Dss Analysis And Decision Support System Information Technology Essay

Abstract

During our study and research on DSS we came to mutual agreement that DSS is an ever evolving domain. Lot of research has been carried out on the usage of DSS in many different domains especially in Clinic. But we found that research on the DSS System as a whole (regardless of which domain) has not been conducted many times in the past. Based on the initial study we have identified the following problems 1. There is no universally accepted definition for DSS, 2. There have been a many reports of failure of DSS systems.

In the research paper below we have tried to define DSS system based on the Characteristics and the Targeted users. Paper also covers the decision making process, the decision analysis cycle, Framework of DSS which form the base of the DSS. We have also made an attempt to formulate the Critical success factors of the DSS and Reasons for the failure of DSS.

We have tried to collect most of our data through secondary research which involves collating of data from existing research documents and books.

In 1960 J. C. R. Licklider wrote a paper on his observation of how the interaction between man and computer can improve the quality and competency in recognizing and problem solving. His paper proved to be like a guide to many future researches on DSS. In 1962 with use of hypertext online system helped in storage and retrieval of documents and creation of digital libraries. SAGE (Semi Automatic Ground Environment) built by Forrester is probably the first data driven computerized DSS. In 1964 Scott Morton built up an interactive model driven management decision system which could help managers make important management decisions. In 1970 John D.C. Little noted that the requirement for designing models and system to make a management decision was completeness to data, simplicity, ease of control and robustness, which till date are relevant in improving and evaluating modern DSS. By 1975, he built up a DSS called Brandaid which could support promotion, advertising, pricing and product related decisions. In 1974 the focus was on giving managers with information which was from accounting and transaction processing system with use if MIS(Management Information Systems) but MIS was found to not helping out managers with making key decisions. Hence in 1979 Scott Morton and Gorry argued that MIS just primarily focused on structured decisions and hence the system which also supports unstructured and semi-structured decision should be termed as Decision support systems.

Gorry and Scott Morton coined the phrase ‘DSS’ in 1971, about ten years after MIS became popular. (David Arnott, An Analysis of Decision Support Systems Research, p.1) Decision support system now-a-days are critical for the daily operation and success of many organizations. Due to which there is a huge investment being made on development, customization, implementation and upgradation of these systems.

Despite the rapid growth of information technology over the past decade, the success of Decision Support System remains questionable due to the lack of insufficient studies on the outcomes. As David Arnott and Gemma Dodson stated in Decision Support System Failure (David Arnott, Gemma Dodson, p.1) “The development of a decision support system is a risky affair. The Volatile task environment and dynamic nature of managerial work means that DSS Projects are prone to Failure.”

As per David Arnott and Gemma Dodson definition above its very important to understand why organization take such a big risk and invest in a Decision support system. (Efraim Turban, Ramesh Sharda, Decision Support and Business Intelligence Systems, 8th Edition, p.12) Some of the factors why company use DSS Systems suggested by Efraim and Ramesh are:

Speedy Computation

Improved Communication and Collaboration

Increase Productivity of group members

Improved data management

Managing Giant Data warehouses

Quality Support

Agility Support

Overcoming cognitive limits in processing and storing information

The paper here deals with the study of how decision analysis happens in DSS, Problems and there types, Why DSS are required or implemented by organization, Decision making process, Types of DSS, Reason for the failure of DSS, Critical success factor of DSS.

Activities that require decision making form a set or a group of problems, varying from structured problem to unstructured problem. As Simon States “The boundary between well structured and ill structured problems is vague, fluid and not susceptible to formalization.” (The structure of ill structured problems, 1973, Herbert A. Simon) the Decision making process, decision made and the style of making decision can be influenced by the personality of the individual and their cognitive style, and which is one of the major reasons for different decision aids being sought.

(Management Information System 8/E Raymond McLeod, Jr. and George Schell)

Decision types in terms of problem structure:

Structured problems can be solved with algorithms and decision rules.

A structured decision can be defined as one in which three components of a decision-the data, process, and evaluation. Structured decisions are made on a regular basis in business environments. If a rigid framework is placed for the decision making process it helps to solve the problem.

