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DECISION SUPPORT SYSTEMS
SEMINAR BY
CLINCY CLEETUS
I.M.COM.
SCHOOL OF
BUSINESS MANAGEMENT
AND LEGAL STUDIES,
KARIAVATTOM CAMPUS
CONCEPT OF DECISION
MAKING
• Decision making has been taken from the word DECIDE which
means TO CUT OFF or TO COME TO A CONCLUSION.
• Decision may be regarded as a CHOICE whereby a decision
maker comes to a conclusion about a given situation.
• Decision making on the other hand, is a process of selecting
one optimum alternative from among alternatives of a course
of action.
• It involves the entire process of establishing goals, searching
for alternatives and developing plans.
• It includes all the activities of coordinating, information
processing, problem solving and evaluating that usually
precede a decision
DECISION MAKING PROCESS
• Decision making, in organizations is regarded as a rational
process.
• HERBERT A. SIMON has given a model to describe the
decision making process.
• The model comprise of three major phases namely,
A. INTELLIGENCE PHASE
B. DESIGN PHASE
C. CHOICE PHASE
A. INTELLIGENCE PHASE
In this phase , the decision maker scans the environment and
identifies the problem or opportunity
B. DESIGN PHASE
• The decision maker finds alternative course of action to solve
the problem.
• Inventing or developing various alternatives is a time
consuming and crucial activity as the decision maker has to
explore all possible alternatives and cannot take a risk of
missing any alternative, as the missed out one be the best
one.
• Developing alternatives is a creative activity which can be
enhanced by various aids such as brain storming, checklists,
analogies etc….
C. CHOICE PHASE
• At this stage, one of the alternatives developed in the design
phase is called a decision.
• One among the various alternatives formulated is selected.
• For selecting an alternative , a detailed analysis is made.
• Having made the decision, it is implemented.
However at any phase, the decision maker may
return to a previous phase. For example, the decision maker
in the choice phase may reject all alternatives and return to
the design phase for developing more alternatives.
TYPES OF
DECISIONS
PURPOSE OF DECISION
MAKING
•STRATEGIC PLANNING
•MANAGEMENT CONTROL
•OPERATIONAL CONTROL
LEVEL OF
PROGRAMMABILITY
•PROGRAMMED/STRUCTURED
•NON-PROGRAMMED/
UNSTRUCTURED
•SEMI –STRUCTURED
TYPES OF DECISIONS
Organizational decisions differ in a
number of ways. These differences affect the development of
alternatives and the choice among them. They also affect the
design of information system support for decision
alternatives.
I. ON THE BASIS OF PURPOSE
OF DECISION MAKING
ROBERT B. ANTHONY(1965) has differentiated
organizational decisions into three categories, they are:
1. STRATEGIC PLANNING DECISIONS: are those
decisions in which the decision maker develops objectives and
allocates resources to achieve these objectives.
• Decisions in this category are of long period and usually
involve a large investment and effort.
• Such decisions are taken by strategic planning level or top
level managers.
• Eg;- introduction of a new product, acquisition of another firm
etc…………….
2. MANAGEMENT CONTROL DECISIONS: are taken
by middle level managers and deal with the use of resources
in the organization.
• Analysis of variance, product mix, planning decisions, fall in
this category.
3. OPERATIONAL CONTROL DECISIONS : deal with
the day to day problems that affect the operation of the
organization.
• This type of decisions are normally taken by the managers
at the operational or bottom level of the management
hierarchy in the organization.
• For example, production scheduling decisions like the product
to be produced for the day or items and quantities to be
ordered are operational control decisions.
II. ON THE BASIS OF LEVEL OF PROGRAMMABILITY
Simon (1965) proposed two types of decisions programmed and non-
programmed decisions, also known as structured and unstructured decisions
(Gorry and Scott morton,1971)
1.PROGRAMMED/ STRUCTURED DECISIONS
Well defined and some specified procedure or some decision rule may be
applied to reach a decision.
These decisions are routine and repetitive and require little time for
developing alternatives in the design phase.
Decisions of this kind can be delegated to lower levels in an organization
These decisions are traditionally been made through habit, by operating
procedures or with other accepted tools.
Modern techniques for making such decisions involve operations
research, mathematical analysis, modeling and simulation etc….
2. NON-PROGRAMMED / UNSTRUCTURED DECISIONS
These decisions are not well defined and have no pre-
specified procedure or decision rule.
These decisions are novel ones, which may range from one
timed decisions relating to a crisis to decisions relating to
recurring problems where conditions change so frequently and
to such an extend that decision rules cannot be specified.
Sufficient time has to be spend in the design phase of these
decisions.
Decisions of these kind are usually handled by strategic
planning level managers.
Unstructured decisions tend to be solved through the
judgment, intuition and the rule of the thumb.
3.SEMI-STRUCTURED DECISIONS
Fall some where between the structured and
unstructured continuum.
The decisions of this category fall somewhere
between structured decisions, which are routine and
unstructured decisions which are unique and non-
repetitive.
Modern approaches to such decisions
include special data analysis on computers,
heuristic techniques etc…….
Cannot be delegated to lower levels.
 Eg: Introduction of a new product, planning
for R and D.
Meaning of decision support systems
The term DSS refers to a class of systems which support the
process of making decisions. The emphasis is on SUPPORT rather than on
automation of decisions . DSS allows the decision maker to retrieve data and test
alternative solutions during the process of problem solving.
DEFINITIONS
In 1984, Freyenfeld proposed the following empirical definition
of DSS based on discussions with some 30 supplier, user and academic
organizations.
A DECISION SUPPORT SYSTEM IS AN INTERACTIVE DATA
PROCESSING AND DISPLAY SYSTEM WHICH IS USED TO ASSIT IN A
CONCURRENT DECISION MAKING PROCESS, AND ALSO CONFORMS TO THE
FOLLOWING CHARACTERISTICS:
i) IT IS SUFFICIENTLY USER-FRIENDLY TO BE USED BY THE DECISION
MAKER(S) IN PERSON
ii) IT DISPLAYS ITS INFORMATION IN A FORMAT AND TERMINOLOGY WHICH
IS FAMILIAR TO THE USER(S) AND
iii) IT IS SELECTIVE IN ITS PROVISION FOR INFORMATION AND AVOIDS ITS
USER(S) IN INFORMATION OVERLOAD.
