Year: 2020

Management Notes

Value of Information

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The value of information is a very slippery concept as information per se does not have any universal value. Its value is related to the person who uses it, when he uses it and for what he uses it. Any assessment of the value of information is therefore related to the value of the decision-making supported by such information.

For example consider two persons lost in the Sahara desert. One person has an adequate supply of drinking water (more than he could desire) and another has exhausted his supply. If one were to approach these two individuals with information about a drinking water well in the surrounding, such information will obviously have greater value for the one who has exhausted his water supply. For the one who is thirsty, this information is the most valuable piece of information for him at that point of time as it will determine if he will survive. If by chance the information reaches this thirsty person late and he dies of thirst, then the value of the same information becomes zero. So we can see that the same information can have different value for different people at different points in time. Hence, it will be fair to conclude that value of information is relative. There is no absolute value of information.

However, the normal mathematical and economical explanation of value of information suggests that if an event occurs whose expectation was low and information of its occurrence is known then such information is valuable. For example let us say that the reader of this text gets the information that an earthquake of 10.5 magnitude in the Richter scale is going to hit India and the epicenter of the earthquake will be the very spot on which he is located, then that information is more valuable to him than the information (say) that he has to pass his BCA exam to get into the MCA course. In the former case the information is more valuable to him as he is not expecting it but in the latter case he already knows the information with certainty and expects it fully and hence the value for such information is less. All market mechanisms work on this model of information. This has already been dealt with in the earlier chapter and as Eq.1 suggests, the value of information of an event is the negative logarithm of the probability of occurrence of the event. Therefore, the more unlikely the event the more its information value, if communicated correctly. This is also exhibited in our behavior as eons of evolutions have shaped us in a manner that we tend to attach more value to unlikely events. In a game setting for example, say a horse racing game, if the odds of a horse winning the race are more then the payoff for that horse is less. Similarly, if a stock in the stock market is likely to outperform the market then the price payoff for that stock is not high, but if a stock that is likely to underperforms, performs beyond expectation, then the price payoff become very high. Thus, we can that the issue of value of information is a complicated one.

Normative Value of Information

Marschak (1971) and McGuire (1972) made seminal contributions to this field of work. Decision theory has developed this concept further and the basic assumption is that we always have some preliminary information about the occurrence of events that are related to our decisions. This information or knowledge is represented by an a priori assignment of probability of occurrence to the event and hence a calculated payoff. The a priori probability might be objective or subjective as the case may be and with the knowledge of additional information the probabilities are modified resulting in a change in the expected payoffs. This approach is however, only good for theoretical discussions as its practical applicability is poor. The problem for such cases has to be highly structured, which is rarely the case in management.

Subjective Value of Information

It is the subject view of the information available. It is the subjective perception or impression of the information. This subjective value approach varies widely with individuals. In the subjective valuation of information, no probabilities are calculated. Subjective value of information is the person’s (receiver’s) comprehensive impression about the information content.

Management Notes

Organizational Decision Making

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Decision making can be defined as selecting between alternative courses of action. Management decision making concerns the choices faced by managers within their duties in the organization. Making decisions is an important aspect of planning. Decision making can also be classified into three categories based on the level at which they occur.

Strategic Decisions: These decisions establish the strategies and objectives of the organization. These types of decisions generally occur at the highest levels of organizational management.

Tactical Decisions: Tactical decisions concern the tactics used to accomplish the organizational objectives. Tactical decisions are primarily made by middle and front-line managers.

Operational Decisions: Operational decisions concern the methods for carrying out the organizations delivery of value to customers. Operational decisions are primarily made by middle and front-line managers.

Decisions can be categorized based on the capacity of those making the decision.

Personal Decisions: Personal decisions are those primarily affecting the individual though the decision may ultimately have an effect on the organization as a result of its effect on the individual. These types of decisions are not made within a professional capacity. These decisions are generally not delegated to others.

Organizational Decisions: An organizational decision is one that relates or affects the organization. It is generally made by a manager or employee within their official capacity. These decisions are often delegated to others.

Strategies:

Marginal Analysis

Marginal analysis helps organizations allocate resources to increase profitability and benefits and reduce costs. An example from indeed.com is if a company has the budget to hire an employee, a marginal analysis may show that hiring that person provides a net marginal benefit because the ability to produce more products outweighs the increase in labor costs.

SWOT Diagram

This tool helps a manager study a situation in four quadrants:

  • Strengths: Where does the organization excel compared to its competition? Consider the internal and external strengths.
  • Weaknesses: What could the organization improve?
  • Opportunities: How can the organization leverage its strengths to create new avenues for success.
  • Threats: Determine what obstacles prevent the organization from achieving its goals.

