Management Information Systems Essay Sample
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- Category: database
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Management Information Systems Essay Sample
Ques 1): What is MIS? Define the characteristics of MIS? What are the basic Functions of MIS? Give some Disadvantage of MIS?
Answer: Management Information Systems (MIS):-
MIS is the discipline which focuses on the management of information and communications technology elements within business organizations. Specifically, MIS places distinct emphasis on the three core facets through which an organization processes information – people, processes, and information technologies. Accordingly, students who enroll in the MIS program learn to design, build, implement, and manage information systems that will support the information processing needs of an organization. The program is oriented toward the design of systems that will improve an organization’s operational efficiency, add value to existing products, engender innovation and new product development, enhance or add new features to distribution channels and other elements of commercial systems, support collaboration of distributed teams, and help managers make better decisions. Typically, this focus involves the use of advanced information and communications technologies.
1. It supports transaction handling and record keeping. 2. It is also called as integrated database Management System which supports in major functional areas. 3. It provides operational, tactical, and strategic level managers with east access to timely but, for the most, structured information. 4. It supports decision –making function which is a vital role of MIS. 5. It is flexible which is needed to adapt to the changing needs of the organization. 6. It promotes security system by providing only access to authorized users. 7. MIS not only provides statistical and data analysis but also works on the basis on MBO (management by objectives). MIS is successfully used for measuring performance and making necessary change in the organizational plans and procedures. It helps to build relevant and measurable objectives, monitor results, and send alerts. 8. Coordination: MIS provides integrated information so that all the departments are aware of the problem and requirements of the other departments. This helps in equal interaction of the different centers and connects decision centers of the organization. 9. Duplication of data is reduced since data is stored in the central part and same data can be used by all the related departments. 10. MIS eliminates redundant data.
11. It helps in maintaining consistency of data. It is divided into subsystems. Handlings with small systems are much easier than an entire system. This helps in giving easy access of data, accuracy and better information production. 12. MIS assembles, process, stores, Retrieves, evaluates and disseminates the information.
Function of MIS:-
1. The main function of MIS is to help the managers and the executives in the organization in decision making. 2. Large quantities of data like customer’s information, competitor’s information, and personnel records, sales data, accounting data etc is collected from internal sources like the company records and external sources like annual reports and publications. 3. The collected data is organized in the form of a database. 4. The data from the database is processed and analysed by using different tools and techniques. 5. The results of the analysis is properly presented to the managers to help them in decision making.
Disadvantages of MIS:
1. Highly sensitive requires constant monitoring.
2. Budgeting of MIS extremely difficult.
3. Quality of outputs governed by quality of inputs.
4. Lack of flexibility to update it.
5. Effectiveness decreases due to frequent changes in top management
6. Takes into account only qualitative factors and ignores non-qualitative factors like morale of worker, attitude of worker etc.
Ques 2): Explain Knowledge based system? Explain DSS and OLAP with example?
Answer: Knowledge based system(KBS):-
KBS is a system of data and information used for decision making. The system is automated to work on the knowledge based data and information required in a particular domain of management activity. The processing is done based on the past decisions taken under suitable conditions. Decision making is based on the fact that the condition is similar to the past situation hence the decision is also is similar. Examples of KBS are intelligent systems, robotics, neural networks etc.
There are two types of knowledge bases.
a.) Machine readable knowledge bases: The knowledge base helps the computer to process through. It makes the data in the computer readable code which makes the operator to perform easier. Such information sare used by semantic web. Semantic web is a web that will make a description of the system that a system can understand. b.) Human readable knowledge bases: They are designed to help people to retrieve knowledge. The information need to be processed by the reader. The reader can access the information and synthesize their own.
Decision Support Systems (DSS):-
DSS is an interactive computer based system designed to help the decision makers to use all l the resources available and make use in the decision making. In management many a time problems arise out of situations for which simple solution may not be possible. To solve such problems you may have to use complex theories. The models that would be required to solve such problems may have to be identified. DSS requires a lot of managerial abilities and managers judgment.
You may gather and present the following information by using decision support application:
1. Accessing all of your current information assets, including legacy and relational data sources, cubes, data warehouses, and data marts. 2. Comparative sales figures between one week and the next. 3. Projected revenue figures based on new product sales assumptions. 4. The consequences of different decision alternatives, given past experience in a context that is described.
