Mis

MAJOR CHALLENGES OF INFORMATION SYSTEMS

Information System has brought revolution in the business world for getting its maximum
benefits. So, building, operating and maintaining information system is a challenging activity
now a days.

1. Globalization Challenge :- It deals with the identification of business and system
requirements to survive in globalized economy.
2. Information Architecture Challenge :- It deals with what should be the architectural
design of the information system to achieve the overall goal of the organization.
3. Strategic Business Challenge :- It deals with how to derive maximum benefits of
information technology to make an organization competitive and effective.
4. Responsibility & Control Challenge :- It deals with whether information systems are
ethical and socially responsible and can the system be controlled or not controlled by the
people?
5. Information System Investment Challenge :- It deals with what is the business value of
the information system? Generally it is very difficult to find out the value of information
systems to the organization because the benefits from information system are intangible.
6. Product & Service Challenge :- Traditional products that are tangible such as
automobile can be difficult to be deliver in the global market. However, electronic
products like software, music, books and electronic services can be delivered to
customers electronically over the networks, through Internet or other electronic means.
7. State, Regional & National Laws :- Every state, region and country has a set of laws
that must be obeyed by organizations operating in the country. These laws deals with
issues like trade secrets, patents, copyrights and privacy.

Keeping track of all these laws and incorporating them into the procedures and computer
systems of multi-national and transnational organizations is a difficult and time
consuming challenge for managers, which require expert legal advice.

MANAGEMENT INFORMATION SYSTEM (MIS)

MIS deals with information that is systematically and routinely collected in accordance with a
well-defined set of rules. The information provided by an MIS helps the managers to make
planning and control decisions. Managers often use historical data on an organization’s activities
from database to make planning and control decisions. This data base is an essential component
of an MIS.

According to Cannith, “ MIS is an approach that visualize the business organisation as a single
entity composed of various inter-related and interdependent sub-systems looking together to
provide timely and accurate information for management decision making, which leads to the
optimization of overall enterprise goals”.

According to David J. Olson, “A Management Information System is an integrated user
machine system for providing information to support the operations, management, analysis and
decision-making functions in an organisation”.

Thus, effective management information systems must be developed to provide modern
managers with the specific marketing, financial, production and personnel information products
they required to support their decision making responsibilities.

OBJECTIVES OF MIS

a) To store and manage data efficiently from all the functional areas of the business.
b) To provide information quickly as and when required & process the collected data and
derive information out of them.
c) To provide information for planning, organizing and controlling purposes.
d) To smooth up the flow of data through various levels of organization.
e) To speed up the execution of the results with the reliable data available.

CHARACTERISTICS OF MIS

MIS is a comprehensive coordinated set of Information Sub-systems, which are rationally
integrated and transform data into information. The following are the characteristics of MIS :

• It is a sub-system concept. The system is viewed as a single entity but it is broken down
into sub-systems that can be implemented one at a time.
• It provides a comprehensive view look of the inter-related sub-systems that operate with
an organisations.
• It is a rationally integrative system. All the sub-systems are integrated so that the
activities of each are inter-related with those of others.
• It provides relevant information to management.
• It enhances productivity, provides higher levels of service to organizations & individuals.
• It enhances manager’s ability to deal with unanticipated problems, facilitates the
organization’s normal management processes.
• It is management oriented as well as management directed.

ADVANTAGES OF MIS

MIS is a process of collection and storing of the data useful for the organization. The following
points can summarize the importance of MIS :-

1. It facilitates Planning : MIS improves the quality of plans by providing relevant
information for sound decision – making. Due to increase in the size and complexity of
organizations, managers take help of information systems to know about the status of
operations.
2. In minimizes Information overload : MIS change the larger amount of data in to
summarized form and there by avoids the confusion which may arise when managers are
flooded with detailed facts.

3. MIS encourages De-centralization : Decentralization of authority is possibly when
there is a system for monitoring operations at lower levels. MIS is successfully used for
measuring performance and making necessary change in the organizational plans and
procedures.
4. It brings Co-ordination : MIS facilities integration of specialized activities by keeping
each department aware of the problem and requirements of other departments. It connects
all decision centers in the organization.
5. It makes control easier : MIS serves as a link between managerial planning and control.
It improves the ability of management to evaluate and improve performance. The used
computers has increased the data processing and storage capabilities and reduced the
cost.
6. MIS assembles, process , stores , Retrieves , evaluates and Disseminates the information.
7. It helps in minimizing risk in decision making.
8. It helps the executives to avail the information regarding the functional areas quickly.
9. It processes the data and derives information out of them & helps in preparing corporate
report.

