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MB0047 : Explain the different control issues in management information systems.

Control Issues in Management Information Systems.

Answer : Control

Control is the process through which manager assures that actual activities are according to standards leading to achieving of common goals. The control process consists of measurement of progress, achieving of common goals and detects the deviations if any in time and takes corrective action before things go beyond control. Information systems operate in real world situations which are always changing and there are lots of problems. Information systems are vulnerable to various threats and abuses. Some of the points are memory, communications links, microwave signal, telephone lines etc.

Security Control

The resources of information systems like hardware, software, and data, need to be protected preferably by build in control to assure their quality and security.

Types of Security Control:

  • Administrative control
  • Information systems control
  • Procedural control
  • Physical facility control

Administrative Control

Systems analysts are actually responsible for designing and implementing but these people need the help of the top management in executing the control measure. Top executives provide leadership in setting the control policy. Without their full support, the control system cannot achieve its goal.


Information System Control

Information system control assures the accuracy, validity and proprietary of information system activities. Control must be there to ensure proper data entry processing techniques, storage methods and information output. Accordingly management information system control are designed to see or monitor and maintain quality, security of the input process, output and storage activities of an information system.

Input Control

As we know whatever we give to computer the computer processes that and returns the result to us. Because of this very fact, there is a need to control the data entry process. The types of input control are:

  • Transaction Codes: Before any transaction can be input into the system, a specific code should be assigned to it. This aids in its authorization.
  • Forms: a source document or screen forms should be used to input data and such forms must adhere to certain rules.
  • Verification: Source document prepared by one clerk can be verified by another clerk to improve accuracy.
  • Controltotals: Data entry and other system activities are frequently monitored by the use of control-total. For example, record count is a control-total that consist of counting the total number of source documents or other input records and compare them at other stage of data entry. If totals do not match, then a mistake is indicated.
  • Check digit: These are used for checking important codes such as customer number to verify the correctness.
  • Labels: It contains data such as file name, and date of creation so that a check can be made that correct file is used for processing.
  • Character and field checking: Characters are checked for proper mode – numeric, alphabetic, alphanumeric fields – to see if they are filled in properly.


Processing Control

Input and processing data are so interrelated that we can take them as first line of defense. Once data is fed into the computer, controls are embedded in various computer programs to help, detect not only input errors but also processing errors. Processing – controls are included to check arithmetic calculations and logical operations. They are also used to ensure that data are not lost or do not go unprocessed. Processing control is further divided into hardware and software control.

Output Control

These are developed to ensure that processed information is correct, complete and is transmitted to authorized user in a timely manner. The output control are mostly of same kind as input control e.g. Output documents and reports are thoroughly and visually verified by computer personnel and they are properly logged and identified with rout slips

Storage Control

Control responsibility of files of computer programs and databases is given to librarian or database administrator. They are responsible for maintaining and controlling access to the information. The databases and files are protected from unauthorized users as accidental users. This can be achieved with the help of security monitor. The method includes assigning the account code, password and other identification codes. A list of authorized users is provided to computer system with details such as type of information they are authorized to retrieve or receive from it.

Procedural Control

These methods provide maximum security to operation of the information system.  Standard procedures are developed and maintained manually and built in software help display so that every one knows what to do. It promotes uniformity and minimize the chance of error and fraud. It should be kept up-to-date so that correct processing of each activity is made possible.


Physical Facility Control

Physical facility control is methods that protect physical facilities and their contents from loss and destruction. Computer centers are prone to many hazards such as accidents, thefts, fire, natural disasters, destructions etc. Therefore physical safeguards and various control procedures are required to protect the hardware, software and vital data resources of computer using organizations.

Physical Protection Control

Many type of controlling techniques such as one in which only authorized personnel are allowed to access to the computer centre exist today. Such techniques include identification badges of information services, electronic door locks, security alarm, security policy, closed circuit TV and  dust control etc., are installed to protect the computer centre.

Telecommunication Controls

The telecommunication processor and control software play a vital role in the control of data communication activity. Data can be transmitted in coded from and it is decoded in the computer centre itself. The process is called as encryption.

Computer Failure Controls

Computers can fail for several reasons like power failures, electronic circuitry malfunctions, mechanical malfunctions of peripheral equipment and hidden programming errors. To protect from these failure precaution, any measure with automatic and remote maintenance capabilities may be required.


