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M.P.BIRLA INSTITUTE OF MANAGEMENT

A DIAGNOSTIC STUDY ON

APPLICATION OF COMPUTERISED MARKETING DSS IN

SELECT CORPORATE ENTERPRISES IN BANGALORE

A DISSERTATION SUBMITTED IN PARTIAL FULFILMENT OF THEREQUIREMENTS FOR THE AWARD OF MBA DEGREE OF

BANGALORE UNIVERSITY

BY

Mr. BHARATH B. K.Register Number: 06XQCM6016

Under the guidance and supervisionOf

Dr. K V PRABHAKARSenior Professor

M P Birla Institute of ManagementBangalore

M P BIRLA INSTITUTE OF MANAGEMENTAssociate Bharatiya Vidya Bhavan

Race Course Road, BANGALORE - 560001

APRIL 2008

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DECLARATION

I, Mr. Bharath B.K. bearing the Reg. Nos.- 06XQCM6016 hereby

declare that this dissertation entitled - A Diagnostic Study of the

Application of Computerized Marketing DSS in select

Corporate Enterprises in Bangalore is the result of my own

research work carried out under the guidance and supervision of

Dr. K. V. Prabhakar, Senior Professor, M P Birla Institute of

Management, Bangalore. I also declare that this dissertation has

not been submitted earlier to any Institute/organization for the

award of any degree or diploma.

Place: Bangalore

Date: (Mr. Bharath B.K.)

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PRINCIPALS CERTIFICATE

This is to certify the project report entitled A Diagnostic Study

of the Application of Computerized Marketing DSS in select

Corporate Enterprises in Bangalore has been prepared by

Mr. Bharath B.K. bearing the Reg. Nos. 06XQCM6016 under

the guidance ofDr. K. V. Prabhakar, Senior Professor M.P.Birla

Institute of Management (Associate Bharatiya Vidya Bhavan),

Bangalore. This has not formed the basis for the award of any

degree/ diploma or similar title by any University or Institution.

Place: Bangalore Principal

Date: (Dr. N. S. Malavalli)

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GUIDES CERTIFICATE

This is to certify that the Project report entitled A Diagnostic

Study of the Application of Computerized Marketing DSS in

select Corporate Enterprises in Bangalore by BHARATH B.K.

bearing Reg. No. 06XQCM6016 is a bonafide work done under

my guidance during the academic year 2007-08 in partial

fulfillment of the requirements for the award of MBA degree by

Bangalore University. To the best of my knowledge this report has

not formed the basis for the award of any other degree/diploma or

similar title by any University/Institution.

Place: Bangalore (Dr. K.V. Prabhakar)

Date:

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ACKNOWLEDGEMENTS

The Dissertation is truly an enriching experience. This activity involves the

immense contribution and guidance of many people. Hence I script this page

to extend my sincere gratitude to all those who have always encouraged and

assisted me in this endeavour.

A heartfelt thank you to all my respondents, who spent their valuable time

replying to my questionnaire.

Dr. Nagesh. S. Malavalli,Principal, M. P. Birla Institute of Managementfor his constant and dedicated service to brighten our careers.

Dr. K V Prabhakar, my internal guide and my professor, to whom I am

deeply grateful for his constant support and guidance.

I thank Priyashree S. my MENTOR for her constant support and

encouragement through out my career.

I thank my friend Vinay K.P. whose encouragement has made me a true

management professional. My heartfelt gratitude to him.

To all my faculty, friends and classmates for being a constant source of

encouragement and support.

To my family for always having stood by my convictions and encouraging

me to perform better.

Thank You.

BHARATH B K

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C O N T E N T S Page. No

EXECUTIVE SUMMARY 03

Chapters

PART A (THEORITICAL SETTING) 09

1. INTRODUCTION 10

1.1. Background of the study. 11

1.2. Statement of the problem. 12

2. REVIEW OF LITERATURE 13

2.1 Purpose of review. 142.2 Methodology. 14

2.3 Conclusion. 38

3. RESEARCH METHODOLOGY 39

3.1 Type of Research. 40

3.2 Sampling Technique. 41

3.3 Sampling size. 41

3.4 Instrumentation Techniques. 41

3.5 Actual collection of data. 42

3.6 Research Limitations. 42

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PART B (SURVEY FINDINGS) 43

4.1. DATA ANALYSIS AND INFERENCES. 44

PART C (CONCLUSIONS AND RECOMMENDATIONS) 54

5.1. Major conclusions emerging from the study. 55

5.2. Specific Recommendations 56

ANNEXURE: 57

1. Select bibliography including websites used. 58

2. Questionnaire 59

3. Directions for further research 63

TABLES AND CHARTS

Table 1: Awareness about MDSS tools in marketing 45

Table 2: Level of Management Where MDSS Is Implemented 47

Table 3: Necessity of an MDSS that is evolved out of Industry

Best Practices 49

Chart 1: Graph showing the level of awareness about MDSS tools 46

Chart 2: Level of Management where MDSS is implemented 47

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EXECUTIVE SUMMARY

EXECUTIVE SUMMARY

A decision is a reasoned choice among alternatives. Making decisions is part

of the broader subject of problem solving. The decisions made by managers

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and knowledge workers have a great impact on corporate success. In

organizations, with the availability of rich information, making decisions is

the foundation for the success. Decision Support System is a computer-based

system that is used by managers for decision-making. It is a set of tools, data,

models and other resources that managers and analysts use to understand,

evaluate and solve problems that are useful in making unanticipated semi-

structured and unstructured decisions. DSS can also be defined as a class of

computer-based information systems designed to help managers select one

of the best alternative solutions to a problem. It helps the managers in

decision processes when decision-making is semi-structured, and improves

the effectiveness of decision-making by giving interactive information

support to managers.