DISADVANTAGES OF MIS

MIS enhances the overall throughput of the organization. But it has certain limitations :

 The qualities of the outputs of MIS are basically governed by the qualities of inputs and
processes.
 It may not have requisite flexibility to quickly update itself with the changing needs of
time, especially in a fast changing and complex environment.
 MIS effectiveness decreases due to frequent changes in top management, organizational
structure and operational team.
 It can not replace managerial judgment in making decisions in different functional areas.

DECISION SUPPORT SYSTEMS (DSS)

DSS is identified as a system intended to support managerial decision makers in semi-structured decision
situations. A properly designed DSS is an interactive software-based system intended to help decision
makers compile useful information from a combination of raw data, documents, personal knowledge, or
business models to identify and solve problems and make decisions. They assist management decision
making by combining data, sophisticated analytical models and tools, and user-friendly software into that
support semi-structured or unstructured decision making. For Example, advertising managers may use an
electronic spreadsheet program to do what-if analysis as they test the impact of alternative advertising
budgets on the forecasted sales of new products.

According to Scott-Norton (1971), “DSS is an interacting computer-based system that helps the decision
maker in the use of data and models in the solution of unstructured problems”

According to Sprague & Carlson (1982), “Decision Support System (DSS) are interactive, computer-
based information systems that use decision models and specialized databases to assist the decision
making process of managerial end users.”

Therefore, DSS are designed to be ad-hoc, quick-response systems that are initiated and controlled by
business decision makers. A DSS provide managers with analytical modeling, simulation, data retrieval
and information presentation capabilities.

FEATURES OF DSS

Decision Support Systems (DSS) have many characteristics that allow them to be effective management
support tools. Not all DSSs work the sane. Some of characteristics of DSS are :

1) Provide Rapid Access to Information : Some DSSs provide fast and continuous access to
information. Example – the gauges on the dashboard of a car are used to see how the vehicle is
running.
2) Handles Large Amount of Data from Different Sources : DSS allow decision makers to access
data that resides in different databases on different computer systems or networks. Other sources
of data can be accessed via the Internet or corporate Intranet.

3) Provide Report and Presentation Flexibility : Managers can get the information they want,
presented in a format that suits their needs.
4) Offer both Textual and Graphical Orientation : DSSs can produce text, tables, line drawings,
pie charts, trend lines, etc. By using their preferred orientation, managers can use a DSS to get a
better understanding of a situation and to convey this understanding to others.
5) Support Drill-Down Analysis : A manager can get more details by drilling down through data.
Example – a manager can get more detailed information for a project by viewing the overall
project cost of drilling down and seeing the cost for each phase, activity and task.
6) Perform Complex Analysis & Comparisons : Marketing research surveys can be analyzed in a
variety of ways using programs that are part of a DSS.

CAPABILITIES OF DSS

1. DSS provide support decision makers mainly for semi structured and unstructured situations, by
bringing together human judgment and computer.
2. DSS provide support to several interdependent and sequential decisions to individuals as well as
to groups.
3. It support all phases of the decision-making process.
4. DSS attempt to improve the effectiveness of decision making.
5. A DSS usually utilizes models for analyzing decision-making situations.
6. Improve the effectiveness rather than the efficiency
7. Combine the use of models or analytical techniques with data access functions
8. DSS facilitates flexibility, adoptability and a quick response.

SIMON’S MODEL OF DECISION MAKING

The word ‘Decision’ is derived from Latin word which means ‘to come to a conclusion’. Decision-
making is a process of selecting one optimum alternative from several alternatives. So, decision is an end
or final product of the decision-making process. Herbert A. Simon has given a model to describe the
decision-making process. The model consists of 3 major phases :

I. Intelligence – In this phase, scanning of the environment and identification of the problem or
opportunity is done. Scanning of the environment may be continuous or intermittent. This
phase also involves problem finding and problem formulation.

‘Problem’ is defined as the difference between something expected and reality. In this phase,
once the problem is identified, then the problem is simplified by determining its boundaries,
breaking it down into smaller manageable sub-problems or focusing on the controllable elements.

