
more participants modify the workspace simultaneously, with the
image or notes then electronically captured upon completion of
the discussion. The participants in this scenario are usually in
physically-separate locations. This GDSS is popular in group
engineering and design organizations, due to its interactive and
computer-aided environment.
Unlike single-user DSSs, GDSSs impact groups of decision
makers at any given time. It is clear that groups often make
decisions differently from the way an individual does. GDSSs
provide the medium to generate and brainstorm ideas, formulate
discussions, resolve differences, settle political issues, and
reach a group consensus. GDSSs reinforce the fact that group
activities are economically necessary for the production of
ideas, especially when democratic values are encouraged within
the company. GDSSs increase participation in the decision making
process, help to set priorities, document the activities of
meetings, and provide a forum to evaluate ideas objectively
(Laudon & Laudon, 1996).
By taking different locations and transmission times into
account, GDSSs foster collaboration within the corporation, so
that critical decisions are studied and made using multiple
inputs and global views. This reduces the chance for error in
decision making and helps to build broad support for problem
resolutions and implementation plans within the corporation.
Decision Support Systems: Business Applications
This paper is the fourth in a five-topic series within the
subject area of decision support systems. This paper will
provide an overview of some different applications of decision
support systems now in use for business decisions, describing
their importance, functionality, and impact on their respective
organizations. The paper will close with some conclusions on the
business applications of DSSs.
Decision support systems are widely used in business for a
variety of routine and complex decision processes. Current uses
as noted in the literature available on decision support systems
include:
* Auto loan approval and credit scoring * Financial and investment management * Operations scheduling in manufacturing environments * Selecting Health care insurance systems * Legal services and strategy.
Charlotte, NC-based NationsBank processes over 100,000
automobile loan applications every month. Due to the volume,
quick turnaround time on loan approval is crucial. NationsBank
Dealer Financial Services created a pilot DSS, Credit Connection,
to establish a real-time credit processing and decision service
linking banks, dealers, and credit bureaus for instantaneous
credit decisions (Anonymous, 1996).
The benefits to NationsBank are significant. What used to
take days to process now takes minutes in a near-paperless
environment. Electronic loan applications gathered by Credit
Connection are fed directly into NationsBank's loan processing
software, Credit Revue, where the loan/no-loan decision is made
and the decision transmitted immediately back to the requesting
institution. Although NationsBank had to upgrade their
telecommunications and computer platforms to keep up with the
demand, the strategy seems to have paid off. NationsBank now
projects that the current application processing rate will at
least double, enhancing NationsBank's lead in on-line banking.
NationsBank's Credit Connection/Revue is an example of a DSS
that often feeds data into credit scoring DSSs. Credit scoring
is a versatile tool that can help lenders increase their revenues
by precisely identifying desirable prospects and accounts amongst
a pool of candidates. This helps the creditors to better know
their prospects and gear their products, offers, and marketing
strategies to match them. Credit scoring, coupled with data
warehousing and mining applications, has also allowed
institutions to create predictive risk models that are now
specific to a particular type of portfolio (Friedland, 1996). A
variety of tailored credit scores are used by banks and credit
bureaus, including: auto loan, mortgage, home equity, credit
card, small business, household goods.
Using different credit scores, lenders and issuers can build
successful strategies throughout the entire credit or lending
cycle: from marketing, to application screening and account
management. Credit scoring has helped financial organizations to
streamline operations, cut costs, reduce losses, improve customer
service, and retain clients.
In addition to loan and credit-service organizations, DSSs
have become an important tool for those institutions that need to
conduct decision support for financial modeling and investment
services. British Gas uses Comshare's Commander Decision to
model their data warehouse of financial information, setting
alert and exception levels to direct their attention accordingly
(Newing, 1996). Many investment houses use a variety of systems,
including Reuter's Triarch and Dow Jones Telerate, to obtain raw
financial information and redisplay it in various ways to keep
their fund managers constantly informed on changing market
factors so that immediate portfolio adjustments can be made
(Essinger, 1995).
DSSs are also used extensively in manufacturing operations
to assist with production scheduling problems. Lipske (1996)
notes that production scheduling problems with parallel machines
and sequence-dependent setup times are extremely difficult to
solve. User-driven DSSs are often used in this environment to
apply the heuristics required to achieve the "good" and "near-
optimal" throughput improvement needed to take full advantage of
resources, time, and supplies. The information supplied by the
manufacturing DSSs gives production schedulers the flexibility
required to minimize underutilized machines and take advantage of
the "right" product mix for the given operation.
Rising health-care costs have been a major concern to the
government, corporations, and employees alike. The motivation of
employees is to minimize their health care insurance costs, just
as it is with most employers. These competing motivations have
pit the employee against the employer. Gupta & Scott (1996) note
that companies are now developing DSSs to find the right mix of
health care choices and selections for their employees in order
to contain the skyrocketing costs. These systems will help
employees in making the right health-care selection for their
families by analyzing different health insurance policies,
coverages, deductibles, and premium structures.
