2. 2
Different perspectives on decision making.
There are three different perspectives on decision making. These include the following:
1. Economic/Biological Perspective
2. Behavioural Perspective
3. Cognitive Perspective
1. Economic/Biological Perspective
- The economic or biological perspective is taken form evolutionary theory.
- In this perspective, decision making is regarded as a matter of simple cost/benefit analysis.
- This perspective assumes that organisms decide and act always to optimize their welfare.
2. Behavioural Perspective
- The behavioural perspective is taken from early works in Psychology.
- This perspective uses Stimulus-Response (S-R) pairings theory.
- In this perspective, decision making is regarded largely as a function of learning and past
experience.
3. Cognitive Perspective
- In the cognitive perspective, decision making behaviour is regarded as largely governed by
mental events in the brain and nervous system.
- These mental events include the following:
Sensations
- We are constantly bombarded with environmental stimuli of all kinds – sounds, touch,
sights, smells, etc.
- These stimuli are transformed into electrical energy at our sense organs.
- This is a lower level process.
Perception
- Once the sensory information arrives at the respective parts of the brain, there is a process of
filtering and initial processing of sensory information material making sense of it, or forming patterns.
- A great deal of sensory information is lost at this stage.
- This is also a lower level process.
Memory
- The filtered information then enters one of our memory systems, short term memory or long term
memory.
- This is also a lower level process.
Thinking
- In the short term memory, information is available for more intensive processing known as
thinking.
- Thinking is a higher level process.
3. 3
Factors that influence decision making.
There are five different factors that influencing decision making. These are as follows:
1. Subjective Probability of Outcome
2. Subjective Utility of Outcome
3. Risk of Outcome
4. Stress
5. Cognitive Style
1. Subjective Probability of Outcome
- People seem to subjectively evaluate decision alternatives.
- If one has 4 courses of action or alternatives, with associated subjective probabilities of
success of A=0.12, B=0.65, C=0.54, and D=0.83, then the decision maker is likely to choose
alternative D, as decision.
- However, people typically ranks alternatives rather than assigning explicit probability
scores.
- Also people may obtain the wrong probability through ignorance, failure to think through
logically, etc.
2. Subjective Utility of Outcome
- Decision making is influenced by personal values associated with a particular course of
action or decision alternative.
- In this case, the decision maker is likely to ignore that distasteful or valueless alternative
and consider other alternatives in making a decision.
3. Risk of Outcome
- Another factor affecting decision making is the perceived risk of outcome or the magnitude
of consequences associated with a particular course of action or decision alternative.
- For example, some people will carry an umbrella with them all the time.
- Their decision is not based on their subjective probability of it raining, but on the need to
counter the risk of getting wet or missing an appointment.
4. Stress
- Stress factors to do with life events can significantly influence one’s decision making.
- One’s spouse may be very ill, or one may have financial problems, or may have a career in
jeopardy.
- Stress factors like these can severely impair or even paralyse his or her ability to make
decisions.
4. 4
5. Cognitive Style
- There are individual differences in the way people think and make decisions.
- Some focus more on details and some on more general observations or relationships.
- Some people are heavily influenced by personal feelings, others by more objective
considerations.
- The above cognitive characteristics and others, collectively referred to as cognitive style,
influence an individual’s decision making behaviour.
Case Study
- A small mail order company, selling innovative consumable items to people in the 50+ age bracket
is interested in expanding its profits. An external marketing consultancy has identified the following
four possibilities, with associated probabilities of success:
1. Increase range of stock keeping same mailing list (0.4)
2. Produce an E-Commerce World Wide Web site (0.7)
3. Maintain current stock range, expanding mailing list (0.5)
4. Increase range of stock, expanding mailing list (0.6)
- The managing director of the company has looked at the possible actions and has associated some
further probabilities, in terms of the values to him and the business, which are (1=0.5, 2=0.1, 3=0.8,
4=0.4).
Which action should he take and why? Show all your working.
Probabilities Action 1 Action 2 Action 3 Action 4
Marketing Company 0.4 0.7 0.5 0.6
Company Director 0.5 0.1 0.8 0.4
Multiply above 0.2 0.07 0.4 0.24
- Action 3 has the resulting higher probability, so the action that the managing director should
take, is to maintain current stock range, expand mailing list. The company being small can’t
afford to maintain an e-commerce site. Also target clients in the 50+ age group are unlikely to
interact with the internet.
5. 5
Principal characteristics of Management Information Systems
(MIS), Management Science (MS) and Decision Support Systems
(DSS).
Description of Principal Characteristics
Keen and Scott Morton describes the principal characteristics of Management Information
Systems (MIS), Management Science (MS) and Decision Support Systems (DSS) as follows:
Management Information Systems (MIS)
-Main impact of MIS has been on structured tasks, where standard operating procedures,
decision rules, and information flows can be reliably predefined.
-Main payoff of MIS has been in improving efficiency by reducing costs, turnaround time,
and so on, and by replacing clerical personnel or increasing their productivity.
- Relevance of MIS for managers’ decision making has mainly been indirect (e.g. by
providing reports and access to data).
-MIS applications are routine and done periodically.
Management Science/Operations Research (MS/OR)
-Impact of MS/OR has mostly been on structured problems (rather than tasks), in which the
objective, data and constraints can be prespecified.
-Payoff of MS/OR has been in generating better solutions for general categories of problems
(e. g. inventory).
-Relevance of MS/OR for managers has been in the provision of detailed recommendations
and new methods handling complex problems.
-Applications are nonroutine, as needed.
Decision Support Systems (DSS)
-Impact of DSS has been on decisions in which there is sufficient structure for computer and
analytic aids to be of value but where the manager’s judgment is essential.
-Payoff of DSS has been in extending the range and capability of managers’ decision
processes to help them improve their effectiveness.
-Relevance of DSS for managers has been in the creation of a supportive tool, under their
own control, that does not attempt to automate the decision process, predefine objectives, or
impose solutions.
-DSS applications are also nonroutine, as needed
Discussion
- MIS is usually organized along functional areas. Thus, there are marketing MIS, accounting
MIS, and so on. A DSS, on the other hand, is basically a problem-solving tool and is often
used to address ad hoc and unexpected problems.
- MIS is usually developed by the IS department because of its permanent infrastructure
nature while DSS is usually an end-user tool.
- DSS can provide decision support within a short time. An MIS can provide quick decision
support only to situations for which the models and software were prewritten.
6. 6
- Because of its unstructured nature, DSS is usually developed by a prototype approach. MIS,
on the other hand, is often developed by a structured methodology such as the system
development life cycle (SDLC).
- A DSS can evolve as the decision maker learns more about the problem. Many
computerized applications, including MIS, are developed in a way that requires detailed
specifications to be formalized in advance.
- Of particular interest is the point that MIS is concerned with structured tasks, that
management science is concerned with structured problems, and that DSS is concerned with
ill-structured problems.
- It is also important to note that in both the MIS and MS domains, the output from the
systems is almost totally prescribed i.e. an answer is produced, and the need for post-output
judgement is limited. In the use of DSS, post-output judgement, assessment, and comparison
of decision alternatives is characteristic.
The framework for DSS proposed by Sprague and Watson.
- A framework for DSS development is has been provided by Sprague and Watson.
- The framework defines three main elements relating to their development and use:
1. Technology levels
2. People involved
3. Developmental approach.
1. Technology Levels
- Technologically, this framework defines three classes of development platforms.
- These include: Specific DSS (DSS Applications); DSS Integrated Tools (Generators or
Engines); and DSS Primary Tools.