Decision Matrix

A decision matrix can provide clarity when dealing with different choices and variables. It is like a pros/cons list, but decision-makers can place a level of importance on each factor. According to Dashboards, to build a decision matrix:

  • List your decision alternatives as rows
  • List relevant factors as columns
  • Establish a consistent scale to assess the value of each combination of alternatives and factors
  • Determine how important each factor is in choosing a final decision and assign weights accordingly
  • Multiply your original ratings by the weighted rankings
  • Add up the factors under each decision alternative
  • The highest-scoring option wins

Pareto Analysis

The Pareto Principle helps identify changes that will be the most effective for an organization. It’s based on the principle that 20 percent of factors frequently contribute to 80 percent of the organization’s growth. For example, suppose 80 percent of an organization’s sales came from 20 percent of its customers. A business can use the Pareto Principle by identifying the characteristics of that 20 percent customer group and finding more like them. By identifying which small changes have the most significant impact, an organization can better prioritize its decisions and energies.

Steps:

Make long-term goals and use them to measure your decisions.

All too often, organizations find themselves endlessly running around in pursuit of short-term goals. Money that has been committed to a year-long project gets overrun or set off because flashy or short-term priorities arise and resources are redirected. As a result, you typically end up with an awful lot of confusion and a lack of overall progress.

To avoid this problem, nail down your high-priority, long-term goals from the outset. Then as your organization makes decisions, ask yourself whether what you’re doing aligns with those goals. This should be a constant process, returning again and again to check your organizational activity against your goals.

When you apply this method successfully, you will engage more reliably in short-term projects that support your long-term goals. Over time, this will push your organization forward.

Align your goals with your core values

Ideally, these should flow from your organization’s mission and core values. Your organization’s goals may evolve over time, but its values should be much less mutable.

Your organizational values confer a coherent sense of identity and continuity to your organization. They should be clearly understood and agreed upon by your decision-makers. As you evaluate your goals, make sure that they are aligned with your core values.

Assess (and reassess) spending

One way to evaluate your priorities as they are being realized today is to take a look at your spending. Often, you may think you’re prioritizing a particular goal or effort, while your budget tells a different story.

Make sure your organizational spending reflects your identified priorities. If not, you need to take a second look. And as with any such check-in, it’s essential to make this a regular assessment to continuously verify that you’re on track.

Understand the impacts of your decisions.

Some decisions may be discrete and routine, having neat boundaries and only significantly impacting the matter directly at hand. But more often, organizational decisions may have wide-ranging consequences, especially if they will touch on policy or processes.

As your organization considers varying possibilities, make sure to weight second and third-order effects. These consequences can provide crucial context for the decision at hand.

Remember your personnel.

Organizations tend to depend on the quality of their employees to succeed. If your decisions make it difficult for your employees to be productive in their work environment, it will damage your prospects for long-term success even if your decisions appear to advance a short-term goal.

Evaluate the effect your decisions will have on your employees’ ability to perform their jobs and factor this component into your decisions accordingly.

The most effective decision-making should lead to improved work toward your long-term goals, which should be driven by core values. You should constantly reevaluate your spending and assess likely consequences of your actions. If you follow these steps thoroughly, you will have assembled a framework for successful organizational decision-making.

Advantages of Decision Making

Increase People’s Participation

Decision making in the organisation is done by a group of peoples working in the organisation. It is not carried out by a single individual rather than by a group of people. Each people actively participates in decision making of the organisation. They are free to present their creative ideas without any boundations.

Also, none of them is individually criticized for any failure but the whole group is responsible to handle. This increases the participation level of different people in the organisation.

Gives More Information

Good decision-making process acquires enough information before taking any action. In decision making, there is a large number of peoples involved. It is undertaken by the whole group rather than by a single individual. Each person gives his perspective to handle a particular situation.

They all represent there facts and figures according to their skill. This generates enough information which can be used for better understanding of the situation. This helps managers in taking corrective decisions.

Provide More Alternatives

Companies are able to get different alternatives for a particular situation through group decision making. There are different people working as a group for proper decisions. Each person looks differently to a particular problem.

They give their own perspectives and ideas for it. This way there are different options available to choose. All the alternatives are properly analysed in light of handling situation. The best one is chosen to arrive at a better result.

Improves the Degree of Acceptance and Commitment

Companies always face the chances of conflict among its staff working in the organisation. Through group decision making each person gets equal right to share his views and ideas.

Here decisions are not imposed on the peoples but are created with their participation. It develops a sense of loyalty and belongingness among people towards the business. They easily accept the decisions taken and are committed to their roles.

Helps In Strengthening the Organisation

It helps in improving the strength of the organisation. Decision making provides a platform to each individual working in an organisation to equally represent their ideas. Everybody gets an equal right to take part in managing the organisation.