The decision support system is a model based set of procedures for

processing data and judgments to assist the manager in his/her decision

making. In real sense a DSS is an approach/methodology for supporting the

solution for a specific non-structured management problem. It makes use of

data, provides an easy use interface and can incorporate the decision maker's

own insights. Additionally a DSS usually makes use of an interactive

process. It supports all phases of decision making and may include a

knowledge component. A DSS can be web-based for use by many people at

several locations. The application of DSS in the marketing domain assumes

enormous significance because most of the marketing problems are ill

structured and for which the algorithm are not easily available.

Research Focus

1. Utility of Marketing Decision Support System

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An emerging trend in the realm of marketing has been the increased

application of decision support system (DSS) technology to aid with

decision-making. The concept of a MDSS addresses this need for decision-

making support. Developing a sound and robust marketing strategy has

never been an easy task. The success or failure of a companys marketing

effort depends on the interaction of numerous internal and external factors,

combined with the knowledge and intuition of the decision-makers

themselves.

Marketing decision-making requires a comprehensive analysis of

environment both inside and outside the firm. It requires a wide range of

strategic information, including hard and soft information, and it requires

managers to deal with issues that involve a high degree of uncertainty,

subjectivity, and ambiguity. Marketing also involves managers intuition,

judgment, and personal vision.

2. Application of DSS in strategic/tactical/operational marketing

decisions

The way in which marketing professional utilize DSS in taking decisions

depends on whether he is a part of strategic/tactical/operational level of

management.

A person at strategic level needs to take unstructured decisions where it is

not possible to supply most of the decision procedures that are to be

followed, in advance.

A person at tactical level focuses on monitoring and controlling operation

and providing information to the higher levels of the organization. Here, the

scope of the decision is contained within the business; the function is

complex; and it has a time limit of a few days to few months. Managerial

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level decision-making is referred to as semi-structured decision-making

because solutions and problems are clear and often require judgment and

expertise for semi-structured decisions. Some procedures can be specified in

advance but not to the extent where a specific recommendation can be made.

A person at operational level focuses on structured decision where in the

procedures to follow for a given situation can be specified in advance.

At an operational level, the routine day-to-day business process occurs.

Information systems at this level are designed to automate repetitive

activities and improve the efficiency of business process. The decisions at

this level are day-to-day, highly structured and recurring.

Our research investigation seeks to analyze how marketing professional at

various level of management utilize DSS in decision making.

Research Objectives

To find out the awareness of using MDSS tools in marketing. Level of management where MDSS is implemented To identify the barriers to the application of MDSS to the marketing

problems

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To prepare a road map for the application of MDSS to the marketingproblems

Research technique used

The data for the study are collected both from the primary source and the

secondary source. The project employs systematic, objective and

quantitative Research techniques using a well structured questionnaire,

which form the primary information collected. The secondary data sources

are management journals and websites. Using the primary data, separate

tables have been furnished under specific headings; have been analyzed with

the help of descriptive analysis and percentage, and interpreted. Detailed

charts have been developed showing the data obtained from the respondents,

the data collected from the respondents were organized, processed and

tabulated to depict the results. The sample size comprises of 30 respondents.

Major Research Findings

67% of the Respondents knew about MDSS. Around 27% of the respondents

felt that MDSS would be used at the Top management level, 60% believed

that MDSS is useful at Middle management level and only 13% felt it is

used at the operational or lower management level.

Recommendations

There is need to create awareness among the marketing professionals about

the usage of MDSS for better decision making. A need for training them on

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such tools holds key to success of any companies marketing strategies. A

need to monitor the usage of MDSS tools so as to improve it.

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PART 'A'

THEORETICAL

SETTING

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CHAPTER-1

INTRODUCTION

1.1. BACKGROUND OF THE STUDY.

IMPORTANCE OF DSS IN MARKETING

Developments in computer industry, has made it possible that several tasks

could be carried out much more easily. Automating the collection and

analysis of marketing information would result in timely marketing and sales

decisions and their quality would be largely improved. Later on information

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technology also facilitated the automation of the sales and marketing support

in a Management Information System. This would lead to improved

efficiency of the marketing and sales staff.

Hardware and software costs continually decrease and the computer

programs become more and more powerful. Its main barriers such as

computer power (for PCs) and software compatibility have largely

disappeared.

However, does information technology provide the required return on

investment?

Which added value has been created by this new technology?

Realizing the advantages of a DSS is difficult. Substantial productivity

benefits such as cost reductions can be properly measured. It is evident that

the use of a DSS also has soft benefits. For example, it is reasonable to

assume that this marketing tool is able to make more effective and better

founded decisions, because marketers have information to their disposal that

is more recent, more comprehensive and more reliable. The importance of

information in commercial processes is largely recognized.

Unanswered remains the question to what extent information provided by

the DSS can improve a marketers performance.

1.2. STATEMENT OF THE PROBLEM.

Are Computerized Decision Support Systems helping

marketers in their decision-making apparatus?

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CHAPTER-2REVIEW

OF

LITERATURE.

2.1 PURPOSE OF REVIEW.

The purpose of literature review is to explore the historical and current

scenarios in Computerized Decision Support Systems. The literature survey

is done to understand what is a DSS? To understand the different levels of

decision making in an organization. To understand the conceptual

framework of a Decision Support System, understand the various decision

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models and the architecture of Decision Support Systems. Over and above,

the purpose of the literature review is to understand in totality the foundation

of the research problem.

2.2 METHODOLOGY.

The Book DECISION SUPPORT SYSTEM in the 21st

Century by George

M. Marakas has been referred to understand the above mentioned concepts

and is presented below.

