Modeling and Analysis Decision Support Systems
Static and Dynamic Model
- A static model takes a single snapshot of the system. The decision is made on that snapshot.
- A decision whether to buy a product, a quarterly or annual income statement, the decision to invest are static.
- A dynamic model is time dependent.
- Determining how many checkout points should be open in a supermarket—this needs to take into account the time of day because different numbers of customers arrive during each hour.
Certainty, Uncertainty, and Risk
- In 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.
- Certainty models are relatively easy to develop and solve, and they can yield optimal solutions.
- In decision making under uncertainty, the decision maker considers situations in which several outcomes are possible for each course of action.
Certainty, Uncertainty, and Risk
- The decision maker does not know, or cannot estimate, the probability of occurrence of the
Possible outcomes.
- It is more difficult than making decision under certainty because there is insufficient information.
- A decision made under risk (also known as a probabilistic, or stochastic) is one in which
The decision maker must consider several possible outcomes for each alternative, each with a
Given probability of occurrence.
- Risk analysis is a decision-making method that analyzes the risk (based on assumed known probabilities) associated with different alternatives.
Modeling with Spreadsheets
- Spreadsheet packages were quickly recognized as easy- to-use implementation software for the development of a wide range of applications in business, engineering, mathematics, and science.
- Spreadsheets include extensive statistical, forecasting, and other modeling and database management capabilities, functions, and routines.
- These DSS-related spreadsheets include solver(solver.com), What’s best(lindo.com),
Barbicel (promland.com), Neural Tools, Evolver, @RISK (palisade.com), and GRG-2(MS Excel).
Decision Tables and Trees
- Decision tables conventionally organize information and knowledge in a systematic tabular manner to prepare for it analysis.
- Decision under certainty can be modelled by most linear programing techniques.
- Decision under risk needs more deliberation due to a lot more possibilities—among the most commonly used techniques are decision tables and tree.
Decision Tables and Trees
- A decision tree shows the relationships of the problem graphically and can handle complex situation in a compact form.
- TreeAge Pro(treeage.com), PrecisionTree (palisade.com),
psychwww.com/mtsite/dectree.html and Mind Tools (mindtools.com).
Mathematical Programming Optimization
- Linear programming
- Product Mix
- Transportation Problem
- Simplex Method
- Non-Linear programming
- Travelling salesman
- Vehicle routing problem
Multiple Goals
- Managers want to attain simultaneous goals, some of which may conflict.
- In addition to earning money, the company wants to grow, develop its products and
Employees, provide job security to its workers.
- Managers want to satisfy the shareholders and at the same time enjoy high salaries and
Expense accounts, and employees want to increase their take-home pay and benefits.
To solve this kind of problems, common methods are:
- Utility theory
- Goal programming
- Expression of goals as constraints, using LP
- A point system
Sensitivity Analysis
- Sensitivity analysis attempts to assess the impact of a change in the input data or
Parameters on the proposed solution.
- Sensitivity allows flexibility and adaptation to changing conditions and to the requirements
Of different decision-making situations.
- Sensitivity analysis tests relationships such as the following:
- The impact of changes in external (uncontrollable) variables and parameters on the outcome variables(s)
- The impact of changes in decision variables on the outcome variable(s).
- The effect of uncertainty in estimating external variables
- The effects of different dependent interactions among variables
- The robustness of decisions under changing conditions
What-if Analysis
- What-if analysis is structured as What will happen to the solution if an input variable, and
Assumption, or a parameter value is changed?
- For example, what will happen to the total inventory cost if the cost of the carrying
Inventories increases by 10 per cent?
- A spreadsheet tool is a good example. A manager can analyze a cash flow problem by
Changing parameters’ values and see the differences without any involvement
Of computer programmers.
Goal Seeking
- Goal seeking calculates the values of the inputs necessary to achieve a desired level of an output (goal). It represents a backward solution approach.
- For example, what annual R&D budget is needed for an annual growth rate of 15 per cent by 2012?
Simulation
- A simulation model is a mathematical model that calculates the impact of uncertain inputs
And decisions we make on outcomes that we care about, such as profit and loss, investment
Returns, etc.
- A simulation model will include:
- Model inputs that are uncertain numbers/ uncertain variables
- Intermediate calculations as required
- Model outputs that depend on the inputs -- These are uncertain functions
- Simulation is imitation of some real thing, or a process. The act of simulating something
Generally involves representation of certain key characteristics or behaviors of a selected
Physical or abstract system.
- Simulation involves the use of models to represent real life situation.
Simulation Techniques
- Simulation techniques can be used to assist management decision-making, where analytical
Methods are either not available or inappropriate.
- Typical business problems where simulation could be used to aid management
Decision-making are
- Inventory control.
- Queuing problems.
- Production planning.
Advantages of Simulation
- It is useful for sensitivity analysis of complex systems.
- It is suitable to analyze large and complex real life problems that cannot be solved by the
Usual quantitative methods.
- It is the remaining tool when all other techniques become intractable or fail.
- It can be used as a pre-service test to try out new policies and decision rules for
Operating a system.
Disadvantages of Simulation
- Sometimes simulation models are expensive and take a long time to develop.
- Each application of simulation is ad hoc to a great extent.
- The simulation model does not produce answers by itself.
- It is the trial and error approach that produces different solutions in repeated runs .It does
Not generate optimal solutions to the problems.
Types of Simulation
- Time dependent and time independent simulation: In time dependent simulation know the
Precise time when the event is likely to occur, but in case of time independent simulation it
Is not important to know the time when the event is occur.
- Corporate and financial simulations: The corporate and financial simulation is used in
Corporate planning, especially the financial aspects. The models integrate production,
Finance, marketing, and other functions.
- Visual interactive simulation: It uses computer graphic displays to present the consequences
Of change in the value of input variations in the model.
Steps of Simulation Process
- Identify the problem : The simulation process is used to solve a problem only when the
Assumptions required for analytical models are not satisfied.
- Identify the decision variables and decide the performance: The inventory control situation,
The demand, lead time and safety stock are identified as decision variables and measure
The performance.
- Construct a simulation model : For developing a simulation model, an intimate
Understanding of the relationships among the elements of the system being required.
Steps of Simulation Process
- Testing and validating the model: Any simulation model must represent the system under
Study. This requires comparing a model with actual system validation process.
- Designing of the experiment: It refers to controlling the conditions of the study, such as the
Variables to include. In this situations where observations are taken but the conditions of
The Study are not controlled.
- Run the simulation model : The computer to get the results in the form of operating
Characteristics.
- Evaluating the result : The simulation process is complete, then select the best course of
Action, otherwise make desired changes in model decisions variables.