Manager may sometimes find it difficult to solve such problems. E.g. – In a sales problem if there is multiple decision variables modeled as a simple linear problem but having multiple optima, it becomes difficult to take a decision. Since any of the multiple optima would give optimum results. But the strategy to select the one most suitable under conditions prevailing in the market, requires skills beyond the model. It would take some trials to select a best strategy. Under such circumstances it would be easy to take decision if a ready system of databases of various market conditions and corresponding appropriate decision is available. A system which consists of database pertaining to decision making based on certain rules is known as decision support system. It is a flexible system which can be customized to suit the organization needs. It can work in the interactive mode in order to enable managers to take quick decisions.
You can consider decision support systems as the best when it includes high-level summary reports or charts and allow the user to drill down for more detailed information. A DSS has the capability to update its decision database. Whenever manager feels that a particular decision is unique and not available in the system, the manager can chose to update the database with such decisions. This will strengthen the DSS to take decisions in future.. There is no scope for errors in decision making when such systems are used as aid to decision making. DSS is a consistent decision making system. It can be used to generate reports of various lever management activities. It is capable of performing mathematical calculations and logical calculation depending upon the model adopted to solve the problem.
Benefits of DSS:-
• Improves personal efficiency
• Expedites problem solving
• Facilitates interpersonal communication
• Promotes learning or training
• Increases organizational control
• Generates new evidence in support of a decision
• Creates a competitive advantage over competition
• Encourages exploration and discovery on the part of the decision maker
• Reveals new approaches to thinking about the problem space
Online Analytical Processing (OLAP):-
OLAP refers to a system in which there are predefined multiple instances of various modules used in business applications. Any input to such a system results in verification of the facts with respect to the available instances. A nearest match is found analytically and the results displayed form the database. The output is sent only after thorough verification of the input facts fed to the system. The system goes through a series of multiple checks of the various parameters used in business decision making. OLAP is also referred to as a multi dimensional analytical model. Many big companies use OLAP to get good returns in business. The querying process of the OLAP is very strong. It helps the management take decisions like which month would be appropriate to launch a product in the market, what should be the production quantity to maximize the returns, what should be the stocking policy in order to minimize the wastage etc. A model of OLAP may be well represented in the form of a 3D box. There are six faces of the box. Each adjoining faces with common vertex may be considered to represent the various parameter of the business situation under consideration. E.g.: Region, Sales & demand, Product etc.
Ques 3): What are Value Chain Analysis & describe its significance in MIS? Explain what is meant by BPR? What is its significance? How Data warehousing & Data Mining is useful in terms of MIS?
Answer: Business Process Re-engineering:-
The existing system in the organization is totally reexamined and radically modified for incorporating the latest technology. This process of change for the betterment of the organization is called as Business process re-engineering. This process is mainly used to modernize and make the organizations efficient. BPR directly affects the performance. It is used to gain an understanding the process of business and to understand the process to make it better and re-designing and thereby improving the system. BPR is mainly used for change in the work process. Latest software is used and accordingly the business procedures are modified, so that documents are worked upon more easily and efficiently.This is known as workflow management.
Significance of BPR:
Business process are a group of activities performed by various departments, various organizations or between individuals that is mainly used for transactions in business. There may be people who do this transaction or tools. We all do them at one point or another either as a supplier or customer. You will really appreciate the need of process improvement or change in the organizations conduct with business if you have ever waited in the queue for a longer time to purchase 1 kilo of rice from a Public Distribution Shop (PDS-ration shop). The process is called the check-out process. It is called process because uniform standard system has been maintained to undertake such a task. The system starts with forming a queue, receiving the needed item form the shop, getting it billed, payment which involves billing, paying amount and receiving the receipt of purchase and the process ends up with the exit from the store.
It is the transaction between customer and supplier. The above activities takes place between the customer and supplier which forms the process steps this example explains the business process. The business process may be getting admission to the college and graduating from the college, building house, and implementing new technology to an organization (Example EDUNXT in SMUDE), etc A Process can be represented by triangle and following figure shows continuous process of Business. Business process reengineering is a major innovation changing the way organizations conduct their business. Such changes are often necessary for profitability or even survival.