2.2.1 INTRODUCTION

A decision is a reasoned choice among alternatives. Making decisions is part

of the broader subject of problem solving. The decisions made by managers

and knowledge workers have a great impact on corporate success. In

organizations, with the availability of rich information, making decisions is

the foundation for the success. Decision Support System is a computer-based

system that is used by managers for decision-making. It is a set of tools, data,

models and other resources that managers and analysts use to understand,

evaluate and solve problems that are useful in making unanticipated semi-

structured and unstructured decisions. DSS can also be defined as a class of

computer-based information systems designed to help managers select one

of the best alternative solutions to a problem. It helps the managers in

decision processes when decision-making is semi-structured, and improves

the effectiveness of decision-making by giving interactive information

support to managers.

A majority of DSS research is focused on the application of new technology

to managerial tasks at the operational and tactical levels. Though DSS is

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used to solve semi-structured problems at all levels of an organization,

resources are deployed or structured at any given moment. DSS provides

support to the selection process and allows managers who are directly

responsible for the outcome to make the final selection.

DSS helps in making the organization more effective by giving it a

competitive advantage. In today's environment, managers will be

empowered to make their own decisions based on the organized feedback

from peers, customers or from top management and hence knowledge-based

organizations depend heavily on the computer-based DSS. It clearly offers

management a powerful tool in meeting demands of managerial work. They

are also known as business intelligence systems or analytics.

DSS helps human decision-making performance and problem solving by

enabling the users to examine alternative solutions using the `what if '

analysis. This analysis helps to make changes to the data associated with a

problem and observe how these changes influence the results.

2.2.2 DECISION - MAKING LEVELS OF AN ORGANIZATION

Each level of an organization has different responsibilities and therefore

needs different information.

Structured Decisions

These decisions involve situations where proper structuring of the procedure

to be followed when a decision is needed can be specified in advance. At an

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operational level, the routine day-to-day business process occurs.

Information systems at this level are designed to automate repetitive

activities and improve the efficiency of business process. The decisions at

this level are day-to-day, highly structured and recurring. Structured decision

are those in which the procedures to follow for a given situation can be

specified in advance.

Semi-structured Decisions

Some decisions can be pre-specified but they are not bad for a definite

recommended action. At the managerial level of the organization, the

functional managers focus on monitoring and controlling operation actuaries

and providing information to the higher levels of the organization. Here, the

scope of the decision is contained within the business; the function is

complex; and it has a time limit of a few days to few months. Managerial

level decision-making is referred to as semi-structured decision-making

because solutions and problems are clear and often require judgment and

expertise for semi-structured decisions. Some procedures can be specified in

advance but not to the extent where a specific recommendation can be made.

Unstructured Decisions

These decisions involve situations where it is not possible to supply most of

the decision procedures that are to be followed, in advance. At the executive

level of the organization, managers focus on long term strategic issues

facing the organization. Managers at this level are the CEO, President, Vice-

president and the Board of Directors. This level deals with complex

problems with broad and long-term requirements of the organization. The

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decisions here are unstructured because the problems are complex and non-

routine. For unstructured decisions, few or no procedure can be specified in

advance.

A highly structured decision includes easily determined objectives that are

not subject to conflict, are clearly defined, or those selecting alternative

courses of action and ascertainable outcomes. In a highly unstructured

decision, the objectives of the situation often conflict, the alternatives

available to the decision-maker are difficult to implement and the effect of a

particular course of action or selection of an alternative carries with it a high

degree of uncertainty.

The decisions at three levels of organization are as follows:

Decision-makers at the strategic level require the more summarized ad hoc

unscheduled reports, forecast and internal intelligence to support their

unstructured planning. The strategic decision is one which will affect the

entire organization for a long period of time. They affect organizational

objectives and policies. Data are generally in the unstructured form. It is

necessary for a firm to develop successful decision strategies to confront in

order to survive and succeed. In the long run, these strategies force the shape

and the structure of competition in the industry, in addition to cost leadership,

differentiation, innovation growth and alliance. There are many other

competitive strategies that are implemented with information systems. It

attempts to use the information system in customer and supplier relation

systems. In a customer and supplier relationship, the focus is on significantly

improving the quality of service to customer and suppliers in a firms

actuaries. A major emphasis on Strategic Information System (SIS) has been

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to find ways to build switching costs into the relationships between a firm

and its customers or suppliers.

The primary objective of strategic managers is to maximize the value

delivered to all the enterprise's stakeholders. This objective is accomplished

by establishing and maintaining a unifying vision and strategic direction.

The conflicting objectives of asset managers and value chain managers must

be reconciled. Competitive advantage must be identified and exploited for

the enterprise to win and grow (Reid et al., 1996). To support the

competitive position of the enterprise, the following strategic management

processes are operated (Reid et al., 1996):

1. Provide situational awareness-both internal and external2. Facilitate decision-making3. Develop and deploy plans-at both strategic and operational

performance levels

4. Manage enterprise performance5. Establish the required performance management infrastructure. This

activity is generally performed only for a particular enterprise.

6. Monitor performance7. Communicate performance8. Evaluate performance9. Improve performance

Two important factors determine strategic planning decisions: Time and

Uncertainty. This has led to recent investigation of Strategic Planning

models that enable the decision-maker to adopt strategic policies which

hedge against uncertainty, and respond to events as they unfold in time.

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Decision-makers at the operational level require specified internal reports

with detailed current and historical data comparisons. A tactical decision

will affect the manner in which a part of organization does business for a

limited period of time in the future. These decisions are made by middle

managers to determine how an entire category of future decisions will be

taken. An operational decision is one that affects a particular activity

currently taking place in the organization. These decisions relate to activities

whose tasks, goals, and resources have already been defined by strategic and

tactical decisions.