It develops a sense of cooperation and unity among individuals working there. They all come together and work towards the accomplishment of the company’s goals. This increases the overall productivity of the organisation and strengthens its overall structure.

Improves the Quality of Decisions

Decision making helps in taking quality decisions at the right time. There are different experts engaged by organisations in their decision-making group. These peoples have through knowledge and creative thinking.

They analyse each and every aspect of every alternative available to them for handling situations. Best among the different alternatives available is chosen. It enables in quality decision making which helps in easy attainment of objectives.

Limitations:

Consultation ambiguity: This can be a scenario where a group of employees all feel like they have a vote in a decision or when a manager asks for input but doesn’t consider a group’s views. It’s important for a manager to solicit feedback but to make sure that contributors understand it’s the manager’s final decision.

Avoiding discomfort: Sound management decision making requires leaders who do not confuse their need for comfort with making the best decision. Some of the most effective decisions involve a degree of discomfort for the manager.

Appearing indecisive: Sometimes, a systematic decision making process has a downside. Being too rigorous in evaluating every possible angle can draw out the process and open the risk of appearing indecisive. Keep stakeholders informed about the timeline for a decision.

Blind spots: People have particular perspectives and ways of thinking that can create blind spots, which may be important for an effective decision but cannot be readily apparent. It can be helpful to seek input from trusted colleagues to provide a different perspective.

Groupthink: This occurs when a group’s members want to minimize conflict and reach a comfortable decision at the expense of a critical evaluation of other ideas and viewpoints. It’s important to explore alternatives a group may not have considered.

Management Notes

Decision Making and Management Information System

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Management Information System (MIS) is an organized approach that collects, processes, stores, and distributes information to support decision-making within an organization. It integrates people, technology, processes, and data to provide timely, accurate, and relevant information. MIS transforms raw business data into structured reports and summaries that help managers analyze trends, monitor performance, and plan future strategies. It is widely applied in finance, marketing, human resources, and operations. The main objective of MIS is to ensure that the right information reaches the right people at the right time.

In today’s competitive business environment, information plays a critical role in organizational success. A Management Information System (MIS) acts as a backbone for businesses by converting raw data into meaningful insights. It ensures that managers at different levels—top, middle, and operational—can access updated and reliable data for strategic, tactical, and operational decision-making.

MIS combines the use of software, hardware, and communication technologies with systematic data management techniques. For example, financial reports, inventory tracking, and sales forecasts are common MIS outputs that help organizations align resources effectively. MIS not only improves efficiency and accuracy in reporting but also reduces duplication of effort by centralizing data processing.

Role of Management Information Systems in Decision-Making:

1. Providing Accurate and Timely Information

One of the most important roles of MIS in decision-making is delivering accurate and timely information. Decisions often fail when they are based on outdated or incorrect data. MIS ensures that managers receive real-time insights from reliable sources such as transaction records, financial statements, or performance dashboards. This minimizes uncertainty and improves the quality of choices made at strategic, tactical, and operational levels. With quick access to updated data, managers can respond faster to challenges and opportunities, improving overall business agility and competitiveness.

2. Supporting Structured and Unstructured Decisions

MIS helps in managing both structured and unstructured decisions. Structured decisions, like preparing budgets or calculating payroll, are repetitive and routine. MIS automates these processes by generating accurate outputs quickly. Unstructured decisions, such as entering a new market or launching a new product, require more analytical inputs. MIS assists by providing forecasting tools, trend analyses, and scenario modeling. Thus, MIS plays a dual role by handling routine activities efficiently while also offering valuable support in complex, non-routine decision-making situations. This balance enables organizations to operate efficiently and strategically.

3. Enhancing Strategic Planning

Strategic decisions require long-term planning that affects the entire organization. MIS supports strategic planning by providing comprehensive reports, market trends, competitor analysis, and financial projections. For example, when a company considers international expansion, MIS supplies information about demand patterns, economic forecasts, and investment feasibility. By integrating both internal and external data, MIS empowers top-level management to make informed choices about growth opportunities, diversification, or mergers. The role of MIS here is crucial because it reduces the risks associated with large-scale business strategies and ensures alignment with long-term goals.

4. Improving Operational Efficiency

Operational decision-making deals with day-to-day activities such as inventory management, production scheduling, and customer service. MIS enhances operational efficiency by providing real-time monitoring systems and automated reporting. For instance, managers can quickly track stock levels, detect shortages, and order supplies before disruption occurs. Similarly, service-based firms use MIS to monitor customer complaints and response times. By reducing delays and redundancies, MIS ensures smooth operations and cost savings. This operational efficiency strengthens productivity, helps maintain customer satisfaction, and provides a reliable foundation for higher-level decision-making.