A SET OF WELL INTEGRATED, USER FRIENDLY, COMPUTER BASED TOOLS
THAT COMBINE DATA WITH VARIOUS DECISION MAKING MODELS-
QUANTITATIVE AND QUALITATIVE TO SOLVE SEMI-STRUCTURED AND
UNSTRUCTURED PROBLEMS.
DSS
EVOLUTION OF DSS
The notion of DSS as a formal concept was coined by G.
Anthony Gorry and Michael S.Scott Morton. They felt a
need for a framework to channel computer applications
towards management decision making and developed a
grid known as
THE GORRY AND SCOTT MORTON GRID. The grid is based
on Simon’s concept of programmed and non programmed
decisions and Robert N. Anthony’s management levels.
MANAGEMENT LEVELS
DEGREE
OF
PROBLEM
STRUCTURE
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Operational Control Management Control Strategic Planning
Structured
Semi Structured
Unstructured
Accounts
receivable
Order Entry
Inventory Control
Budget analysis-
engineered costs
Short term forecasting
Tanker Fleet mix
Warehouse and
Factory location
Production
scheduling
Cash
management
PERT/COST
systems
Variance analysis-
overall budget
Budget preparation
Sales and production
Mergers and
Acquisitions
New product
planning
R &D planning
CHARACTERISTICS OF DSS
The main characteristics of DSS are:
The computer must support the manager but not replace
his/her judgments .It should therefore, neither try to provide THE
ANSWER nor impose a predefined sequence of analysis
The main payoff of computer support is for semi-structured
problems, where parts of the analysis can be systemized for the
computer, but where the decision maker’s insight and judgements are
needed to control the process.
Effective problem solving is interactive and is enhanced by
a dialogue between the user and the system. The user
explores the problem situation using the analytic and
information providing capabilities of the system as well as
human experience and insights.
Decision support and repetitiveness of
decisions
• Highly repetitive decisions ( whether structured, semi structured or
unstructured) can frequently benefit from DSS.
• If decision process is basically the same each time, a model can be
tailored to fit the process, even for a single decision maker.
• Demonstrable benefits of such systems are faster decision making,
improved consistency and accuracy and improved methods for
analyzing and solving problems.
• On the other extreme, non repetitive, one-time decisions require
decision support of a very different nature.
• The primary requirement of systems to support one time decisions is
flexible access to a database and other forms of information such as
external databanks.
• Emphasis tends to be on the search phase of decision making, where
different kinds of data in different forms are needed for each decision
process.
OBJECTIVES OF DSS
Peter G.W.Keen and Michael S.Scott Morton(1978)
defined three objectives that DSS should achieve:
 Assist managers in making decisions to solve
semi-structured problems.
 Support manger’s judgement rather than to replace it .
 Improve the manager’s decision making effectiveness
rather than its efficiency.
Importance of DSS/WHY AN ORGANIZATION
NEEDS A DSS
1. Fast computation
2. Enhanced productivity
3. Data transmission
4. Better decisions
5. Competitive edge
CLASSIFICATION OF DSS
1. FILE DRAWER SYSTEMS
• These allow immediate access to data items.
• They are basically online mechanized versions of manual filing
systems.
• The user is non managerial line personnel.
Eg:- Status inquiries for inventory information
Account balance
Monitoring loads and capacities
2. DATA ANALYSIS SYSTEMS
• These allow manipulation of data by means of either analysis
operations tailored to the task and setting or general analysis
operations.
• They are typically used by non-managerial personnel to analyze
files containing current or historical data.
Eg:- Budget analysis system
Financial system for analyzing alternative investment
opportunities.
3. ANALYSIS INFORMATION SYSTEMS
• These provide access to a series of databases and small models. These are extensions of
data analysis systems.
Eg:- A marketing DSS containing internal sales data, promotion and pricing data
plus access to external databases.
4. ACCOUNTING MODELS
• These calculate the consequences of planned actions on the basis of accounting
definitions.
•They typically generate estimates of income, balance sheets etc….based on the variations
in input values to the definitional formulas.
Eg:- Monthly budgeting systems for operational decision making
Short term financial planning.
5. REPRESENTATIONAL MODELS
• These estimate the consequences of actions on the basis of models that represent some
non definitional characteristics of the system such as probabilities of occurrence.
• They include all simulation models that contain elements beyond accounting
definitions.
Eg:- Risk analysis model using estimated probability distributions for each of the
key factor.
6. OPTIMIZATION MODELS
• These provide guide lines for action by generating the optimal
solution consistent with a series of constraints.
•They are used for repetitive decisions that can be described
mathematically and where specific objective , such as minimizing cost ,
is the goal.
Eg:- system for scheduling training classes under a complex set of
constraints and a material usage optimization system.
7. SUGGESTION MODELS
• These compute a specific suggested decision for a fairly structured
and repetitive decision.
•Their purpose is to bypass other(less efficient) procedures for
generating the suggestion.
Eg:- Insurance renewal rate calculation system and a model price
cardboard boxes on a standard set of dimensions and decision rules.
COMPONENTS OF DECISION
SUPPORT SYSTEMS
There are three main software components of a DSS. They are:
1. DATA BASE MANAGEMENT SYSTEMS
The data in the decision support system database are
managed by the DBMS, which covers
 COMPILATION OF DATA
 MANIPULATION OF DATA
 DATA GENERATION
 DATA UPDATING
 DATA MAINTENANACE
 DISSEMINATION OF DATA
2. MODEL MANAGEMENT SYSTEMS
Model management systems stores and access models that managers use to make
decisions.
Such models are used for designing a manufacturing facility, analyzing financial health of
an organization, forecasting demand for a product or a service and determining the quality of
a particular batch of products.
Although most models are quantitative, decision makers use qualitative models also to
make decisions.
The model builder, a component of many model bases, provides a structured framework
for developing models by helping decision makers, identify the variables and the
interrelationships among the variables in the model.
A model builder creates, identifies, process, stores, updates and maintains different
decision making models and ensures that these models are consistently applied when
decisions are made.
The model builder also contains a model dictionary for consistency in the definitions and
uses of models.
Some of the models are:
•Statistical Models
•Production Models
•Marketing Models
•Financial and Accounting Models
•Strategic Models.
These models are extensively used in different
functional areas of a business.
3. SUPPORT TOOLS
 Support Tools ( Online help, Pull down menus, User
interfaces, Graphical analysis, Error correction mechanisms)
facilitate user’s interactions with the system.
