Data, Models and Decisions (Fach) / Types of DSS (Lektion)
In dieser Lektion befinden sich 11 Karteikarten
Different types of DSS.
Diese Lektion wurde von hannemac erstellt.
- file drawer systems Systems that provide access to data items. Examples include real-time equipment monitoring, inventory recorder and monitoring systems. Simple query and reporting tools that access OLTP fall into this category.
- data analysis systems Systems that support the manipulation of data by computerized tools trailored to a specific task and setting or by more general tools and operations. Examples include budget analysis and variance monitoring, and analysis of investment opportunities. Most data warehouse applications would be categorized as data analysis systems.
- analysis information systems Systems that provide access to series of decision-orientated database and small models Examples include sales forecasting based on a marketing database, competitor analyses, and product planning and analysis. OLAP systems fall into this category.
- accounting and financial models Models that calculate the consequenses of possible actions. Examples include estimating of profitability of a new product; analysis of operational plans using a goal-seek capability, break-even analysis, and generating estimates of income statements and balanced sheets. These types of models should be used with "What if?" or sensifivity analysis.
- representational models Models that estimate the consqeuences of actions of the basis of simulation models that include causal relationships and accounting definitions. Examples include a market response model, risk analysis model, and equipment and production simulations.
- optimization models Models that provide guidelines for action by generating an optimal solution consistent with a series of constraints. Examples include scheduling systems, ressource allocation, and material usage optimization.
- suggestion models Models that perform the logical processing leading to a specific suggested decision for a fairly structured or well-understook task. Examples include insurance renewal rate calculation, an optimal bond-bidding model, a log-cutting DSS, a credit scoring. Hinweis: Model liegt stark an der Grenze eines DSS, wegen fehlender Interaktion.
- data-driven DSS A data-driven DSS provides access to and manipulation of large databases of structured data and, especially, a time-series of internal company and external data. Data-driven DSS wird "On-Line Analytical Processing" (OLAP) provide the highest level of funcionality and decision support that is linked to analysis of large collections of historical data.
- model-driven DSS Model-driven DSS emphasize access to an manipulation of a model. Model-driven DSS use data and parameters provided by decision makers to aid them in analyzing a situation, but they are not usally data intensitive. Very large databases are usally not needed for model-driven DSS, but data for a specific analysis may need to be extracted from a large database.
- knowledge-driven DSS This systems suggest or recommend actions by manager. They use business rules and knowledge bases. These DSS are person-computer systems with spezialised problem-solving expertise. The "expertise" consists of knowledge about a particular domain, understanding of problems within that domain, and "skill" at solving some of there problems.
- document-driven DSS This system is envolving to help managers gather, retrieve, classify and manage unstructured documents, including Web pages. A document-driven DSS intregrates a variety of storage and processing technologies to provide complete document retrieval and analysis.