Specific DSS (DSS Applications)
- A specific DSS is the actual DSS application used by an end user.
- If a specific DSS for a given application is available off-the-shelf, one need not spend
resources building it in-house.
DSS Integrated Tools (Generators or Engines)
- A DSS Integrated Tool (Generator or Engine) is an integrated development software
package that provides a set of capabilities for building a specific DSS quickly, inexpensively,
and easily.
- A popular PC-based generator is Microsoft Excel.
DSS Primary Tools
- These fundamental elements, including, programming languages, graphics softwares,
editors, query systems, etc. facilitate the development of either a DSS Generator or a specific
DSS.
7. 7
2. People Involved
- Sprague suggests that there are 5 roles taken on by people involved in developing and using
a DSS within an organisation:
The end-user – generally the manager who is responsible for making important decisions
with the aid of the DSS.
An intermediary – perhaps a staff assistant who might assist the user in operating the DSS.
The DSS builder – someone who is capable of generating a specific DSS from a DSS
generator.
The technical supporter - The person in this role develops additional systems features, when
they are needed, as part of the DSS generator.
The toolsmith – a computer science engineer or systems expert, who can develop new tools
appropriate for interacting with a DSS or quickly extracting relevant data from a database,
etc.
3. Developmental Approach
- Most DSSs are developed through the prototyping process.
- What happens is that:
(1) A team, including the manager and the people involved in building the system, together
attempt to define the problem.
(2) Builder(s) goes away and develops an initial system to support the decision-making
required.
(3) The manager uses the initial system for a short period of time and provides feedback.
(4) Team evaluates the feedback.
(5) The system is modified and/or expanded in the light of the feedback.
(6) The new version of the system is then used by the manager for another short period.
(7) The whole cycle is repeated a number of times until the final system emerges.
Which roles map onto particular levels
--- Although Sprague and Watson have mentioned five human roles in the development and
use of DSS, different technology levels chosen require different combinations of these roles.
--- If a specific DSS is adopted, all roles other than the end user is unnecessary.
--- If a DSS generator is used to build a Specific DSS, The Toolsmith may not be necessary.
--- If an End User is well-conversant about use of his or her Specific DSS, an Intermidiary is
not necessary.
Relationship of adaptive design to other systems development methodologies
--- In case of development of very large and complex decision support systems, prototyping
is combined with other systems development methodologies.
Problems of cost and excessive time in fully adopting this approach
8. 8
--- Fully adopting this approach requires use of an well-represented team to work on the
development of a DSS through formal meetings and adequate number of iterations of
development works, which increases the costs and time required.
Potential benefits
The DSS Development methodology suggested by Sprage and Watson has the following
benefits:
--- Short development time
--- Short user reaction time
--- Adequate user understanding of the system
--- Generally, low cost development.
The subsystems of a typical Decision Support Systems (DSS) and
their functions.
- A typical Decision Support Systems (DSS) has three major subsystems: Data Management
Subsystem, Model Management Subsystem, and User Interface Subsystem.
- Diagramme (Visual 3.3)
The Data Management Subsystem
- The data management subsystem is composed of the following elements:
-- DSS Database
-- Database Management System (DBMS)
-- Data Directory
-- Query Facility
The Database
- A database is a collection of interrelated data organized to meet the needs and structure of an
organization.
- The data in the DSS database is extracted from internal and external data sources, and it also
includes personal data belonging to one or more users.
Data Base Management System (DBMS)
- The database is created, accessed, and updated by a DBMS.
- Most DSS are built with a standard commercial DBMS.
The Query Facility
- The query facility includes a special query language, such as, a standard Database 4GL,
and/or SQL.
The Directory (Data Dictionary)
- The data directory is a catalog of all the data in the database.
- The data dictionary contains data definitions.
9. 9
The Model Management Subsystem
- The Model management Subsystem of a DSS is composed of the following elements:
-- Model Base
-- Model Base Management System (MBMS)
-- Modelling Language
-- Model Directory
-- Model Execution, Integration, and Command Processor
Model Base
- A model base contains routine and special statistical, financial, forecasting, management
science, and other quantitative models that provide the analysis capabilities in a DSS.
- The models in the model base can be divided into four major categories: strategic, tactical,
operational and model-building blocks and routines.
Model Base Management System (MBMS)
- Stores, retrieves, and manages a wide variety of different types of models in a logical and
integrated manner.
- Creates models easily and quickly, either from scratch or from existing models or from
model building blocks.
- Allows users to manipulate models and integrate them with database and DSS.
Modeling Languages
- Because DSS deal with semistructured or unstructured problems, it is often necessary to
customize models.
- This can be done with high level languages, such as, fourth-generation languages and
special modeling languages, such as, IFPS/Plus.
Model Directory
- A model directory is similar to a database directory.
- It is a catalogue of all the models and other software in the model base.
Model Execution, Integration, and Command Processor
- Model execution is the process of controlling the actual running of the model.
- Model integration means combining the operations of several models when needed (such as
directing the output of one model to be processed by another one).
- A model command processor is used to accept and interpret modeling instructions from the
dialog component and to route them to the MBMS, the model execution or the integration
functions.
10. 10
The User Interface (Dialog) Subsystem
- The term user interface covers all aspects of communication between a user and the DSS.
- It includes not only the hardware and software, but also factors that deal with ease of use,
accessibility, type of user, and human-machine interaction.
- The user interface subsystem is managed by software called the User Interface Management
System (UIMS).
The importance of the user interface subsystem of a Decision
Support System (DSS).
- To a user, the user interface is the system, because the user sees only this part of the DSS.
- If the system looks awkward or is not ergonomic or the response time is particularly slow,
there may be frustration but the user may well get used to these problems in time.
- But, if the dialogue type used is inappropriate, the user may simply stop learning to use a
system and rely on his or her unaided ability to make decisions.
- So it is vitally important to match dialogue type to the user.
- User interface is particularly more important for DSS users because most users of DSS are
not computer experts, are busy people, and may be skeptical of the advantages of a DSS.
The major features or capabilities of a User Interface
Management System (UIMS) in the context of DSS.
- A DSS user interface is managed by a software called the User Interface Management
System (UIMS).
- The UIMS is composed of several programs that provide capabilities as mentioned below:
--- Provides graphical user interface.
--- Accommodates the user with a variety of input devices.
--- Presents data with a variety of formats and out put devices.
--- Gives users help capabilities, prompting, diagnostic and suggestion routines, or any other
flexible support.
--- Provides interactions with the database and the model base.
--- Stores input and output data.
--- Has windows to allow multiple functions to be displayed concurrently.
--- Provides training by examples (guiding users through the input and modeling process).
--- Provides flexibility and adaptiveness so the DSS can accommodate different problems and
technologies.
--- Interacts in multiple, different dialog styles.
11. 11
Types of database structure that can be found in a DSS.
Four different types of database structure can be found in a DSS. These are described below.
Relational
- Data are organised in the form of two-dimensional tables, based upon the mathematical
theory of relations.
- The idea is to group related data items (fields) into as many tables (records) as required.
- Advantages of this type of structure are: simple for users to learn, can be easily modified
and may be accessed in a number of formats.
Hierarchical
- Data is ordered in a top down fashion, creating logical links between related data items.
- It looks like a tree or an organization chart.
- It is used mainly in transaction processing where processing efficiency is a critical element.
- A disadvantage of this format is that it tends to create data duplication.
Network
- Network structure is similar to the hierarchical model.
- It permits more complex links, including lateral connections between related items.
- It has the advantage of saving storage space through the sharing of some items and possible
speeding up of data access.