 The better the interface , the greater are the chances that users will accept the
system.
 Although managers recognize the power and potential of DSS, the main problem to
its adoption is a lack of people with training in computer technologies. In such an
environment, good interfaces can make or break the system.
FUNCTIONS OF DECISION
SUPPORT SYSTEMS
There are five functions of decision supporting system
facilitating managerial decision making. They are:
1. MODEL BUILDING
Model building allows decision makers to identify appropriate model for
solving the problem at hand.
It takes into account input variables, interrelationships among the
variables, problem assumptions and constraints.
For example, a marketing manager of Videocon is charged with the
responsibility of developing a sales forecasting model for colour TV sets.
A model builder uses a structured framework to
identify variables like demand, cost and profit, analyze the relationships among
these variables, identify the assumptions, if any and identify the constraints viz,
the production capacity of the plant.
All this information are then integrated by a system
into a decision making model, which can be updated and modified whenever
required.
2. WHAT – IF ANALYSIS
The process of assessing the impact of changes to model variables, the values of
the variables, or the interrelationships among variables.
This helps managers to be proactive , rather than reactive, in their decision making
This analysis is critical for semi-structured and unstructured problems because
the data necessary to make such decisions are often either not available or
incomplete.
Hence , managers normally use their intuition and judgement in predicting the long
term implications of their decisions.
Spreadsheet packages such as Excel and Lotus 1-2-3, have WHAT-IF
applications.
3. GOAL SEEKING
The process of determining the input values required to achieve a
certain goal.
For example, house buyers determine the monthly payment they can afford (say rs.
7000) and calculate the number of such payments required to pay the desired
house.
4. RISK ANALYSIS
Allows managers to assess the risks associated with various alternatives.
Decisions can be classified as low risk, medium risk, and high risk.
A Decision Support System is particularly useful in
medium risk and high risk environments.
5. GRAPHICAL ANALYSIS
Helps managers to quickly digest large volumes of data and visualize
the impacts of various courses of action
First, lotus system enabled users to easily display and print information
in a graphic form.
Spreadsheets have sophisticated graphical analysis capabilities. Eg:-
Microsoft excel gives the user a wide choice of graphs and charts in
many colours and patterns.
DEVELOPMENT OF
DSS
 The development of a DSS is determined by the types of information and the
facilities needed for taking the decision.
 Support systems are developed using programming languages or produced
by packages specially for incorporating decision support development tools.
Programming languages used to develop DSS are:
c++
BASIC
SQL
Advantages of using programming languages are:
Application development is speedy.
Many are end user oriented.
Declarative rather than procedural.
The selection of a language depends on factors such as:
Availability of language and support for it.
Amount of manipulation Vs. presentation of data.
Need to document and maintain the program.
Frequency of use and number of users.
In general , there are three methods of developing a DSS:
1. DSS GENERATOR
2. DSS SHELLS
3. CUSTOM MADE SOFTWARE
1. DSS GENERATOR
 A DSS generator is comprised of programs such as data
management tools, electronic spreadsheets, report generators(user
friendly programs that allow decision makers to produce customized
reports), statistical packages, query languages and model building
tools, that help in the development of a DSS.
 Some known DSS generators are FOCUS, EXCEL and
LOTUS 1-2-3
2. DSS SHELLS
 A DSS Shell is used to build a customized DSS.
 Shells eliminate the need for developing the DBMS, model
management system and user interfaces, because skeletal versions of
these models are already available in the shell.
 A user can simply connect the shell with the appropriate models in
order to have a fully functional DSS.
 The main disadvantage of a shell is that it may have to be customized
to meet the needs of the decision maker or the user may have to adopt
the problem to the tool.
3. CUSTOM – MADE SOFTWARE
 It is designed and developed by an organization, who is committed to DSS
technology but cannot find a suitable generator or shell.
 The software is developed using a procedural language, such C or a 4GL, such as
FOCUS.
 Organizations may also choose to combine shells and customized software.
 The primary disadvantage of this approach is that the system may be expensive
and time consuming to develop and organizations may run into unexpected
bottlenecks and cost overruns.
SUPPORT FOR DIFFERENT PHASES
This section describes the types of information support
systems that can be provided for each phase of decision making.
SUPPORT FOR INTELLIGENCE PHASE
The database needed in this phase is very comprehensive. In general it should
cover the three environments:
1.Societal
2.Competitive
3.Internal
The concept of DSS does not imply that all the data is in computerized databases.
It does imply, however, that the data is systematically collected and stored and is
accessible to the user of the system.
The primary requirement of DSS for intelligence is the ability to search the
database for opportunities and problems.
The search process has different characteristics depending on whether it can be
structured and whether it is continuous or ad hoc.
These differences are summarized in three types of search:
1.Structured , continuous search:
 Some problem areas, such as inventory balances and product prices
relative to competitors , are relatively structured and can be examined
regularly.
 Periodic reporting systems providing condition data support this type of
search.
 DSS permit the scope, number and frequency of information outputs to
be extended with scanning of all known indicators of potential problems or
opportunities.
 Output can be produced on a periodic basis or whenever a problem or
opportunity is detected.
 DATA ANALYSIS SYSTEMS and SUGGESTION SYSTEMS can support this
type of search.
2. structured, ad hoc search:
Many problems do not occur frequently enough to
be handled by regular search. However , the search process can be structured.
Eg:- Plant location may be a problem for an expanding company,
but it may not occur with sufficient frequency to justify a database and regular
scanning for plant location sites.
Instead, the intelligence process is structured, but it
is applied only when other indicators suggest the need for it.
System Support for structured ad hoc search involves intelligence
algorithms( pre established logic for scanning and search) or expert systems and
report formats.
ANALYSIS INFORMATION SYSTEMS and REPRESENTATIONAL MODELS
may be used.
3. Unstructured Search:
• Support for unstructured search is primarily based on
flexible access to data base.
• The user needs to be able to perform such functions as retrieval,
presentation, scanning, analysis and comparison on data in order to discover
new relations and new conclusions that have not previously been defined.
• Interactive systems enhance the performance of unstructured
search by allowing the user to change parameters of the problem and quickly
see their effect.