5. Facilitating Tactical Decision-Making

Middle managers often engage in tactical decision-making, such as allocating resources, setting departmental goals, or adjusting marketing campaigns. MIS plays a significant role here by providing comparative reports, performance metrics, and cost-benefit analyses. For example, sales managers can analyze which products perform best in specific regions and adjust promotional strategies accordingly. By offering insights into departmental operations, MIS helps managers choose the most effective course of action. Tactical decisions bridge the gap between daily operations and long-term strategy, and MIS ensures they are based on accurate and well-structured data.

6. Assisting in Problem Identification and Solution

MIS supports decision-making by helping managers identify problems at an early stage. For example, a sudden decline in sales can be highlighted through MIS-generated sales reports and customer feedback summaries. Once the problem is identified, MIS provides tools to analyze root causes, such as shifts in consumer demand, pricing issues, or supply chain disruptions. Additionally, MIS can suggest alternative solutions through simulation models or trend analysis. This role is vital in ensuring that decisions are proactive rather than reactive, reducing the risks of delayed responses and business losses.

7. Enabling Data-Driven Decision-Making

In modern business environments, decisions must be data-driven rather than based on intuition alone. MIS enables managers to base their decisions on reliable data sets such as financial performance, customer behavior, or operational efficiency. For instance, in marketing campaigns, MIS provides demographic data, purchase trends, and feedback analysis, ensuring that strategies are targeted and effective. This reduces the risks of poor decisions and improves overall accuracy. By combining data collection, analysis, and presentation, MIS strengthens decision-making with measurable evidence instead of guesswork, aligning choices with actual business performance.

8. Supporting Coordination and Communication

Decision-making requires smooth coordination among departments such as finance, marketing, production, and HR. MIS acts as a central platform for communication by providing standardized reports and dashboards accessible across the organization. For example, production managers can align their schedules with sales forecasts provided by marketing teams through MIS. This cross-functional integration ensures that decisions are not taken in isolation but consider interdepartmental requirements. By supporting transparent communication, MIS reduces duplication of efforts, prevents conflicts, and helps managers make collaborative decisions that are beneficial for the entire organization.

9. Reducing Decision-Making Risks

Every decision involves some degree of risk. MIS reduces risks by equipping managers with forecasting tools, trend analysis, and scenario simulations. For example, before launching a new product, managers can use MIS to simulate demand forecasts, estimate costs, and analyze competitor responses. This reduces uncertainty and prepares the organization for different outcomes. By systematically organizing historical and real-time data, MIS helps decision-makers evaluate both potential opportunities and risks. In this way, MIS not only improves confidence in decision-making but also minimizes the chances of costly business mistakes.

10. Enhancing Performance Monitoring and Feedback

Decision-making is incomplete without performance evaluation. MIS provides managers with tools to monitor outcomes and compare them against planned objectives. For instance, after implementing a new marketing strategy, MIS can generate performance reports on sales, customer engagement, and ROI. This feedback helps managers evaluate the effectiveness of their decisions and take corrective action if necessary. By offering continuous monitoring and feedback, MIS creates a cycle of improvement, ensuring that decision-making becomes more refined over time. It enables managers to adapt quickly and maintain business competitiveness.

11. Implementation and Evaluation

While you make your decisions with specific goals in mind and have the documentation from management information systems and trend analysis to support your expectations, you have to track company results to make sure they develop as planned. Management information systems give you the data you need to determine whether your decisions have had the desired effect, or whether you have to take corrective action to reach your goals. If specific results are not on track, you can use management information systems to evaluate the situation and decide to take additional measures if necessary.

Management Notes

Decision Making Concepts

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Decision-making is a cognitive process that results in the selection of a course of action among several alternative scenarios.

Decision-making is a daily activity for any human being. There is no exception about that. When it comes to business organizations, decision-making is a habit and a process as well.

Effective and successful decisions result in profits, while unsuccessful ones cause losses. Therefore, corporate decision-making is the most critical process in any organization.

In a decision-making process, we choose one course of action from a few possible alternatives. In the process of decision-making, we may use many tools, techniques, and perceptions.

In addition, we may make our own private decisions or may prefer a collective decision.

Usually, decision-making is hard. Majority of corporate decisions involve some level of dissatisfaction or conflict with another party.

Decision-Making Process

Following are the important steps of the decision-making process. Each step may be supported by different tools and techniques.

Step 1: Identification of the Purpose of the Decision

In this step, the problem is thoroughly analyzed. There are a couple of questions one should ask when it comes to identifying the purpose of the decision.

  • What exactly is the problem?
  • Why the problem should be solved?
  • Who are the affected parties of the problem?
  • Does the problem have a deadline or a specific time-line?

