Skip to main content

PROCESS OF MIS

The MIS implementation process (Table 3) involves a number of sequential steps (Murdick and Ross, 1975):

1. First establish management information needs and formulate broad systems objectives so as to delineate important decision areas (e.g., general management, financial management or human resources management). Within these decision areas there will be factors relevant to the management decision areas, e.g., general management will be concerned about its relationship with the managing board, institute-client relationships and information to be provided to the staff. This will then lead the design team to ask what information units will be needed to monitor the identified factors of concern. Positions or managers needing information for decision making will be identified.

2. Develop a general description of a possible MIS as a coarse design. This design will have to be further refined by more precise specifications. For efficient management of information processing, the MIS should be based on a few databases related to different sub-systems of the organization.

3. Once the information units needed have been determined and a systems design developed, decide how information will be collected. Positions will be allocated responsibility for generating and packaging the information.

4. Develop a network showing information flows.

5. Test the system until it meets the operational requirements, considering the specifications stipulated for performance and the specified organizational constraints.

6. Re-check that all the critical data pertaining to various sub-systems and for the organization as a whole are fully captured. Ensure that information is generated in a timely manner.

7. Monitor actual implementation of the MIS and its functioning from time to time.

Methodology for implementing MIS

1. Understand the organization

2. Analyse the information requirements of the organization

3. Plan overall strategy

4. Review

5. Preliminary analysis

6. Feasibility assessment

7. Detailed fact finding

8. Analysis

9. Design

10. Development

11. Cutover

12. Obtain conceptual schema

13. Recruit database administrator

14. Obtain logical schema

15. Create data dictionary

16. Obtain physical schema

17. Create database

18. Modify data dictionary

19. Develop sub-schemas

20. Modify database

21. Amend database

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 never be p
Post a Comment
Read more