• In some cases system support may include ANALYSIS
INFORMATION SYSTEMS and REPRESENTATIONAL MODELS ; in some other cases
system support may be a FILE DRAWER with fast access to database
SUPPORT FOR DESIGN PHASE
Support for the design phase should provide for iterative
procedures in considering alternative courses of action.
The following iterative steps are typical:
1.Support in understanding the problem.
A correct model of the situation needs to be applied
or created, and the assumptions of the model tested.
2. Support for generating solutions.
The generation of possible courses of action is aided
by:
oThe model itself: The manipulation of the model frequently provides insight
leading to generation of solution ideas.
oThe database retrieval system: The retrieval capabilities yield data useful in
generating solution ideas.
3. Support for testing feasibility of solutions.
The requirements for the design phase can be met by a variety of models.
System type Design phase support Example
Data analysis Understanding the
problem.
•Analysis of factual data.
•Budget variance analysis to identify reasons
for variance.
Analysis information
systems
Understanding the
problem. and generating
solutions
•Casual models for analysis and presentation
of casual relationships and inferential data
analysis.
•Sales analysis model to identify sales
problems and to suggest alternatives for
solutions.
Accounting models Understanding the
problem and generating
solutions.
And testing feasibility
•Deterministic models.
•Budget planning model to show effect on
financial statements of various factors being
examined.
Representational
models
Understanding the
problem and generating
solutions. And testing
feasibility.
•Deterministic and probabilistic models.
•Material requirements planning model to
understand the nature of scheduling
problems and to generate and test solutions
for feasibility.
SUPPORT FOR CHOICE PHASE
A DSS, by definition, does not make a choice.
 However optimization models and suggestion models can be used
to rank the alternatives and otherwise apply decision choice procedures to
support the choice of the decision maker.
 An important consideration in evaluating alternatives is the
sensitivity of the solutions to changes in the assumptions on which the
decision is to be made or in the conditions which are expected to occur.
 Sensitivity analysis is performed most easily when a quantitative
model is available for manipulation.
THE BENEFIT OF DSS
1. The ability to examine more alternatives.
2. The ability to achieve a better understanding of the business.
3. The ability to respond quickly to unexpected situations.
4. The ability to carryout ad hoc types of reporting and analysis.
5. The ability to provide timely information for control of ongoing
operations.
6. The ability to save time and costs.
7. The ability to make better decisions.
THE RISKS OF DSS
1. Lack of quality assurance.
2. Lack of data security.
3. Failure to specify correct requirements.
4. Failure to understand design alternatives.
DIFFERENCE BETWEEN
DSS AND MIS.
MIS DSS
The main focus is on the structured tasks and
the routine decisions
Focus is mainly on the semi / un-structured
tasks, which demand the managerial
judgment.
Identifies the information requirement. Develops certain tools for using in the decision
process.
Data storage is of great importance The main emphasis is on the data –
manipulation.
Delivers system depending on the frozen
requirements.
Current data can be used in the Decision
Support System
Only the in – direct access to the data by the
managers is provided
Managers enjoy direct access to the data.
Very much dependent on the computer
expert.
Depends on the managerial judgment.
Access to the data possibly requiring a ‘wait’
for the manager’s turn
Waiting is not at all required
MIS manager may not completely understand
the nature of the decision
Manager possesses the knowledge about the
nature of the decision and the decision making
environment
Main stress is on the efficiency. Main emphasis is laid on the effectiveness.
GROUP DECISION SUPPORT
SYSTEMS
A Group Decision Support System is an interactive
computer based system that facilitates the solution of semi
structured problems by a set of decision makers working together
in a group.
Just like a DSS , a GDSS includes a database, a model base and
a software supporting group processes.
Software might be used to summarize member’s ideas , to
report votes, to calculate the weights of decision alternatives
and to anonymously record ideas.
In a group decision support situation, a group facilitator
coordinates the use of technology in the process of conducting
the meeting.
Group Decision Support can take place in four
scenarios:
1. The decision room
2. The local decision network
3. Teleconferencing
4. Remote decision making
1. DECISION ROOM
• The decision room is like a traditional
meeting room, with addition of
computer and computer based tools.
• Each member has a computer
terminal.
• Members can interact with each other,
both verbally and by computer.
• The room has specific computer based
tools which aids in group decisions.
2. THE LOCAL DECISION NETWORK
 Group members are located at workstations in the privacy of their offices.
 A central processor stores the GDSS software, and the LAN provides
intercommunication.
3. TELECONFERENCING
Decision rooms are located in major cities so that groups can meet with each
other.
If the groups are composed of members or subgroups that are geographically
dispersed, tele/computer/video conferencing provides for interactive connection
between two or more decision rooms.
The interaction will involve transmission of computerised and audiovisual
information.
4. REMOTE DECISION MAKING GROUPS
Geographically remote locations are tied together via long distance telecommunication
network.
Group Decision Support Software
• GDSS are designed to facilitate active participation by all members.
• The software does everything from facilitating the exchange of information among
members to providing automated tools for discussion and problem solving.
• Some of the advanced features that group decision making software supports are
planning, uncertainty reduction, resource allocation and consensus building.
• GDSS can facilitate planning through the use of electronic brainstorming tools and
planning tools such as PERT.
• GDSS also encourage choice, using tools for weighing prefernces , voting and
consensus building.
• Tools that are used for resolving conflicts include displaying member’s opinion and
automatic mediation.
Benefits of GDSS
• Anonymous input methods encourage people to participate more openly
and equally in group discussions.
• Anonymous communications also reduce the probability that any one
member will dominate the discussion and facilitate “democratic”
interaction.
• Members are more willing to suggest controversial views.
Risks of GDSS
• Because diverse ideas are offered, achieving a consensus view in a GDS
environment can be difficult.
EXPERT SYSTEMS
• An expert system is a computer application that guides
the performance of ill-structured tasks which usually require
experience and specialized knowledge(ie, expertise).