BPR is employed when major IT projects such as ERP are undertaken. Reengineering involves changes in structure, organizational culture and processes. Many concepts of BPR changes organizational structure. Team based organization, mass customization; empowerment and telecommutingare some of the examples. The support system in any organization plays a important role in BPR. ES, DSS, AI (discussed later) allows business to be conducted in different locations, provides flexibility in manufacturing permits quicker delivery to customers and supports rapid paperless transactions among suppliers, manufacturers and retailers. Expert systems can enable organizational changes by providing expertise to non experts. It is difficult to carry out BPR calculations using ordinary programs like spreadsheets etc. Experts make use of applications with simulations tools for BPR. Reengineering is basically done to achieve cost reduction, increase in quality, improvement in speed and service. BPR enable a company to become more competitive in the market. Employees work in team comprising of managers and engineers to develop a product. This leads to the formation of interdisciplinary teams which can work better than mere functional teams. The coordination becomes easier and faster results can be achieved. The entire business process of developing a product gets a new dimension. This has led to reengineering of much old functional process in organizations.
Data Warehouse is defined as collection of databasewhich is referred as relational database for the purpose of querying and analysis rather than just transaction processing. Data warehouse is usually maintained to store heuristic data for future use. Data warehousing is usually used to generate reports. Integration and separation of data are the two basic features need to be kept in mind while creating a data warehousing. The main output from data warehouse systems are; either tabular listings (queries) with minimal formatting or highly formatted “formal” reports on business activities. This becomes a convenient way to handle the information being generated by various processes. Data warehouse is an archive of information collected from wide multiple sources, stored under a unified scheme, at a single site. This data is stored for a long time permitting the user an access to archived data for years. The data stored and the subsequent report generated out of a querying process enables decision making quickly. This concept is useful for big companies having plenty of data on their business processes. Big companies have bigger problems and complex problems. Decision makers require access to information from all sources. Setting up queries on individual processes may be tedious and inefficient. Data warehouse may be considered under such situations. Data warehouse Architecture:
Data mining is primarily used as a part of information system today, by companies with a strong consumer focus -retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among “internal” factors such as price, product positioning, or staff skills, and “external” factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to “drill down” into summary information to view detail transactional data. With data mining, a retailer could use point-of-sale records of customer purchases to send targeted promotions based on an individual’s purchase history. By mining demographic data from comment or warranty cards, the retailer could develop products and promotions to appeal to specific customer segments.
Data Mining is a collaborative tool which comprises of database systems, statistics, machine learning, visualization and information science. Based on the data mining approach used, different techniques form the other discipline can be used such as neural networks, artificial intelligence, fuzzy logic, knowledge representation, high performance Data mining refers to extracting or mining knowledge from large amount of data. There may be other terms which refer data mining such as knowledge mining, knowledge extraction, data/pattern analysis, data archeology, and data dredging. The Knowledge discovery as a process may consist of following steps:
1. Data Cleaning: It removes noise and inconsistent data.
2. Data integration: It is where multiple data sources are combined. 3. Data selection: Data relevant to the analysis task are retrieved from the database. 4. Data transformation: Data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance. 5. Data mining: An essential process where intelligent methods are applied in order to extract data patterns. 6. Pattern evaluation: To identify the truly interesting patterns representing knowledge based on some interesting measure. 7. Knowledge presentation: Visualization and knowledge representation techniques are used to present the mined knowledge to the users.
The best example for data mining which is so close to our lives is Google. The success of Google depends on the use of data mining techniques in the analysis of data in the search engine to meet your search demand.
Ques 4): Explain DFD & Data Dictionary? Explain in detail how the information requirement is determined for an organization?
Data flow diagrams represent the logical flow of data within the system. DFD do not explain how the processes convert the input data into output. They do not explain how the processing takes place. DFD uses few symbols like circles and rectangles connected by arrows to represent data flows. DFD can easily illustrate relationships among data, flows, external entities an stores. DFD can also be drawn in increasing levels of detail, starting with a summary high level view and proceeding o more detailed lower level views.
A number of guidelines should be used in constructing DFD.