Unstructured problems have no structure in Simon’s phases.

These decisions have the same components as structured ones-data, process, and evaluation- but there nature is different. For example, decision maker use different set of data and process to reach a decision or goal. In addition, as the nature of the decision is different a few numbers of people within the organization are even qualified to evaluate the decision and to confirm one.

Semi structured problems have structured and unstructured phases.

Most of the DSS System is focused on Semi Structured decision. Characteristics of this type of decisions of this type are

Having some agreement on the data, process, and/or evaluation to be used,

Efforts to maintain a level of human-judgement in the decision making process.

To determine which Support system is required it is necessary to analyze thoroughly and understand the limitations and ill effects, which the decision maker are manifested with.

Apart from which it is also important to understand the objectives of the system.

(Management Information System 8/E Raymond McLeod, Jr. and George Schell)

Decision Support System Objectives:

Efficiency of the system.

Making decisions.

To support managers, not to replace people.

Used when the decision is “semi structured” or “unstructured.”

Incorporate a database.

Incorporate models.

It is also important that like any other computer based system the DSS should be:

Simple

Robust

Easy to Use

Adaptive

Easy to communicate with.

Now that we have a brief idea about the type of problems that are faced by the managers and the qualities that the DSS system should pertain understanding the decision making process would give an insight to the how a decision is made.

Decision Making:

(Administrative Behavior, Herbert Simon, 1947) Herbert Simon in 1947 defines decision as “the behavioral and cognitive processes of making rational human choices, that is, decisions.”

It states that any decision making is a behavioral and cognitive process of making choices from a set of options available. So, it is important for the DSS, to be accurate enough for making a choice from many different options available. To make accurate choices from the options available DSS takes help from constrains defined and the goals that it has to achieve.

(Administrative Behavior, Herbert Simon, 1947) Simon states in his journal

“The human being striving for rationality and restricted within the limits of his knowledge has developed some working procedures that partially overcome these difficulties. These procedures consist in assuming that he can isolate from the rest of the world a closed system containing a limited number of variables and a limited range of consequences.”

Intelligence:

Identify reality.

Get problem/opportunity understanding.

Obtain required information.

Design:

Make decision criteria.

Make decision alternatives.

Look for related unmanageable events.

Identify the links between criteria, alternatives, and events.

Choice:

Logically assess the decision alternatives.

Make recommended actions that best meet the decision criteria.

Implementation:

Consider the decision analysis and assessment.

Evaluate the cost of the recommendations.

Have confidence in the decision.

Make an implementation plan.

Secure required supplies.

Set implementation plan into act.

Based on the Decision making process by Simon and the problem structure described above we can define the accuracy of decisions can be measured by the following criteria:

The methods or technique with which it achieves the desired results or goals; and

The efficiency with which the goals and sub goals are obtained.

By this we mean members of the organization may focus on the method and technique used to reach to the result or goal, but the administrative management must pay attention to the efficiency with which the desired result was obtained.

To understand the efficiency of the decision made it is necessary to analysis the decision made. Decision Analysis in itself is a vast field and deals with many methodologies to measure the efficiency of the decision.

Decision Analysis:

(Ronald Howard, 1965, Decision Analysis: Applied Decision Theory)Decision Analysis is a discipline which was developed to deal with the challenges of making important decisions which involved handling major uncertainty, long-term targets and complex value issues. Decision Analysis comprises the philosophical, theoritical, methodological, and professional practice necessary to formalize the analysis of important decisions.

(Ronald Howard, 1965, Decision Analysis: Applied Decision Theory) “Decision analysis is a logical procedure for the balancing of the factors that influence a decision. The procedure incorporates uncertainties, values, and preferences in a basic structure that models the decision. Typically, it includes technical, marketing, competitive, and environmental factors. The essence of the procedure is the construction of a structural model of the decision in a form suitable for computation and manipulation; the realization of this model is often a set of computer programs.”