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2.2.3 CONCEPTUAL FRAMEWORK OF DECISION SUPPORT

SYSTEM

A decision support system is a computer based procedure that allows a

manager to directly interact with data using various methods of analysis to

integrate, and analyze and interpret information; The heart and soul of DSS

is datadifferent types of data from a wide variety of sources. Decision

Support Systems couple the intellectual resources of individuals with the

capabilities of the computer to improve the quality of decisions. Typically, a

DSS contains data describing the market, consumers competitors, economic

and social trends and the organization performance. The DSS has methods

for analyzing data. These methods range from simple procedure such as

computing ratios to drawing graphs to sophisticated statistical techniques

and mathematical models. By combining personal computers and user-

friendly software, the DSS allows managers to independently retrieve data,examining the relationships, and even create unique reports to meet their

specific needs. The DSS clearly offers management a powerful tool and is

rapidly becoming an integral component of managerial work. The speed

with which todays information becomes yesterdays news continues to

increase at a staggering rate. The leveraging of technology that will allow

the manager to be effective in such a high speed environment is what

decision support all about. DSS is expected to extend the decision makers

capacity of processing the mountain of information involved in making a

decision.

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The following chart describes the simple mechanism of DSS:

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Common DSS Characteristics

1. Employed in semi structured or unstructured decision context

2. Intended to support decision makers

3. Supports all phases of the decision-making process

4. Focuses on the effectiveness of the decision making process

5. Utilizes underlying data and models

6. Facilitates learning on the part of the decision maker

7. Provides support for all levels of management from top executives to line

managers

8. Can support for multiple or interdependent decisions

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ADVANTAGES OF DSS USAGE

Various authors have investigated the assumed benefits of Decision Support

Systems.

1. Administrative advantages (time and labor savings),

2. More efficient use of management time,

3. Better decision making, handling more complex problems,

4. Better judgment of problems, superior trust in decisionmaking,

5. Better data usage (recency, accuracy, access and availability),

6. Better planning and control,

7. Possibility to research more alternatives,

8. Communication advantage.

Thus DSS is a truly dynamic system that requires knowledge beyond that of

the Typical Information System professional. The decision makers of

tomorrow will rely on this technology to support their daily activities and to

assist them in managing the ever-growing knowledge base necessary to

make informed and effective decisions.

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2.2.4 DECISION MODELS

Decision models can be classified into a number of ways. One common

high-level classification divides the models in terms of their explicit

inclusion of time as an element. Models that do not explicitly acknowledge

time are called static models and those that do are referred to as dynamic

models. Another widespread method of classification is by technique or

logical focus.

Abstract Model Types:An abstract model focuses on the mathematical precision with which various

outcomes can be predicted. Theoretically, this type of model can determine

which alternative should result in the most favorable outcome. Based on this

prediction, a decision-maker then develops a strategy for implementing the

proposed alternative and thus solves the problem.

Deterministic Models:In the deterministic model, no variable can take more than one value at any

given time. The same output value will always result from any given set of

input variables. Most of the traditional models of the normative economic

theory are deterministic as are many of the common models used in financial

analysis. These model types are particularly useful when a problem structure

contains a large number of elements and complex relationships. Common

deterministic models include linear programming, nonlinear programming,

and differential equations.

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Stochastic Models:In a stochastic model, at least one of the variables is uncertain and must be

described by some probability function. These model types are often referred

to as probabilistic models because they explicitly incorporate uncertainty

into their structure. When constructing a stochastic model, the decision-

maker distributes the input value of one or more variables around some

mean value and the output variables take the form of a frequency

distribution rather than a discrete set of value.

Domain-specific Models:The advances in the science disciplines prompt the need for highly specific

types of decision-making techniques and contexts. In these cases, each

discipline develops its own set of abstract mathematical modeling techniques

to serve its needs. Models of supply and demand are unique to the toolbox of

economists. Other well-known domains such as operations research,

sociology, medicine and meteorology rely on their own unique abstract

modeling techniques. In the design of DSS for a specific problem domain,

the designer must be aware of any domain specific models available for

inclusion in the model base.

Conceptual ModelsConceptual models can be identified as analogies to the problem in context.

Implicit in this perspective is the notion that even though all problems are

unique, no problem is completely new. Experience from a past problem can

be used to assist in forecasting events and outcomes in the new context.

Using this method, decision-makers can recall and combine a variety of past

experiences and contexts to create an accurate conceptual model of the

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current situation and to assign probabilities to those event components that

remain uncertain.

Tools and Technologies of DSS

Decision Support System includes many key terms the meaning of which

helps us to get a good result.

Extraction, Transformation, and Loading (ETL)

During the ETL (Extraction, Transformation, and Loading) process, the

desired data is identified and extracted from many different sources,

including database systems and applications. Depending on the source

system's capabilities (e.g., operating system resources), some

transformations may take place during this extraction process. After

extracting data, it has to be physically transported to the target system or an

intermediate system for further processing. Depending on the chosen way of

transportation, some transformations can be done during this process, too.

For example, an Structured Query Language (SQL) statement, which

directly accesses a remote target through a gateway, can concatenate two

columns as part of the SELECT statement; this leads the manager to take a

particular decision.

Online Analytical Processing (OLAP)

Online Analytical Processing is a significant improvement of the model-

based management, which manipulates data from a variety of sources that

has been stored in a static data warehouse. The software can cause various

views and representations of the data and this helps in making the context

explicit. OLAP can pull the data, create pictures and make the user see the

model running. It creates various models and helps in finding out the best. It

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allows the users to analyze information that has been summarized into

multidimensional views and hierarchies.

Relational Online Analytical Processing (ROLAP)

When we need to handle large data and try to leverage functionalities

inherent in the relational database, we need Relational Online Analytical

Processing. This methodology relies on manipulating the data stored in the

relational database to give the appearance of the traditional OLAP's slicing

and dicing functionality. In essence, each action of slicing and dicing is

equivalent to adding a `WHERE' clause in the SQL statement.