II. Design – In this phase, the decision-maker makes a checklist of alternative courses of action
to solve the problem. These alternatives can be developed by various methods such as
brainstorming, interviews, etc.
III. Choice – At this stage, the decision-maker selects one alternative from the various
alternatives developed in design phase. This alternative after thorough analysis is further
implemented.

TYPES OF DSS

Decision Support Systems (DSS) are interactive computer-based systems that help decision makers use
communications technologies, data, documents, knowledge and/or models to complete decision process
tasks. A DSS may present information graphically and may include an expert system or artificial
intelligence (AI).

Data-Driven DSS

It is also known as data-oriented DSS. It emphasizes access to internal company data through the
company’s TPS and MIS systems and retrieve from it useful information which managers can use as
information for further decisions. It is used to query a database or data warehouse to seek specific
answers for specific purposes.

Communication-driven DSS

It is a hybrid DSS that focuses both the use of communication and decision models. They use network
and communications technologies to facilitate decision-relevant collaboration and communication. Its

purpose are to help conduct a meeting, or for users to collaborate. It supports more than one person
working on a shared task.

Document-driven DSS

These systems have also been called as text-oriented DSS. Document-driven DSSs are more common,
targeted at a broad base of user groups. The purpose of such a DSS is to search web pages and find
documents on a specific set of keywords or search terms. It manages, retrieves, and manipulates
unstructured information in a variety of electronic formats. It integrates a variety of storage and
processing technologies to provide complete document retrieval and analysis.

Knowledge-driven DSS

Knowledge-driven DSS can suggest or recommend actions to managers. These DSS are person-computer
systems that provide specialized problem-solving expertise stored as facts, rules, procedures, or in similar
structures. The "expertise" consists of knowledge about a particular domain and understanding of
problems within that domain. These systems are also called suggestion DSS.

It is essentially used to provide management advice or to choose products/services. The typical
deployment technology used to set up such systems could be client/server systems, the web, or software
running on stand-alone PCs.

Model-driven DSS

Model-driven DSSs are complex systems that help analyze decisions or choose between different options.
They emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model.
Model-driven DSS use limited data and parameters provided by users to assist decision makers in
analyzing a situation; they are not necessarily data-intensive.

Web-based DSS

Web-based decision support system is a computerized system that delivers 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. Ex - Homes.com provides a nation-wide listing of homes for
sale, apartments for sale and mortgages available.

In a nutshell, data-driven DSS will use faster, real-time access to larger, better integrated databases.
Model-driven DSS will be more complex, yet understandable, and systems built using simulations and
their accompanying visual displays will be increasingly realistic. Communications-driven DSS will
provide more real-time video communications support. Document-driven DSS will access larger
repositories of unstructured data and the systems will present appropriate documents in more useable
formats. Finally, knowledge-driven DSS will likely be more sophisticated and more comprehensive. The
advice from knowledge-driven DSS will be better and the applications will cover broader domains.

COMPONENTS OF DSS

DSS include a database of data used for query and analysis, a software system with models, a user
interface and other analytical tools.

 DSS Database : It is a huge data warehouse that contains a subset of corporate data that has been
combined with external data. It is a collection of current or historical data from a number of
applications or groups. The data in DSS databases are generally extracts or copies of production
databases.

 DSS Software system : It contains the software tools that are used for data analysis. It contain
various OLAP tools, data mining tools, a collection of mathematical and analytical models. A
model is an abstract representation that depicts the relationship of a phenomenon. The most
common models used in DSS are Statistical model, Optimization model, Forecasting model,
Sensitivity analysis model.

(a) Statistical Model – This model has library which contain statistical functions like mean,
median, deviations and scatter plots. This model has the ability to project future outcomes by
analyzing a series of data. Example – Finding impact of differences in age, income and other
miscellaneous factors on product sales.
(b) Optimization Model – It uses linear programming, transportation algorithms to determine
optimal resources allocation to maximize or minimize specified variables such as cost and
time. Example – To determine the proper mix of products within a given market to maximize
profits.
(c) Forecasting Model – They are generally used by organizations to predict the actions of their
competitors. These models use historical data to project future conditions and the sales that
might result from these conditions.
(d) Sensitivity Model – It uses ‘What-If” analysis to determine the impact of changes in one or
more factors on outcomes. Example – “What happens if” we raise the price by 5% or
increase the advertising budget by $100,000?

 User Interface : DSS user interface permits easy interaction between users of the system and the
DSS software tools. Today, DSS are built with Web-based interfaces to take advantage of the
Web’s ease of use, interactivity and capabilities of customization.
 Users : They are managers who have to take short-term decisions for the organizational run.

