Overview of Decision Support Systems
1. Introduction to Decision Support Systems
Inform students that a handout containing
a full set of visuals will be provided to them at the end of this lecture.
During the last session we looked at what we mean by decision making,
considering various approaches to studying decision making, and studying some
of the factors involved.
 |
| English: Roadmap from data collection to decision support. Source: NASA. (Photo credit: Wikipedia) |
2. What
are DSS?
DSS evolved as a means for helping people, specifically senior management, make these non- programmed decisions. A definition of DSS (Sprague &
Carlson, 1982) is:
“Computer-based systems that help
decision-makers confront ill-structured problems through direct interaction
with data and analysis models.”
Keen
and Scott-Morton, 1978 (from Turban course text):
“...couple the intellectual resources of
individuals with the capabilities of the computer to improve the quality of
decisions. It is a computer based
support system for management decision makers who deal with semi-structured
problems.”
3. Evolutions of DSS
There
are two identifiable streams of development contributing to DSS:
·
Computer-based information systems.
·
Operational research and management science.
3.1. Computer-Based Information Systems
The
first commercial computer systems used were known as EDPs – Electronic Data
Processing systems. These were systems, usually running on mainframe computers,
which were dedicated to some particular business transaction in
batch-processing mode – Typically something like calculating payrolls or
updating stock records.
The
first of these was LEO (Lyons Electronic Office) developed by Lyons around
1958. EDP systems were used at lower levels of an organisation to automate
existing, routine paper-based transactions.
Management
Information Systems (MIS) appeared in the 1960s, and were aimed at integrating various
EDP tasks, providing a structured information flow to middle management to
allow these managers to better handle day to day operations. MIS were organised
around a database with inquiry and report generating features. Most large
organisations have built MIS. University
of London has one, which handles a wide range of information – things like
courses, student numbers, room allocation etc. – and allows middle managers,
such as Registry and Admission staff to keep track of huge amounts of
information, and allocate resources efficiently.
3.2. Operational Research and Management Science
These
disciplines deal with strategic and tactical planning, and managing resources
efficiently. They do this by building
and using mathematical models and simulations for solving complex problems, and
the techniques used in OR have saved companies large amounts of money.
4. Operational Researches and the Modelling Process
The basic steps involved in the modelling process
are:
·
Define the problem.
·
Collect and record all relevant data.
·
Examine and analyse data and develop a mathematical or systematic
model of the real life situation.
·
Check the model is valid.
·
Test the model under differing conditions.
·
Select the optimum solution using the model.
·
Implement the results.
·
Keep a check on the model and see it is still valid under changing Circumstances.
·
Always mention the drawbacks: i.e. – have all the questions been asked
in formulating the problem?
·
Implicit it OR is the assumption that the problem can be formulated in
mathematical terms.
Statistical
techniques are used in the feedback process where the model is compared to the
reality and checked to see if there are any significant differences.
Computer
resources are mostly used in setting up the model and providing the mathematical
solution required.
5. OR
Problems
Around
the early 1970s, these two streams came together in the form of DSS, in
response to two things:
•
Advances in technology – initially with access to time sharing
terminals and new software.
•
A growing realisation that MIS were not easily able to support
unstructured strategic decision making, typical of the activities of senior
management.
Further
advances came in the late 1970s, with the emergence of PCs and with integrated
software applications; and also applications generators – software environments
and shells which could allow people who were not expert programmers to build
their own DSS. As we have said then, DSS were traditionally aimed at senior
managers, dealing not with day to day information but with mainly historical
information. By looking at trends in
information stretching back months and years, managers could use DSS to model
various scenarios, in order to make projections into the future – to see trends
and patterns, and then take some action.
So DSS provided ways of manipulating all kinds of information to aid
strategic decision making.
Nowadays,
there is a tendency to call any system which supports decision making a DSS.
For example, there are a number of spreadsheet applications available now, such
as Excel, which incorporate features allowing users to explore a wide range of
decision alternatives – essentially allowing people to ask “What if?”
questions.
There
are also a number of project planning applications on the market. A significant
activity of senior management is planning the work of the organisation, perhaps
establishing goals for project teams, and time scales for various activities
over a 5 or 10 year period. The best of these applications allow users easily
to manipulate all kinds of variables to come to some optimum solution – again,
to explore scenarios by asking “What if?” questions (for example, “What is the
effect on the whole project if we increase Task B time scale by 10%?”).
Also,
nowadays, there is a realisation that DSS can potentially support decision
making throughout an organisation, and newer systems are not just aimed at
senior management.
6. What
Should DSS Do?
Sprague (1989) describes a number of characteristics that an ideal or generic DSS should possess, from a manager’s point of view:
1.