• using an expert system, a non expert can achieve
performance comparable to an expert in that particular p
Differences between DSS and ES
DECISION SUPPORT SYSTEMS EXPERT SYSTEMS
Objective Assists the human Provides “expert” consulting
Who makes decision The human The system
Query type Human quires the machine Machine quires the human
Problem area Complex, wide Narrow domain
Database Includes factual knowledge Includes procedures and data
Evolution Adapts to the changing
environment
Supports a fixed problem domain
A comparison between DSS and ES
PROCESSES FEATURES
Specify objectives Modeling syntax DSS
Retrieve data Data entry DSS
Generate alternatives “what-if” analysis DSS
Infer consequences of alternatives Modeling syntax with IF/THEN/ELSE logic ES
Assimilate numerical and graphical
information
Statistical functions ES
Evaluate set of consequences Financial evaluation functions; optimization ES
Decision support systems, group decision support systems,expert systems-management information system

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Decision support systems, group decision support systems,expert systems-management information system

  • 1. DECISION SUPPORT SYSTEMS SEMINAR BY CLINCY CLEETUS I.M.COM. SCHOOL OF BUSINESS MANAGEMENT AND LEGAL STUDIES, KARIAVATTOM CAMPUS
  • 2. CONCEPT OF DECISION MAKING • Decision making has been taken from the word DECIDE which means TO CUT OFF or TO COME TO A CONCLUSION. • Decision may be regarded as a CHOICE whereby a decision maker comes to a conclusion about a given situation. • Decision making on the other hand, is a process of selecting one optimum alternative from among alternatives of a course of action. • It involves the entire process of establishing goals, searching for alternatives and developing plans. • It includes all the activities of coordinating, information processing, problem solving and evaluating that usually precede a decision
  • 3. DECISION MAKING PROCESS • Decision making, in organizations is regarded as a rational process. • HERBERT A. SIMON has given a model to describe the decision making process. • The model comprise of three major phases namely, A. INTELLIGENCE PHASE B. DESIGN PHASE C. CHOICE PHASE
  • 4. A. INTELLIGENCE PHASE In this phase , the decision maker scans the environment and identifies the problem or opportunity
  • 5. B. DESIGN PHASE • The decision maker finds alternative course of action to solve the problem. • Inventing or developing various alternatives is a time consuming and crucial activity as the decision maker has to explore all possible alternatives and cannot take a risk of missing any alternative, as the missed out one be the best one. • Developing alternatives is a creative activity which can be enhanced by various aids such as brain storming, checklists, analogies etc….
  • 6. C. CHOICE PHASE • At this stage, one of the alternatives developed in the design phase is called a decision. • One among the various alternatives formulated is selected. • For selecting an alternative , a detailed analysis is made. • Having made the decision, it is implemented. However at any phase, the decision maker may return to a previous phase. For example, the decision maker in the choice phase may reject all alternatives and return to the design phase for developing more alternatives.
  • 7. TYPES OF DECISIONS PURPOSE OF DECISION MAKING •STRATEGIC PLANNING •MANAGEMENT CONTROL •OPERATIONAL CONTROL LEVEL OF PROGRAMMABILITY •PROGRAMMED/STRUCTURED •NON-PROGRAMMED/ UNSTRUCTURED •SEMI –STRUCTURED
  • 8. TYPES OF DECISIONS Organizational decisions differ in a number of ways. These differences affect the development of alternatives and the choice among them. They also affect the design of information system support for decision alternatives.
  • 9. I. ON THE BASIS OF PURPOSE OF DECISION MAKING ROBERT B. ANTHONY(1965) has differentiated organizational decisions into three categories, they are: 1. STRATEGIC PLANNING DECISIONS: are those decisions in which the decision maker develops objectives and allocates resources to achieve these objectives. • Decisions in this category are of long period and usually involve a large investment and effort. • Such decisions are taken by strategic planning level or top level managers. • Eg;- introduction of a new product, acquisition of another firm etc…………….
  • 10. 2. MANAGEMENT CONTROL DECISIONS: are taken by middle level managers and deal with the use of resources in the organization. • Analysis of variance, product mix, planning decisions, fall in this category. 3. OPERATIONAL CONTROL DECISIONS : deal with the day to day problems that affect the operation of the organization. • This type of decisions are normally taken by the managers at the operational or bottom level of the management hierarchy in the organization. • For example, production scheduling decisions like the product to be produced for the day or items and quantities to be ordered are operational control decisions.
  • 11. II. ON THE BASIS OF LEVEL OF PROGRAMMABILITY Simon (1965) proposed two types of decisions programmed and non- programmed decisions, also known as structured and unstructured decisions (Gorry and Scott morton,1971) 1.PROGRAMMED/ STRUCTURED DECISIONS Well defined and some specified procedure or some decision rule may be applied to reach a decision. These decisions are routine and repetitive and require little time for developing alternatives in the design phase. Decisions of this kind can be delegated to lower levels in an organization These decisions are traditionally been made through habit, by operating procedures or with other accepted tools. Modern techniques for making such decisions involve operations research, mathematical analysis, modeling and simulation etc….
  • 12. 2. NON-PROGRAMMED / UNSTRUCTURED DECISIONS These decisions are not well defined and have no pre- specified procedure or decision rule. These decisions are novel ones, which may range from one timed decisions relating to a crisis to decisions relating to recurring problems where conditions change so frequently and to such an extend that decision rules cannot be specified. Sufficient time has to be spend in the design phase of these decisions. Decisions of these kind are usually handled by strategic planning level managers. Unstructured decisions tend to be solved through the judgment, intuition and the rule of the thumb.
  • 13. 3.SEMI-STRUCTURED DECISIONS Fall some where between the structured and unstructured continuum. The decisions of this category fall somewhere between structured decisions, which are routine and unstructured decisions which are unique and non- repetitive. Modern approaches to such decisions include special data analysis on computers, heuristic techniques etc……. Cannot be delegated to lower levels.  Eg: Introduction of a new product, planning for R and D.
  • 14.
  • 15. Meaning of decision support systems The term DSS refers to a class of systems which support the process of making decisions. The emphasis is on SUPPORT rather than on automation of decisions . DSS allows the decision maker to retrieve data and test alternative solutions during the process of problem solving.
  • 16. DEFINITIONS In 1984, Freyenfeld proposed the following empirical definition of DSS based on discussions with some 30 supplier, user and academic organizations. A DECISION SUPPORT SYSTEM IS AN INTERACTIVE DATA PROCESSING AND DISPLAY SYSTEM WHICH IS USED TO ASSIT IN A CONCURRENT DECISION MAKING PROCESS, AND ALSO CONFORMS TO THE FOLLOWING CHARACTERISTICS: i) IT IS SUFFICIENTLY USER-FRIENDLY TO BE USED BY THE DECISION MAKER(S) IN PERSON ii) IT DISPLAYS ITS INFORMATION IN A FORMAT AND TERMINOLOGY WHICH IS FAMILIAR TO THE USER(S) AND iii) IT IS SELECTIVE IN ITS PROVISION FOR INFORMATION AND AVOIDS ITS USER(S) IN INFORMATION OVERLOAD.
