MANAGEMENT INFORMATION

SYSTEMS –KMBN-208
UNIT -II
Information, Management and Decision Making -
Attributes of information and its relevance to
Decision Making, Types of information. Models of
Decision Making - Classical, Administrative and
Herbert Simon's Models. Management Support
Systems: Decision Support Systems, Group
Decision Support Systems, and Executive
Information Systems.
 INFORMATION, MANAGEMENT &
DECISION MAKING
INTRODUCTION

▪ Information is needed to survive in the modern

competitive world. Information is needed to create
strong information systems and keep these systems up
to date.
▪ Information is a vital resource for the success of any
organization. Future of an organization lies in using and
disseminating information wisely.
▪ Good quality information placed in right context in
right time tells us about opportunities and problems
well in advance. Good quality information- Quality is a
value that would vary according to the users and uses of
the information.
 ATTRIBUTES OF INFORMATION

▪1. Accuracy

What it means: Information must be correct and free

from errors.
Relevance: Inaccurate information can lead to bad
decisions.
Example: A company deciding on investment options
needs precise financial reports; if profits are
overstated, they might invest recklessly.
2. Completeness
What it means: All necessary information should
be available — nothing critical should be missing.
Relevance: Incomplete information can cause
wrong or delayed decisions.
Example: A doctor diagnosing a patient needs a
full medical history. Missing allergy information
could risk the patient's life.

3. Timeliness
What it means: Information should be available
when needed.
Relevance: Outdated information can lead to
irrelevant or wrong choices.
Example: A stock trader needs up-to-the-minute
stock prices. Acting on yesterday’s prices could
lead to huge losses.
4. Relevance
What it means: Information should directly relate
to the decision being made.
Relevance: Irrelevant information distracts or
misleads decision-makers.
Example: When hiring, information about a
candidate’s hobbies may not be as relevant as their
job experience and skills.

5. Understandability
What it means: Information should be easy to
comprehend.
Relevance: Complex, confusing data can cause
misunderstandings.
Example: A company dashboard that presents
performance data with clear graphs makes it easier
for managers to act quickly.
6. Consistency
What it means: Information should be
consistent over time and across sources.
Relevance: Contradictory information confuses
decision-makers.
Example: If two reports give different figures for
the same sales quarter, managers won’t know
which to trust.

7. Verifiability
What it means: Information should be backed
up with evidence or sources.
Relevance: Decisions based on unverifiable
information are risky.
Example: Before approving a supplier, a
company checks references and certifications to
verify their credibility.
8. Value (Cost-effectiveness)
What it means: The benefit of having the
information should outweigh the cost of obtaining
it.
Relevance: Spending too much on gathering
information for a minor decision is wasteful.
Example: A small shop doesn't need an expensive
market analysis for deciding which soft drink brand
to stock.

Why These Attributes Matter in Decision

Making
Good decisions minimize risks and maximize
opportunities.
Each attribute ensures that decision-makers trust
the information.
Poor information leads to inefficient, costly, or
even dangerous outcomes.
CLASSIFICATION OF INFORMATION
Action & Non Action
Information
Action Information: The information which
induces action is called an action information. For
Example:- when the attendance of the student for a
particular subject suddenly falls down 40% calls for
immediate action.

▪ Non-Action Information: Non-Action Information

which is communicates only the status of a
situation is a no action information. for Example:-
While watching the live cricket match you
understand that India's Current Run rate is 4 per
over whereas its required run rate is 7 Per over. You
have this information but this is non-action
information.
Recurring & Non Recurring
Information
Recurring Information: The Information
that is generated at regular information. For
Example:-The monthly Sales reports, Account
statements.

▪ Non-Recurring Information: Non

Repetitive in nature. For Example: The
financial analysis or the report on the market
research.
Recurring Information: The Information that is
generated at regular information. For Example:-The
monthly Sales reports, Account statements.

▪ Non-Recurring Information: Non Repetitive in

nature. For Example: The financial analysis or the
report on the market research.

▪ Internal Information: The information is

generated through the internal sources of the
organization.