Decision-making consists of assigning values on the outcomes of interest to the decision-maker. Thus, decision analysis evaluates the decision-makers trade-offs between monetary and non-monetary outcomes and also establishes in quantitative terms his preferences for outcomes that are risky or distributed over time.

Ronald A. Howard in his paper Advances: Foundations of DA Revisited goes on to discuss the “Pillars of Decision Analysis”

The First Pillar: Systems Analysis

Systems analysis grew out of World War II and was concerned with understanding dynamic systems. Key notions were those of state variables, feedback, stability, and sensitivity analysis. The field of systems engineering is currently in a state of resurgence. Decision analysis and systems engineering have many complementary features (Howard, 1965, 1973).

The Second Pillar: Decision Theory

Decision theory is concerned primarily with making decisions in the face of uncertainty. Its roots go back to Daniel Bernoulli (Bernoulli, 1738) and Laplace. Bernoulli introduced the idea of logarithmic utility to explain the puzzle called the St. Petersburg paradox. In the most influential book on probability ever written (Laplace, 1812), Laplace discusses the Esperance mathematique and the Esperance morale. Today we would call these the mean and the certain equivalent.

The Third Pillar: Epistemic Probability

Jaynes taught that there is no such thing as an objective probability: a probability reflects a person’s knowledge (or equivalently ignorance) about some uncertain distinction. People think that probabilities can be found in data, but they cannot. Only a person can assign a probability, taking into account any data or other knowledge available. Since there is no such thing as an objective probability, using a term like “subjective probability” only creates confusion. Probabilities describing uncertainties have no need of adjectives.

This understanding goes back to Cox (2001), Jeffreys (1939), Laplace (1996) and maybe Bayes, yet somehow it was an idea that had been lost over time. A famous scientist put it best over 150 years ago:

The actual science of logic is conversant at present only with things either certain, impossible, or entirely doubtful, none of which (fortunately) we have to reason on. Therefore the true logic for this world is the calculus of Probabilities, which takes account of the magnitude of the probability which is, or ought to be, in a reasonable man’s mind. (Maxwell, 1850)

The Fourth Pillar: Cognitive Psychology

In the 1960s few appreciated the important role that cognitive psychology would play in understanding human behaviour. At the time of DAADT, we just did our best to help experts assign probabilities. In the 1970s the work of Tversky, Kahneman, and others provided two valuable contributions. First, it showed that people making decisions relying only on their intuition were subject to many errors that they would recognize upon reflecting on what they had done. This emphasized the need for a formal procedure like decision analysis to assist in making important decisions. The second contribution was to show the necessity for those who are assisting in the probability and preference assessments to be aware of the many pitfalls that are characteristic of human thought. Tversky and Kahneman called these heuristics — methods of thought that could be useful in general but could trick us in particular settings. We can think of these as the “optical illusions” of the mind.

An important distinction here is that between “descriptive” and “normative” decision-making. Descriptive decision-making, as the name implies, is concerned with how people actually make decisions. The test of descriptive decision-making models is whether they actually describe human behaviour. Normative decision-making is decision-making according to certain rules, or norms, that we want to follow in our decision-making processes.

The underlying premise of decision analysis is to distinguish between a good decision and a good outcome. A good decision is termed as logical decision which is based on the information, values, and preferences of the decision-maker. A good outcome is one that benefits the end user. The aim is to arrive at good decisions in all situations which would go on to ensure as high a percentage of good outcomes. But at times it may be observed that even a good decision has achieved a good outcome. But for majority of the situations we may face making good decisions is the best way to ensure good outcomes.

A decision can be defined as a choice among alternatives that will yield uncertain futures, for which we have preferences. To explain the formal aspects of decision analysis the image of the three-legged stool shown in Figure 3.1 (Howard, 2000).