• Choose meaningful names for the symbols on the diagram.
• Number the processes consistently. The numbers do not imply the sequence.
• Avoid over complex DFD.
• Make sure the diagrams are balanced.
The data dictionary is used to create and store definitions of data, location, format for storage and other characteristics. The data dictionary can be used to retrieve the definition of data that has already been used in an application. The data dictionary also stores some of the description of data structures, such as entities, attributes and relationships. It can also have software to update itself and to produce reports on its contents and to answer some of the queries. A schedule is made for the development of the system. While preparing the schedule due consideration is given to the importance of the system in the overall information requirement. Due regard is also given to logical system development. For example, it is necessary to develop the accounting system first and then the analysis. Further, unless the systems are fully developed their integration is not possible.
This development schedule is to be weighed against the time scale for achieving certain information requirement linked to a business plan. If these are not fully met, it is necessary to revise the time schedule and also the development schedule, whenever necessary decisions with the financial decisions. The system development schedule is linked with the information requirements which in turn, are linked with the goals and objectives of the business. The selection of the architecture, the approach to the information system development and the choice of hardware and software are the strategic decisions in the design and development of the MIS in the organisation. The organisations which do not care to take proper decisions in these areas suffer from over-investment, under-utilisation and are not able to meet the critical information requirements.
Hardware and Software Plan:-
Giving due regard to the technical and operational feasibility, the economics of investment is worked out. Then the plan of procurement is made after selecting the hardware and software. One can take the phased approach of investment starting from the lower configuration of hardware going over to higher as development takes place. The process is to match the technical.
Ques 5): What is ERP? Explain its existence before and its future after? What are the advantages & Disadvantages of ERP? What is Artificial Intelligence? How is it different from Neural Networks?
Answer: Enterprise Resource Planning:-
To be considered an ERP system, a software package must provide the function of at least two systems. For example, a software package that provides both payroll and accounting functions could technically be considered an ERP software package. However, the term is typically reserved for larger, more broadly based applications. The introduction of an ERP system to replace two or more independent applications eliminates the need for external interfaces previously required between systems, and provides additional benefits that range from standardization and lower maintenance to easier and/or greater reporting capabilities. Examples of modules in an ERP which formerly would have been stand-alone applications include: Manufacturing, Supply Chain, Financials, Customer Relationship Management (CRM), Human Resources, Warehouse Management and Decision Support System. Enterprise Resource Planning is a term originally derived from manufacturing resource planning that followed material requirements planning . MRP evolved into ERP when “routings” became a major part of the software architecture and a company’s capacity planning activity also became a part of the standard software activity.
ERP systems typically handle the manufacturing, logistics, distribution, inventory, shipping, invoicing, and accounting for a company. Enterprise Resource Planning or ERP software can aid in the control of many business activities, like sales, marketing, delivery, billing, production, inventory management, quality management, and human resource management. ERP systems saw a large boost in sales in the 1990s as companies faced the Y2K problem in their legacy systems. Many companies took this opportunity to replace their legacy information systems with ERP systems.
This rapid growth in sales was followed by a slump in 1999, at which time most companies had already implemented their Y2K solution. ERPs are often incorrectly called back office systems indicating that customers and the general public are not directly involved. This is contrasted with front office systems like customer relationship management (CRM) systems that deal directly with the customers, or the eBusiness systems such as eCommerce, eGovernment, eTelecom, and eFinance, or supplier relationship management (SRM) systems. ERPs are cross-functional and enterprise wide. All functional departments that are involved in operations or production are integrated in one system. In addition to manufacturing, warehousing, logistics, and information technology, this would include accounting, human resources, marketing, and strategic management. ERP II means open ERP architecture of components. The older, monolithic ERP systems became componentoriented.
ERP Before and After:-
Prior to the concept of ERP systems, departments within an organization (for example, the human resources (HR)) department, the payroll department, and the financial department) would have their own computer systems. The HR computer system (often called HRMS or HRIS) would typically contain information on the department, reporting structure, and personal details of employees. The payroll department would typically calculate and store paycheck information. The financial department would typically store financial transactions for the organization. Each system would have to rely on a set of common data to communicate with each other. For the HRIS to send salary information to the payroll system, an employee number would need to be assigned and remain static between the two systems to accurately identify an employee. The financial system was not interested in the employee-level data, but only in the payouts made by the payroll systems, such as the tax payments to various authorities, payments for employee benefits to providers, and so on. This provided complications. For instance, a person could not be paid in the payroll system without an employee number.