E. Fremont Magee, a partner in the firm of Piper and Marbury
in Baltimore, Maryland, uses Law Choice software to help in
deciding among candidates for arbitration panels in medical
malpractice claims. Magee enters biographical information on the
candidates into the system, where the information is weighted by
importance and scored according to criteria that is favorable to
his client. Several different candidate mixes are generated by
the system, since the composition of the panels is subject to
negotiation. This gives Magee the ability to respond to his
opponent's reasoning and adjust his decision matrix accordingly
(Nagel, 1992).
Decision support systems and their applications have found
their way into just about every sector of business, from loan
processing to legal services. Vendors are eager to provide
solutions; Consumers are eager to apply solutions within their
business to become more efficient, favorably affect the bottom
line, and stay with, or ahead, of their competitors. The
computer and the decision maker(s), through the use of a single-
user and/or group DSSs, have become a team. However, this team
approach has pitfalls and limitations -- especially with respect
to ethical dilemmas and moral responsibility. The next, and
final paper in this series will explore these issues in more
detail.
Decision Support Systems: Ethical Implications
This paper is the last in a five-topic series within the
subject area of decision support systems. This paper will
discuss the ethical implications of using decision support
systems to make and implement business decisions and how ethical
issues impact the way DSSs are used for business decisions. The
paper will close with some conclusions on ethics and decision
support system use.
Decision supports systems have become an integral part of
the decision making process for both public and private
corporations. DSSs aid in the design of marketing strategies,
help to minimize manufacturing costs, improve corporate-consumer
relations, and can predict the risk and reward outcomes of
alternative investment practices. DSSs promise a variety of
benefits for better decision making: help in coping with complex
issues, managing uncertainty, and introducing consistency and
efficiency to organizations.
However, the use of DSSs raise a variety of ethical
concerns. Johnson and Mulvey (1996) note that the ethical issues
tend to center on the values and trade-offs made within the
systems, leading to the following troubling questions:
* How are risks to human health and life managed? * Is it fair to put dollar amounts on human life? * Do DSS models bring hidden bias into decision making? * How accountable are system designers in relation to clients, users, and others affected by their DSSs?
These are tough questions to address, and they usually receive
attention only when a DSS contributes to an incorrect decision:
a misdiagnosis; a rejected housing loan for those in need; an
incorrect engineering decision that puts lives in danger; the
rejection of a qualified college applicant.
Mallach (1994) also raises a series of ethical concerns.
DSSs and data warehouses often store information that customers
may view as "private." In storing this information for strategic
reasons, are corporations invading one's privacy? Is there
information contained within the DSS that was not obtained
properly and may belong to someone else? Once the data is in the
system, is it safe to assume that it is being used for the
purposes in which it was provided? As a corporation's database
grows, and more and more professionals learn how to access the
data, the chance for its unethical misuse increases. Bias in
decision making can be introduced by intentionally including
and/or excluding populations of data based on criteria that is
not relevant to the business decision at hand.
Information is property, has value, and can be sold or
leased to others. If there are questions as to how and why a
company is using data, these same questions remain if the data is
shared with other companies that also have an interest in using
it. Consumers often given personal and financial information to
companies in return for a product or service, only to find that
their names were "sold" to a direct marketing firm, who then sold
them to telemarketing agency. This results in highly targeted,
and often unwanted, solicitations back to the consumer based on
information volunteered for another purpose entirely.
DSS use is subject to human judgment and fallibility. DSSs
can provide perfect information, but the information could then
be misinterpreted or used inappropriately -- especially when the
system is relied upon to make the decision rather than support a
decision. DSS parameters can be modified and tuned. As such,
slight or imperceptible errors can be introduced into the
decision making process, whether they were intentional or not.
Ideally, checks and balances are in place to prevent errors and
correct errors when they are detected. However, they do not
always work. Anyone who has had an error on their credit report
can attest to that, and to the fact that it takes quite an effort
to get the error fixed. When DSSs are coupled with a massive
bureaucracy, and when the output from DSSs are regarded as
"fact," lives are inevitably impacted in a negative way.
The operations of organizations that use DSSs are often
greatly impacted, both procedurally and culturally, by their use
(Jelassi, Klein, & Mayon-White, 1992). The use of DSSs can
become so routine that human intuition on a decision is
disregarded in favor of hard facts and system output.
Individuals who rely on the system become slaves to it. In a
society that rewards speed and efficiency, it is easy to
understand why this is the case.
DSSs are as diverse as the techniques they deploy in their
applications. Analytical techniques such as linear programs,
decision analysis, and Monte Carlo simulations are used in a
variety of situations, ranging from overseeing engineering-plant
changes to flying an airplane on auto pilot. The critical factor
in these techniques and applications is that the human using the
DSS becomes dependent on the DSS itself for vital recommendations
and actions. The line between the human decision and a system's
advice becomes blurred -- and with it morality and
responsibility.
Johnson (1994) notes that DSS vendors and consultants can
argue that they are not responsible for decisions made using
their products because the systems they create are only meant to
supplement decision making and be advisory in their capacity.