Object-Orientated (OO) Model
- Based upon the principle of OO programming, OO organised databases allow analysis of
data in terms of natural relationships between real world objects.
- Such relationships are organised into inheritance hierarchy, where sibling objects inherit
properties of parent objects.
- Conventional data and related procedural code can be encapsulated within an object.
- This gives the advantage of rapid data access.
- This model is important in modern distributed systems and image greedy applications.
12. 12
Data sources for the database component of a DSS
There are three different sources of data for DSS databases. These are described below.
Internal
- These data are about people, products, services, processes, etc. of an organisation.
- For example, client and sales data are normally kept in sales or marketing databases; data
about employees and their pay are usually stored in personnel databases; and so on.
External
- These are data used by an organisation, but originating from an external source.
- There are many sources of external data.
- They range from commercial databases, government, financial and research institutions,
internet, etc. to data collected by sensors and from satellites.
Personal
- DSS users may have expertise and knowledge that can be stored for future use.
- These may include, managers’ subjective estimates of sales, opinions about what
competitors are likely to do, or how financial markets will fare, interpretation of news
articles, or their own heuristic rules to explain or reason about any recommendations made,
etc.
Example of DSS Application Drawing Data from Three Sources:
- A DSS application for the Chief Executive Officer of ………………… Institute of IT.
- Internal Data – Student data; Data about teachers; Data about employees; Data about
courses, etc.
- External Data – Data about courses run by other similar institutes; Government Policies, etc.
- Personal Data – The CEO’s subjective estimates of probable number of students in the next
year, thoughts about what competing institutes are likely to do, interpretation of relevant
news articles, etc.
13. 13
The factors and components which make up the database
component of a DSS.
The factors and components which make up the database component of a DSS are described
below:
Relationship of the Database to a DSS
- The relationship of a database to a DSS may be represented by the following Diagramme
(Presentors’ Guide, Visual 4.5).
Role of Data Base Management System (DBMS)
- The role of DBMS in the database component include the following capabilities:
- Capture/extract data for inclusion in a DSS database
- Update (add, delete, edit, change) data records and files
- Interrelate data from different sources
- Retrieve data from the database for queries and reports
- Provide comprehensive data security
- Perform complex data manipulation tasks based on queries
- Manage data through a data dictionary
Data Directory (Data Dictionary)
- The data directory is a catalog of all the data in the database.
- It contains the data definitions, and its main function is to answer questions about the
availability of data items, their source, and their exact meaning.
- For large databanks, the data directory shows the contents and nature of data available in
different databases.
Sources of Data
There are three different sources of data for DSS databases. These are described below.
Internal
- These data are about people, products, services, processes, etc. of the organisation.
- For example, client and sales data are normally kept in sales or marketing databases; data
about employees and their pay are usually stored in personnel databases; and so on.
External
- These are data used by an organisation, but originating from an external source.
- There are many sources of external data.
- They range from commercial databases, government, financial and research institutions,
internet, etc. to data collected by sensors and from satellites.
14. 14
Personal
- DSS users may have expertise and knowledge that can be stored for future use.
- These may include, managers’ subjective estimates of sales, opinions about what
competitors are likely to do, or how financial markets will fare, interpretation of news
articles, or their own heuristic rules to explain or reason about any recommendations made,
etc.
Case.
Big Store PLC have a retail outlet in every major capital city in the world. They have a
charge card, which enables users to purchase goods on credit; settling their account on a
monthly basis. In order to apply for the card users must fill in a form relating to their
financial status. This is then input at any store and processed via a ‘dumb terminal’ over a
Data Communication System (DCS) at their head office in New York. The system, which
does this, is a DSS, with a large database component. Management is unhappy with the
processing times involved in this centralized system and are currently inviting proposals from
Information Systems developers to improve the processing of the system.
Discuss the type of new DSS system you would recommend; utilizing the rapidly expanding
technologies of: powerful and affordable personal computers, distributed DSS, Local Area
Networks, and client/server architecture.
Powerful Personal Computer
- Personal Computers (PC) have become enormously powerful these days.
- They are many times more powerful than former mainframe computers in terms of speed
and volume of data they can store and process.
- Because of their power and low cost PCs with little higher configuration can be used as
servers in distributed systems.
Distributed DSS
Distributed DSS systems posses five important characteristics:
- Workstations are dispersed throughout an organisation.
- Workstations are interconnected via telecommunications systems.
- Common database(s) are shared by all.
- All workstations are centrally coordinated with an information resource management plan.
- Input/output operations are done within user departments.
Local Area Network (LAN)
The LAN technology has also evolved a lot.
With the introduction of optical fibres, enormous bandwidths of interconnectivity is now
possible.
Owing to decreases in telecommunications costs and developments in Internet technology,
LANs can now interact at high speeds with other LANs at distant locations in the world.
15. 15
Client/Server Architecture
- Workstations/PCs (clients) carry out all the processing, but data is held centrally on host
(server).
- There is a DBMS in each client controlling requests for data to/from the database held on a
central server.
- The DBMS can also be on a server, where all database processing is done on the server and
clients submit their requests for processing.
Type of new DSS system recommended for the case
- A Distributed Architecture may be implemented
- A central Database residing in a powerful server computer (High-end PC) in the Head office
at New York
- Application processing by the local stores, transferring of only valid data to the central
database in New York
- Client-Server architecture between the retail outlet PCs and the central database server
- Accompanying Human decision-making workload distributed among officials at the local
stores
- Application processing time, therefore, reduced because of distributed system architecture.
The main types or categories of decision models used in DSS
model base software.
Eom & Lee (1990), in their survey of 203 Decision Support Systems, looked at the main
types, or categories of models used in DSS model base software. Following is a description
of these types:
Data analysis systems
- These models Present simple representations of data to users.
- Examples include, summaries, graphical representations, etc.
Analysis information systems
- These models use small statistical models in processing data.
- They provide management information.
Accounting models
- These models calculate consequences of planned actions on financial data.
- They also support financial planning.
Representational models (simulations)
- These models estimate future consequences of actions.
- They are used to find good enough solutions.
16. 16
Optimisation models
- These models generate optimal or best solutions using formulae or algorithms.
Suggestion models
- These models produce specific decisions for structured tasks.
- As specific decisions are generated for the users, their remains small role for human
judgment.
- The above six categories of models are based on a classification scheme which goes from
models which do not determine the decision maker’s actions to those which potentially
provide the correct answer.
Case.
Traditionally, when large Information Systems companies roll out custom network
applications and encounter performance problems, their first reaction is often to add such
things as higher speed switches, to migrate to fast backbone networks. If this does not work,
they tend to find other ways of solving the problem by spending lots of money, hoping it will
go away. Such steps do not normally address the real problems of poorly coded applications
and attempts to rebuild and roll out the application again waste time and money. DSS
modelling can help to avoid this problem by using modeling techniques in the design stage to
find a good enough solution to the problem by experimentation.
Discuss the different DSS modeling techniques available and recommend an appropriate one
for the problem outlined above.
Answer:
Many techniques exist for designing different types of models. Some useful techniques are
discussed below:
Decision Analysis Technique
- The decision analysis technique (decision tables and decision trees) is a relatively simple
technique for modelling situations where one has to find the best solution from a relatively
small number of alternatives.
Mathematical Technique
- Based on mathematical theory, it is a relatively unbiased approach to allocate scarce
resources amongst activities; or optimise measurable goals.
- This technique is used to create models that can find best solutions from large numbers of
alternatives.
- Examples include, linear programming, goal programming, etc.
Simulation
- Simulation is a technique for finding a good enough solution to a problem.