MB0047 : a. Explain Management Science models in detail.

 b. Estimate the completion time of each activity whose optimistic time

 estimate is 5 seconds and the pessimistic time estimate are 20 seconds. The

 most likely time estimate is 9 seconds.

Answer :- Management Science Models

These models are developed on the principles of business management, accounting and econometrics. In many areas of management, the proven methods of management control are available which can be used for the management decision. There are also several management systems, which can be converted into the Decision Support System models.

Some of these models can be used straight away in the design of the Decision Support System. While some others require the use of management principles and practices, most of the procedure based decision-making models belong to this category. One can develop a model for selection of vendor for procurement of an item, based on the complex logical information scrutiny. Such models take away the personal bias of the decision-maker.

Budgeting Models

Controlling the business performance through the budget system is an accepted management prac­tice. In this approach, various budgets are prepared, viz., the Sales Budget, the Production Budget, the Capacity Budget, the Manpower Budget, the Expense Budget, and the Inventory Budget, etc. Using these budgets the profits are estimated.

Break-even Analysis Model

This model is simple but very useful for determining the volume of business activity at which there is no loss or profit. The model is used to decide the alternatives based on the cost, volume and price.This model can be built for the company, for the product groups or for any activity, where you can identify the fixed cost, the variable cost and the revenue at each activity level in terms of the units demanded. The advantage of this model is that it tells you as to what the break-even point for the given level of costs and revenue is. If there are possibilities of altering the costs, it would tell its impact on the break-even point, i.e., if the price is reduced, the revenue will come down and the break-even point will further go up.

Return on Investment Analysis

The investment decisions are very common in the business organisations and they are of two types. First, one has to invest in one among the several alternatives which are competing with each other. The second decision the management has to take is how to allocate the total funds to the various investment projects.

Corporate Model of Return on Investment

This model is popularly known as DuPont Model where the composition and the analysis of the Return on Investment is shown. This model is better than the above discussed individual ratio model and its analysis as this model provides an insight into the relationships of the various factors affecting the return on investment.

Model for Cash Budgeting

Cash budgeting is a continuous process. With careful cash planning, a company should be able to maintain sufficient cash balance for its needs, yet not be in a position where it is holding excessive cash. This kind of planning will help raise the short-term loans and simultaneously focus on the issues which are affecting the financial management.

Estimate the completion time of each activity

to = The optimistic time estimate. tm = The most likely time estimate.
tp = The pessimistic time estimate.

The activity time estimate te

The activity time estimate te = 5+4(9) +20 / 6   = 10.1667

MB0047 : There are two investment plans in the market whose details are given below based on which you need to decide which investment plan you need to select. Suggest which investment plan you prefer and why?


Plan A

Plan B

Investment in Rs. Million



Savings/ gain per year in Rs. Million



No. of years savings or gain would occur



Discount Rate



In above investment analysis, the Net Present Value (NPV) is calculated and compared with all the investment alternatives.

NPV = (PV of further Cash flow) – Investment = PV – 1

The formula used for the present value PV is:

Where T is a number of period, in which an amount S for each period is to be received and i is a discount rate.

Plan A

PV = 1.0   1- (1+0.12)-5        = 3.605 Therefore, NPV = 3.605 – 3 = 0.605


Plan B

PV = 0.75    1- (1+0.12)-5        = 2.704 Therefore, NPV = 2.704 – 2.7 = 0.004


From the above data: since Plan A has more NPV we should select plan A.

MB0047 : Explain with an example of your own the difference between data, information, knowledge and wisdom.

There is probably no segment of activity in the world attracting as much attention at present as that of knowledge management. This arena of activity  quickly found there didn’t seem to be a wealth of sources that seemed to make sense in terms of defining what knowledge actually was, and how was it differentiated from data, information, and wisdom. What follows is the current level of understanding piece together regarding data, information, knowledge, and wisdom.

According to Russell Ackoff, a systems theorist and professor of organizational change, the content of the human mind can be classified into five categories:

  • Data: symbols
  • Information: data that are processed to be useful; provides answers to “who”, “what”, “where”, and “when” questions
  • Knowledge: application of data and information; answers “how” questions
  • Understanding: appreciation of “why”
  • Wisdom: evaluated understanding.

Ackoff indicates that the first four categories relate to the past; they deal with what has been or what is known. Only the fifth category, wisdom, deals with the future because it incorporates vision and design. With wisdom, people can create the future rather than just grasp the present and past. But achieving wisdom isn’t easy; people must move successively through the other categories.