The legs of the stool are the three elements of any decision: what you can do, the alternatives; what you know, the information you have; and what you want, your preferences. Collectively, the three legs represent the decision basis, the specification of the decision. Note that if any leg is missing, there is no decision to be made. If you have only one alternative, then you have no choice in what you do. If you do not have any information linking what you do to what will happen in the future, then all alternatives serve equally well because you do not see how your actions will have any effect. If you have no preferences regarding what will happen as a result of choosing any alternative, then you will be equally happy choosing any one. The seat of the stool is the logic that operates on the decision basis to produce the best alternative. We shall soon be constructing the seat to make sure that it operates correctly.

Decision Analysis provides a formal language for communication for the people involved in the decision-making process. During this, the basis for a decision becomes clear, not just the decision itself. The views may differ on whether to adopt an alternative because individuals possess different relevant information or because they may value the consequences differentlly.

Decision analysis Cycle:

The professional practice of decision analysis is decision engineering. Creating a focused analysis requires the continual elimination of every factor that will not contribute to making the decision. This winnowing has been a feature of decision analysis since the beginning (Howard, 1968, 1970). Since DAADT, the process has been described as a decision analysis cycle, depicted in Figure 3.4 (Howard, 1984a).

The application of decision analysis can be modeled in form of an iterative procedure called the Decision Analysis Cycle.

Decision Analysis Cycle:

The procedure is divided into three phases:

Deterministic phase: the variables affecting the decision are defined and relations between the variables established, the values are assigned, and the importance of the variables is measured upto a acceptable level of certainity.

Probabilistic phase: the associated probability assignments on values are derived. We also take into account the assessment of risk preference, which identifies the best possible solution in the face of uncertainty.

Informational phase: the results of the first two phases are reviewed to determine the economic value of eliminating uncertainty in each of the important variables in the problem.It is the most important phase among the three because it evaluates in monetary terms to have the perfect information.

Decision Support System:

There is no universally accepted definition for the DSS system as of now. It is the major reason we have to rely on the Characteristics and Objectives of the DSS to understand the system. Below are a few famous definition for the DSS we would refer to formulate a definition for the system.

(Decision Support Systems: An Organizational Perspective, Keen & Scott-Morton, 1978) Keen and Scott define DSS as “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 semi structured problems.”

If we correlate the definition from Keen and Morton and Simons definition stating

We understand that the base of the DSS system is to support the manager. But one of the drawbacks of the definition from Keen and Morton is that they state that the system deals with only semi structured problems but the present DSS system also handles Unstructured and Structured issues.

Peter Keen in 1980 defined DSS as “Personal System to assist Manager must be built from the Managers perspective and must be based on a very detailed understanding of how the manager makes decision and how the manager organization functions.” (Donald R. Moscato, 2004, p.1)

In the above definition Peter Keen tries to define DSS in terms of the implementation and customization of DSS and states that it should be done based on Managers perspective, styles of decision making and the organizations function. Drawback with this definition is that it defines DSS as a personnel system and with the introduction of Group DSS and Communication DSS the definition becomes obsolete.

Bonczek, Holsapple and Whinston (Foundations of Decision Support Systems, Bonczek, Holsapple and Whinston, 1981, p.19) argued “the system must possess an interactive query facility, with a query language that … is … easy to learn and use”.

The above definition tries to explain that DSS systems should be interactive and should have a language of its own so that constrains of the decision and the goals can be addressed to the system and is easy to understand and use. (We have stated in the section objectives of DSS).

(Daniel J Power, 2001, p.1)Sprague and Carlson (1982) define Decision Support Systems broadly as interactive computer based systems that help decision-makers use data and models to solve ill-structured, unstructured or semi-structured problems.

Sparague and Carlson explained the DSS system as an interactive system and which can help managers solve ill-structured, unstructured and semi-structured problem. If you observe the definition is a co-relation of definition provided by Peter Keen, Keen & Scott-Morton – 1978 and Bonczek, Holsapple and Whinston-1981 by removing there drawbacks.

A few more definition that we thought explains DSS are as follows:

Marakas in 2002 (Marakas, 2002, p.4) stated the following is a formal definition of DSS: “A decision support system is a system under the control of one or more decision makers that assists in the activity of decision making by providing an organized set of tools intended to impose structure on portions of the decision-making situation and to improve the ultimate effectiveness of the decision outcome.”