Multidimensional Online Analytical Processing (MOLAP)

This is the more traditional way of OLAP analysis. In MOLAP, data is

stored in a multidimensional cube. It effects excellent performance and can

perform complex calculations. In a Management Decision Support system,

managers need to look into the summary rather than the detail report. Hybrid

Online Analytical Processing (HOLAP) technologies attempt to combine the

advantages of MOLAP and ROLAP. For summary-type information,

HOLAP leverages cube technology for faster performance. When detail

information is needed, HOLAP can "drill through" from the cube into the

underlying relational data. Web based DSS deliver decision support

information or decision support tools to a manager or business analyst using

a "thin-client" Web browser like Netscape Navigator or Internet Explorer

that is accessing the global Internet or a corporate intranet. Web-based DSS

can be communications-driven, data-driven, document-driven, knowledge-

driven, model-driven or hybrid. Web technologies can be used to implement

any category or type of DSS. Web-based means the entire application is

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implemented using Web technologies; Web-enabled means key parts of an

application like a database remain on a legacy system, but the application

can be accessed from a Web-based component and displayed in a browser.

Using a web infrastructure for building DSS improves the rapid

dissemination of the best practices analysis and decision-making framework

and it should promote more consistent decision-making on repetitive

decision tasks across a geographically distributed organization.

Data Warehouse

Data Warehouse is a collection of integrated, subject oriented databases

designed to support DSS function, where each unit of data is non-volatile

and relevant. Data Warehouse is a batch updated and structured for rapid

online queries and management solutions. So it has become an important

component in DSS. DW contains a large amount of data that are subject-

oriented, integrated, and time-variant as well as a huge stable collection of

data that supports the decision-making process of managers.

Dialogue Management

This refers to the hardware and software components that create the user

interface for the DSS. Software support for dialogue is in terms of packages

that can be accessed via high-end languages and is used to construct user

interface and data definition language, which describes the dialogue

component of DSS architecture. It also focuses on command language and

menu dialogues that create a restrictive context for building DSS.

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Database Management

DBM (Database Management System) is a key component of DSS because

it reduces the cost of building and using DSS, by increasing data control and

sharing and reducing data redundancy. There are five different data models

for the database component of DSS: Record, relational, hierarchical,

network and rule model. The record of flat file model is common in DSS

that uses time series data. DSS database ware is limited in scope and

definitely not integrated because of the problems with data processing

efficiency and storage overload that degrade the performance.

Model Base Management

The modeling component gives decision-makers the ability to analyze the

problem and moves a Management Information System (MIS) to full DSS.

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2.2.5 DECISION SUPPORT SYSTEM ARCHITECTURE

DSS Architecture

DSS architecture refers to the mechanism and structure for the integration of

the dialogue, database and model management components of a DSS. There

are four types of architectures for building and integrating the DSS software

system: The DSS Network, with multiple dialogue, modeling and database

components that can communicate with each other through a component

interface; the DSS Bridge, which provides a standard interface with local

dialogue and modeling components that link to remote modeling and

database components; the DSS Sandwich, which integrates multiplemodeling components by using single dialogue and database components;

and the DSS Tower, where data extraction is used to integrate diverse

database components and other components are similar to the network

architecture.

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Components of DSS

The components of DSS include Management Decision Support System,

Management Information System, Executive Information System, File

Drawer System and Knowledge Management.

DSS is a competitive knowledge-rich world in which managers take

decisions about what to do with their organizations' resources. Large number

of decisions are made every day and range from those that are simple to

those that are very complex. Every one of these decisions involves the use of

knowledge of varying kinds and amounts, and many of them can benefit

from (or even require) the use of a component known as Knowledge

Management. Knowledge management is the basis of any decision-making

process. First, one should know which information should be put where and

how to manage them. Preparing a databank from information is a part of

knowledge management. DSS basically starts from creating a correct and

proper database. So, the first component for any DSS is knowledge

management. From databases, executives draw the information they need in

a refined way. The process of raw data converting to useful information for

the executives is known as EIS (Executive Information Systems). EIS is

there to support the senior management. It helps organizations to enhance

their knowledge processing and their ability to learn and adapt. EIS enables

the use of interactive information exchanges by access to electronic

networks. It can provide quantitative diagnostics to monitor the progress of

decisions. In terms of organizational impact, EIS can improve the

managerial decision-making process because it naturally will support, or

create pressure for, a formal planning system, for it is known that an

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advantage of formal planning is that it fosters lateral communication, formal

reporting, and rationality.

When information passes from executives to the managers, it is already a

refined one. But to make decisions more simple and result-oriented, middle

level managers tune them to fine information. This process is called as MIS

(Management Information System). After going to MIS, top level managers

use the DSS components and take the final decision, which is known as

management decision support system.

With the tools, technology, models and components of DSS, today it is

essential to capture the trend of outsourcing.

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2.2.6 GAINING COMPETITIVE ADVANTAGE WITH

COMPUTERIZED DECISION SUPPORT

Many companies have isolated decision support capabilities that are hard to

use or hard to access. For example, a data mart may have been built for

accessing customer data, a project management system may exist for

tracking large-scale projects, or Excel analyses may be routinely used in a

specific business decision process. In general, managers are experiencing

information overload and are having difficulty finding the right information

when it is needed. Potentially, innovative Decision Support Systems can

yield competitive advantage for an organization or at least maintain an

organizations competitive position.

Evidence indicates managers can now use sophisticated data-driven and

document-driven DSSs to obtain information that was buried for many years

in filing cabinets or archived on computer storage systems. Model-driven

DSSs can reduce waste in production operations and improve inventory

management. Knowledge-driven DSSs can help managers evaluate

employees or help technical staff diagnose problems. Communications-

driven DSSs can support teams working all over the world. Inter-

organizational DSSs can support a companys suppliers and customers.

Real-time decision support systems are now possible for tactical decision

support.

A decision support system creates a competitive advantage if three criteria

are met. First, once the DSS is implemented, it must be used and it must

become a major or significant strength or capability of the organization.

