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The planning phase of the system life cycle includes setting organizational objectives, setting the overall mission of the information and decision support system, analyzing environmental factors, and forming an overall strategy for developing the information and decision support system. Analysis includes investigating current and future needs and the strengths and weaknesses of the existing system. Design involves setting the objectives of the new system and developing both a general and detailed systems design. Implementation includes preparing, installing, reviewing, and evaluating the system in terms of organizational objectives. In many cases, the review and evaluation performed during implementation lead to new planning and a new beginning for the system life cycle.
The six stages of data processing system (DPS) growth include: (1) initiation, (2) contagion, (3) control, (4) integration, (5) data administration, and (6) maturity. All organizations start at stage 1 and progress to later stages of data processing growth. Depending on the data processing growth and experience, different organizations have different approaches to implementing the system life cycle.
The development and use of a DPS (Data Processing System), an MIS (Management Information System), or a DSS (Decision Support System) is based on a systems framework or foundation. The systems approach, the use of models, the model of the organization, and the theory of management will be discussed in this section and related to DPS, MIS, and DSS.
The real world is complex and dynamic. Due to this fact, the systems approach normally uses models of the system under investigation instead of the system itself. A model is an abstraction or an approximation of reality. There are a number of different types of models, and some are listed below:
A verbal model, as the name implies, is based on words. It is a verbal or narrative description of reality. Both spoken and written descriptions are considered verbal models. Reports, documents, and conversations concerning a system are all verbal models. Examples include the following: a salesperson describing the competition to a sales manager, a report describing the functioning of a new piece of manufacturing equipment, and a statement about the economy or future sales.
A physical model is a physical representation of reality, An engineer may develop a physical model of a chemical reactor, a builder may develop a scale model or mock-up of a new shopping center, the marketing research department may develop a prototype of a new product, and a doctor may build a plastic skeleton. These are all examples of physical models. Of course, such childhood toys as dolls and model cars are also examples of physical models.
A schematic model is a graphic representation of reality. Graphs, charts, figures, diagrams, illustrations, and pictures are all types of schematic models. Actually, you have already been studying and using schematic models throughout this book. Flowcharts, HIPO diagrams, grid charts, decision tables, structure charts, and organizational charts are all schematic models. Most data processing related models are schematic.
A mathematical model is a mathematical representation of reality. They are used in data processing, accounting, finance, management science, and production management. For example, the following mathematical model might be developed to determine the total cost of a project:
TC = (V)(X) + FC,
TC = Total cost
V =Variable cost per unit
X = Number of units produced
FC = Fixed cost
The above model assumes that the total cost of a project can be divided into fixed and variable costs. In other words, this model assumes that there are no semi variable costs. In developing any model, it is important to make it as accurate as possible. An inaccurate model will usually lead to an inaccurate solution to the problem.
Since a data processing system, a management information system, and a decision support system all attempt to benefit a particular organization, it is useful to develop a model of the organization. The most appropriate type to use in this setting is a schematic model.
The actual procedures used during the system life cycle depend on the organization. Organizations that have had previous experience with information and decision support systems usually have refined system life-cycle procedures. Furthermore, managers and decision makers in these firms do not need to be trained or educated about the potential and hazards of a computerized information and decision support system.
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