Step 2: Information Gathering

A problem of an organization will have many stakeholders. In addition, there can be dozens of factors involved and affected by the problem.

In the process of solving the problem, you will have to gather as much as information related to the factors and stakeholders involved in the problem. For the process of information gathering, tools such as ‘Check Sheets’ can be effectively used.

Step 3: Principles for Judging the Alternatives

In this step, the baseline criteria for judging the alternatives should be set up. When it comes to defining the criteria, organizational goals as well as the corporate culture should be taken into consideration.

As an example, profit is one of the main concerns in every decision making process. Companies usually do not make decisions that reduce profits, unless it is an exceptional case. Likewise, baseline principles should be identified related to the problem in hand.

Step 4: Brainstorm and Analyze the Choices

For this step, brainstorming to list down all the ideas is the best option. Before the idea generation step, it is vital to understand the causes of the problem and prioritization of causes.

For this, you can make use of Cause-and-Effect diagrams and Pareto Chart tool. Cause-and-Effect diagram helps you to identify all possible causes of the problem and Pareto chart helps you to prioritize and identify the causes with the highest effect.

Then, you can move on generating all possible solutions (alternatives) for the problem in hand.

Step 5: Evaluation of Alternatives

Use your judgment principles and decision-making criteria to evaluate each alternative. In this step, experience and effectiveness of the judgment principles come into play. You need to compare each alternative for their positives and negatives.

Step 6: Select the Best Alternative

Once you go through from Step 1 to Step 5, this step is easy. In addition, the selection of the best alternative is an informed decision since you have already followed a methodology to derive and select the best alternative.

Step 7: Execute the decision

Convert your decision into a plan or a sequence of activities. Execute your plan by yourself or with the help of subordinates.

Step 8: Evaluate the Results

Evaluate the outcome of your decision. See whether there is anything you should learn and then correct in future decision making. This is one of the best practices that will improve your decision-making skills.

Process and Modeling in Decision-Making

There are two basic models in decision-making:

  • Rational models
  • Normative model

(i) Rational models

The rational models are based on cognitive judgments and help in selecting the most logical and sensible alternative. Examples of such models include – decision matrix analysis, Pugh matrix, SWOT analysis, Pareto analysis and decision trees, selection matrix, etc.

A rational decision making model takes the following steps −

  • Identifying the problem,
  • Identifying the important criteria for the process and the result,
  • Considering all possible solutions,
  • Calculating the consequences of all solutions and comparing the probability of satisfying the criteria,
  • Selecting the best option.

(ii) Normative model

The normative model of decision-making considers constraints that may arise in making decisions, such as time, complexity, uncertainty, and inadequacy of resources.

According to this model, decision-making is characterized by −

  • Limited information processing – A person can manage only a limited amount of information.
  • Judgmental heuristics – A person may use shortcuts to simplify the decision making process.
  • Satisfying – A person may choose a solution that is just “good enough”.

Dynamic Decision-Making

Dynamic decision-making (DDM) is synergetic decision-making involving interdependent systems, in an environment that changes over time either due to the previous actions of the decision-maker or due to events that are outside of the control of the decision-maker.

These decision-makings are more complex and real-time.

Dynamic decision-making involves observing how people used their experience to control the system’s dynamics and noting down the best decisions taken thereon.

Sensitivity Analysis

Sensitivity analysis is a technique used for distributing the uncertainty in the output of a mathematical model or a system to different sources of uncertainty in its inputs.

From business decision perspective, the sensitivity analysis helps an analyst to identify cost drivers as well as other quantities to make an informed decision. If a particular quantity has no bearing on a decision or prediction, then the conditions relating to quantity could be eliminated, thus simplifying the decision making process.

Sensitivity analysis also helps in some other situations, like −

  • Resource optimization
  • Future data collections
  • Identifying critical assumptions
  • To optimize the tolerance of manufactured parts

Static and Dynamic Models

Static models

  • Show the value of various attributes in a balanced system.
  • Work best in static systems.
  • Do not take into consideration the time-based variances.
  • Do not work well in real-time systems however, it may work in a dynamic system being in equilibrium
  • Involve less data.
  • Are easy to analyze.
  • Produce faster results.

Dynamic models

  • Consider the change in data values over time.
  • Consider effect of system behavior over time.
  • Re-calculate equations as time changes.
  • Can be applied only in dynamic systems.

Simulation Techniques

Simulation is a technique that imitates the operation of a real-world process or system over time. Simulation techniques can be used to assist management decision making, where analytical methods are either not available or cannot be applied.

Some of the typical business problem areas where simulation techniques are used are –

  • Inventory control
  • Queuing problem
  • Production planning

Operations Research Techniques

Operational Research (OR) includes a wide range of problem-solving techniques involving various advanced analytical models and methods applied. It helps in efficient and improved decision-making.

