Skip to main content

Understanding Decision Support System (DSS)

A Decision Support System (DSS) is a class of information systems (including but not limited to computerized systems) that support business and organizational decision-making activities. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from a combination of raw data, documents, personal knowledge, or business models to identify and solve problems and make decisions.


Typical information that a decision support application might gather and present are:

  • an inventory of all of your current information assets (including legacy and relational data sources, cubes, data warehouses, and data marts),
  • comparative sales figures between one week and the next,
  • projected revenue figures based on new product sales assumptions.

Development Frameworks

DSS systems are not entirely different from other systems and require a structured approach. Such a framework includes people, technology, and the development approach.[10]

DSS technology levels (of hardware and software) may include:

  1. The actual application that will be used by the user. This is the part of the application that allows the decision maker to make decisions in a particular problem area. The user can act upon that particular problem.
  2. Generator contains Hardware/software environment that allows people to easily develop specific DSS applications. This level makes use of case tools or systems such as Crystal, AIMMS, and iThink.
  3. Tools include lower level hardware/software. DSS generators including special languages, function libraries and linking modules

An iterative developmental approach allows for the DSS to be changed and redesigned at various intervals. Once the system is designed, it will need to be tested and revised for the desired outcome.

DSS components may be classified as:

  1. Inputs: Factors, numbers, and characteristics to analyze
  2. User Knowledge and Expertise: Inputs requiring manual analysis by the user
  3. Outputs: Transformed data from which DSS "decisions" are generated
  4. Decisions: Results generated by the DSS based on user criteria
Labels:

Comments

Post a Comment

Popular posts from this blog

Advantages and Disadvantages of EIS Advantages of EIS Easy for upper-level executives to use, extensive computer experience is not required in operations Provides timely delivery of company summary information Information that is provided is better understood Filters data for management Improves to tracking information Offers efficiency to decision makers Disadvantages of EIS System dependent Limited functionality, by design Information overload for some managers Benefits hard to quantify High implementation costs System may become slow, large, and hard to manage Need good internal processes for data management May lead to less reliable and less secure data
1 comment
Read more

Inter-Organizational Value Chain

The value chain of   a company is part of over all value chain. The over all competitive advantage of an organization is not just dependent on the quality and efficiency of the company and quality of products but also upon the that of its suppliers and wholesalers and retailers it may use. The analysis of overall supply chain is called the value system. Different parts of the value chain 1.  Supplier     2.  Firm       3.   Channel 4 .   Buyer
Post a Comment
Read more

Big-M Method and Two-Phase Method

Big-M Method The Big-M method of handling instances with artificial  variables is the “commonsense approach”. Essentially, the notion is to make the artificial variables, through their coefficients in the objective function, so costly or unprofitable that any feasible solution to the real problem would be preferred, unless the original instance possessed no feasible solutions at all. But this means that we need to assign, in the objective function, coefficients to the artificial variables that are either very small (maximization problem) or very large (minimization problem); whatever this value,let us call it Big M . In fact, this notion is an old trick in optimization in general; we  simply associate a penalty value with variables that we do not want to be part of an ultimate solution(unless such an outcome is unavoidable). Indeed, the penalty is so costly that unless any of the  respective variables' inclusion is warranted algorithmically, such variables will ...
Post a Comment
Read more