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Second, the DSS must be unique and proprietary to the organization. Third,

the advantage provided by the DSS must be sustainable until an adequate

payback is received, usually at least three years. Managers who are

searching for strategic investments in information technology need to keep

these three criteria in mind. Just because a vendor says a product will create

a competitive advantage doesnt make the claim true. A competitive

advantage means an organization does something important much better

than its competitors.

The widespread use of computer technology has changed the way companies

do business. Information technology has altered relationships between

companies and their suppliers, customers and rivals. Porter and Millar

(1985) discuss two specific ways that information technology can affect

competition: by altering industry structures and by supporting cost and/or

differentiation strategies. A common approach used to identify opportunities

to change the structure and profitability of an industry is to examine five

competitive forces. Michael Porter (1979) argued that the power of buyers,

the power of suppliers, the threat of new entrants, the threat of substitute

products and the rivalry among existing competitors determines the

profitability of an industry. How a company uses information technology

can affect each of the five competitive forces and can create the need and

opportunity for change. For example, information technology has altered the

bargaining relationships between companies and their suppliers, channels

and buyers. Today it is easy for information systems to cross company

boundaries. These inter-organizational systems have become common and,

in some instances, they have changed the boundaries of the participating

industries. Decision support systems can reduce the power of buyers and

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suppliers. Decision support systems can erect new barriers that reduce the

threat of entrants. Decision support systems can help differentiate products

and services and reduce the threat from substitutes. Also, decision support

systems can help managers reduce the cost of rivalry actions and, in some

cases, reduce the need for competitive actions and reactions.

Decision support systems can potentially help a firm create a cost advantage.

Decision support systems can provide many benefits including improving

personal efficiency and reducing staff needs, expediting problem solving and

increasing organizational control. Managers who want to create a cost

advantage should search for situations where decision processes seem slow

or tedious and where problems reoccur or solutions are delayed or

unsatisfactory. In some cases, DSSs can reduce costs where decision makers

have high turnover and training is slow and cumbersome, and in situations

where activities, departments and projects are poorly controlled.

Also, DSSs can create a major cost advantage by increasing efficiency or

eliminating value chain activities. For example, a bank or mortgage loan

firm may reduce costs by using a new DSS to consolidate the number of

steps and minimize the number of staff hours needed to approve loans.

Technology breakthroughs can sometimes continue to lower process costs,

and rivals who imitate an innovative DSS may nullify or remove any

advantage.

Decision support systems can potentially create a differentiation advantage.

Providing a DSS to customers can differentiate a product and possibly

provide a new service. Differentiation increases profitability when the price

premium charged is greater than any added costs associated with achieving

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the differentiation. Successful differentiation means a firm can charge a

premium price, and/or sell more units, and/or increase buyer loyalty for

service or repeat purchases. In some situations, competitors can rapidly

imitate the differentiation, and then all competitors incur increased costs for

implementing the DSS.

Finally, decision support systems can be used to help a company better focus

on a specific customer segment and hence gain an advantage in meeting that

segments needs. Management information systems and decision support

systems can help track customers, and DSSs can make it easier to serve a

specialized customer group with special services. Some customers wont pay

a premium for targeted service, and larger competitors also target specialized

niches using their own DSSs.

It is important to recognize that some firms have no competitive advantage.

Firms can achieve a competitive advantage by making strategic changes, and

firms can lose a competitive advantage when competitors make strategic

changes. Implementing computerized decision support does not necessarily

create a competitive advantage. In fact, most decision support does not have

such a broad enterprise-wide impact.

Decision support systems can be important and useful and very necessary,

and yet not provide a competitive advantage. Many consulting firms and

vendors focus on gaining competitive advantage from a data warehouse or abusiness intelligence system, and that can happen. Many DSS projects do

not, however, deliver such results and the projects probably were not

intended to create competitive advantage.

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A study identified a number of companies that had gained an advantage

from information systems. Some of those systems were decision support

systems, but most were transaction processing systems. The following DSS

examples are from their paper: Air Products, a vehicle scheduling system;

Cigna, a risk assessment system; IBM, a marketing management system;

Owens-Corning, a materials selection system; and Procter & Gamble, a

customer response system. Most companies wisely do not provide many

details on their success with computerized decision support. Competitive

responses and technology have had a negative impact on how some of the

aforementioned systems are perceived today.

If a company is trying to develop a decision support system that provides a

competitive advantage, managers and analysts should ask how the proposed

DSS affects company costs, customer and supplier relations, and managerial

effectiveness. Managers should also attempt to assess how the proposed

strategic system will impact the structure of the industry and the behavior of

competitors. Finally, companies must continuously improve their

information and decision support technology to gain and maintain any

competitive advantage.

Harbridge House (a Boston Firm) has conducted a survey and ranked clear-

cut decisions needed as the most important of 10 management practices. Asthe trends are changing, it is very difficult to rely upon trial and error

approach to management; in case of decision is unstructured/ill-

structured/semi-structured decision making situations. Managers must learn

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sophisticated new tools and techniques. Using these technological tools, its

extremely rewarding in making effective decision

2.3 CONCLUSION.

Thus from the literature survey conducted, a good understanding of

DSS model, advantages of DSS and Competitive Advantage of using

have been understood.

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CHAPTER-3RESEARCH

METHODOLOGY

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3.1 TYPE OF RESEARCH.

Our research investigation adopted a systematic, objective and quantitative

research techniques using a well-structured questionnaire. The questionnaire

is designed keeping in mind the various features offered by DSS. The

research investigation is restricted to the various marketing managers and

business development mangers in the Bangalore. The primary data was

collected using a well structured questionnaire. The questionnaire was meant

to know the knowledge of DSS of the managers. The level at which they had

been exposed to the Decision Support System.