It encompasses techniques such as simulation, mathematical optimization, queuing theory, stochastic-process models, econometric methods, data envelopment analysis, neural networks, expert systems, decision analysis, and the analytic hierarchy process.

OR techniques describe a system by constructing its mathematical models.

Heuristic Programming

Heuristic programming refers to a branch of artificial intelligence. It consists of programs that are self-learning in nature.

However, these programs are not optimal in nature, as they are experience-based techniques for problem solving.

Most basic heuristic programs would be based on pure ‘trial-error’ methods.

Heuristics take a ‘guess’ approach to problem solving, yielding a ‘good enough’ answer, rather than finding a ‘best possible’ solution.

Group Decision-Making

In group decision-making, various individuals in a group take part in collaborative decision-making.

Group Decision Support System (GDSS) is a decision support system that provides support in decision making by a group of people. It facilitates the free flow and exchange of ideas and information among the group members. Decisions are made with a higher degree of consensus and agreement resulting in a dramatically higher likelihood of implementation.

Following are the available types of computer based GDSSs −

(i) Decision Network

This type helps the participants to communicate with each other through a network or through a central database. Application software may use commonly shared models to provide support.

(ii) Decision Room

Participants are located at one place, i.e. the decision room. The purpose of this is to enhance participant’s interactions and decision-making within a fixed period of time using a facilitator.

(iii) Teleconferencing

Groups are composed of members or sub groups that are geographically dispersed; teleconferencing provides interactive connection between two or more decision rooms. This interaction will involve transmission of computerized and audio visual information.

Management Notes

MIS as Technique for Programmed Decision

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A programmed decision is used to solve routine, repetitive but complex problems. These techniques are also called as Quantitative Techniques. The managers working at lower-level of management make these decisions.

Various Approaches or Techniques for making programmed decisions are:

  1. Linear Programming

Linear Programming is a quantitative technique. It is used to decide how to distribute the limited resources for achieving the objectives. Here, linear, means the relationship between variables, and programming means taking decisions systematically. Linear programming is used when two or more activities are competing for limited resources. For e.g. product mix decisions, inventory management decisions, etc. Linear programing is used for Agriculture, Industry, Contract biding and Evaluation of tenders.

  1. Decision Tree

A decision tree is a diagram which shows all the possible alternatives of a decision. All this information can be seen at one glance. It is also easy to understand. A decision tree is like a horizontal tree. The base of the tree is called the Decision Point. From this point, the different alternatives and sub-alternatives are shown as branches and sub-branches. The manager must study all the alternatives very carefully and select the best alternative.

  1. Game Theory

A game is a situation involving at least two people. Each persons decision is based on what he expects the other to do. Game theory is used for deciding about competitive pricing. For e.g. A company may increase the price of its product when it feels that the competitor may also increase the price. For e.g. Pepsi will increase its price if it feels that Coca Cola will also increase its price. Here, both decisions- makers adapt to each other’s decisions.

  1. Simulation

Simulation technique is used to decide about complex problems. The effect of the decision is observed in a simulated situation and not in a real situation. For e.g. A company can find out the effectiveness of its new advertisement by first showing it to few people before telecasting it on TV.

  1. Queueing Theory

This technique is used to find solutions to the waiting list problems in case of airline reservations, railway reservations, college admissions, etc. Queueing theory helps to find out the optimum number of service facilities required and the cost of these services. For e.g. A transport company may introduce more vehicles to carry the passengers in the waiting list. This will prevent the passengers from going to the competitor’s company.

  1. Network Techniques

Managers use network techniques like PERT (Program Evaluation Review Technique) and CPM (Critical Path Method) for complex projects, where many activities have to be completed. With the help of these techniques, complex projects can be completed as per the schedule. Network techniques save time and cost.

  1. Probability Decision Theory

Probability Decision theory is based on the assumption that the future is uncertain. There is a chance that a certain event may or may not take place. Based on available data and subjective judgement of the manager, various probabilities are assigned (given) to alternative courses of action (decision). The likely / possible outcomes of different alternatives are evaluated, and the most likely alternative is selected.

  1. Payoff Matrix

Payoff matrix is a statistical technique, which helps managers to choose the best alternative. A payoff is the return or reward for selecting the best alternative. The best alternative can be a combination of many alternatives or a single alternative. For e.g. A manager may decide to increase sales and profit by increasing advertising, improving quality of the product, reducing the price, etc. Each alternative or a combination of alternatives may provide an expected reward.

Management Notes

Database Concepts

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A database intends to have a collection of data stored together to serve multiple applications as possible. Hence a database is often conceived of as a repository of information needed for running certain functions in a corporation or organization. Such a database would permit not only the retrieval of data but also the continuous modification of data needed for control of operations. It may be possible to search the database to obtain answers to queries or information for planning purposes.