  • 17. A SET OF WELL INTEGRATED, USER FRIENDLY, COMPUTER BASED TOOLS THAT COMBINE DATA WITH VARIOUS DECISION MAKING MODELS- QUANTITATIVE AND QUALITATIVE TO SOLVE SEMI-STRUCTURED AND UNSTRUCTURED PROBLEMS. DSS
  • 18. EVOLUTION OF DSS The notion of DSS as a formal concept was coined by G. Anthony Gorry and Michael S.Scott Morton. They felt a need for a framework to channel computer applications towards management decision making and developed a grid known as THE GORRY AND SCOTT MORTON GRID. The grid is based on Simon’s concept of programmed and non programmed decisions and Robert N. Anthony’s management levels.
  • 19. MANAGEMENT LEVELS DEGREE OF PROBLEM STRUCTURE ……………………………………………………………………………. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Operational Control Management Control Strategic Planning Structured Semi Structured Unstructured Accounts receivable Order Entry Inventory Control Budget analysis- engineered costs Short term forecasting Tanker Fleet mix Warehouse and Factory location Production scheduling Cash management PERT/COST systems Variance analysis- overall budget Budget preparation Sales and production Mergers and Acquisitions New product planning R &D planning
  • 20. CHARACTERISTICS OF DSS The main characteristics of DSS are: The computer must support the manager but not replace his/her judgments .It should therefore, neither try to provide THE ANSWER nor impose a predefined sequence of analysis The main payoff of computer support is for semi-structured problems, where parts of the analysis can be systemized for the computer, but where the decision maker’s insight and judgements are needed to control the process. Effective problem solving is interactive and is enhanced by a dialogue between the user and the system. The user explores the problem situation using the analytic and information providing capabilities of the system as well as human experience and insights.
  • 21. Decision support and repetitiveness of decisions • Highly repetitive decisions ( whether structured, semi structured or unstructured) can frequently benefit from DSS. • If decision process is basically the same each time, a model can be tailored to fit the process, even for a single decision maker. • Demonstrable benefits of such systems are faster decision making, improved consistency and accuracy and improved methods for analyzing and solving problems. • On the other extreme, non repetitive, one-time decisions require decision support of a very different nature. • The primary requirement of systems to support one time decisions is flexible access to a database and other forms of information such as external databanks. • Emphasis tends to be on the search phase of decision making, where different kinds of data in different forms are needed for each decision process.
  • 22. OBJECTIVES OF DSS Peter G.W.Keen and Michael S.Scott Morton(1978) defined three objectives that DSS should achieve:  Assist managers in making decisions to solve semi-structured problems.  Support manger’s judgement rather than to replace it .  Improve the manager’s decision making effectiveness rather than its efficiency.
  • 23. Importance of DSS/WHY AN ORGANIZATION NEEDS A DSS 1. Fast computation 2. Enhanced productivity 3. Data transmission 4. Better decisions 5. Competitive edge
  • 25. 1. FILE DRAWER SYSTEMS • These allow immediate access to data items. • They are basically online mechanized versions of manual filing systems. • The user is non managerial line personnel. Eg:- Status inquiries for inventory information Account balance Monitoring loads and capacities 2. DATA ANALYSIS SYSTEMS • These allow manipulation of data by means of either analysis operations tailored to the task and setting or general analysis operations. • They are typically used by non-managerial personnel to analyze files containing current or historical data. Eg:- Budget analysis system Financial system for analyzing alternative investment opportunities.
  • 26. 3. ANALYSIS INFORMATION SYSTEMS • These provide access to a series of databases and small models. These are extensions of data analysis systems. Eg:- A marketing DSS containing internal sales data, promotion and pricing data plus access to external databases. 4. ACCOUNTING MODELS • These calculate the consequences of planned actions on the basis of accounting definitions. •They typically generate estimates of income, balance sheets etc….based on the variations in input values to the definitional formulas. Eg:- Monthly budgeting systems for operational decision making Short term financial planning. 5. REPRESENTATIONAL MODELS • These estimate the consequences of actions on the basis of models that represent some non definitional characteristics of the system such as probabilities of occurrence. • They include all simulation models that contain elements beyond accounting definitions. Eg:- Risk analysis model using estimated probability distributions for each of the key factor.
  • 27. 6. OPTIMIZATION MODELS • These provide guide lines for action by generating the optimal solution consistent with a series of constraints. •They are used for repetitive decisions that can be described mathematically and where specific objective , such as minimizing cost , is the goal. Eg:- system for scheduling training classes under a complex set of constraints and a material usage optimization system. 7. SUGGESTION MODELS • These compute a specific suggested decision for a fairly structured and repetitive decision. •Their purpose is to bypass other(less efficient) procedures for generating the suggestion. Eg:- Insurance renewal rate calculation system and a model price cardboard boxes on a standard set of dimensions and decision rules.
  • 28. COMPONENTS OF DECISION SUPPORT SYSTEMS There are three main software components of a DSS. They are:
  • 29. 1. DATA BASE MANAGEMENT SYSTEMS The data in the decision support system database are managed by the DBMS, which covers  COMPILATION OF DATA  MANIPULATION OF DATA  DATA GENERATION  DATA UPDATING  DATA MAINTENANACE  DISSEMINATION OF DATA
  • 30. 2. MODEL MANAGEMENT SYSTEMS Model management systems stores and access models that managers use to make decisions. Such models are used for designing a manufacturing facility, analyzing financial health of an organization, forecasting demand for a product or a service and determining the quality of a particular batch of products. Although most models are quantitative, decision makers use qualitative models also to make decisions. The model builder, a component of many model bases, provides a structured framework for developing models by helping decision makers, identify the variables and the interrelationships among the variables in the model. A model builder creates, identifies, process, stores, updates and maintains different decision making models and ensures that these models are consistently applied when decisions are made. The model builder also contains a model dictionary for consistency in the definitions and uses of models.
  • 31. Some of the models are: •Statistical Models •Production Models •Marketing Models •Financial and Accounting Models •Strategic Models. These models are extensively used in different functional areas of a business.