ERP software, among other things, combined the data of formerly separate applications. This made the worry of keeping numbers in synchronization across multiple systems disappears. It standardized and reduced the number of software specialties required within larger organizations.
Advantages and Disadvantages:-
Advantages – In the absence of an ERP system, a large manufacturer may find itself with many software applications that do not talk to each other and do not effectively interface. Tasks that need to interface with one another may involve
• A totally integrated system
• The ability to streamline different processes and workflows
• The ability to easily share data across various departments in an organization
• Improved efficiency and productivity levels
• Better tracking and forecasting
• Lower costs
• Improved customer service
• Customization in many situations is limited
• The need to reengineer business processes
• ERP systems can be cost prohibitive to install and run
• Technical support can be shoddy
• ERP’s may be too rigid for specific organizations that are either new or want to move in a new direction in the near future.
Artificial Intelligence is the science and technology based on various functions to develop a system that can think and work like a human being. It can reason, analyze, learn, conclude and solve problems. The systems which use this type of intelligence are known as artificial intelligent systems and their intelligence is referred to as artificial intelligence. It was said that the computer don’t have common sense. Here in AI, the main idea is to make the computer think like human beings, so that it can be then said that computers also have common sense. More precisely the aim is to obtain a knowledge based computer system that will help managers to take quick decisions in business. Artificial Intelligence can be classified into various branches like Natural Language Processing (NLP), Speech Recognition, Automated Programming, Machine Learning, Pattern Recognition and Probabilistic Networks. Most of the software developed for AI have been through Prolog, C++, Java and LISP.
These programming languages provide facility of creating various functions of business activity, extension of a function, handling dynamic situations in business, providing uniformity in application etc. A business decision making process depends upon the level of risk and uncertainty involved in the problem. To model the uncertainty and risk of natural language used in developing a AI for business application the concept of fuzzy logic is used. For problems related finance applications apart from fuzzy logic concepts, two other concepts of AI are being researched. These are genetic algorithm and chaotic models. AI is also being applied to the functions of marketing like – Selling, Forecasting, and Communication etc.
Artificial Intelligence and Neural Networks:-
Artificial intelligence is a field of science and technology based on disciplines such as computer science, biology, psychology, linguistics, mathematics and engineering. The goal of AI is to develop computers that can simulate the ability to think, see, hear, walk, talk and feel. In other words, simulation of computer functions normally associated with human intelligence, such as reasoning, learning and problem solving. AI can be grouped under three major areas: cognitive science, robotics and natural interfaces. Cognitive science focuses on researching on how the human brain works and how humans think and learn. Applications in the cognitive science area of AI include the development of expert systems and other knowledge-based systems that add a knowledge base and some reasoning capability to information systems. Also included are adaptive learning systems that can modify their behavior based on information they acquire as they operate. Chess-playing systems are some examples of such systems.
Fussy logic systems can process data that are incomplete or ambiguous. Thus, they can solve semi-structured problems with incomplete knowledge by developing approximate inferences and answers, as humans do. Neural network software can learn by processing sample problems and their solutions. As neural nets start to recognize patterns, they can begin to program themselves to solve such problems on their own. Neural networks are computing systems modeled after the human brain’s mesh like network of interconnected processing elements, called neurons. The human brain is estimated to have over 100 billion neuron brain cells. The neural networks are lot simpler in architecture. Like the brain, the interconnected processors in a neural network operate in parallel and interact dynamically with each other. This enables the network to operate and learn from the data it processes, similar to the human brain.
That is, it learns to recognize patterns and relationships in the data. The more data examples it receives as input, the better it can learn to duplicate the results of the examples it processes. Thus, the neural networks will change the strengths of the interconnections between the processing elements in response to changing patterns in the data it receives and results that occur. For example, neural network can be trained to learn which credit characteristics result in good or bad loans. The neural network would continue to be trained until it demonstrated a high degree of accuracy in correctly duplicating the results of recent cases. At that point it would be trained enough to begin making credit evaluations of its own. Genetic algorithm software uses Darwinian (survival of the fittest), randomizing and other mathematics functions to simulate evolutionary processes that can generate increasingly better solutions to problems.