Purpose of Database

A database should be a repository of data needed for an organization’s data processing. That data should be accurate, private, and protected from damage. It should be accurate so that diverse applications with different data requirements can employ the data. Different application programmers and various end-users have different views upon data, which must be derived from a common overall data structure. Their methods of searching and accessing of data will be different.

Advantage of Using Database

  • Database minimizes data redundancy to a great extent.
  • The database can control the inconsistency of data to a large extent.
  • Sharing of data is also possible using the database.
  • Database enforce standards.
  • The use of Databases can ensure data security.
  • Integrity can be managed using the database.

Various Levels of Database Implementation

The database is implemented through three general levels. These levels are:

  • Internal Level or Physical level
  • Conceptual Level
  • External Level or View Level

The Concept of Data Independence

As the database may be viewed through three levels of abstraction, any change at any level can affect other levels’ schemas. Since the database keeps on growing, then there may be frequent changes at times. This should not lead to redesigning and re-implementation of the database. The concepts of data independence prove beneficial in such types of contexts.

  • Physical data independence
  • Logical data independence

Basic Terminologies Related to Database and SQL

(i) Relation

In general, a relation is a table, i.e., data is arranged in rows and columns. A relation has the following properties:

  • In any given column of a table, all the items are of the same kind, whereas items in different columns may not be of the same kind.
  • For a row, each column must have an atomic value, and also for a row, a column cannot have more than one value.
  • All rows of a relation are distinct.
  • The ordering of rows in a relationship is immaterial.
  • The column of a relation are assigned distinct names, and the ordering of these columns is immaterial.

(ii) Tuple

The rows of tables in a relationship are generally termed as Tuples.

(iii) Attributes

The columns or fields of a table is termed as Attributes.

(iv) Degree

The number of attributes in a relation determines the degree of relation.  A relation having three attributes is said to have a relation of degree 3.

(v) Cardinality: The number of tuples or rows in a relation is termed as cardinality.

Management Notes

Type of Databases

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Databases are structured collections of data used to store, retrieve, and manage information efficiently. They are essential in modern computing, supporting applications in business, healthcare, finance, and more. Different types of databases cater to various needs, ranging from structured tabular data to unstructured multimedia content.

  • Relational Database (RDBMS)

Relational Database stores data in structured tables with predefined relationships between them. Each table consists of rows (records) and columns (attributes), and data is accessed using Structured Query Language (SQL). Relational databases ensure data integrity, normalization, and consistency, making them ideal for applications requiring structured data storage, such as banking, inventory management, and enterprise resource planning (ERP) systems. Popular relational databases include MySQL, PostgreSQL, Microsoft SQL Server, and Oracle Database. However, they may struggle with handling unstructured or semi-structured data, requiring additional tools for scalability and performance optimization.

  • NoSQL Database

NoSQL (Not Only SQL) databases are designed for scalability and flexibility, handling unstructured and semi-structured data. NoSQL databases do not use fixed schemas or tables; instead, they follow different data models such as key-value stores, document stores, column-family stores, and graph databases. These databases are widely used in big data applications, real-time analytics, social media platforms, and IoT. Popular NoSQL databases include MongoDB (document-based), Cassandra (column-family), Redis (key-value), and Neo4j (graph-based). They offer high availability and horizontal scalability but may lack ACID (Atomicity, Consistency, Isolation, Durability) compliance found in relational databases.

  • Hierarchical Database

Hierarchical Database organizes data in a tree-like structure, where each record has a parent-child relationship. This model is efficient for fast data retrieval but can be rigid due to its strict hierarchy. Commonly used in legacy systems, telecommunications, and geographical information systems (GIS), hierarchical databases work well when data relationships are well-defined. IBM’s Information Management System (IMS) is a well-known hierarchical database. However, its inflexibility and difficulty in modifying hierarchical structures make it less suitable for modern, dynamic applications. Navigating complex relationships in hierarchical models can be challenging, requiring specific querying techniques like XPath in XML databases.

  • Network Database

Network Database extends the hierarchical model by allowing multiple parent-child relationships, forming a graph-like structure. This improves flexibility by enabling many-to-many relationships between records. Network databases are used in supply chain management, airline reservation systems, and financial record-keeping. The CODASYL (Conference on Data Systems Languages) database model is a well-known implementation. While faster than relational databases in certain scenarios, network databases require complex navigation methods like pointers and set relationships. Modern graph databases, such as Neo4j, have largely replaced traditional network databases, offering better querying capabilities using graph traversal algorithms.

