Decision Support Systems DSS

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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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1st 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 products defects which
  affect humans 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.
• Business decision problems are often 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
  T-shirt production in a factory is 10 tons. This
  limit can be expressed as a constraint C lt 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 /
  (1i)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 lt 8 hours.
• E.g. minimum number of basketballs to produce in
  a factory per month (Y) Y gt 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 months 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 Solvers 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
  customers 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.