Decision Support Systems (DSS)
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Decision Making and Problem
Solving
• Problem solving is a critical activity for any
business organization.
• Once a problem has been identified, the
problem-solving process begins with decision
making.
• A well-known model developed by Herbert
Simon divides the decision-making phase of the
problem-solving process into three stages:
intelligence, design, and choice.
• This model was later incorporated by George
Huber into the following expanded model – see
next slide.
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How Decision Making Relates to Problem
Solving
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st
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stage: Intelligence
• Potential problems or opportunities must be
identified and defined.
• This is the most important step because if the
wrong problem is identified and defined, the
entire effort of problem solving is wasted.
• Symptoms are not problems.
• To distinguish symptoms and real problems, we
need to gather data describing the problem.
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Gather data about the problem
• Environmental resources and constraints are
investigated during the 1st stage (Intelligence) to
gain understanding of the problem.
• Study the environment which may include
suppliers, customers and competitors (from
market research) etc.
• Competitors: sell at prices 10% to 15% lower.
• Suppliers: increase costs of goods sold to 30% or
more because of recent oil price hike.
• Customers: complain about product’s defects
which affect human’s health seriously.
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Decision Support System (DSS)
• A DSS may be expressed as a
mathematical program, with some
symbolic representations to represent real
world objects, quantities and meanings, to
solve business decision problems, e.g.
shared resource allocation problems
(see Tutorial D or case 18 of “Advanced
Cases in MIS”).
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Shared resource allocation
problems
• They have resource allocation limits which can
be expressed using mathematical constraints.
• e.g. maximum available cotton (C) in stock for Tshirt production in a factory is 10 tons. This limit
can be expressed as a constraint: C <= 10 tons.
• Some constraints may conflict with another, and
thus finding solutions or the best solution is a
difficult mathematical problem called
optimization or linear programming problem.
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Programmed Decisions
• Easy to computerize using rules, procedures,
quantitative methods or mathematical formulae. E.g.
• Simple financial model: Profit = Revenue - Cost
• Simple Present-value cash flow model: P = F / (1+i)n
where P = present value, F = future single payment ($),
i = interest rate, n = number of years.
• Problems/decisions solved by operational or
transactional processing systems (TPS) are easily
programmed, they are structured problems.
• Routine, summary reports produced by MIS are also
structured problems.
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Nonprogrammed Decisions
• Nonprogrammed decisions
– Rules and relationships not well defined
– Problem is not routine, exceptional cases
– Not easily quantifiable
– Determining an appropriate training program
for new employees is an example of
unstructured problem.
– E.g. interviewing new employees is also a
nonprogrammed or unstructured decision.
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Optimization (using Solver)
• finds the best solution out of many combinations
of possibilities
• E.g. find the appropriate number of products a
factory should produce to meet a profit goal, given
certain constraints and assumptions. E.g.
• E.g. of a problem constraint: there is a limit on the
number of working hours per day (X) for each
machine in the factory: X <= 8 hours.
• E.g. minimum number of basketballs to produce in a
factory per month (Y): Y >= 40000 balls
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Heuristics
• They are “rules of thumb”, guesses or estimates
based on vast experience, or commonly
accepted guidelines.
• E.g. when the inventory level for a certain item
drops below 20 units, an experienced manager
would order 4 month’s supply as a good guess
to avoid out of stock without too much excess
inventory.
• Heuristics are used in optimization for efficiency
especially when there are many complicated
problem constraints, changing cells and decision
variables.
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Excel Solver’s optimizing routines
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DSS: What-if (sensitivity) analysis
• Makes several forecasts for different
possible economic situations (or scenarios)
which could be good, bad or stable by
using varying estimates of inputs such as
growth rate, oil price, labour change,
salaries etc.
• The varying estimates of inputs reflect
uncertainties of real economy.
• In Supply/Demand, elasticity can be done
using what-if analysis.
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Decision Support Systems (DSS)
• A CBIS that focuses on decision-making
effectiveness for various decision-making levels:
mostly for mid and top levels of management,
less for bottom level management.
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Characteristics of DSS
• Predictive nature – output information is for future
events rather than descriptive of past events, should
help reduce risks in future. E.g. forecasts of future
economic conditions, projections of new product
sales, forecasts of changing target customer groups.
• Summary form – output information is not detailed,
but concerned with global data. E.g. managers are
not interested in the details of customer’s invoices but
more interested in the overall buying trend in the
summaries of sales groups.
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Characteristics of DSS
• Ad hoc basis – strategic planning information
is produced irregularly but with a specific
purpose. E.g. Managers may request
marketing analysis information about a new set
of stores (or a new product) when they are
considering adding new stores in the region.
• Unexpected information – economic forecast
for the economy and for the industry may often
find surprises to managers. E.g. marketing
survey in above may produce store locations
that had not been expected.
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Characteristics of DSS
• External data – most of the inputs into DSS are
from sources external to the firm. Information
such as investment opportunities, rates of
borrowed capital, census data, economic
conditions must be obtained from databases
outside the firm e.g. government databases.
• Subjectivity - input data into DSS are usually
highly subjective (personal opinions based on
experiences) and their accuracy may be a
suspect. E.g. rumors about future stock market
trends reported by brokers.
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