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BATTELLE COLUMBUS LABS OH 
DECISION SUPPORT SYSTEMS: THEORY.(U) 
1976 J J JARVIS 


DAAG29-76-0-0100 
































r DECISION SUPPORT SYSTEMS: THEORY 

Jj'Inal Repact - Delivery Order No. 0999-01 
DAAG29— 76 — D—0100 • 

J r 

Presented to: 

Dr. Clarence Glese, Director, AIRMICS 
Major Edward H. Ely, COTR 

Presented by: 

/ John J .I Jarvis 


School of Industrial & Systems Engineering 
Georgia Institute of Technology 

Atlanta, Georgia 30332 









"The views, opinions, and/or findings contained in this report 
are those of the author and should not be construed as an official 
Department of the Army position, policy, or decision, unless so 
designated by other documentation." 




EXECUTIVE SUMMARY 


The purpose of this project was to research, collect, and evaluate all 
available information on Decision Support Systems (DSS) Theory. A 
biographical search resulted in over 600 articles, books and other docu¬ 
ments directly and indirectly related to DSS. This bibliography is in¬ 
cluded at the end of this report. The report is based on reading and 
evaluation of between 75 and 85 separate documents from the bibliography. 

DSS are interative, conversational computer systems supporting decision 
makers. DSS rely heavily on human intuition, judgement, and experience 
as an integral part of the decision process. DSS designers emphasize 
the interfacing mechanisms between the decision maker and the computer. 
Terns characterizing DSS include: computer-based, interactive, con¬ 
versational, flexible, adaptable, convenient, quick, helpful, and re¬ 
liable. Color graphics terminals, light pens, joy sticks, digitizer 
pads, and similar devices usually replace or augment the typewriter 
terminal in the design of DSS to enchance the man-machine interface. 

DSS design draws on three important methodologies: Operations Research 
and Management Science (OR/MS), Computer Science, and Behavioral Science. 
DSS utilizes the modelling and analysis techniques of OR/MS to identify 
and evaluate alternative courses of action. Computer Science contributes 
the necessary expertise in information storage, processing, and retrieval. 
Finally, DSS design accounts for those elements of individual and group 
behavior to ensure the best utilization of the decision maker in the inter 
active decision making process and acceptance by the decision maker and 
the organization of the DSS. 

DSS is not a separate science; it is an idea whose time has come. Namely, 
that by combining the computer's computational power with the decision 
maker's intuition and judgement in an interactive manner, better decisions 
will result than by either the computer or human taken separately. 








TABLE OF CONTENTS 


Page 


i. EXECUTIVE SUMMARY i 

1. INTRODUCTION 2 

1.1 Purpose 2 

1.2 Methods 2 

1.3 Organization of the Report 2 

2. DEFINITIONS AND CHARACTERISTICS OF A DECISION SUPPORT SYSTEM 3 

2.1 Evolution of DSS 3 

2.2 A Problem Class for DSS 3 

2.3 Some Definitions of a DSS 4 

2.4 Some Characteristics of a DSS 6 

3. STRUCTURE OF A DECISION SUPPORT SYSTEM 8 

3.1 Concept of a DSS 8 

3.2 Structure of a DSS 8 

3.3 Interfacing is the Key to DSS 10 

3.4 DSS Draws on Other Methodologies 13 

4. CONCLUSIONS AND RECOMMENDATIONS 17 

4.1 Conclusions 17 

4.2 Recommendations 17 

Bibliography 18 


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1. INTRODUCTION 


1.1 P urpose 

The United States Army Institute for Research in Management Information and 
Computer Science (AIRMICS) contracted with the School of Industrial and 
Systems Engineering at the Georgia Institute of Technology to research and 
collect all available information in Decision Support Systems (DSS). The 
information was to include all past, current, and proposed research. This 
report discusses the results of that activity as well as a requested eval¬ 
uation of the information in relation to the Management Information Systems 
mission of the U. S. Army Computer Systems Command. 

Activities within this contract focused on DSS Theory. A separate con¬ 
tract, with Dr. Lesley G. Callahan as principle investigator, was intended 
to focus on DSS applications. 