Importance of Marakas definition is that it takes into consideration the tools that a manager can use to work with DSS system (can term it as third party tools in some cases) other that the query language or the normal interactive screen of the DSS.

From the above example it is pretty clear that to define a DSS not only we will have to study the characteristics and the tools, types of DSS but also the framework of the DSS to select a definition or to define one.

(Ralph H. Sprague, Hugh J. Watson, Decision Support System – Putting Theory into practice, 3rd edition, 1993, p.4)

Characteristics of DSS:

They tend to be aimed at the less well structured, underspecified problems that upper level managers typically face.

They attempt to combine the use of models or analytic techniques with traditional data access and retrieval function

They specifically focus on features which make them easy to use by non-computer people in an interactive mode

They emphasize flexibility and adaptability to accommodate changes in the environment and the decision making approach of the user.

Framework of DSS:

From (Daniel J Powers, 2001, p.1) we come to know that the framework for the Decision support system should be based on the following factors: (by this Daniel J Power meant “System should be discussed and explained in terms of four descriptors to maintain better communication:”)

Dominant Technological Component

The Targeted Users

Purpose

Deployment Technology

(Daniel J Powers, 2001, p.1) And the Five generic categories of DSS are:

Communication Driven

Data Driven

Document Driven

Knowledge Driven

Model Driven decision support system.

(Daniel J Powers, 2001, p.1) DSS Deployment technology can be:

Mainframe Computers

A client server LAN

Web Based Architecture

Marakas (2002) meant that it is important to understand the type of DSS to determine the best design and approach of a new DSS.

In 1976 Steven Alter, a doctoral student created a taxonomy of seven DSS types on Gorry and Scott-Morton framework based on a study of 56 DSSs. In 1980, Steven Alter (Daniel J Power, 2001, p.2) proposed his taxonomy of Decision Support Systems. Alter’s seven category typology is still relevant for discussing some types of DSS, but not for all DSS. Alter’s idea was that a Decision Support System could be categorized in terms of the generic operations it performs, independent of type of problem, functional area or decision perspective.

His seven types included:

File Drawer Systems

Data Analysis Systems

Analysis Information Systems

Accounting and Financial models

Representational Models

Optimization Models

Suggestion Models.

Alter’s first three types of DSS have been called data oriented or data driven; the second three types have been called model oriented or model driven; and Alter’s suggestion DSS type has been called intelligent or knowledge driven DSS.

Importance of Alters Study was:

Supports concept of Developing Systems that address particular decisions.

Makes clear that DSS need not be restricted to a particular Application Type.

Based on Alters study Daniel J Power formulated an expanded framework. The purpose of expanded DSS framework is to help people understand and apply the framework to integrate, evaluate, implement and select appropriate means for supporting and informing decision-makers.

Expanded Framework suggested by Daniel J Power (Daniel J Power, Expanded DSS framework, June 2001, p.5)

Dominant DSS

Component

Target Users:

Internal / External

Purpose:

General /Specific

Deployment

Technology

Communications

Communications-

Driven DSS

Internal teams, now

expanding to external

partners

Conduct a meeting or Help users collaborate

Web or Client/

Server

Database

Data-Driven DSS

Managers, staff, now

Suppliers

Query a Data Warehouse

Main Frame, Client/

Server, Web

Document base

Document-Driven

DSS

Internal users, but

the user group is expanding

Search Web pages or

Find documents

Web or Client/

Server

Knowledge base

Knowledge-

Driven DSS

Internal users, now

Customers

Management Advice

or Choose products

Client/Server, Web,

Stand-alone PC

Models

Model-Driven

DSS

Managers and staff,

now customers

Crew Scheduling or

Decision Analysis

Stand-alone PC or

Client/Server or Web

(Ralph H. Sprague, Hugh J. Watson, Decision Support System – Putting Theory into practice, 3rd edition, 1993, p.4) Three Technology Levels:

Specific DSS – System which actually accomplishes the work might be called the specific DSS.