  • 32. 3. SUPPORT TOOLS  Support Tools ( Online help, Pull down menus, User interfaces, Graphical analysis, Error correction mechanisms) facilitate user’s interactions with the system.  The better the interface , the greater are the chances that users will accept the system.  Although managers recognize the power and potential of DSS, the main problem to its adoption is a lack of people with training in computer technologies. In such an environment, good interfaces can make or break the system.
  • 33. FUNCTIONS OF DECISION SUPPORT SYSTEMS There are five functions of decision supporting system facilitating managerial decision making. They are:
  • 34. 1. MODEL BUILDING Model building allows decision makers to identify appropriate model for solving the problem at hand. It takes into account input variables, interrelationships among the variables, problem assumptions and constraints. For example, a marketing manager of Videocon is charged with the responsibility of developing a sales forecasting model for colour TV sets. A model builder uses a structured framework to identify variables like demand, cost and profit, analyze the relationships among these variables, identify the assumptions, if any and identify the constraints viz, the production capacity of the plant. All this information are then integrated by a system into a decision making model, which can be updated and modified whenever required.
  • 35. 2. WHAT – IF ANALYSIS The process of assessing the impact of changes to model variables, the values of the variables, or the interrelationships among variables. This helps managers to be proactive , rather than reactive, in their decision making This analysis is critical for semi-structured and unstructured problems because the data necessary to make such decisions are often either not available or incomplete. Hence , managers normally use their intuition and judgement in predicting the long term implications of their decisions. Spreadsheet packages such as Excel and Lotus 1-2-3, have WHAT-IF applications.
  • 36. 3. GOAL SEEKING The process of determining the input values required to achieve a certain goal. For example, house buyers determine the monthly payment they can afford (say rs. 7000) and calculate the number of such payments required to pay the desired house. 4. RISK ANALYSIS Allows managers to assess the risks associated with various alternatives. Decisions can be classified as low risk, medium risk, and high risk. A Decision Support System is particularly useful in medium risk and high risk environments.
  • 37. 5. GRAPHICAL ANALYSIS Helps managers to quickly digest large volumes of data and visualize the impacts of various courses of action First, lotus system enabled users to easily display and print information in a graphic form. Spreadsheets have sophisticated graphical analysis capabilities. Eg:- Microsoft excel gives the user a wide choice of graphs and charts in many colours and patterns.
  • 39.  The development of a DSS is determined by the types of information and the facilities needed for taking the decision.  Support systems are developed using programming languages or produced by packages specially for incorporating decision support development tools. Programming languages used to develop DSS are: c++ BASIC SQL Advantages of using programming languages are: Application development is speedy. Many are end user oriented. Declarative rather than procedural.
  • 40. The selection of a language depends on factors such as: Availability of language and support for it. Amount of manipulation Vs. presentation of data. Need to document and maintain the program. Frequency of use and number of users. In general , there are three methods of developing a DSS: 1. DSS GENERATOR 2. DSS SHELLS 3. CUSTOM MADE SOFTWARE
  • 41. 1. DSS GENERATOR  A DSS generator is comprised of programs such as data management tools, electronic spreadsheets, report generators(user friendly programs that allow decision makers to produce customized reports), statistical packages, query languages and model building tools, that help in the development of a DSS.  Some known DSS generators are FOCUS, EXCEL and LOTUS 1-2-3
  • 42. 2. DSS SHELLS  A DSS Shell is used to build a customized DSS.  Shells eliminate the need for developing the DBMS, model management system and user interfaces, because skeletal versions of these models are already available in the shell.  A user can simply connect the shell with the appropriate models in order to have a fully functional DSS.  The main disadvantage of a shell is that it may have to be customized to meet the needs of the decision maker or the user may have to adopt the problem to the tool.
  • 43. 3. CUSTOM – MADE SOFTWARE  It is designed and developed by an organization, who is committed to DSS technology but cannot find a suitable generator or shell.  The software is developed using a procedural language, such C or a 4GL, such as FOCUS.  Organizations may also choose to combine shells and customized software.  The primary disadvantage of this approach is that the system may be expensive and time consuming to develop and organizations may run into unexpected bottlenecks and cost overruns.
  • 44. SUPPORT FOR DIFFERENT PHASES This section describes the types of information support systems that can be provided for each phase of decision making.
  • 45. SUPPORT FOR INTELLIGENCE PHASE The database needed in this phase is very comprehensive. In general it should cover the three environments: 1.Societal 2.Competitive 3.Internal The concept of DSS does not imply that all the data is in computerized databases. It does imply, however, that the data is systematically collected and stored and is accessible to the user of the system. The primary requirement of DSS for intelligence is the ability to search the database for opportunities and problems. The search process has different characteristics depending on whether it can be structured and whether it is continuous or ad hoc. These differences are summarized in three types of search:
  • 46. 1.Structured , continuous search:  Some problem areas, such as inventory balances and product prices relative to competitors , are relatively structured and can be examined regularly.  Periodic reporting systems providing condition data support this type of search.  DSS permit the scope, number and frequency of information outputs to be extended with scanning of all known indicators of potential problems or opportunities.  Output can be produced on a periodic basis or whenever a problem or opportunity is detected.  DATA ANALYSIS SYSTEMS and SUGGESTION SYSTEMS can support this type of search.
  • 47. 2. structured, ad hoc search: Many problems do not occur frequently enough to be handled by regular search. However , the search process can be structured. Eg:- Plant location may be a problem for an expanding company, but it may not occur with sufficient frequency to justify a database and regular scanning for plant location sites. Instead, the intelligence process is structured, but it is applied only when other indicators suggest the need for it. System Support for structured ad hoc search involves intelligence algorithms( pre established logic for scanning and search) or expert systems and report formats. ANALYSIS INFORMATION SYSTEMS and REPRESENTATIONAL MODELS may be used.
  • 48. 3. Unstructured Search: • Support for unstructured search is primarily based on flexible access to data base. • The user needs to be able to perform such functions as retrieval, presentation, scanning, analysis and comparison on data in order to discover new relations and new conclusions that have not previously been defined. • Interactive systems enhance the performance of unstructured search by allowing the user to change parameters of the problem and quickly see their effect. • In some cases system support may include ANALYSIS INFORMATION SYSTEMS and REPRESENTATIONAL MODELS ; in some other cases system support may be a FILE DRAWER with fast access to database
  • 49. SUPPORT FOR DESIGN PHASE Support for the design phase should provide for iterative procedures in considering alternative courses of action. The following iterative steps are typical: 1.Support in understanding the problem. A correct model of the situation needs to be applied or created, and the assumptions of the model tested. 2. Support for generating solutions. The generation of possible courses of action is aided by: oThe model itself: The manipulation of the model frequently provides insight leading to generation of solution ideas. oThe database retrieval system: The retrieval capabilities yield data useful in generating solution ideas.