1.2 M ethods 

After extensive meetings with AIRMICS personnel, a comprehensive bibli¬ 
ography search was initiated. This activity continued throughout the 
contract period. It included a library search, and contact with organizations 
and individuals involved in DSS activities. The bibliographical search pro¬ 
duced over 600 titles, including several books. The complete bibliography 
appears at the end of this report. While the bibliographical search con¬ 
tinued, a companion activity was initiated. This consisted of reading and 
evaluation of approximately a dozen books and major articles on the subject 
of DSS. This evaluation produced a second list of 50-60 documents which 
appeared to be important to the development of DSS. These documents were 
read and evaluated, and they, in turn, pointed to other documents. In total, 
between 75 and 85 separate documents were read and evaluated. 

A second major activity included visits to U. S. Army installations and 
agencies. Discussions were held with personnel at (1) Fort Lee, Virginia, 

(2) Military Personnel Center (MILPERCEN) in Alexandria, Virginia, and (1) 
USAMSSA in the Pentagon in Washington, D. C. These discussions centered on 
the nature of DSS and its potential for application within the U. S. Army and 
their own particular activities. 

1.3 Orga n ization of the Report 

Chap f er II gives several definitions of DSS and some of its distinguishing 
characteristics. In Chapter III, the concept and structure of a DSS is out¬ 
lined. Chapter IV presents conclusions and recommendations. An extensive 
bibliogiaphy of DCS literature is presented at the end of this report. 


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2. DEFINITIONS AND CHARACTERISTICS OF A DECISION SUPPORT SYSTEM 


2.1 Evolution of DSS 


During the period from the late 1950's through the early 1970's rapid 
success was achieved in the use of the computer in decision making activ¬ 
ities. As computers became larger and more sophisticated, more complex 
decision making tasks were turned over to them. Often, when the computer 
could not be programmed to make the decision, it could be utilized to pro¬ 
duce large volumes of information which a human could use in reaching 
his decision. 

By the early 1970's a few notions had become apparent. First, the volume 
of information computers were generating had exceeded the capacity of 
humans. Decision makers were being presented boxes of printouts, even 
though it was obvious to all concerned that they wouldn't look at more than 
a few numbers. 

The second observation was slower in crystallizing. Breakthroughs in com¬ 
puterized (as opposed to computer aided) decision making were occurring 
less frequently. In short, the computer had about reached its saturation 
point in replacing man's decision making activities. And yet, many decision 
making activities remained virtually untouched by the computer. 

Beginning in the late 1960's computer scientists and users were examining 
the feasibility of interactively linking the "intuition, judgement and ex¬ 
perience" of the human with the computational power of the computer to 
achieve even greater heights in decision making. Out of this single idea 
was born the field of Decision Support Systems. 

2.2 A Problem Class for DSS 


Simon (1960) developed a distinction between "programmed" and "non-programmed" 
decisions. Keen and Morton (1978) modified these terms to "structured" and 
"unstructured" to free them from an implied computer environment; they also 
added an intermediate term, "seraistructured" decisions. 

Structured. Decisions "...do not involve a manager. ...the decision is well 
enough understood to have been given to clerks or ...automated through the 
computer." 

Semistructured Decisions involve some judgement and subjective analysis, but 
this alone is not adequate, due to problem size or computational complexity. 

Unstructured Decisions "...are those that are either not capable of being 
structured or that have not yet been examined in depth and so appear to the 
organization as unstructured." 

Semistructured decisions provide a natural class for application of DSS. A 
combination of the human's judgement and the computer's computational power 
can extend the human's effectiveness in such decision making activities. 




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Anthony (1965) developed three categories of managerial activities - stra¬ 
tegic planning, management control, and operational control. Although, for 
DSS, this distinction seems much less important, since DSS has applications 
in each activity so long as the decision is semistructured. 

Donovan and Madnick (1976) categorize DSS into two types: institutional 
and ad hoa. Institutional DSS, currently the most common, are those 
supporting repetitive or recurrent decisions. Ad hoc DSS, currently few 
in number, support decisions which occur infrequently or are not usually 
anticipated. 

.3 Some Definitions of a DSS 


Much of the initial focus and direction in DSS was provided by Professor 
Peter C. W. Keen, Michael S. Scott Morton and their students; as well as 
Eric D. Carlson and his colleagues. Figure 2.1 presents some of the def¬ 
initions which have evolved from this early work. 

By examining the words separately, one might accept the erroneous conclu¬ 
sion that DSS consists of any system which supports decision making activities. 
This definition is far too broad and, as can be seen from examination of 
Figure 2.1, is not intended by researchers and practitioners in the field. 