There are three alternative types of evaluation procedures for DSS

performance: subjective judgment, expert observation and objective

measurement.

A subjective judgment requests from users to assess their experiences.

In most cases this means that a user has to fill out a questionnaire.

Expert observation also uses a subjective judgment, however in this case of

non-participating observers and experts in the field of DSS to judge DSS

users.

Objective measurement is usually related to empirical experiments and

comparison.

Objective, empirical measurement is considered as the most reliable and

consequently most preferred way to acquire data. Unfortunately, this is a

very expensive method and in practice it is often selected for expert

observation and subjective judgment. In representing the results of the

investigations to DSS performance, in each case we will report the method

used. This will enable the reader to estimate such a method at its true value.

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3.2 SAMPLING TECHNIQUE

POPULATION: The information is generated from the marketers whose

company implements DSS in various operations in Bangalore.

SAMPLING UNIT: Marketers are selected from the companies in

Bangalore.

SAMPLING METHOD: The selection of Marketers is non-probability

(convenience).

3.3 SAMPLE SIZE:

30 Marketers from various companies in Bangalore were chosen and

interviewed. The number of corporate entities choosen for this purpose is

12 ( twelve). These include Banks, FMCG companies, Industrial

Marketing Companies and IT Companies.

3.4 INSTRUMENTATION TECHNIQUES.

QUESTIONNAIRE: The research instrument used here is a questionnaire,

which helped us to generate the database adequately. The questionnaire has

close-ended questions, as the study is very specific.

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3.5 ACTUAL COLLECTION OF DATA.

A. SECONDARY DATA:

Secondary data is gathered from numerous sources. A tedious and time-

consuming library research gave desired information. The secondary data is

collected from the internal research, such as journals and internet.

B. PRIMARY DATA:

Survey research: Questionnaire has been used to gather data. Survey has

been designed to capture wide variety of information. This research has

helped to identify the satisfaction levels of the Marketers in using DSS tools.

It has also helped to learn about the awareness and expectations of the

marketers and also answered an important question

Do you think MDSS tools can help you in taking better decisions which

would hence increase the productivity of the company?

3.6 RESEARCH LIMITATIONS:

Time and resource constraints Effective decision making is dependent on several other factors like

environmental factors which we have not considered due to lack of

time and resources

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PART BSURVEY FINDINGS

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DATA ANALYSIS

AND

INFERENCES

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RESEARCH OBJECTIVE 1:

To find out the awareness of using MDSS tools in marketing

Table 1: AWARENESS ABOUT MDSS TOOLS IN MARKETING

Sl. Nos. Level of Awareness Respondents Percentage(%)

1 YES 29 67

2 NO 11 33

TOTAL 30 100

Source: Field Investigation

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Chart 1:

CHART SHOWING THE LEVEL OF AWARENESS

ABOUT MDSS TOOLS

AWARENESS LEVEL OF MDSS TOOLS

YES

67%

NO

33%

YES

NO

Inference: This table is designed to identify the awareness of

MDSS tools in marketing domain.

a) 67% of the respondents are aware of the MDSS tools.

b) 33% of the respondents are unaware of the MDSS tools.

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TABLE 2:

LEVEL OF MANAGEMENT WHERE MDSS IS IMPLEMENTED

SL.NO. LEVEL OF MGMT RESPONDENTS

PERCENTAGE

(%)

1 STRATEGIC 8 26.67

2 TACTICAL 18 60

3 OPERATIONAL 4 13.33

TOTAL 30 100.00


CHART 2:


8

26.67

18

60

4

13.33

0

10

20

30

40

50

60


RESPONDENTS 8 18 4

PERCENTAGE (%) 26.67 60 13.33

STRATEGIC TACTICAL OPERATIONAL

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Inference:

This table is designed to identify various levels of management where

MDSS are implemented.

a) 26.67% of respondents use MDSS tools in strategic level

b) 60% of respondents use MDSS tools in tactical level

c) 13.33% of respondents use MDSS tools in operational level.

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TABLE 3

NECESSITY OF AN MDSS THAT IS EVOLVED OUT OF

INDUSTRY BEST PRACTICES

SL NO. OPTION RESPONDENTS

PERCENTAGE

(%)

1 YES 18 60

2 NO 12 40

TOTAL 30 100


Inference:

The above table identifies the need for MDSS to be evolved out of the

industry best practices

a) 60% of the respondents felt that it would be better if the MDSS is

evolved out Industry Best Practices.

b) However 40% of the respondents felt there was no need for such

thought.

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To identify whether MDSS helps the marketers in making better

decisions this would increase the productivity of the company.

Null Hypothesis:

H0: DSS cannot find total solutions to the marketing problems

Alternate Hypothesis:

H1: DSS can find total solutions to the marketing problems

Test Statistics used here is Z-test

Formula

Z = | P Ps |

_________

(P*Q)/N

P -> Assumed population mean

Ps -> sample population

Q = 1-P

N -> Total number of samples

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Do you think MDSS tools can help you in taking better

decisions which would increase the productivity of the

company?

Number of Respondents who said YES: 19 (nineteen only)

Number of respondents who said NO: 11 (eleven only)

Assumption: Population Mean = .5

Calculations:

N = 30

Ps Sample population = 19 / 30 = .634

P = .5

Q = 1-.5 = .5

Test statistics = Z = | P Ps |

________

(P*Q)/N

Z = | .5 - .634 |

________ = .134/.091 = 1.472

(.5*.5)/30

Tabulated value for 95% level of significance is 1.96

Calculated value = 1.472

Cal value < tabulated value,

Hence we reject Ho

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INFERENCE:

Since calculated value is less than tabulated value we reject our null

hypothesis and accept the alternate hypothesis.

Hence we conclude there is significant difference in the productivity of

the company after implementing MDSS. Hence we can conclude that

DSS can find total solutions to the marketing problems.