17. 17
- It is mainly used in representational models.
- Because it is usually costly to produce computer simulations, it is only used when a problem
is too complex for mathematical techniques.
Heuristics
- Heuristic refers to a common sense rule or principle or guideline.
- Heuristic procedure can be described as finding rules, through a trial and error search process, that
help to solve intermediate subproblems of a complex problem, and thereby finding methods that
ultimately lead to a computational algorithm or general solution of it.
Influence Diagrams
- An influence diagram shows the dependency of a variable on the value of another.
- It is an easy and convenient way of mapping all variables in a decision problem where there are
small number of alternatives.
Recommendation:
- The Simulation technique may be recommended for the problem outlined in the case as large
companies can afford it and as simulation applications are getting cheaper, particularly in the network
area.
- There are a number of advantages of the simulation technique that can lead to better network design
planning.
- Advantages include: simulation theory is relatively straightforward; DSS builder constantly
interfaces with manager; shows which variables are important; real life probabilities are used as
opposed to predictive ones; time compression in terms of a feeling of the long term in minutes; easy
to obtain a wide range of performance measures; etc.
- Disadvantages include: slow and costly construction; optimal solution cannot be guaranteed;
solutions and inferences not transferable; and may, at times, be presented as such an attractive option
that superior methods, when they exist, may be ignored.
18. 18
Ways in which models may be categorized with a model base of a
Decision Support System.
Strategic Models
- Strategic models are used to support top management’s strategic planning responsibilities.
- Potential applications may include, developing corporate objectives, planning for mergers and
acquisitions or diversifications, etc.
Tactical Models
- Tactical models are used mainly by middle managements to assist in allocating and controlling
organizational resources.
- Examples of tactical models include, labor requirements planning, sales promotion planning, plant
layout determination, etc.
Operational Models
- Operational models generally support operational managers and supervisors of an organization in
their day-to-day activities or very short-term decisions.
- Typical decisions involve, daily or weekly production scheduling, inventory control, maintenance
planning and scheduling, etc.
Analytical Models
- Analytical models are used to perform analysis on data.
- They include statistical models, management science models, data mining algorithms, financial
models, etc.
Case
University
Strategic
- Major campus expansion; affiliation with other universities; development of a new school or college.
Tactical
- Development of a new course; opening a new department; marketing plans for the year.
Operational
- Course scheduling for the semester; specific admission decisions on MBA applicants.
Analytical
- Grade distributions for modules and courses; Ethnic balance monitoring across courses.
19. 19
- In the case of a university, a strategic model may support senior management to appreciate whether
or not a major campus expansion will be profitable; a tactical model may support middle management
to generate and evaluate alternative designs for development of a new course; an operational model
may support operational management to generate and evaluate alternative course schedules for a
semester.
Restaurant
Strategic
A model to support decision making regarding opening of a new branch of the restaurant in another
city.
Tactical
A model to determine optimal amount of money to be spent on advertising about the restaurant at any
given time.
Operational
A model to decide the optional number of sandwiches to be prepared for sale at a park where a
peaceful demonstration is going to be held during lunch time on a holiday.
Analytical
A model to study the correlation of sales of different fast food items with various customer categories.
- In the case of a restaurant, a strategic model may support senior management to appreciate whether
or not the opening of a new branch will be profitable; a tactical model may support middle
management to generate and evaluate alternative amounts of money that can be spent for
advertisement in order to find an optimum; an operational model may support operational
management to generate and evaluate alternative numbers of sandwiches in order to find an optimum.
20. 20
How user interface design of a DSS potentially more important
than in traditional software systems development
- User interface is potentially more important for decision support systems because, the
probability is that the majority of intended users are:
----- Not experts in communicating with computers;
----- Busy people, with a limited amount of time to learn; and
----- Are Likely to be sceptical of the advantages of using a DSS to help their decision
making.
- Decision Support systems are mostly used by managers, many of whom do not have much
exposure to information technology. As such, DSS interface needs to be highly user-friendly
for them, otherwise they will not feel encouraged to use the system.
- Managers are very busy people. According to Mintzberg, managers have 10 different roles.
Performing in so many different roles require them to remain busy all the time.
- Managers are also not always very clear about the philosophy and methodology of DSS
applications, and as such, are often sceptical of the advantages of using a DSS to help their
decision making.
Describe the issues relating to dialogue design for DSS user
interfaces.
There are three major issues relating to dialogue design for DSS user interfaces.
These are: Characteristics of intended users; Dialogue types to be used; and Data presentation
formats.
Characteristics of Users
- There are a large number of ways we can characterise users.
Experience Level
- Complete novices have virtually no knowledge of any computer system.
- Casual experts have a working knowledge of a particular system.
- Associative experts are those who frequently interact with a number of systems but do not
have a good grasp of what the system is actually doing.
- Expert professionals have a profound grasp of how the system is constructed and how it
operates.
Cognitive Style
- Cognitive style is the usual and systematic way in which people think.
- Some concentrate on details of an information display, while others look at the whole
picture and attempt to extract patterns of meaning from it.
- Some people concentrate on the specific, some on the general.
- Some people sum up a situation in terms of how it relates to their past experience; others are
more objective.
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Cognitive Complexity
- This has a number of aspects:
- Differentiation – the number of elements a person seeks out and assimilates in the cognitive
process.
- Discrimination – the way in which a person assigns stimuli to different categories.
- Integration – the number and completeness of rules that the person uses in the cognitive
process.
Field Dependence/Independence
- Field independent people tend to perceive patterns of data, which are independent of their
context.
- Field dependent people find it difficult to separate data from the context in which it is
embedded.
Thinking Mode
- Systematic thinkers search data in a planned, sequential manner, looking for causal
relationships, setting up formulae and rules.
- Heuristic thinkers search data on a trial-and-error basis, using common sense to find
patterns or solutions - effectively intuitive thinking.
Dialogue Styles
- There are potentially a number of alternative dialogue styles available for interaction: menu;
command language; question and answer; form filling; natural language interface; object
manipulation; handwriting recognition; voice interaction, multimedia, virtual reality, etc.
- If one wants to give the user a lot of power over the system, a command or query language
is the most appropriate.
- Some DSS now provide a range of dialogue styles.
Data Display Formats
- Data can be presented as raw numbers or as summary data.
- Alternatively, numerical data can be transformed into different types of graphs.
- Also there are new approaches to data presentation, including, Speech synthesis; Visual
interactive modeling; Hypertext and hypermedia, etc.
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Uses with differing cognitive styles cope with, or prefer different
types of dialogue and data presentation formats; in relation to the
interface component of DSS.
- Zmud did a study on how users with different cognitive styles cope with, or prefer different
types of dialogue and data presentation formats.
Users with High Cognitive Complexity
- People who were measured as having high cognitive complexity have the following
characteristics:
a. Search for and use more data or information; b. Prefer summary or aggregate information
rather than raw data; c. Use more rules in processing data or integrating information; d. Use
more complex information; and e. Generate more alternative decisions.
Field Dependent/Independent Users
- Field dependent people, i.e. those who can’t cope with data divorced from its context, prefer
detailed quantitative information, and require more decision time than field independent
people.
Systematic Thinkers
- People who think systematically, prefer more quantitative information, and need more time
to arrive at decisions than heuristic thinkers.
- However, other studies have produced conflicting results.
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The major features of a User Interface Management System
(UIMS) in the context of MSS.
- Major features of a User Interface Management System are mentioned below:
----- Provides graphical user interface.
----- Accommodates the user with a variety of input devices.
----- Presents data with a variety of formats and out put devices.
----- Gives users help capabilities.