BENEFITS/ADVANTAGES OF DSS

1. DSS improves managerial effectiveness and provides extensive range of support to management.
2. Expedites problem solving (speed up the progress of problems solving in an organization)
3. Encourages exploration and discovery on the part of the decision maker.
4. Reveals new approaches to thinking about the problem space.

5. Provides a detailed quantitative analysis in very short time.
6. Facilitates quicker analysis of variances to anticipate outcomes with the help of efficient and ad-
hoc query facilities.
7. Facilitates the faster analysis of unstructured decision-making which improves the response in
unexpected decision-making situation.

DISADVANTAGES OF DSS

1. It cannot replace human decision-making such as creativity, intuition & imagination.
2. Languages and command interface are not sophisticated enough to allow for natural language
processing.
3. Their general design prevents their generalize use to multiple decision-making process.

GENERAL USES OF DSS IN ORGANIZATION

1. Ad hoc data retrieval

With the use of computer and application software, DSSs help the decision-maker to reduce the
workload of handling a large amount of data, while improving efficiency and accuracy. The basic
functionality of DSSs is to provide the decision-maker with the ability to retrieve information
selectively on an ad hoc basis. DSSs have the capability to retrieve data selectively, as well as to
aggregate and summarize data.

2. Information presentation

DSS often present computational results in a variety of formats. The formats include traditional
ones like tables and graphics as well as new patterns like animation, audio, and video. By means
of the visual aids, users can recognize the relationship of a collection of data and understand some
complicated results more easily.

3. Multiple decision aids

DSSs provide interactive decision aids that combine data retrieval, stylized displays, and model-
based processing to satisfy some particular decision needs. For example, a decision aid can help a
decision-maker to choose from many alternative solutions that might come from different
databases, which are to be drawn out under different control conditions.

ANALYTICAL MODELLING ACTIVITIES OF DSS

Using a DSS involve 4 basic types of analytical modeling activities :-

1. What-If Analysis - An end user makes changes to variables, or relationships among variables,
and observes the resulting changes in the values of other variable. Example : What if we cut
advertising by 20% - What would happen to sales? What if we change a revenue amount or a tax
rate formula –What would happen to Net profit after Taxes?

2. Sensitivity Analysis – It is special type of ‘What If’ analysis involving repeated changes to only
one variable at a time. In this, value of only one variable is changed repeatedly, and the resulting
changes on other variables are observed. Example : Cut advertising by Rs. 100 repeatedly, and
see its relationship to sales.

3. Goal-Seeking Analysis - It reverses the direction of the analysis done in ‘What-If’ and
‘Sensitivity’ analysis. In this, target value (goal) is set up for a variable and then repeatedly
change other variables until the target value is achieved. Example : Let the target value is Rs. 2
crore for net profit after taxes for a small business. Now, one can repeatedly change the value of
revenue or expenses in a spreadsheet model until the goal of Rs. 2 crores is achieved. So, this
form of analytical modeling helps to answer the question, “How can we achieve Rs. 2 crore in net
profit after tax?” Thus, goal-seeking analysis is another important method of decision-support.

4. Optimization Analysis – It is a more complex extension of ‘Goal-Seeking’ analysis. In this,
rather than setting target value for a variable, the goal is to find the optimum value for one or
more target variables, given certain constraints. Then one or more other variables are changed
repeatedly, subject to the specific constraints, until the best values for the target values are
achieved. Example : What is the best amount of advertising to have, given our budget and choice
of media? What is the highest possible level of profit that could be achieved by varying the values
for selected revenue source and expense categories. For optimization analysis, the commonly
used software is the solver tool of MS-Excel.

DIFFERENCE BETWEEN MIS & DSS

Management Information Systems Decision Support Systems
Provide information about the performance of Provide information and decision support
the organization techniques to analyze specific problems or
opportunities
Information is in Prespecified & fixed format Information format is in Ad-hoc, flexible and
adaptable format
Information is produced by extraction and Information is produced by analytical modeling
manipulation of business data of business data
Emphasis is on data storage Emphasis is on data manipulation
Focus is on structured tasks and routine Focus is on semi / unstructured tasks, which
decisions require managerial judgment
Emphasis is on efficiency Emphasis is on effectiveness
Presentation is in form of reports Presentation is in form of graphics

Mis

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