DSS Involves Computers 

Virtually all the definitions include the term computer in them. Even 
McCosh and Morton limit their focus to computer systems. The computer is 
there for its computational power - including data searching and reduction, 
modelling and analysis, and informational display. In may ways, in a DSS, 
the computer can be viewed as a powerful calculator. 

DSS Involves Humans 

Such terms as support, assist, meet with, extend, aid, and help imply the 
use of the human component (Manager, Decision Maker) as a part of the DSS. 
Current state-of-the-art computers still cannot model human intuition, 
judgement and experience. However, careful design of a DSS, involving both 
the human and the computer, which accounts for the abilities of the human 
and the computer, can extend the capabilities of both. 

The Linkage is Important 

DSS is serious in its attempt at integration of the human and computer com¬ 
ponents into a single system, and thus the linkage is important. Such terms 
as flexible, interactive, conversational, and relevent information become 
significant. 

Emphasis is placed on the human ability to transmit, receive and process 
information. Computer transmissions employ charts and graphs instead of 
tables, limited amounts of information, color coded and spatial displays, 
etc. Human transmissions occur in english-like commands, or through flex¬ 
ible control devices such as light pens or joy sticks, etc. 

By far and away, the greatest concern in the design of a DSS is the Linkage 


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"A COMPUTERIZED SYSTEM WHICH IS DESIGNED SPECIFICALLY TO HELP 
MANAGERS MAKE DECISIONS". (ALTER, 1975) 

"INTERACTIVE, COMPUTER-BASED SYSTEMS WHICH ARE DESIGNED TO AID 
PROFESSIONAL DECISION MAKERS IN SOLVING UNSTRUCTURED PROBLEMS" 
(GRACE, 1976) 

"THE FLEXIBLE SUPPORT OF DECISION MAKERS WITH COMPUTER-BASED 
INFORMATION". (CARLSON & MORTON, introduction to Carlson, 1977) 

"COMPUTER SYSTEMS DESIGNED TO MEET WITH MANAGER'S EXISTING 
ACTIVITIES AND NEEDS WHILE EXTENDING THEIR CAPABILITIES". (KEEN 
& STABELL, introduction to Keen & Morton, 1978) 

"THE USE OF COMPUTERS TO: 

!• ASSIST MANAGERS IN THEIR DECISION PROCESS IN 
SEMISTRUCTURED TASKS. 

2. SUPPORT, RATHER THAN REPLACE, MANAGERIAL JUDGEMENT 

3. IMPROVE THE EFFECTIVENESS OF DECISION MAKING RATHER 
THAN ITS EFFICIENCY". (KEEN & MORTON, 1978) 

"SUPPORTING THE DECISION PROCESSES OF MANAGERS WITH FLEXIBLE 
ACCESS TO MODELS AND RELEVANT INFORMATION". (MCCOSH & MORTON, 1978) 

"INTERACTIVE, CONVERSATIONAL COMPUTER SYSTEMS SUPPORTING 
DECISION MAKERS'.'. (JARVIS, 1978) 


FIGURE 2.1 SOME DEFINITIONS OF A DECISION SUPPORT SYSTEM 


between the human and the computer. 


2.4 Some Characteristics of a DSS 


Examining the definitions in Figure 2.1 and the variety of applica¬ 
tions typified by the bibliography, we are able to identify a set of basic 
characteristics possessed by most DSS's. One such list of characteristics 
is presented in Figure 2.2. WE shall discuss each characteristic in turn, 
and indicate how it relates to DSS. 

Computer-Based - As already indicated, DSS implies the use of a computer. 

Interactive - The human is involved in the system. Further, the human 
role is active (guiding, controlling) rather than passive (observing). 

Conversational - The human uses english-like commands to operate the DSS. 

Flexible - The DSS is able to operate under different control sequences. 

That is, the human is able to combine the different modules of the DSS in 
various ways in processing various problems. The DSS is able to process 
abreviated commands as the human learns the communication language. 

Adaptable - The DSS is somewhat dynamic; capable of changing or being easily 
changed as the decision environment changes. New modules are easily added. 

Convenient - It doesn't require the human to input volumes of data during 
operation. It uses existing data sets whenever possible. It accepts 
abbreviated input, menu selection formats, digitizer pad data, so as to 
reduce the human burden. 