----- Provides interactions with the database and the model base.
----- Stores input and output data.
----- Provides colour graphics, 3D graphics, and data plotting.
----- Has windows to allow multiple functions to be displayed concurrently.
----- Can support communications among and between users and builders of MSS.
----- Provides training by examples.
----- Provides flexibility and adaptibility.
----- Interacts in multiple, different dialog styles.
----- Captures, stores, and analyzes dialogue uses (tracking) to improve the dialogue system.
Eight different dialogue design styles for DSS user interfaces.
- I would like to describe four of the eight different dialogue design styles using the
description of Majchrzak.
- Majchrzak has given a comparative description of four dialogue types on a number of
dimensions:
Dimension Menu Form-Filling Command GUI
Speed Slow at times Moderate Fast Could be slow
Accuracy Error free Moderate Many errors Error free
Training Time Short Moderate Long Short
User Preference Very high Low Prefer (Trained) High
Power Low Low Very high Moderate
Flexibility Limited Very limited Very high Moderate/High
Direction of
Control
The system The system The user System and User
- Another four different dialogue design styles are described below:
- Natural language processing has advanced to the stage where naturalistic front-ends can be
built to database management systems (DBMS).
- Voice recognition systems are not yet sufficiently developed to allow natural interaction,
but are improving with research.
- Multimedia systems are now becoming standard in PC systems.
- Virtual reality systems give three dimensional interfaces.
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The principal characteristics of a traditional decision-room based
GDSS.
- The principal characteristics of a traditional decision-room based GDSS may be described
under its four major components, including, Hardware, Software, People, and Procedures.
Hardware
- Ideally, each member of the group should have a terminal or a workstation of his or her
own.
- A range of input devices may be used, including, keyboard, touch sensitive screens,
graphics tablets, etc.
- Also, there are high resolution screens or large viewing screens to communicate with group
members present who do not have an individual workstation.
Software
- DSS software includes, a database, a model base, and a flexible, easy to use interface.
- In addition, a GDSS has specially developed applications software to support groups in
making decisions.
- This has a number of features, as detailed below.
- Basic Features
--- Standard text editing facilities.
--- Learning facilities.
--- On-line help.
--- Various ways of displaying data – worksheets, spreadsheets, decision trees, etc.
--- Database management facilities.
- Features for group support:
--- Numerical and graphical summaries of the group’s ideas and votes.
--- Menus to prompt for input of data or votes by group members.
--- Software to support transmission of data between individuals or to a central computer.
- Special programs for:
--- Calculation of weights for decision alternatives.
--- Anonymous recording of ideas.
--- Formal selection of a group leader.
--- Elimination of irrelevant ideas in brainstorming.
--- Analysing group interactions.
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People
- People involved include, Users, Chairman, Facilitator, Devil’s Advocate, Note Takers,
Observers, etc.
- The facilitator is a kind of systems manager, who, in effect, sits in between the people and
the technology of the system, calling up and displaying particular specialised information.
Procedures
- These are rules that govern how people behave in the meeting.
- They might include how verbal discussion takes place and the agenda, or flow of events in
the meeting.
Whether it is likely that other GDSS configurations, based on
temporal and spatial separation of participants, will become
widespread over a five year time period.
- There are four types of GDSS, based on spatial and temporal separation of participants i.e.
how they fit along two dimensions:
--- The physical proximity of group members.
--- The duration of the decision making session.
Draw Diagramme here.
Decision Room
- Close Proximity – Short Duration.
- This is like a traditional one-off meeting, but with computer support.
- People sit in a room around a curved table.
- In the simplest form, only the facilitator would have a workstation.
- More typically, each member would have a workstation, all networked together.
- The communication between people can be either verbal, or by means of displaying data via
the computer network, or typically, both.
Teleconferencing
- Geographical Separation – Short Duration.
- Here, two or more decision rooms, separated by perhaps thousands of miles, are linked
together with audio, visual and computer links for special, scheduled meetings.
Local Decision Network
- Close Proximity – Ongoing Duration.
- This configuration is suited to a situation where a group of colleagues – perhaps members of
a research group and located in the same building deal with problems on a regular basis.
- Rather than meet in a special room, people hold a meeting with each person in their own
office, communicating through a LAN based GDSS software.
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Remote Decision Making
- Geographical Separation – Ongoing Duration.
- This differs from teleconferencing in that communication and decision making is ongoing,
although participants may be thousands of miles apart.
- Meetings may be called at just a few minutes notice, using a wide area network to
communicate.
- Yes, other GDSS configurations will become widespread over a five-year time period.
Move towards dispersed group works
- Modern telecommunications facilities are permitting people to decentralize and distribute
group works geographically to suitable locations.
Developments in telecommunications
- Developments in broadband networks supporting real-time video and audio transmissions
allow people to hold audio and video conferences at a much cheaper cost that before.
Advances in software
- User-friendly Electronic Meeting Systems (EMS) and other Group Support Systems (GSS)
softwares now allow ordinary people to login and interact from distant locations.
The term Group Decision Support Systems (GDSS).
- According to DeSanctis and Gallupe, Group Decision Support Systems (GDSS) is an
interactive computer-based system which facilitates solution of unstructured problems by a
set of decision makers working together as a group.
The major characteristics of a Group Decision Support System
(GDSS).
- Major characteristics of a GDSS may be summarized as follows:
--- The GDSS is a specially designed information system, not merely a configuration of
already existing system components.
--- A GDSS is designed with the goal of supporting groups of decision makers in their work.
Therefore, the GDSS should improve the decision-making process or the outcomes of groups.
--- A GDSS is easy to learn and to use. It accommodates users with varying levels of
knowledge regarding computing and decision support.
--- The GDSS may be designed for one type of problem or for a variety of group-level
organizational decisions.
--- The GDSS is designed to encourage activities such as idea generation, conflict resolution,
and freedom of expression.
--- The GDSS contains built-in mechanisms that discourage development of negative group
behaviors such as destructive conflict, miscommunication, and groupthink.
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The benefits of making decisions in groups.
- Potential benefits of making decisions in groups include the following:
--- Groups are better than individuals at understanding problems
--- People are accountable for decisions in which they participate
--- Groups are better than individuals at catching errors
--- A group has more information (knowledge) than any one member. Groups can combine
that knowledge and create new knowledge. As a result, there are more alternatives for
problem solving, and better solutions can be derived.
--- Synergy during problem solving may be produced
--- Working in a group may stimulate the participants and the process.
--- Group members will have their egos embedded in the decision, so they will be committed
to the solution.
--- Risk propensity is balanced. Groups moderate high-risk takers and encourage
conservative.
--- Participation of members in a decision means less likelihood of resistance in
implementation.
Relevant design issues relating to the development of GDSS.
- There are a number of relevant design issues relating to the development of GDSS, which
emerged from a body of research on the dynamics of group decision making.
- These are discussed below:
1. How to Encourage Participation of All Group Members
- Everyone present in a GDSS meeting is there because they have something valuable to
contribute and that can happen only if participation of all group members is encouraged.
2. Issues of Group Dynamics
- Groups, whether an ad hoc meeting of two people or a formal planned meeting involving
several individuals, develop dynamics of their own which are often quite unpredictable.
- In the context of GDSS, it is important to gain an understanding of these group dynamics, in
order to provide possible design solutions.
3. Positive Phenomena in Group Decision Making (Process Gains)
- The idea is to enhance the positive aspects of group decision making, including, Synergy;
Trapping Errors; Stimulation; Learning, etc.