▪ External Information: The Information is

Generated through the External sources of the
organization.
 Classification by Application
In terms of applications, information can be
categorized as −
▪ Planning Information −
Planning Information: These are the information needed for
establishing standard norms and specifications in an
organization. This information is used in strategic, tactical,
and operation planning of any activity. Examples of such
information are time standards, design standards.
Control Information: This information is needed for
establishing control over all business activities through
feedback mechanism. This information is used for controlling
attainment, nature and utilization of important processes in a
system. When such information reflects a deviation from the
established standards, the system should induce a decision or
an action leading to control.
Knowledge Information: Knowledge is defined as
"information about information". Knowledge information is
acquired through experience and learning, and collected from
archival data and research studies.
Organizational Information: Organizational information
deals with an organization's environment, culture in the light
of its objectives. Karl Weick's Organizational Information
Theory emphasizes that an organization reduces its
equivocality or uncertainty by collecting, managing and using
these information prudently. This information is used by
everybody in the organization; examples of such information
are employee and payroll information.
Functional/Operational Information: This is operation
specific information. For example, daily
schedules in a manufacturing plant that refers to the detailed
assignment of jobs to machines or machines to operators. In a
service oriented business, it would be the duty roster of
various personnel. This information is mostly internal to the
organization.
 Database Information: Database information construes large
quantities of information that has multiple usage and
application. Such information is stored, retrieved and managed
to create databases. For example, material specification or
supplier information is stored for multiple users.
Information is a vital resource for the success of any
organization. Future of an organization lies in using and
disseminating information wisely. Good quality information
placed in right context in right time tells us about opportunities
and problems well in advance.
Types of Information
The words Data and Information may look similar and many people use
these words very frequently, but both have lots of differences between
them.
Data are plain facts. The word "data" is plural for "datum." When data
are processed, organized, structured or presented in a given context so
as to make them useful, they are called Information.
Information is data that has been processed in such a way as to be
meaningful to the person who receives it. it is anything that is
communicated.
Data is the term, that may be new to beginners, but it is very
interesting and simple to understand. It can be anything like name of a
person or a place or a number etc. Data is the name given to basic facts
and entities such as names and numbers. The main examples of data
are weights, prices, costs, numbers of items sold, employee names,
product names, addresses, tax codes, registration marks etc.
Data is the raw material that can be processed by
any computing machine. Data can be represented
in the form of:
Numbers and words which can be stored in
computer's language, Images, sounds, multimedia
and animated data as shown.
Types of Information
Based on Anthony's classification of
Management, information used in business for
decision-making is generally categorized into
three types −
▪ Strategic Information − Strategic information
is concerned with long term policy decisions that
defines the objectives of a business and checks
how well these objectives are met.
▪ For example, acquiring a new plant, a new
product, diversification of business etc., comes
under strategic information.
▪ Tactical Information − Tactical
information is concerned with the information
needed for exercising control over business
resources, like budgeting, quality control,
service level, inventory level, productivity
level etc.
▪ Operational Information − Operational
information is concerned with plant/business
level information and is used to ensure
proper conduction of specific operational
tasks as planned/intended. Various operator
specific, machine specific and shift specific
jobs for quality control checks comes under
this category.
 Decision Making
Decision-making is a cognitive process
that results in the selection of a course of
action among several alternative scenarios.
▪ Decision-making is a daily activity for
any human being. There is no exception
about that. When it comes to business
organizations, decision-making is a habit
and a process as well.
▪ Effective and successful decisions result in
profits, while unsuccessful ones cause
losses. Therefore, corporate
decision-making is the most critical
process in any organization.
 Decision Making Models
The Classical Model
The classical model of decision-making is a
rational and systematic approach to making
choices, commonly used in Management
Information Systems (MIS) and decision
sciences. It assumes that decision-makers are
fully informed, rational, and seek to
maximize utility.

Classical Model of Decision-Making:

Key Steps:
Problem Identification
Information Gathering
Generation of Alternatives
Evaluation of Alternatives
Choice of Best Alternative
Implementation
Monitoring and Feedback