Ques 6): Distinguish between closed decision making system & open decision making system? What is ‘What – if’ analysis? Why is more time spend in problem analysis & problem definition as compared to the time spends on decision analysis?
Answer: Closed decision-making system:-
The decision-making systems can be classified in a number of ways. There are two types of systems based on the manager’s knowledge about the environment. If the manager operates in a known environment then it is a closed decision-making system. The conditions of the closed decision-making system are:
a) The manager has a known set of decision alternatives and knows their outcomes fully in terms of value, if implemented. b) The manager has a model, a method or a rule whereby the decision alternatives can be generated, tested, and ranked for selection. c) The manager can choose one of them, based on some goal or objective criterion.
Examples are a product mix problem, an examination system to declare pass or fail, or an acceptance of the fixed deposits.
Open decision-making system:-
If the manager operates in an environment not known to him, then the decision-making system is termed as an open decision-making system. The conditions of this system in contrast closed decision-making system are: a) The manager does not know all the decision alternatives.
b) The outcome of the decision is also not known fully. The knowledge of the outcome may be a probabilistic one. c) No method, rule or model is available to study and finalise one decision among the set of decision alternatives.
d) It is difficult to decide an objective or a goal and, therefore, the manager resorts to that decision, where his aspirations or desires are met best. Deciding on the possible product diversification lines, the pricing of a new product, and the plant location, are some decision-making situations which fall in the category of the open decision-making systems. The MIS tries to convert every open system to a closed decision-making system by providing information support for the best decision. The MIS gives the information support, whereby the manager knows more and more about environment and the outcomes, he is able to generate the decision alternatives, test them and select one of them. A good MIS achieves this. What if analysis Decisions are made using a model of the problem for developing various solution alternatives and testing them for best choice.
The model is built with some variables and relationship between variables. In reality, the considered values of variables or relationship in the model may not hold good and therefore solution needs to be tested for an outcome, if the considered values of variables or relationship change. This method of analysis is called ‘what if analysis.’ For example, in decision-making problem about determining inventory control parameters (EOQ, Safety Stock, Maximum Stock, Minimum Stock, Reorder level) lead time is assumed fairly constant and stable for a planning period. Based on this, the inventory parameters are calculated. Inventory manager wants to know how the cost of holding inventory will be affected if lead time is reduced by one week or increased by one week. The model with changed lead time would compute the cost of holding inventory under new conditions. Such type of analysis can be done for purchase price change, demand forecast variations and so on. Such analysis helps a manager to take more learned decisions. ‘What if analysis’ creates confidence in decision-making model by painting a picture of outcomes under different conditions?
A decision is made but such decision needs to be analysed for conditions and assumptions considered in the decision model. The process is executed through analytical modelling of problem and solution.
The starting point of a problem definition is the information gathered in the problem analysis stage. The different aspects surrounding the design problem have been analysed and should be taken into account in the problem definition. For defining a problem this implies that it is not sufficient to describe the existing state. Therefore, we speak consciously of the situation someone is or is not content with. A description of the situation is therefore a description of a state plus the relevant causal model(s), including the assumed patterns of behaviour of the people and organizations involved. A situation is only a problem if the problem-owner wishes to, and want to do something about it. This implies that a situation must be conceivable that is more desirable than the present one: the goal situation. The existing situation, however, can also be formulated in such a manner that a problem does arise. A problem definition is usually set up at the end of the problem analysis phase.
You can use problem analysis to gather information that helps you determine the nature of a problem encountered on your system. The problem analysis information is used to:
• Determine if you can resolve the problem yourself.
• Gather sufficient information to communicate with a service provider and quickly determine the service action that needs to be taken.
The method of finding and collecting error information depends on the state of the hardware at the time of the failure. This procedure directs you to one of the following places to find error information: • Hardware Management Console (HMC) error logs
• The operating system’s error log
• The control panel
• Advanced System Management Interface (ASMI) error logs Hence more time is spent Problem Analysis and Problem Definition.