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MIS AND SUPPORT TO BUSINESS SYSTEM COMPONENTS BUSINESS SYSTEM MANAGEMENT INFORMATION SYSTEM INPUTS Raw materials, plants and machinery,manufacturing,selling arrangement,accounting Data from transaction of purchase, production and sales, receipts and payments PROCESS Purchasing,manufacturing,selling, accounting Transaction processing and data processing. OUTPUT Quantity of production, sales,stock, income and profit. Computation of production in number, sales in value, stocks in weight. SENSOR Profit Income less assigned cost COMPARISON UNIT Expectation of profit vs actual profit Algebraic comparison module to compare income vs budgeted income profit vs budgeted profit. CORRECTIVE UNIT Managing director Business decision Marketing manager Pricing, advertising and promoting decisons.

MIS and System Concepts T he MIS is an arrangement of data processing and information systems in an orderly manner to support the management in achieving the business objectives. The MIS boundaries cross the limits of the organization and draw the data from the source external to the organization. MIS follows a generalized model of a system as stipulated into the theory and performs on the principle of feedback and control. It works on the principle of control by exception. MIS is designed to provide the information which is exceptional in nature form the point of view of business. The exceptions could be abnormal events, surprising developments, shocking news, or something that was not consistent with the exceptions. The MIS must catch all such points and reports then to the concerned management. It must, therefore, recognize all such possible points and provide a measure for comparison with the actual performance. Unless such a feature is included, the MIS will be supplying m

DDS MODELS 1. BEHAVIOURAL MODELS : these models are useful in understanding the behaviour amongst the business variables. The decision maker can then make decision giving due regards to such behavioural relationship. Examples : REGRESSION MODELS TIME SERIES ANALYSIS MARKET RESEARCH METHODS RATIO ANALYSIS 2. MANAGEMENT SCIENCE MODELS: These models are developed on the principles of business management,the proven methods of management control are available which can be used for the management decision. Examples : BUDGETING MODEL BREAK-EVEN ANALYSIS MODEL RETURN ON INVESTMENT MODEL MODEL FOR CASH BUDGETING COST ACCOUNTING SYSTEMS 3. OPERATIONAL RESEARCH MODELS: These models are mathematical models. They represent a real life problem situation in terms of the variables, constants and parameters expressed in algebraic equations. Examples: LINEAR PROGRAMMING MODEL INVENTORY CONTROL MODELS A-B-C ANALYSIS

Decision Support Systems Origins In the 1960s, researchers began systematically studying the use of computerized quantitative models to assist in decision making and planning (Raymond, 1966; Turban, 1967; Urban, 1967, Holt and Huber, 1969). Ferguson and Jones (1969) reported the first experimental study using a computer aided decision system. They investigated a production scheduling application running on an IBM 7094. In retrospect, a major historical turning point was Michael S. Scott Morton's (1967) dissertation field research at Harvard University. Scott Morton’s study involved building, implementing and then testing an interactive, model-driven management decision system. Fellow Harvard Ph.D. student Andrew McCosh asserts that the “concept of decision support systems was first articulated by Scott Morton in February 1964 in a basement office in Sherman Hall, Harvard Business School” (McCosh email, 2002) in a discussion they had about Scott Morton’s dissertation. Du

DECISION SUPPORT SYSTEM A decision support system is a way to model data and make quality decisions based upon it. Making the right decision in business is usually based on the quality of your data and your ability to sift through and analyze the data to find trends in which you can create solutions and strategies for. DSS or decision support systems are usually computer applications along with a human component that can sift through large amounts of data and pick between the many choices. While many people think of decision support systems as a specialized part of a business, most companies have actually integrated this system into their day to day operating activities. For instance, many companies constantly download and analyze sales data, budget sheets and forecasts and they update their strategy once they analyze and evaluate the current results. Decision support systems have a definite structure in businesses, but in reality, the data and decisions that are based on it are flui