DSS Generator – This is a set of related hardware and software which provides a set of capabilities to quickly and easily build a specific DSS.

DSS Tool – These are hardware or software elements which facilitates the development of a specific DSS or DSS Generator.

Based on the details above we would like to define DSS as

DSS can be defined as use of computer application that can help managers, staff members, or people who interact within the organization to make decisions and identify problems by using available data and communication technology.

It is also very important to understand the reason for the failure of DSS. And what are the factors that could cause the failure of system and which factors are to be termed as the success factors of DSS.

Reason for Failure of DSS System:

Despite the benefits that DSS offers the implementation of such system has been limited. Some of the reasons can be the following:

Proper evaluation of the DSS preceding and during DSS development.

DSS output does not fit the producer’s decision-making style.

Complexity involved while operating the DSS.

Post Implementation support.

Benefits from these systems are not always realized

Other than the above reason few disadvantages of the DSS system are:

Over dependency for Decision making

Assuming it to be correct.

Unanticipated effects

Deflect personal responsibilities

Information overload.

Considering the above reason, to increase the rate of success of DSS implementation and customization, the following factors should be considered and managed.

Critical Success Factors of DSS:

Hartono (Hartono et al, 2006, p.257) uses the following words to describe their interpretation of Critical Success Factors: “Success antecedents are those key factors that organizations can manage so that the management information system is favorably received and the implementation is deemed as successful”

(Johannes Johansson; Bjorn Gustafson, Critical Success Factors affecting Decision Support System Success, from an end-user perspective,2009, p.1)Johannes Johansson and Bjorn Gustafson identified three factors that significantly affect end-users perceived net benefits, namely Data Quality, Problem Match and Support Quality.

(S. Newman1, T. Lynch, and A. A. Plummer; Success and failure of decision support systems: Learning as we go, p.1)The case study “HotCross,” a DSS under development to evaluate crossbreeding systems in northern Australia, provided evidence of a shift in the development process because greater emphasis was put on the learning process of breeding program design by end-users rather than emphasis on learning how to use the DSS itself. Greater end user involvement through participatory learning approaches (action learning, action research, and soft systems methodologies), iterative prototyping (evolving development processes), as well as keeping DSS development manageable and small in scope, will provide avenues for improving the rate of DSS adoption.

(Johannes Johansson; Bjorn Gustafson, 2009, p.13)

Implementation Factors:

Management Support – One of the main factor that significantly effects the overall success of pre-implementation

Champion – someone who actively supports the project and supplies it with important and relevant resources

Resource – Resources are the money, people and time that are required to complete the project

End-User Participation – This leads to better communication of their needs and helps to ensure that the system is implemented successfully. The importance of this factor stretches further than just in the implementation situation. By obtaining a high user participation in the implementation phase the system is more likely to be accepted once implemented.

Team Skill – the right people with the right sets of skills in a project is of great significance

Source System – existing data quality in an organization have a profound effect on the success of a new system. Data needs to be consistent in the whole organization in order to benefit

Development Technology – The technology on which the system is built will affect the overall performance of the system

(Johannes Johansson; Björn Gustafson , Critical Success Factors affecting Decision Support System Success, from an end-user perspective,2009, p.14)

Implementation Success:

Organizational Implementation Success – The implementation is not successful unless the system it produces is accepted in the organization.

Project Implementation Success – Success in projects can be measured by how well the different teams meet budgets, critical deadlines and functional goals

Technical Implementation Success – implementations are rather large-scaled since they need to incorporate underlying systems, this also increases the complexity of such implementations and the team should me technically sound to cope with the difficulties.

(Johannes Johansson; Björn Gustafson , 2009, p.14)

System Success:

Data Quality – It concerns the quality of data that are provided to the system and by the system.

Systems Quality – This factors focus is on the system itself and is commonly measured by the systems flexibility, integration, response time and reliability.

Perceived Net Benefits – a system with high Data Quality and System Quality can lead to perceived net benefits for various users such as stakeholders, decision makers and ultimately the organization

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