Characteristics of the Classical Model

Assumes complete information
Emphasizes logic and objectivity
Ideal for structured problems
Often supported by MIS tools (like DSS, ERP
systems)
Example in MIS Context: Inventory
Management
Scenario: A retail manager wants to optimize
inventory levels using a Decision Support
System (DSS) within an MIS.
1. Problem IdentificationThe store is either
overstocked (causing high storage costs) or
understocked (missing sales).
2. Information GatheringThe MIS gathers
data on sales trends, seasonal demand,
supplier lead times, and storage capacity.
3. Generation of AlternativesPossible
inventory strategies are created (e.g., reorder
when stock reaches 50, 75, or 100 units).
4. Evaluation of AlternativesEach strategy
is simulated in the system to estimate cost
and service level.
5. Choice of Best AlternativeThe strategy
that balances cost and service level best is
selected.
6. ImplementationThe new reorder policy is
input into the automated inventory system.
7. Monitoring and FeedbackSales and
inventory reports are regularly reviewed for
performance.
 2. Hiring Decision (Using HRIS – Human
Resource Information System)
Problem Identification Company needs to
hire a new software engineer.

Information Gathering HRIS pulls data from

candidate profiles, test scores, interview
feedback, and past hiring trends.

Generate Alternatives Final shortlist

includes 3 candidates.
Evaluate Alternatives Each candidate is
scored based on skills match, cultural fit, and
compensation expectations.

Choose Best Alternative Candidate B has

the best score.

Implementation Offer letter is generated

and sent through HRIS.

Monitoring & Feedback Performance is

tracked over probation period to assess
hiring quality.
The Administrative Model
Administrative Model, also known as the Behavioral
Model, was developed as a response to the
limitations of the Classical Model. It recognizes that
decision-making often occurs in uncertain and
complex environments, leading to a more realistic
approach.
Features of the Administrative Model:
Bounded Rationality:
Decision-makers operate within the constraints of
limited information, cognitive limitations, and time
pressures. They cannot consider every possible
alternative, so they settle for satisfactory solutions
rather than optimal ones.
Satisficing:
Instead of maximizing utility, decision-makers look for
alternatives that are "good enough." This involves choosing
the first satisfactory option that meets acceptable criteria.

Incremental Decision-Making:
Decisions are often made in small steps, with adjustments
based on feedback and changing circumstances.
This approach allows organizations to remain flexible and
adapt to new information.

Focus on Organizational Politics:

The Administrative Model recognizes that decision-making
often involves negotiation and influence among various
stakeholders within an organization.
This model reflects the reality of decision-making in
organizations, where uncertainty and bounded rationality
play significant roles.
Example in MIS Context: Software Vendor
Selection

Problem Identification
A small company needs a new CRM software
but lacks IT expertise.
Limited Information Search
MIS collects only basic info from a few
well-known vendors due to time and budget
constraints.
Generation of Alternatives
Only 2–3 options are considered (e.g., Zoho,
Salesforce, HubSpot).
Satisficing Choice
The company chooses Zoho—not the most
powerful, but easiest to use and affordable.
Implementation
The CRM is rolled out with limited
customization.
Feedback and Adjustment
Minor adjustments are made based on user
feedback over time.
 Common CRM Process Stages
These CRMs generally follow a similar core
process:
Lead Capture – Getting contact info from
prospects.
Lead Management – Qualifying and nurturing
leads.
Deal Management – Moving qualified leads into
opportunities/deals.
Contact & Account Management – Tracking
customer data.
Sales Automation – Automating repetitive
tasks.
Analytics & Reporting – Measuring
performance.
Post-Sales Support – Managing customer
 Zoho CRM Process
1. Lead Capture
Captures leads via web forms, email, social
media, or manual entry.
Lead assignment rules and scoring.
2. Lead Management
Leads can be automatically assigned to reps.
Workflow automation for follow-ups and lead
nurturing.
3. Deal (Opportunity) Management
Convert leads to contacts, accounts, and deals.
Customizable pipeline stages.
Blueprint feature to guide reps through sales
stages.
4. Contact & Account Management
Detailed views of customer interactions.
Integration with email and telephony.
5. Automation
Rules-based workflows, macros, and custom
functions.
Zoho CommandCenter for orchestrated
customer journeys.
6. Analytics
Advanced dashboards, KPIs, and forecasting
tools.
7. Support Integration
Integrated with Zoho Desk for customer
support.
 Sales force CRM Process
1. Lead Capture
Web-to-lead forms, integrations, and
third-party tools.
Einstein Lead Scoring for AI prioritization.
2. Lead Management
Campaign tracking, auto-assignment, and
nurturing workflows.
Integration with Pardot/Marketing Cloud for
marketing automation.
3. Opportunity Management
Convert leads to contacts, accounts, and
opportunities.
Customizable sales stages with AI insights.
4. Contact & Account Management
360-degree customer view.
Timeline and activity history.
5. Automation
Process Builder and Flow for complex
automation.
Apex for custom code-based automation.
6. Reporting & Analytics
Real-time dashboards and reports.
Salesforce Einstein for AI-powered insights.
7. Service Integration
Connected with Service Cloud for customer
support.
 HubSpot CRM Process
1. Lead Capture
Forms, chatbots, landing pages, and social
media.
CRM auto-populates contact info from emails
and interactions.
2. Lead Management
Lead scoring via workflows.
Marketing and sales alignment through
shared timeline.
3. Deal Management
Deals are managed via visual pipelines
(drag-and-drop).
Multiple pipelines supported.
4. Contact & Company Management
Automatic enrichment of contact/company records.
Unified timeline view.
5. Automation
Workflow automation for emails, task creation, and
deal progression.
Sequences for sales follow-ups.
6. Reporting
Basic reporting is free; custom reports in paid tiers.
Revenue attribution and sales performance
analytics.
7. Customer Service
Ties into HubSpot Service Hub for support tickets,
knowledge base, etc.
Herbert Simon’s Model of
Decision-Making