Quick - The DSS operates within an environment of fast access and turn¬ 
around. Typically DSS's are operational on time-shared computers or 
dedicated mini computers. 

Helpful - It requires limited external documentation to operate the DSS. 

It can "help" the human when he/she doesn't remember a command or can't 
remember what to do next. It is polite, forgiving, and guides the user 
out of mistakes. 

Reliable - The DSS is free of bugs; it recovers from human error. The com¬ 
puter hardware is not prone to breakdown. 


• COMPUTER-BASED 

•INTERACTIVE 

% 

•CONVERSATIONAL (ENGLISH-LIKE COMMANDS) 

.FLEXIBLE 

•ADAPTABLE (DYNAMIC) 

•CONVENIENT (DATA INPUT) 

•QUICK (ACCESS AND TURNAROUND) 

«HELPFUL (LIMITED EXTERNAL DOCUMENTATION REQUIRED) 
•RELIABLE (HIGH SYSTEM INTEGRITY) 


FIGURE 2.2 CHARACTERISTICS OF A DECISION SUPPORT SYSTEM 


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3. STRUCTURE OF A DECISION SUPPORT SYSTEM 


3.1 Concept of a DSS 

Figure 3.1 illustrates the basic concept of a DSS. On the one hand, we 
have the decision maker. The decision maker has certain goals and ob¬ 
jectives within the environment in which he/she operates. Tti is, together 
with his/her intuition, judgement, and experience, establishes the human 
component. 

The other major component is the DSS, itself. It is normally operational 
within a computer environment. It has data available to it, either its 
own or else data available from some other data base. In addition, the 
DSS possesses its own information processing activities, including data 
reduction, modelling, and analysis. These activities are often highly 
sophisticated, although there are divergent points-of-view in this respect. 

Some DSS designers argue that the manager/decision maker will not utilize 
a DSS which employs sophisticated information processing components. 

These designers prefer rules-of-thumb, graphs, tables, and other intuitive 
procedures to complex linear programming models, and the like. The think¬ 
ing is that the decision maker doesn't trust something he doesn't under¬ 
stand. This is only partly true. 

First, managers/decision makers will come to trust something which they 
have seen "work" many times, even though they don’t understand it. A good 
example is the automobile. Many people use it without knowing why it works. 
Second, many DSS designers argue that managers/decision makers being pro¬ 
duced by current university programs, are much more knowledgeable in the 
use of sophisticated decision models. 

An outside factor in the design and operation of the DSS is the environ¬ 
ment within which the decision maker operates. This environment includes 
his/her superiors and their reaction to computer based decision making. 

The usual pattern of DSS development is that the process is initiated by 
a casual request for help in some decision making activity. From this 
point on, the DSS designer charges, full steam ahead, with a grandiose 
system in mind. This is often a mistake. If the decision maker and envi¬ 
ronment are not highly receptive to computer methods, it is better to be¬ 
gin slowly, producing a series of small successes while gaining the con¬ 
fidence of all concerned. 

3.2 Structure of a DSS 

Burch and Strater (1974) identified five basic activities (functions) asso¬ 
ciated with an effective information system. These are (1) interrogation, 
(2) modelling, (3) filtering, (4) monitoring, and (S) externally. We mav 
also employ these terms (slightly modified) to describe the basic structure 
of a DSS. Brief descriptions of each are: 

'n't >. - interaction between the human and the DSS. Either the 


(H) 




FIGURE 3.1 CONCEPT OF A DECISION SUPPORT SYSTEM 


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human and the DSS may request information from (initiate) or provide in¬ 
formation to (react) the other. 

Modelling - the process of employing mathematical or computer models 
(eg., linear programming, simulation, queueing, heuristics, artificial 
intellegence, other algorithms) to develop strategies and information. 

Filtering - reducing, summarizing, aggregating, and otherwise combining 
(in a less sophisticated manner than modelling) data into information. 
Examples include means, variances, plots, bar charts, etc. 

Monitoring - continuously observing the data and provide informational 
comments to the decision maker on an automatic basis. An example might be, 
"THE MACHINE UTILIZATION RATIO EXCEEDED 75% THIS PERIOD:" 

Gathering - the process of obtaining data from sources external to the DSS. 
Sources include other parts of the organization or outside the organization. 