4. Negative Phenomena in Group Decision Making (Process Losses)
- The idea is to reduce or eliminate the negative aspects of group decision making, including,
Air time fragmentation; Attenuation blocking; Concentration blocking; Attention blocking;
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Conformance; Evaluation apprehension; Diffusion of responsibility; Socialising; Domination;
Information overload; Incomplete task analysis; Sub-optimal coordination strategies; etc.
5. GDSS Software Support
- GDSS software support features are built to enhance process gains and reduce or eliminate
process losses.
6. GDSS Should Support Newly Formed Groups
- GDSS may query group members on how they wish the group to operate, and help them in
using the system during decision making.
7. Using GDSS to Select Appropriate People to Serve as Group Members
- Different meetings may require different people with specific skills and expertise.
- GDSS may contain appropriate selection software to assist managers identify appropriate
persons from database to constitute decision making or project teams.
8. Practical Issues
Subgroups/Use of ‘Break-out’ Rooms
--- Some GDSS facilities also include smaller rooms next to the main decision room, equipped with
networked PCs.
--- These are useful to hold meetings of subgroups, each tackling a sub-problem.
Adequate network communications
--- People tend to become frustrated if the system response speed drops during a meeting, so
a high bandwidth LAN is vital.
Well-designed interfaces
--- If even a couple of people have difficulty in mastering interaction with the system, the
productivity of the whole group can noticeably suffer, so it is vital to have well-designed
interfaces.
9. Implementation Alternatives
- There are a number of options for implementation:
--- Build a system permanently at a user site.
--- Bring a portable system to a user site by a vendor when needed.
--- Permanently install at a vendor site.
--- Use a Value Added Data Service (VADS) or Web-Based GDSS.
- Some other relevant design issues may include, Enhance structuredness of unstructured
decisions; Preserve anonymity of participants; Organizational involvement; Ergonomic
considerations, etc.
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Expert System (ES) and essential differences between DSS and ES
- Expert System (ES) is a decision-making or problem-solving computer package intended to
attain or surpass the levels of performance of a human expert in some highly specialized and
usually very narrow field.
- This is an applied Artificial Intelligence technology wherein expertise of a human expert is
transferred to the computer.
- The ES can make inferences and arrive at a specific conclusion.
Essential Differences between DSS and ES
Emergence through different historical pathways
- Decision Support Systems evolved through developments in Computer Science and
Management Science.
- Logic models developed in Management Science began to be implemented on DSS with the
help of developments in concepts and tools in Computer Science.
Different structures
- A Decision Support System consists of a Database, a Model Base, and an Interface.
- An Expert System consists of a Knowledge Base, an Inference Engine, User Interface, An
Explanation Subsystem or Justifier, a Knowledge Refining System, and in some systems, a
Blackboard or Workplace.
Different functions or uses
- ES is used to directly derive a specific answer or solution to a specific question or problem.
- It gives what may be regarded as ‘the correct answer’ to a question; or ‘the solution’ to a
problem, which is normally expected from an expert.
- DSS, on the contrary, gives decision alternatives only, that have to be analysed or evaluated
by the decision maker who has to make a choice himself or herself from among them.
Difference in terms of granularity
- In the case of ES, knowledge can be represented in more flexible ways, in contrast to the
algorithmic approach of DSS models.
- This is why many times more rules are required to represent knowledge in ES compared to
DSS.
Impact, Payoff, and Relevance
-- In comparison with the properties of DSS, the impact of ES has been on both structured
and ill-structured tasks; main payoff has been in confidence building among users in their
decision making; and the relevance has been both direct and indirect.
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Consist of three major activities: Development, Consultation, and
Improvement. Discuss, citing practical examples for each activity.
- ES construction & use consist of three major activities: development, consultation, &
improvement.
Development
- The development of an expert system involves the construction of a problem-specific
knowledge base by acquiring knowledge from experts or documented sources.
- The knowledge is then separated into factual and procedural ones.
- Development activity also includes the construction (or acquisition) of an inference engine,
a blackboard, an explanation facility, and any other required software, such as interfaces.
- The process of developing ES can be lengthy.
- The ES shell is a tool often used to expedite development.
- Exsys from Exsys corporation, is a windows-based ES shell.
Consultation
- Once the system has been developed and validated, it can be deployed to users.
- The ES conducts a bidirectional dialog with the user, asking for facts about a specific
incident.
- The ES asks questions, and the user answers them; additional questions can be asked and
answered; and, finally, a conclusion is reached.
- This effort is made by the inference engine, which chooses heuristic search techniques to be
used to determine how the rules in the knowledge base are to be applied to each specific
problem.
- The consultation environment is also used by the builder during the development phase to
test the system.
Improvement
- Expert systems are improved several times through a process called rapid prototyping
during their development.
The evolving concept of MSS is being influenced by the proposal
of various models of DSS and ES integration.
- Different researchers have proposed different models of making this integration.
Meador, Keen and Guyote’s Model
- Meador, Keen and Guyote suggest that decision making is a process which is composed of
8 steps.
- The first 7 steps are those typically covered by DSS functions, while the 8th is an expert
system function.
- These steps are:
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Step 1. Specification of objectives, parameters, and probabilities.
Step 2. Retrieval and management of data.
Step 3. Generation of decision alternatives.
Step 4. Inference of consequences of decision alternatives.
Step 5. Assimilation of verbal, numeric, and graphical information.
Step 6. Evaluation of sets of consequences.
Step 7. Explanation and implementation of decision.
Step 8. Evaluation of the decision in a broader context.
- A DSS can produce a decision on the basis of data used and the models in the DSS
modelbase, but typically is weak at evaluating how that decision will fit in with the business
context or strategy.
- In the 8th step, an ES is emplyed, as an expert, evaluating the decision in a broader context.
Goul, Shane & Tonge’s Model
- Goul, Shane & Tonge regard the ES not as an add-on, but as an integral part of a DSS,
guiding decision making at each step.
- In this model, the ES might guide the DSS and the user at each stage: giving advice on
which objectives and parameters to specify initially; formulating models; choosing models;
developing and refining models; suggesting which data to collect; which information to
present, etc.
- An ES can also improve the organisation of data in a DSS database.
- An ES can also act as an intelligent database manager, extracting relevant data and
performing high level logical operations on it.
- Integration of ES and DSS also allows the possibility of having better interfaces to DSS.
Convergence of Above Models in terms of an Evolving MSS Model
- Global integration can include several MSS technologies and even crossovers to other
organizations to form inter-organizational systems.
- Corporate MSS, as a global integrated system, may include DSS, MIS, EIS and ES; an
internet-based video conferencing system for group work; EDI for transaction processing,
etc.
- A comprehensive global integrated system has been proposed by Forgionne and Kohl.
ODSS differ from DSS, in terms of purpose, policies,
construction, focus, and support
DSS ODSS
Purpose Improve performance of an
individual decision maker or a
small group
Improve efficiency and effectiveness of
organizational decision making
Policies Sell the system to an individual Sell the system to an organization
Construction Usually an informal process Formal undertaking
Focus On individual and his or her
objectives
On functions to be performed
Support Usually to one individual, one unit,
in one location.
Across functional areas, hierarchical levels,
and geographically dispersed units.
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- A DSS provides analytical support enabling decision makers to perform what-if analyses on
data to generate, evaluate, and compare decision alternatives, whereas, an ODSS may also
provide informational support to them.
- However, current trend is towards integration of these different types of information
systems under the concept of MSS.
The concept and structure of an ODSS, proposed by Jacob and
Pirkul (1992).
ODSS Concepts
- In the context of an ODSS, an organisation is regarded as a network, the nodes of which are
a DSS-human information processing pair.