This is one of the foundational theories in

Management Information Systems (MIS) and
organizational behavior. He proposed that
decision-making is a step-by-step process
involving bounded rationality, and he
broke it down into three key phases:
Herbert Simon’s Decision-Making Model
(IDC Model)
Intelligence Phase
Identify and understand the problem or
opportunity.
Design Phase
Develop possible solutions or alternatives.
Choice Phase
Select the most appropriate course of action.
Later, some versions also add a 4th phase:
Implementation.
 Example in MIS Context: Improving Customer
Support Efficiency Using a Helpdesk System
1. Intelligence MIS reports show that customer
support response time is too slow. Management
reviews logs, feedback forms, and service KPIs.

2. DesignThe team evaluates possible solutions: A)

Hire more agents, B) Implement a ticketing system, C)
Add chatbots. MIS tools (dashboards, simulations) are
used to model impacts.

3. ChoiceAfter reviewing cost, speed, and scalability,

management chooses to implement a helpdesk
ticketing system with automated workflows.

4. Implementation (optional)The system is

deployed. MIS tracks the new response times and
customer satisfaction. Adjustments are made as
needed.
Why It’s Important in MIS
Emphasizes rational process even under
constraints.
Matches the structure of many MIS tools like
Decision Support Systems (DSS) and Expert
Systems.
Bridges both data analysis and practical
decision-making.
Summary Table
Example Intelligence- Reports, data mining, trend
analysis
Design- Modeling tools, simulations, what-if
analysis
Choice- Ranking tools, dashboards, decision
matrices Implementation Workflow systems,
automation, monitoring
 Management Support Systems, Functions,
Components, Benefits, Challenges
Management Support Systems (MSS) are
computer-based systems that provide managers
with tools to organize, evaluate, and efficiently
manage departments within an organization.
These systems facilitate strategic planning,
operational control, and other managerial
functions by delivering pertinent information in a
timely manner. Essentially, MSS help
decision-makers make informed choices by
integrating data from various sources and
streamlining the decision-making process. They
combine features of decision support systems,
executive information systems, and
knowledge-based or expert systems.
 Functions of Management Support
Systems:
Decision Support:
MSS provide tools and analytical capabilities to
support complex decision-making processes. They
offer simulations, what-if analyses, and decision
models that help managers evaluate different
scenarios and make informed choices.

Information Storage and Retrieval:

MSS collect, store, and manage vast amounts of
data from various sources. They provide managers
with easy access to relevant information, ensuring
that data is organized, up-to-date, and readily
available for decision-making.
Reporting and Communication:
MSS generate detailed reports and dashboards that
summarize key performance indicators (KPIs), metrics, and
trends. These systems facilitate communication by
presenting data in a clear, concise format, enabling
managers to share insights and updates with stakeholders
effectively.
Strategic Planning:
MSS support long-term planning by providing tools for
forecasting, trend analysis, and market research. They help
managers to identify opportunities, set strategic goals, and
develop plans to achieve these objectives. ensuring
alignment with the organization's vision and mission.
Performance Monitoring and Control:
MSS enable managers to monitor organizational
performance in real-time, track progress towards goals,
and identify deviations from plans. They provide alerts and
feedback mechanisms that help in taking corrective actions
and ensuring that operations stay on course.
 Benefits of Management Support Systems:

Improved Decision-Making:
MSS provide accurate, relevant, and timely information,
enabling managers to make informed decisions. The systems
offer analytical tools and models that help evaluate different
scenarios, reducing uncertainty and enhancing the quality of
decisions.