In Figure 3.2 we have utilized these terms in the design of the elements of 
a DSS. Carlson (1976) refers to the data base exhibited in Figure 3.2 as 
an "extracted" data base compiled from relevant sources (in the decision 
maker's view). 


Intermediaries 

It is not essential that the decision maker interact directly with the 
DSS. Some decision makers, particularly high-level managers, will not 
spend time doing anything except reading executive summaries. They are 
going to delegate responsibility for developing recommendations to others 
in the organization, who will interact directly with the DSS. These "others" 
are called intermediaries in the DSS literature. 

3.3 Interfacing is the Key to DSS 

In addition to the obvious emphasis on giving the decision maker exactlv 
what he/she needs, no more and no more less, the greatest concern in DSS is 
in how he/she gets it. More generally, the greatest single emphasis in DSS 
is on the interface between the human and the computer. 

The DSS designer is intently involved in expanding the range of man-machine 
communications devices beyond the basic typewriter terminal, card reader, 
and printer. Because humans can process complex patterns of information, 

DSS designers are currently attracted to graphics terminals. Such devices 
can employ colors, flashing backgrounds, lines and points to produce com¬ 
plex graphs, scatter plots, bar charts, etc., as well as basic alpha num¬ 
eric data. 

DSS designers are equally concerned with freeing the decision maker from 
expending a great amount of effort to input commands and information into 
the computer. Devices like light pens, touch panels, joy sticks, and track 
balls, are used to provide menu choices, positional information, and the 
like. Figure 3.3 Illustrates the current range of interfacing mechanisms 
which may be employed between the human and the computer. DSS designers 
attempt to optimally utilize as many of the different mechanisms as possible. 




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DECISION SUPPORT SYSTEM 



FIGURE 3.2 STRUCTURE OF A DECISION SUPPORT SYSTEM 



























often employing several of these interfacing methods in the same DSS. 

3.4 DSS Draws on Other Methodologies 

Decision Support Systems development draws on three other important areas: 
(1) Operations Research/Management Science, (2) Computer Science, and (3) 
Behavioral Science. Each of these sciences contributes significantly to 
the design of a DSS. We shall briefly discuss each area and the contri¬ 
bution it makes. 

Operations Research/Management Science 

Operations Research and Management Science (OR/MS) have historically 
emphasized the development and use of mathematical and computer models 
and methods in problem solving and decision making. Considerable emphasis 
has been placed on (1) improving our ability to develop more realistic 
models of the decision making environment, and (2) developing efficient 
algorithms and computer codes for analyzing larger and more complex models. 
The ultimate goal of OR/MS has been to supply the decision maker with 
"optimal" or "good" courses of action which are realistic relative to the 
limitations and constraints on the particular decision situation. 

The methods developed by OR/MS may generally be classified into two areas: 
descriptive and prescriptive. Descriptive methods attempt to present 
(describe) characteristics of the decision situation (system) when a par¬ 
ticular course of action is selected. Good examples of such methods are 
queueing and simulation. 

Prescriptive methods attempt to specify (prescribe) the "best" course of 
action available among a number of choices. Two major prescriptive areas 
are optimization and heuristics. Optimization methods produce the very 
best strategy or course of action given the conditions of the model. 
Heuristics attempt to produce good solutions given the conditions of the 
model. 

Obviously an extensive repertoire of useful OR/MS models and methods will 
be vaulable in a DSS. These models are usually modularized so that the 
decision maker can combine them in different ways depending on the decision 
situation and on the results at each step of the process. 

Computer Science 

Computer Science has historically emphasized the information storage, pro¬ 
cessing and retrieval aspects of problem solving and decision making. 

This emphasis has occurrred in both the hardware and software areas. 

Computer hardware has improved continually since its initial introduction. 
Computers have become larger - including more central memory, and greater 
amounts of disc and other peripheral memory. At the same time, computers 
have become increasingly faster - in processing central memory and in 
communication between central and peripheral memory. Both advances in time 
and speed have enabled decision makers to process greater amounts of data 
and to analyze larger models of the decision situation. 


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Computer science lias also concentrated on information storage and retrieval. 
Such concepts as list processing and search methods have come out of this 
concentration. This has also tended to be the historical focus of those 
researchers and practitioners of Management Information Systems (MIS). That 
is, they have concentrated on efficient data base design and handling methods. 