- The human receives decision support from the local DSS, but is also regarded as an expert
in some area, capable of providing decision support or expert knowledge to other nodes in the
network.
- Each node may thus be regarded as a DSS (computer) and ES (human) synergistic system –
a nodal subsystem.
- In addition to these nodal subsystems, there are global subsystems, that are superior nodes
serving one or more of its subordinate nodes in an organization.
ODSS Structure
- Precise structure of an ODSS is influenced by the structure of the organisation itself.
- Many organisations are hierarchically structured.
- In many organisations, however, certain activities occur without reference to this strict
hierarchy.
- Thus there are three possibilities regarding the structure of interaction in an organization,
such as, hierarchical, ad-hoc, and unconventional (Diagrammes).
- Jacob and Pirkul has proposed the following structure of an ODSS.
The Nodal Subsystem Components
The Data and Model Subsystems
- The data and model subsystems are equivalent to those in a conventional DSS.
The Expert Subsystem
- The Expert subsystem contains the expert domain knowledge of the human at the
subsystem.
- The knowledge of the human is held locally in this component and added to over time.
- This component implies a knowledge base, an inference engine and a tool for transferring
and structuring knowledge from the human to the system.
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The internal Communication Subsystem
- This contains the communications knowledge necessary for interaction between members of
the organisation i.e. between the nodes.
- This knowledge has several sub-components (or types of knowledge):
--- Linguistic – the syntax and semantics of the languages used in the problem domains.
--- Presentation – format of information (text, graphical, icons, etc.).
--- Assimilative – knowledge about what new knowledge to accept from other nodes.
--- Peripheral – information relating to a particular node’s expertise and which needs to be
accessed from another node in the organisation (i.e. a register of who has what knowledge;
where particular knowledge is to be found).
--- Access – who is allowed or permitted to access a particular node’s knowledge.
The External Communication Subsystem
- This functions similarly to the internal communication subsystem, but provides links to
entities outside the organisation.
The Organisational Procedure and Policy Subsystem
- This is a knowledge base holding standard procedures and policies of the organization.
Global Subsystems Components
- These include 4 components, which are, effectively, resources available to everyone in an
organisation:
--- Organisational database and modelbase – these two components are similar to a
conventional DSS, except that they are available over a network to anyone in the
organisation.
--- Organisational procedure and policy subsystem – this component holds major
organisation’s rules, procedures, access rights, ways of doing things, etc. Sub-sets of this
knowledge are kept at particular nodes.
--- Multi-participant Decision Support System (MDSS) – this is similar in principle to the
Local Decision Network concepts of a GDSS.
Additional Comments
- While previous ODSS systems were constructed as independent systems in the past, today
they are most likely to be part of an intranet support infrastructure.
- Because of its complexity, ODSS can be integrated with a Group Support System (GSS)
and/or an Enterprise Information System (EIS).
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The major benefits and limitations Artificial Neural Networks
(ANNs).
- Major benefits and limitations of ANNs are as follows:
Major Benefits of ANNs
- Good at tasks that people are good at.
- Suitable for solving unstructured and semi-structured problems.
- Pattern recognition, even form incomplete information.
- Classification, abstraction and generalisation.
- In theory at least, the processing can be computed in parallel resulting in faster
computations.
- Ability to adapt to new data.
- Cope with fault-tolerance situations.
Major Limitations of ANNs
- Not good at tasks that people are not good at.
- Not suitable for basic data processing or conventional arithmetic calculations.
- Need a vast amount of data.
- Does not perform well on tasks that are not performed by people (for example, Arithmetic).
- Limited to classification and pattern recognition.
- Lack of explanatory capabilities.
- Not economically viable for parallel processing.
ANNs learn in supervised and unsupervised modes.
- ANNs learn in two different modes: Supervised and Unsupervised.
- These are explained below:
Supervised Learning
- Simpler.
- Desired output and the value of inputs are known.
- All the algorithms that express relationships in the system are known.
- Possible to compute values of the output for given values of the weights.
- The difference between the actual and desired weights can be computed.
- This difference can be reduced (ideally to zero) by adjusting the values of the weights.
- Using a loan application as an example, details from the application form describing the
applicant are accompanied by knowledge of whether or not the loan was granted, and how the
resulting loan worked out.
- We can also think of supervised learning like a lesson we learn in school where, for each
problem, there is an associated set of answers.
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Unsupervised Learning
- Desired output and the value of inputs are not known.
- The process is only semi-automatic, a human must examine the result to determine when the
training needs to stop.
- The weights and other parameters can be adjusted once the outputs are examined.
- When we were very young, our Mother may have said to us, "If you touch the stove, you
will burn your hand".
- This is an example of supervised leaning, because we were given both sides of the cause
and effect equation.
- However, may be our Mother didn’t tell us everything that we could touch that would result
in burning of our hand, such as, candle flame or lighter flame.
- Instead, we learned to associate things that looked like flames or felt hot as we got close
with the likelihood of burning our hand if we touched it.
- Unsupervised learning is actually how we as humans do the majority of our learning.
Applications of ANNs to decision support.
- An ANN is a computer technology that attempts to build computers that will operate like
the human brain consisting of biological neural networks.
- The machines have the capability of pattern recognition and, therefore, can work with
inadequate or ambiguous information.
- Following are two specific examples of Successful Applications of ANNs to decision
support:
1. Credit Approval with Neural Networks
- Millions of people around the world seek loans daily for many reasons.
- Financial institutions require them to fill out an application that includes lots information.
- Applications processing can be lengthy because some information may be missing or
incomplete.
- Neural networks can make accurate predictions even when some information is missing.
- Neural computing has successfully been used in credit application processing, increasing
the productivity of the processors by 25-35 percent compared to processing done with other
computerized tools.
2. Bankruptcy Prediction
- If a lending institution gives too many high-risk loans, it will have a much higher risk of
going bankrupt itself, unless the interest rate reflects the risk.
- It needs to balance its risk by taking on a mixture of high- and low-risk loans.
- It also must adjust the payback terms for each loan according to the risk.
- A neural network can predict a customer’s likelihood of repayment i.e. risk.
3. Stock Market Prediction System with Modular Neural Networks
- Accurate stock market prediction is a complex problem.
- Neural networks can produce a successful stock market prediction model.
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- Fujitsu and Nikko Securities developed a buying and selling prediction system called
TOPIX.
- In this system, several modular neural networks learned the relationships between past
technical and economic indexes and the timing for when to buy and sell.
- A prediction system made up of modular neural networks was found to be very accurate.
- Simulation of buying and selling stocks using the prediction system showed an excellent
profit.
The phases of the decision making process with examples.
- According to Simon (1977), decision-making process involves three major phases:
intelligence, design, and choice.
- A fourth phase, implementation, was added later.
Intelligence Phase
- In the intelligence phase, reality is examined and a problem or an opportunity is identified
and defined.
- Identification of a problem or an opportunity generates an objective to be achieved, a kind
of future state to be arrived at.
- For example, when a company is incurring loss (a problem), the objective may be “To
increase sales by a certain amount”; or when there is a new market for a company (an
opportunity), the objective may be “To open a new sales center in the new market”.
- In searching for problems, it is necessary to distinguish symptoms from problems, because,
it is possible that symptoms appearing as problems will be identified for solution instead of
the underlying real problems.
- Identified problems may be classified as being structured or programmable, semi-structured,
or unstructured or non-programmable.
- Complex problems may be broken down into smaller sub-problems which may be more
structured and therefore easily solvable.