  • 50. 3. Support for testing feasibility of solutions. The requirements for the design phase can be met by a variety of models. System type Design phase support Example Data analysis Understanding the problem. •Analysis of factual data. •Budget variance analysis to identify reasons for variance. Analysis information systems Understanding the problem. and generating solutions •Casual models for analysis and presentation of casual relationships and inferential data analysis. •Sales analysis model to identify sales problems and to suggest alternatives for solutions. Accounting models Understanding the problem and generating solutions. And testing feasibility •Deterministic models. •Budget planning model to show effect on financial statements of various factors being examined. Representational models Understanding the problem and generating solutions. And testing feasibility. •Deterministic and probabilistic models. •Material requirements planning model to understand the nature of scheduling problems and to generate and test solutions for feasibility.
  • 51. SUPPORT FOR CHOICE PHASE A DSS, by definition, does not make a choice.  However optimization models and suggestion models can be used to rank the alternatives and otherwise apply decision choice procedures to support the choice of the decision maker.  An important consideration in evaluating alternatives is the sensitivity of the solutions to changes in the assumptions on which the decision is to be made or in the conditions which are expected to occur.  Sensitivity analysis is performed most easily when a quantitative model is available for manipulation.
  • 52. THE BENEFIT OF DSS 1. The ability to examine more alternatives. 2. The ability to achieve a better understanding of the business. 3. The ability to respond quickly to unexpected situations. 4. The ability to carryout ad hoc types of reporting and analysis. 5. The ability to provide timely information for control of ongoing operations. 6. The ability to save time and costs. 7. The ability to make better decisions.
  • 53. THE RISKS OF DSS 1. Lack of quality assurance. 2. Lack of data security. 3. Failure to specify correct requirements. 4. Failure to understand design alternatives.
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  • 57. MIS DSS The main focus is on the structured tasks and the routine decisions Focus is mainly on the semi / un-structured tasks, which demand the managerial judgment. Identifies the information requirement. Develops certain tools for using in the decision process. Data storage is of great importance The main emphasis is on the data – manipulation. Delivers system depending on the frozen requirements. Current data can be used in the Decision Support System Only the in – direct access to the data by the managers is provided Managers enjoy direct access to the data. Very much dependent on the computer expert. Depends on the managerial judgment. Access to the data possibly requiring a ‘wait’ for the manager’s turn Waiting is not at all required MIS manager may not completely understand the nature of the decision Manager possesses the knowledge about the nature of the decision and the decision making environment Main stress is on the efficiency. Main emphasis is laid on the effectiveness.
  • 58. GROUP DECISION SUPPORT SYSTEMS A Group Decision Support System is an interactive computer based system that facilitates the solution of semi structured problems by a set of decision makers working together in a group.
  • 59. Just like a DSS , a GDSS includes a database, a model base and a software supporting group processes. Software might be used to summarize member’s ideas , to report votes, to calculate the weights of decision alternatives and to anonymously record ideas. In a group decision support situation, a group facilitator coordinates the use of technology in the process of conducting the meeting.
  • 60. Group Decision Support can take place in four scenarios: 1. The decision room 2. The local decision network 3. Teleconferencing 4. Remote decision making
  • 61. 1. DECISION ROOM • The decision room is like a traditional meeting room, with addition of computer and computer based tools. • Each member has a computer terminal. • Members can interact with each other, both verbally and by computer. • The room has specific computer based tools which aids in group decisions.
  • 62. 2. THE LOCAL DECISION NETWORK  Group members are located at workstations in the privacy of their offices.  A central processor stores the GDSS software, and the LAN provides intercommunication. 3. TELECONFERENCING Decision rooms are located in major cities so that groups can meet with each other. If the groups are composed of members or subgroups that are geographically dispersed, tele/computer/video conferencing provides for interactive connection between two or more decision rooms. The interaction will involve transmission of computerised and audiovisual information.
  • 63. 4. REMOTE DECISION MAKING GROUPS Geographically remote locations are tied together via long distance telecommunication network.
  • 64. Group Decision Support Software • GDSS are designed to facilitate active participation by all members. • The software does everything from facilitating the exchange of information among members to providing automated tools for discussion and problem solving. • Some of the advanced features that group decision making software supports are planning, uncertainty reduction, resource allocation and consensus building. • GDSS can facilitate planning through the use of electronic brainstorming tools and planning tools such as PERT. • GDSS also encourage choice, using tools for weighing prefernces , voting and consensus building. • Tools that are used for resolving conflicts include displaying member’s opinion and automatic mediation.
  • 65. Benefits of GDSS • Anonymous input methods encourage people to participate more openly and equally in group discussions. • Anonymous communications also reduce the probability that any one member will dominate the discussion and facilitate “democratic” interaction. • Members are more willing to suggest controversial views. Risks of GDSS • Because diverse ideas are offered, achieving a consensus view in a GDS environment can be difficult.
  • 67. • An expert system is a computer application that guides the performance of ill-structured tasks which usually require experience and specialized knowledge(ie, expertise). • using an expert system, a non expert can achieve performance comparable to an expert in that particular p
  • 68. Differences between DSS and ES DECISION SUPPORT SYSTEMS EXPERT SYSTEMS Objective Assists the human Provides “expert” consulting Who makes decision The human The system Query type Human quires the machine Machine quires the human Problem area Complex, wide Narrow domain Database Includes factual knowledge Includes procedures and data Evolution Adapts to the changing environment Supports a fixed problem domain
  • 69. A comparison between DSS and ES PROCESSES FEATURES Specify objectives Modeling syntax DSS Retrieve data Data entry DSS Generate alternatives “what-if” analysis DSS Infer consequences of alternatives Modeling syntax with IF/THEN/ELSE logic ES Assimilate numerical and graphical information Statistical functions ES Evaluate set of consequences Financial evaluation functions; optimization ES

Editor's Notes

  1. The horizontal line dotted through the middle of the grid is significant. It separates the problems that had not been subjected to computer processing.