The DSS does not control what users do with the system and cannot
control what happens when users modify the system. On the other
hand, users can argue that when a DSS was purchased or
constructed, they were told it would be of great value and have a
high degree of validity. They could assume that use of the DSS
would not have disastrous consequences. They could also point
out that based on the cost of such systems, that it would be
unreasonable and unconscionable for vendors to disclaim
responsibility for the outcomes of their applications.
In the end, a DSS is a tool, much like a typewriter, phone,
or desktop computer. How the tool is used is up to those who use
it. Ignorance is not bliss when DSSs are heavily relied on to
make decisions that impact the welfare of society. Corporations
are ultimately responsible for their actions. If they choose to
use a DSS to make those actions easier to implement, it is their
choice alone. With risk comes reward, but also responsibility.
References
Anonymous (1996). Network cuts auto loan approval time to
minutes at NationsBank. Bank Systems & Technology, Vol. 33, No.
5, p. 69.
Cobb, I. (1996). Decision warehousing: The latest fad?
Management Accounting-London, Vol. 74, No. 6, 44-46.
Donovan, J. (1994). Business re-engineering with information
technology. Englewood Cliffs, NJ: Prentice Hall.
Elizer, L. (1996). Data availability ... survival of the
fittest! Computer Technology Review, Vol. 16, No. 5, 32-34.
Essinger, J. (1995). Decisions on the information edge.
Euromoney, No. 320, 64-67.
Friedland, M. (1996). Credit scoring digs deeper into data.
Credit World, Vol. 84, No. 5, 19-23.
Gaudin, S. (1996). Go ahead - ask your database anything.
Computerworld, Vol. 30, No. 24, p. 50.
Gaudin, S. (1996). A data mining tool for marketers.
Computerworld, Vol. 30, No. 25, p. 54.
Geiger, J., Pendegraft, N. & Geiger, L. (1996). A PC-based
project management tool. Journal of Systems Management, Vol. 47,
No. 3, 52-57.
Greenburg, I. (1996). Simon & Schuster rewrites the book on
implementing OLAP. InfoWorld, Vol. 18, No. 24, p. 79.
Gupta, O. & Scott, C. (1996). A decision support system for
selecting an employee health care insurance plan. Mid-Atlantic
Journal of Business, Vol. 32, No. 1, 47-61.
Holsapple, C. & Whinston, A. (1993). Recent developments in
decision support systems. New York: Springer Verlag.
Jelassi. T., Klein, M. & Mayon-White, W. (1992). Decision
support systems: Experiences and expectations. New York: North
Holland.
Johansen, R. (1988). Groupware: Computer support for
business teams. New York: Free Press.
Johnson, D. (1994). Computer Ethics (2nd ed.). Englewood
Cliffs, NJ: Prentice Hall.
Johnson, D. & Mulvey, J. (1995). Accountability and computer
decision systems. Communications of the ACM, Vol. 38, No. 12, 58-
64.
Laudon, K. & Laudon, J. (1994). Management information
systems: Organization and technology. Englewood Cliffs, NJ:
Macmillan Publishing.
Lewandowski, A., Serafini, P. & Speranza M. (1991).
Methodology, implementation and applications of decision support
systems. New York: Springer Verlag.
Lipske, K. (1996). A greedy-based decision support system
for scheduling a manufacturing operation. Production & Inventory
Management Journal, Vol. 37, No. 1, 36-39.
Lobley, D. (1996). Software can make hard decisions easy.
American City & County, Vol. 111, No. 6, p. 10.
Mallach, E. (1994). Understanding decision support systems
and expert systems. Boston: Irwin.
Nagel, S. (1992). Applications of decision-aiding software.
New York: St. Martin's Press.
Newing, R. (1996). Decision support is reborn. Management
Accounting-London, Vol. 74, No. 4, p. 46.
Niederman, F., Beise, C. & Beranek, P. (1996). Issues and
concerns about computer-supported meetings: The facilitator's
perspective. MIS Quarterly, Vol. 20, No. 1, 1-22.
Parker, C. (1994). Understanding computers and information
processing (5th ed.). New York: Dryden Press.
Pollard, C. (1996). Electronic meeting systems:
Specifications, potential, and acquisition strategies. Journal of
Systems Management, Vol. 47, No. 3, 22-28.
Radding, A. (1995). Support decision makers with a data
warehouse. Datamation, Vol. 41, No. 5, 53-56.
Robinson, T. (1996). A front-line hope for a back-end tool.
Software Magazine, Vol. 16, No. 7, S6-S10.
Sage, A. (1991). Decision support systems engineering. New
York: John Wiley & Sons.
Silver, M. (1991). Systems that support decision makers:
Decision and analysis. New York: John Wiley & Sons.
Tice, S. (1996). Business process support: Data warehouses
that reinvent the business environment. American Society for
Information Science, Vol. 22, No. 4, 22-26.
Turban, E. (1995). Decision support and expert systems (4th
ed.). Englewood Cliffs, NJ: Prentice Hall.
Vadlamudi, P. & DeMocker, J. (1996). Data warehousing tools
take center stage. InfoWorld, Vol. 18, No. 24, p. 3.
Willett, S. (1996). Andyne's GQL makes it easier. Computer
Reseller News, No. 685, p. 79.