- Identification of a problem also requires establishment of problem ownership, because, an
identified problem may, in fact, be an uncontrollable factor for a company rendering it
unsolvable within its boundaries.
Design Phase
- In the design phase, a model of the decision-making problem is constructed, tested, and
validated.
- The idea is to understand the problem and test alternative solutions using the model.
- Decision makers sometimes develop mental models, especially in time pressure situations.
- Possible alternatives to solve a problem or to realize an opportunity are generated in this
phase.
- Models may be normative or descriptive.
- Normative implies that the chosen alternative is demonstrably the best of all possible
alternatives.
- Optimization models are examples.
- Descriptive models describe things as they are, or as they are believed to be.
- Cognitive maps, narratives, etc. are examples.
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Choice Phase
- The choice phase is the one in which one of the alternatives identified in the design phase is
chosen for implementation.
- In other words, it includes search, evaluation, and recommendation of an appropriate
solution to the model.
- A solution to a model is a specific set of values for the decision variables in a selected
alternative.
- Solving the model is not the same as solving the problem the model represents.
- The solution in the model yields a recommended solution to the problem.
- The problem is considered solved only if this recommended solution is successfully
implemented.
Implementation and Evaluation Phase
- Implementation phase involves implementation of a solution chosen based on a model into
the real world environment.
- After implementation, the choice is evaluated for effectiveness and efficiency.
Distinguishing between decision making in the context of
certainty, risk, and uncertainty.
- Decision making involves selection of a course of action from a number of possible
alternatives.
- To evaluate or compare alternatives, it is necessary to predict the future outcome of each
alternative.
- Pridictability of an outcome depends on decision situation, which can be one of Certainty,
Risk, and Uncertainty.
Decision making varies significantly under these different situations:
Decision Making under Certainty
- It is assumed that, complete knowledge is available so that the decision maker knows
exactly what the outcome of each course of action will be.
- In other words, it is assumed that there is only one outcome for each alternative.
Decision Making under Risk
- A decision made under risk is one in which the decision maker must consider several
possible outcomes for each alternative, each with a given probability of occurrence.
- The decision maker can asses the degree of risk associated with each alternative – called as
calculated risk
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Decision Making under Uncertainty
- In decision making under uncertainty, several outcomes are possible for each course of
action, but the decision maker does not know, or cannot estimate, the probability of
occurrence of the possible outcomes.
Management can be classified as strategic, tactical, and
operational.
According to Robert S. Anthony there are three levels of management in any typical
organization, namely, Strategic, Tactical, and Operational level.
There are significant differences in the types of decisions made at these different levels.
- Management at the Strategic level determines long-term objectives, strategies, and policies
of an organization.
- Most of the decisions made this level are poorly structured to unstructured.
- Also more of planning decisions rather than control decisions are made at this level.
- Typical decisions made at this level include, new product/service introductions,
technological developments, capacity building, functional expansions, mergers, acquisitions,
etc.
- Management at the Tactical level is concerned with effective and efficient use of resources
to achieve objectives set by strategic management.
- Decisions at this level include tactical planning decisions and also management control
decisions.
- Management at the Operational level is generally concerned with carrying out day-to-day
tasks or transactions of an organization.
- Decisions at this level are more about management and control than about planning.
- Planning decisions are generally for short-terms, such as, operational scheduling, etc.
Management Information System (MIS); Executive Information
System (EIS); and Business Intelligence (BI).
Management Information System (MIS)
- The purpose of MIS is to provide information to support managerial decision-making of an
organization.
- This informational support may be provided through on-line query facilities and/or through
printed periodic, adhoc, or exception reports.
- This information is generally based on summarized operational (i.e. internal) data.
- This data may be derived directly from the TPS database itself or, alternatively, operational
data may be periodically stored in a specific reporting database.
- This reporting database might be called a ‘data mart’
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Executive Information System (EIS):
- The purpose of an Executive Information System (EIS) or Executive Support System (ESS)
is to provide senior managers of an organization a system to assist them in strategic planning
and management.
- Their purpose is to analysis, compare and highlight trends to help govern the strategic
direction of a company.
- They are commonly integrated with operational systems, giving managers the facility to
‘drill down’ to find out detailed information on any issue.
Business Intelligence (BI)
- Business Intelligence (BI) is a broad category of applications and technologies for
gathering, storing, analyzing, and providing access to data to help enterprise users make
better business decisions.
- BI applications include the activities of decision support systems, query and reporting,
online analytical processing (OLAP), statistical analysis, forecasting, and data mining.
MIS, EIS and BI support decision – making at the different levels
of management.
Management Information System (MIS)
- MIS serves the management level of an organization, providing managers with reports and,
in some cases, with online access to the organization’s current performance and historical
records.
- Typically, they are oriented almost exclusively to internal, not environmental or external,
events.
- MIS primarily serves the functions of planning, controlling, and decision-making at the
management level.
- Generally they depend on underlying transaction processing systems for their data.
- MIS generally provide answers to routine questions that have been specified in advance and
have a predefined procedure for answering them.
- As a result, these systems are generally not flexible and have little analytical capability.
- Fore instance, MIS reports might list the total pounds of lettuce used in a certain quarter by
a fast food chain or compare total annual sales figures for specific produces to planned
targets.
Executive Information System (EIS):
- EIS are developed primarily to support the following decision making objectives:
- Provide an organizational view of operations.
- Serve the information needs of senior executives and others managers.
- Provide an extremely user–friendly interface compatible with individual decision styles.
- Provide timely and effective corporate level tracking and control.
- Provide quick access to detailed information behind text, numbers or graphics.
- Filter, compress, and track critical data and information.
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Business Intelligence (BI)
- Business Intelligence (BI) applications are mission-critical and integral to an enterprise’s operations.
- They may be enterprise-wide or local to one division or department or project.
- They also may be either centrally initiated or driven by user demands.
The benefits and limitations of ES and the circumstances in which
ES provide appropriate support for management decision-making.
The major benefits of expert systems may include:
- Monetary savings, because fewer human experts are needed;
- Improved quality of decisions and or solutions because they are more consistent and fewer mistakes are
made;
- Their compatibility with various decision styles;
- Their use as a training vehicle;
- The expert is freed from repetitive, time-consuming tasks;
- Scarce expertise is preserved; and
- Their ability to operate in hazardous environments.
Limitations of ES may include:
- Knowledge is not always readily available;
- Expertise can be hard to extract from humans;
- Approaches of different experts to situation assessment may be different;
- Users of ES have natural cognitive limits;
- Most experts have no independent means of checking whether their conclusions are reasonable; and
- Knowledge transfer is subject to a host of perceptual and judgmental biases.
Suitable examples, how a DSS can support different classes of users
in different phases of the decision-making process.
The major classes of DSS users are:
- Management (user, decision maker) looking for more user-friendly systems that can do more general
analysis and aid in decision-making.
- Management may be strategic, tactical, and operational.
- Staff personnel looking for more detail–oriented and complex systems.
- Expert tool users are skilled in applications of one or more types of specialized problem solving tools.
- They perform tasks that problem solvers do not have the technical skills to do.
- Business (systems) analysts have general knowledge of application areas, have formal business
administration education, and considerable skills in DSS construction tools.
- Facilitators in Group DSS controls and coordinates software of GDSS.
DSS provides support:
- For decision makers mainly in semi-structured and unstructured situations by bringing human judgment
and computerized information together;
- For various managerial levels ranging from top executives to line managers;
- To individuals as well as groups, since less structured problems sometimes require several individuals
from different departments and organizational levels;
- To several interdependent and or sequential decisions; and
- For all phases of the decisions making process: intelligence, design, choice, and implementation.