They should provide support for decision making, particularly semi-structured
and unstructured decisions – Sprague reports that this is an expressed requirement
by managers.
2. They
should provide decision support for managers at all levels, particularly
supporting the integration of decision making between levels – managers at different
levels typically work on different aspects of the same problem. Co- ordination of the activities of these
people is a central requirement.
3. They
should support interdependent as well as independent decisions – interdependent
decisions are those where an individual makes part of a decision then passes
the responsibility on to someone else (example of bank loans office). They
might also involve decisions made by some group process.
4. They
should support all phases of the decision making process, for example, Simon
(1960):
ü establishing
a goal;
ü identifying
possible courses of action;
ü selecting
the optimum action plan;
ü Implementing
the chosen plan.
5.
They should support a variety of decision making processes – Simon’s model is
only one of many process models. DSS
should support others.
6.
They should be easy and convenient to use.
7. Scopes of DSS
It is difficult to get an overall view of the scope of DSS.
Probably they are most widely used in the area of financial planning. For example, the Treasury uses DSS to aid in its
forecasting and management of
the economy.
These
systems have a number of economic models embodied in software which process all
kinds of economic data – money supply, GNP, trade data etc., in order to make
forecasts and projections of the state of the economy. Also, a number of City institutions, banks
and large firms use DSS for financial management – things like investment
management.
DSS
are also used in the areas of:
•
transport planning and operation;
•
productivity management;
•
industrial process management;
•
marketing;
•
business planning;
•
auditing;
7.1
Survey of DSS Applications (Eom and Lee 1990)
A
paper by Eom & Lee (1990) discusses a literature survey of DSS applications
from the early 1970s to 1988. They found
203 papers in journals dealing with specific DSS applications. Their selection criteria for including
applications were:
•
Supporting decision makers rather than replacing them.
•
Utilising both data and models.
•
Solving problems with varying degrees of structure (mainly
semi-structured and unstructured).
The main
application area seemed to be corporate financial management (65.5%). Included in this area were papers to do with
marketing, transportation and logistics; as well as production and operations management,
finance and strategic management. Outside the corporate area, applications
focused on natural resources, hospital and health care, education, military,
urban planning and administration and government.
Eom and Lee also looked at the level of
management at which the DSS applications were aimed. They found that 23.6% were aimed at strategic
decision making support: the rest at tactical or operational decisions. They
also claim there seems to be an increasing trend towards DSS for strategic
decision making.
8.
Management Reluctance to DSS
Do senior management readily and regularly use DSS?
This is a difficult question
to answer.
There seems to be a greater acceptance of DSS by management in the USA than in the UK (Eom and Lee’s study
largely focused on US systems).
This
might relate to the way managers in the USA are educated – there has been a longer
tradition in the USA that management is a set of skills that needs to be learnt,
whereas in the UK, management until recently has been regarded as something
that you pick up through experience.
Certainly the development of DSS seems more widespread in the USA than
anywhere else.
A recent conversation with a Management Consultant colleague told me that the use of DSS in the UK
was not well advanced because
of resistance to the
idea of computer- aided
support. This resistance seems to take a number of forms:
·
The notion that “secretaries use computers”. This attitude seems to be declining nowadays
– having a sophisticated PC on one’s desk is a mark of status.
·
Managers are convinced of the
quality of their own, intuitive, unaided decision making.
·
Reluctance because managers do not
understand the models used in the DSS.
·
Time constraints – managers do not
have time to help build or learn to use DSS.
·
Previous experience with inadequate
DSS.
9. Current Trends in DSS
Users
of DSS – There are a trend towards
the use of DSS by other people
in the organisation, besides
senior management. These
might be middle managers,
engineers, R&D staff etc.
• Group
decision support – There is also a trend towards more widespread group working
in business generally. In part this has
been driven by technological developments – better communications, based on
efficient exchange of information by computer networks; and by realisations
that better decisions are likely to be produced if the decision making process
has the input of people with a range of skills and expertise. This has led to attempts to produce GDSS– and
we will be looking at GDSS in later lectures.
•Application
of AI to DSS – As we will see in later lectures, the heart of a DSS is a
Model Base – a collection of software algorithms which process largely numerical
data held in a database. These are
procedural and inflexible – and the output often consists of indecipherable
tables of probability data. The use of AI techniques in DSS construction: such
as frames and rules, and heuristic, rule of thumb reasoning, allows the
possibility of representing knowledge in more flexible ways; ways that are more
useful to humans than the output of traditional algorithmic processing.
Strategic level problems require that manipulation of symbolic rather that
numerical information, and AI and Expert Systems can handle this well.
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