A further elaboration of Ackoff’s definitions follows:

Data… data is raw. It simply exists and has no significance beyond its existence (in and of itself). It can exist in any form, usable or not. It does not have meaning of itself. In computer parlance, a spreadsheet generally starts out by holding data.

Information… information is data that has been given meaning by way of relational connection. This “meaning” can be useful, but does not have to be. In computer parlance, a relational database makes information from the data stored within it.

Knowledge… knowledge is the appropriate collection of information, such that it’s intent is to be useful. Knowledge is a deterministic process. When someone “memorizes” information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge. For example, elementary school children memorize, or amass knowledge of, the “times table”. They can tell you that “2 x 2 = 4” because they have amassed that knowledge (it being included in the times table). But when asked what is “1267 x 300”, they can not respond correctly because that entry is not in their times table. To correctly answer such a question requires a true cognitive and analytical ability that is only encompassed in the next level… understanding. In computer parlance, most of the applications we use (modeling, simulation, etc.) exercise some type of stored knowledge.

Understanding… understanding is an interpolative and probabilistic process. It is cognitive and analytical. It is the process by which I can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between “learning” and “memorizing”. People who have understanding can undertake useful actions because they can synthesize new knowledge, or in some cases, at least new information, from what is previously known (and understood). That is, understanding can build upon currently held information, knowledge and understanding itself. In computer parlance, AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge.

Wisdom… wisdom is an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.). It beckons to give us understanding about which there has previously been no understanding, and in doing so, goes far beyond understanding itself. It is the essence of philosophical probing. Unlike the previous four levels, it asks questions to which there is no (easily-achievable) answer, and in some cases, to which there can be no humanly-known answer period. Wisdom is therefore, the process by which we also discern, or judge, between right and wrong, good and bad. I personally believe that computers do not have, and will never have the ability to posses wisdom. Wisdom is a uniquely human state, or as I see it, wisdom requires one to have a soul, for it resides as much in the heart as in the mind. And a soul is something machines will never possess (or perhaps I should reword that to say, a soul is something that, in general, will never possess a machine).

The following diagram represents the transitions from data, to information, to knowledge, and finally to wisdom, and it is understanding that support the transition from each stage to the next. Understanding is not a separate level of its own.

  • Data represents a fact or statement of event without relation to other things. Ex: It is raining.
  • Information embodies the understanding of a relationship of some sort, possibly cause and effect. Ex: The temperature dropped 15 degrees and then it started raining.
  • Knowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next. Ex: If the humidity is very high and the temperature drops substantially the atmospheres is often unlikely to be able to hold the moisture so it rains.
  • Wisdom embodies more of an understanding of fundamental principles embodied within the knowledge that are essentially the basis for the knowledge being what it is. Wisdom is essentially systemic. Ex: It rains because it rains. And this encompasses an understanding of all the interactions that happen between raining, evaporation, air currents, temperature gradients, changes, and raining.

MB0047 : What are the different types of decision and decision making systems? Explain in detail.
Answer : Types of Decision-making Systems

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:

  • The manager has a known set of decision alternatives and knows their outcomes fully in terms of value, if implemented.
  • The manager has a model, a method or a rule whereby the decision alternatives can be generated, tested, and ranked for selection.
  • The manager can choose one of them, based on some goal or objective criterion. Few examples are a product mix problem, an examination system to declare pass or fail, or an acceptance of the fixed deposits.

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:

The manager does not know all the decision alternatives.

  • The outcome of the decision is also not known fully. The knowledge of the outcome may be a probabilistic one.
  • No method, rule or model is available to study and finalise one decision among the set of decision alternatives.
  • 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.

Types of Decisions

The types of decisions are based on the degree of knowledge about the outcomes or the events yet to take place. If the manager has full and precise knowledge of the event or outcome which is to occur, then the decision-making is not a problem. If the manager has full knowledge, then it is a situation of certainty. If he has partial knowledge or a probabilistic knowledge, then it is decision-making under risk. If the manager does not have any knowledge whatsoever, then it is decision-making under uncertainty.

A good MIS tries to convert a decision-making situation under uncertainty to the situation under risk and further to certainty. Decision-making in the Operations Management is a situation of cer­tainty. This is mainly because the manager in this field has fairly good knowledge about the events which are to take place, has full knowledge of environment, and has pre-determined decision alternatives for choice or for selection.

Decision-making at the middle management level is of the risk type. This is because of the difficulty in forecasting an event with hundred per cent accuracy and the limited scope of generating the decision alternatives.