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PART C

CONCLUSIONS

ANDRECOMMENDATIONS

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CHAPTER 5

CONCLUSIONS

AND

RECOMMENDATIONS

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5.1. MAJOR CONCLUSIONS EMERGING FROM THE STUDY

1. Most of the respondents have an average awareness about the MDSStools.

2. It is found that implementation of DSS system in marketing domainhelps in taking better decisions for the marketers which would lead to

increase in productivity of the company.

3. On an average MDSS tools have been extensively used in operationallevel.

4. Most of the respondents have found DSS tools as user-friendly.

The type of marketing problem can be subdivided into strategic, technical

and operational problems. Action strategy of the company in terms of user

friendliness, flexibility has a pronounced effect on the success of DSS.

The use of a DSS by a marketer reduces his time to come to a decision. As

far as the effectiveness of DSS usage by marketers is concerned DSS usage

implies that various alternatives are taken into account and evaluated and,

consequently, a better decision is made. We can also conclude that DSS is

required mostly in operational level and helps the marketers in taking better

marketing decision. Apart from the characteristics User friendliness,

Marketers rate the DSS tools on their flexibility, reliability, and response

time and data storage capacity.

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5.2. SPECIFIC RECOMMENDATIONS

1. Create awareness among the Marketers about the DSS tools whichwould help them in making better decisions.

2. Give training in the DSS tools which would make it more userfriendly and hence would reduce time and increase speed.

3. Monitor the working of the tools performance and effectivenesswhich would help in improving the performance.

Implementation of an information system does not automatically lead to an

increase of the performance of the decision-maker. The performance

depends on a number of factors:

1. The marketing decision-maker or the characteristics of the Individual

2. The DSS

3. The type of marketing problem

4. The environment

5. Organizational characteristics

6. The action strategies of the enterprise.

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ANNEXURE

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1. SELECT BIBLIOGRAPHY

(a) Books

1. Decision Support Systems in the 21st

Century by George M. Marakas,

Prentice-Hall of India Publication, 2nd

Edition, 2003.

2. MDSS Principles, Tools and Implementation, Gary L Lilien; Arvind

Rangaswamy; Gerrit H Van Bruggen; Katrin Starke, Information Systems

Research; Sep 2004

3. Database Systems Design, Implementation & Management by Bob

Coronel, Thomas Course Technology publications, 2006

4. Decision Support Systems & Intelligence Systems by Efrain Turban and

Jay E Aronson, PHI Publications, 2007

5. Data Analysis, Data Modeling & Classification, by Modell Martin,

McGraw Hill Publications 2000

(b) Websites

1. www.mdssworld.com/2. www.mdss.com3. www.mdss.net4. www.rbac.com/methods/mdss.htm

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2. QUESTIONNAIRE

Sir/Madam,

I, Mr.Bharath B K, am conducting research on Computerized Marketing Decision

System. The research survey is a sequel to project report/dissertation required in partial fulfillment of the Degree of Master of Business Administration. In this

connection, I need some information from you. Below is the questionnaire on the

various facets of Computerized Marketing Decision System. Kindly give your

valuable responses to the questions thereon. I humbly assure you that your responses

will be kept confidential and shall only be used for academic purpose.

Mr.Bharath B K

1) Are you aware of the concept of MDSS (Marketing decision support

system) Tools used in the marketing?

a) Yes

b) No

2) Does your company utilize technical tools for decision making?

a) Yes

b) No

3) Please choose the Level of Management where MDSS s implemented:

a) Top Level Manager/Strategic Level

b) Middle Level Manager/Tactical Level

c) Low Level Manager/Operational Level

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4) Do you think MDSS tools can help you in taking better decisions which

would enhance the productivity of the company?

a) Yes

b) No

If no, Please state the reasons:

5) Does DSS match the Human Intuitive Nature and Analytical Ability in

Decision Making?

a) Yes

b) No

6) Please mention the decision support tools you use in marketing?

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7) How do you take Decisions?

a) Based on Gut Feel (Random Decision Making)

b) Intuitive Decision making (Predictive Decision Making)

c) Based on computer generated results/ reports

d) Mix of the above mentioned options

8) Do you find the necessity of an MDSS that is evolved out of Industry best

Practices?

a) Yes

b) No

9) How important are the following factors in determining the efficiency of

an MDSS?

Rate on a scale of 1-5

LOW HIGH

1 2 3 4 5

a) User friendliness

1 2 3 4 5

b) Flexibility

1 2 3 4 5

c) Response Time

1 2 3 4 5

d) Data storage capacity

1 2 3 4 5

e) Reliability

1 2 3 4 5

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10) On a scale of ten, rate these MDSS TOOL expectations/perceptions:

1. Increase speed.2. Increases marketers productivity.3. Increases control of sales process.4. Reduces marketing costs.5. Increases customer satisfaction.6. Better Documentation.

11) What are the limitations of MDSS tools? Check the appropriate one.

1. Expensive2. Making a perfect tool mix3. Learning Curve4. Lack of awareness5. Others6. (please mention).

....

12. If your company is already using MDSS, Please state your experiences

with the usage of MDSS:

.

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THANK YOU FOR SPARING YOUR VALUABLE

TIME IN GIVING THE RESPONSES. I SHALLGREATLY APPRECIATE YOUR COOPERATION IN

THIS REGARD.

Mr. Bharath B K

3. DIRECTIONS FOR FURTHER RESEARCH

The above study has taken into consideration marketing managers and

business development managers of various companies like Banks, FMCG,

IT, Industrial marketing.

1. The interested researchers can conduct a detailed survey as to how theMDSS is helping, considering the above mentioned sectors

individually. They can obtain a rationale as how each sector uses

MDSS individually.

2. A research w.r.t the cost-benefit analysis of implementation of MDSScan be conducted

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Bharath BK

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