Enhanced Efficiency and Productivity:

By automating data collection, processing, and reporting,
MSS streamline operations and reduce the time and effort
required for routine tasks. This allows managers to focus on
strategic activities, improving overall productivity.

Better Resource Management:

MSS facilitate optimal resource allocation by providing
insights into resource utilization and requirements. Managers
can plan and allocate financial, human, and physical
resources more effectively, ensuring efficient use and
minimizing waste.
There are three types of management support
systems, namely:
a) Decision Support Systems,
b) Executive Information (support) Systems and
c) Expert Systems.

Decision Support Systems

A decision support system (DSS) is a computerized

information system used to support decision-making
in an organization or a business. A DSS lets users sift
through and analyze massive reams of data and
compile information that can be used to solve
problems and make better decisions.

The benefits of decision support systems include more

informed decision-making, timely problem solving and
improved efficiency for dealing with problems with
rapidly changing variables.
Components of a DSS
Database Management System (DBMS): To solve
a problem the necessary data may come from internal
or external database. In an organization, internal data
are generated by a system such as TPS and MIS.
External data come from a variety of sources such as
newspapers, online data services, databases
(financial, marketing, human resources).
Model Management System: It stores and accesses
models that managers use to make decisions. Such
models are used for designing manufacturing facility,
analyzing the financial health of an organization,
forecasting demand of a product or service, etc.
Support Tools: Support tools like online help; pulls
down menus, user interfaces, graphical analysis, error
correction mechanism, facilitates the user interactions
with the system.
Elements of DSS
DSS Database: It contains data from various
sources, including internal data from the
organization, the data generated by different
applications, and the external data mined
form the Internet, etc. The decision support
systems database can be a small database or
a standalone system or a huge data
warehouse supporting the information needs
of an organization. To avoid the interference
of decision support system with the working
of operational systems, the DSS database
usually contains a copy of the production
database.
2. Model base
Some of the commonly used mathematical and
statistical models are as follows:-
▪Statistical Models:
▪Sensitivity Analysis Models:
▪Optimization Analysis Models
2.1 Statistical Models: They contain a wide range of
statistical functions, such as mean, median, mode,
deviations etc. These models are used to establish,
relationships between the occurrences of an event
and various factors related to that event.
It can, for example, relate sale of product to
differences in area, income, season, or other factors.
In addition to statistical functions, they contain
software that can analyze series of data to project
future outcomes.
2.2 Sensitivity Analysis Models
These are used to provide answers to what-if
situations occurring frequently in an organization.
During the analysis, the value of one variable is
changed repeatedly and resulting changes on other
variables are observed.
The sale of product, for example, is affected by
different factors such as price, expenses on
advertisements, number of sales staff, productions
etc. Using a sensitivity model, price of the product
can be changed (increased or decreased) repeatedly
to ascertain the sensitivity of different factors and
their effect on sales volume.
2.3 Optimization Analysis
Models
They are used to find optimum value for a target
variable under given circumstances. They are widely
used for making decisions related to optimum
utilization of resources in an organization. During
optimization analysis, the values for one or more
variables are changed repeatedly keeping in mind the
specific constraints, until the best values for target
variable are found.
They can, for example, determine the highest level of
production that can be achieved by varying job
assignments to workers, keeping in mind that some
workers are skilled and their job assignment cannot
be changed. Linear programming techniques and
Solver tool in Microsoft excel are mostly used for
making such analysis.
 Group Decision Support System (GDSS)
It is a type of interactive computer-based system that
facilitates the solution of unstructured problems by a set of
decision-makers working together as a group. It is a
subcategory of Management Information Systems (MIS)
designed to support meetings and group work.
🔍 Definition:
GDSS provides tools and technologies to help groups
communicate, collaborate, and make decisions. It typically
includes features such as idea generation, voting, ranking,
and electronic brainstorming.
✅ Key Features of GDSS:
Anonymity: Encourages honest input by allowing
anonymous feedback.
Parallel Communication: Multiple participants can input
data simultaneously.
Automated Record Keeping: Captures meeting content
and decisions automatically.
Decision-Making Tools: Voting, ranking, and scenario
analysis tools.
 Example of GDSS in MIS:
Scenario: A company wants to decide the best
location for opening a new branch office.
GDSS Used: A tool like GroupSystems ThinkTank
or Microsoft Teams integrated with decision
support plugins.
Process:
A team of regional managers joins a virtual session
using GDSS software.
Each member anonymously submits potential
locations and reasons.
The group discusses pros and cons using a shared
workspace.
Members use the system to vote or rank the
suggested locations.
The system aggregates the results and presents the
most preferred option.
A decision is made based on group input, supported
by system-generated analytics.
Benefits:
Speeds up decision-making.
Reduces influence of dominant participants.
Supports geographically dispersed teams.
Enhances collaboration and documentation.
📘 Real-world Example:
Company: IBM
Use Case: IBM has used GDSS tools to gather
global input from teams when making
strategic IT infrastructure decisions, enabling
real-time collaboration across continents.
 EIS (Executive Information System)
An Executive Information System (EIS) is a type
of Management Information System (MIS)
designed specifically to support the information
needs of senior executives. It provides easy access
to both internal and external information relevant
to organizational goals and strategic
decision-making.
✅ Definition
An Executive Information System (EIS) is a
specialized decision support system tailored for
top-level management to facilitate strategic
planning by offering quick and easy access to
summarized reports and key performance
indicators (KPIs).
 Key Features of EIS
User-friendly interface
Real-time access to critical data
Data from multiple sources (internal &
external)
Drill-down capabilities (from summary to detail)
Trend analysis and forecasting
Visualization tools like dashboards, graphs, and
charts
🧠 Purpose
Helps executives monitor organizational
performance
Supports strategic decision-making
Provides early warning systems for potential
problems
Tracks key performance indicators (KPIs) and
goals
Example
Scenario: EIS in a Retail Chain (e.g.,
Walmart)
User: CEO or Regional Director
Dashboard Overview:
◦ Total sales (daily, weekly, monthly)
◦ Regional performance comparison
◦ Inventory turnover rates
◦ Customer satisfaction trends
◦ Supplier performance
◦ Profit margins by category