Finally, considerable effort in Computer Science has been directed toward 
development of Artificial Intelligence, The general idea behind artificial 
intellegence is to provide the computer with the ability to "learn" from 
its experience in a particular situation so that it can 'xpand its abilitv. 

Two significant areas of concentration have occurred in theorem proving 
and game playing. An example in game plaving involves computers playing 
chess with humans or other computers, and improving their ability to plav 
from their experience in each game. While not currently too practical, 
Artificial Intellegence has obvious future benefits in DSS. 

Behavioral Science 

Behavioral Science is concerned with the behavior (actions and interactions) 
of individuals and groups. The ultimate success of any DSS depends on an 
awareness of the human decision maker and the organizational environment. 
Several areas of Behavioral Science are central to DSS design and imple¬ 
mentation. 

Individual and group psychology is important to DSS. What will an individual/ 
group accept or reject? What situations make a DSS comfortable or uncomfort¬ 
able? These are improtant research areas, even though they are often over¬ 
looked. For example, most high-level managers reject the use of a type¬ 
writer, but they will accept the use of a function keyboard (it usually looks 
like a calculator). What kinds of interpersonal relationships are established 
during group decision making and in what ways will this affect the outcome? 

Human Engineering issues are clearlv significant. How much information can 
a human reasonably process? What formats are best - tables or figures? 

How manv colors can be used? How manv different interfacing devices can 
a human handle simultaneously? We can continue indefinitely generating such 
relevant questions for human engineering in DSS. 

Researchers into the implementation process obviously provide insight into 
how innovation in organizations occur. Such research results can suggest 
reasonable strategies for DSS implementation in the decision making process. 
Also, greater knowledge of the cognitive process, through which individual 
decision makers arrive at decisions, may suggest (1) wavs to design DSS 
so that thev are more acceptable to decision makers, and (2) DSS designs 
which reflc- t the particular cognitive process of a decision maker. 

Synthesis of These Three Methodologies Into DSS 

DSS attempts to integrate the significant decision making aspects of the 
three methodologies of Operations Research/Management Science, Computer 
Science, and Behavioral Science. In review, these elements are: 


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OR/MS - Modelling and analysis. 


Computer Science - Information storage, processing and 
retrieval. 

Behavioral Science - Individual/group decision making 

behavior. 

Figure 3.4 illustrates the methodologies involved in DSS design, develop¬ 
ment, and implementation. 




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OPTIMIZATION 



FIGURE 3.4 METHODOI.OCIKS BEHIND A DECISION SUPPORT SYS i 











4. CONCLUSIONS AND RECOMMENDATIONS 


4.1 Conclusions 


A number of conclusions may be drawn from this study. Some of these are: 

(1) Decision Support Systems (DSS) are interactive, conversational 
computer systems supporting decision makers. 

(2) DSS emphasize the human-computer interface in decision making 
and problem solving. 

(3) DSS rely heavily on human judgement, intuition, and experience. 

(4) DSS are characterized by such terms as: computer-based, inter¬ 
active, conversational, flexible, adaptable, convenient, quick, 
helpful, and reliable. 

(5) DSS development draws on three other important areas: modelling 
and analysis from Operations Research/Management Science; infor¬ 
mation storage, processing, and retrieval from Computer Science; 
and individual/group decision making behavior from Behavioral 
Science. 

(6) DSS is an outgrowth of the evolution of technologies in Computer 
Science, Operations Research.Management Science, and Behavioral 
Science. 

4.2 Recommendations 


These study results point to several recommendations for continued de¬ 
velopment of DSS. They include: 

(1) An analysis of the models and methods from OR/MS useful in DSS. 

(2) A Human Engineering analysis of the opportunities and limitations 
for human-computer interaction in DSS development. 

(3) An analysis and evaluation of the implementation process in the 
Behavioral Science literature with the goal of developing an 
approach for DSS. 

(4) An evaluation of the Computer Science/Management Information Systems 
literature of information storage, processing and retrieval useful 
for DSS design. 

(5) An evaluation of the available interfacing hardware and software 
useful in DSS design. 

(6) A field test in one or more Army installations to evaluate DSS 
methods and designs. 

(7) An evaluation of the essential team characteristics for successful 
DSS implementation. (This comes from the recognition that an 
individual's objectivity is often marred by his/her advocacy.') 

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•**y: r •» 



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