At the top management level, it is a situation of total uncertainty on account of insufficient knowledge of the external environment and the difficulty in forecasting business growth on a long-term basis.

A good MIS design gives adequate support to all the three levels of management.

MB0047 : a. Compare between E-enterprise and conventional organizational design
b. List the different business models with example

Comparison between Conventional Design and E-Organization

Conventional Organization Design


Top heavy organization structure Flat organization structure
Work & work place location at one place Separation of work from work place location
Manual & document-based work flows Paperless work flows
High administrative & management overheads Low overheads
Inflexible, rigid and longer business process cycles Flexible agile and responsive process cycles
Private business process systems for self use. They are barred for usage to customers, vendors and business partners Public business processes and systems for use by customers, vendors and business partners
Low usage of technology Use internet, wireless and network technologies

Business Model

Business Model



Virtual Store

Provides information about product, and sells and delivers directly to customer or business organization. amazon.com, rediff.com, ebay.com.

Information Store

Provides information of interest and earns revenue from sharing and advertising. yahoo.com, msn.com rediff.com, satyaminfoway.com

Transaction Process

Processes bills for payment, telephone, electricity, money transfer & banking transactions, membership for club registration. icici.com billjunction.com seekandsource.com

Online Marketing

Provides a marketing platform where buyers and sellers can meet to exchange information, negotiate and place order for delivery. Examples are shoes, commodities. eauction.com seekandsource.com

Content Selling

News, music, photos, articles, pictures, greetings are stored and sold at a price. timesofindia.com gartner.com aberdeen.com

Online Service

Offers services to individuals and business at large and generates revenue. Examples: Tours and travels, manpower recruiting and maintenance services. Railway, restaurant, airlines booking. Online maintenance service. Online examination.

Virtual Communities

Provides platform to meet people of common interests. Software user groups, professional groups like doctors, managers, user groups. Linux Group New Groups Application Package User Groups Community Groups


Provides contents. E-books, CDs, lessons, conducts test and offers certification. sifyelearning.com

MB0047 : a. Bring out the relationship between AI and Neural Network.
 b. what is the difference between DSS and ES?

      Answer: – 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.

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.

A neural network is designed to simulate a set of neurons, usually connected by synapses. Each neuron makes a simple decision based on its other input synapses, and places the decision on its output synapses. This model mimics the behavior of a brain, and is considered vital to create a true learning system, though modern computers (barring super-computers) do not have the computational resources to execute a neural network with a sufficient number of nodes to be useful (you would need at least a few million neurons firing in unison to be useful).
Artificial intelligence, of course, is software that is designed to pretend like it’s a living, thinking creature. Older implementations were not learning systems, but rather would take input and offer a conditioned response provided by the programmer ahead of time. These systems seemed to be highly intelligent, so long as you did not leave its realm of preplanned responses. Newer AI systems learn by interacting with the user (for example, remembering their favorite color or music artist), and can sometimes even figure out correlated data based on this information.

However, current AI systems tend to still have limited spheres of knowledge, and without external learning sources, cannot make any intelligent responses or decisions outside this realm of information. The missing component, of course, is a system that is capable of learning information and incorporating what it learns into its current knowledge base. Neural networks hold the promise of bridging this gap in the “learning curve” that AI systems have by allowing the AI to actually learn topics that were not covered during its original “training” or “programming.”

The relationship between these two technologies could be said to be symbiotic in nature; both of these can be implemented without the other (i.e. a NN could be used inside a coffee maker for some advanced coffee-making logic, and an AI can certainly use other sources of information to make valid responses), but the combination of the two would allow for a more realistic AI that would be capable of learning data by making correlations between seemingly unrelated data (which is how humans learn, coincidentally).

Different between expert system and decision support system

  1. DSS aid in problem solving by allowing for manipulation of data & models whereas ES allow experts to ‘teach’ computers about their field so that the system may support more of the decision making process for less expert decision makers.
  2. DSS most often contain equations that the system uses to solve problems or update reports immediately, and the users makes the final decisions on the basis of the information whereas an expert system works from a much larger set of modeling rules, uses concepts from AI to process and store the knowledge base & scans base to suggest a final decision through inference.
  3. DSS only supports the decision making process & a human user is required to weigh all the factors in making a decision whereas ES must acquire knowledge from an expert and apply a large but standard set of probability based rules to make a decision in a specific problem setting.


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