Use Case:
The CEO logs into the EIS dashboard and sees
that sales in the Northeast region are
dropping. With a few clicks, they drill down to
find that a key store is underperforming due to
supply chain delays. This enables the CEO to
initiate corrective action quickly.
 Expert System
An Expert System in Management
Information Systems (MIS) is a type of
computer program that simulates the
decision-making ability of a human expert. It
uses knowledge and inference procedures to
solve complex problems that typically require
human expertise.
✅ Definition:
An Expert System (ES) is a knowledge-based
information system that uses its knowledge
about a specific domain to deliver advice or
make decisions.
 Components of an Expert System:

Knowledge Base: Contains domain-specific

facts and rules.
Inference Engine: Applies logical rules to
the knowledge base to deduce new
information or solve problems.
User Interface: Allows users to interact with
the system.
Explanation Facility: Explains the reasoning
process to the user.
Knowledge Acquisition Facility: Helps
update or expand the knowledge base.
 Example in MIS:
Expert System in Loan Approval in Banking:
A bank might use an expert system to assist loan
officers in determining whether to approve a
loan.
Knowledge Base: Contains rules like:
◦ IF credit score > 750 AND income > $50,000 THEN
approve loan.
◦ IF credit score < 600 THEN reject loan.
Inference Engine: Evaluates a customer's
financial data against rules.
User Interface: Loan officer inputs customer
data.
Output: The system gives a recommendation:
“Loan Approved” or “Loan Rejected” with reasons.
 Other Real-World Examples:
MYCIN – Medical diagnosis system.
DENDRAL – Analyzes chemical compounds.
XCON (by Digital Equipment Corp) –
Configures computer systems.
Role in MIS:
Expert systems enhance decision-making in areas
like:
Strategic planning
Resource allocation
Financial analysis
Inventory management
Human resource planning
They complement MIS by adding expert-level
problem-solving capabilities, especially in
Decision Support Systems (DSS).