  • Object-Oriented Database (OODBMS)

An Object-Oriented Database (OODBMS) integrates database capabilities with object-oriented programming (OOP) principles, allowing data to be stored as objects. This model is ideal for applications that use complex data types, multimedia files, and real-world objects, such as computer-aided design (CAD), engineering simulations, and AI-driven applications. Unlike relational databases, OODBMS supports inheritance, encapsulation, and polymorphism, making it more aligned with modern programming paradigms. Popular object-oriented databases include db4o and ObjectDB. However, OODBMS adoption is lower due to its complexity, lack of standardization, and limited compatibility with SQL-based systems.

  • Graph Database

Graph Database is designed to handle data with complex relationships using nodes (entities) and edges (connections). Unlike traditional relational databases, graph databases efficiently represent and query interconnected data, making them ideal for social networks, fraud detection, recommendation engines, and knowledge graphs. Neo4j, Amazon Neptune, and ArangoDB are popular graph databases that support graph traversal algorithms like Dijkstra’s shortest path. They excel at handling dynamic and interconnected datasets but may require specialized query languages like Cypher instead of standard SQL. Their scalability depends on graph size, and managing large graphs can be computationally expensive.

  • Time-Series Database

Time-Series Database (TSDB) is optimized for storing and analyzing time-stamped data, such as sensor readings, financial market data, and IoT device logs. Unlike relational databases, TSDBs efficiently handle high-ingestion rates and time-based queries, enabling real-time analytics and anomaly detection. Popular time-series databases include InfluxDB, TimescaleDB, and OpenTSDB. They offer fast retrieval of historical data, downsampling, and efficient indexing mechanisms. However, their focus on time-stamped data limits their use in general-purpose applications. They are widely used in stock market analysis, predictive maintenance, climate monitoring, and healthcare (e.g., ECG data storage and analysis).

  • Cloud Database

Cloud Database is hosted on a cloud computing platform, offering on-demand scalability, high availability, and managed infrastructure. Cloud databases eliminate the need for on-premise hardware, reducing maintenance costs and operational complexity. They can be relational (SQL-based) or NoSQL-based, depending on the application’s needs. Examples include Amazon RDS (Relational), Google Cloud Spanner (Hybrid SQL-NoSQL), and Firebase (NoSQL Document Store). Cloud databases enable global accessibility, automated backups, and seamless integration with AI and analytics tools. However, concerns about data security, vendor lock-in, and latency exist, especially when handling sensitive enterprise data.

Management Notes

Database Design

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Database design is the organization of data according to a database model. The designer determines what data must be stored and how the data elements interrelate. With this information, they can begin to fit the data to the database model. Database management system manages the data accordingly.

Database design involves classifying data and identifying interrelationships. This theoretical representation of the data is called an ontology. The ontology is the theory behind the database’s design.

A design process suggestion for Microsoft Access

(i) Determine the purpose of the database

This helps prepare for the remaining steps.

(ii) Find and organize the information required

Gather all of the types of information to record in the database, such as product name and order number.

(iii) Divide the information into tables

Divide information items into major entities or subjects, such as Products or Orders. Each subject then becomes a table.

(iv) Turn information items into columns

Decide what information needs to be stored in each table. Each item becomes a field, and is displayed as a column in the table. For example, an Employees table might include fields such as Last Name and Hire Date.

(v) Specify primary keys

Choose each table’s primary key. The primary key is a column, or a set of columns, that is used to uniquely identify each row. An example might be Product ID or Order ID.

(vi) Set up the table relationships

Look at each table and decide how the data in one table is related to the data in other tables. Add fields to tables or create new tables to clarify the relationships, as necessary.

(vii) Refine the design

Analyze the design for errors. Create tables and add a few records of sample data. Check if results come from the tables as expected. Make adjustments to the design, as needed.

(viii) Apply the normalization rules

Apply the data normalization rules to see if tables are structured correctly. Make adjustments to the tables, as needed.

Determining data to be stored

In a majority of cases, a person who is doing the design of a database is a person with expertise in the area of database design, rather than expertise in the domain from which the data to be stored is drawn e.g. financial information, biological information etc. Therefore, the data to be stored in the database must be determined in cooperation with a person who does have expertise in that domain, and who is aware of what data must be stored within the system.

This process is one which is generally considered part of requirements analysis, and requires skill on the part of the database designer to elicit the needed information from those with the domain knowledge. This is because those with the necessary domain knowledge frequently cannot express clearly what their system requirements for the database are as they are unaccustomed to thinking in terms of the discrete data elements which must be stored. Data to be stored can be determined by Requirement Specification.

Determining data relationships

Once a database designer is aware of the data which is to be stored within the database, they must then determine where dependency is within the data. Sometimes when data is changed you can be changing other data that is not visible. For example, in a list of names and addresses, assuming a situation where multiple people can have the same address, but one person cannot have more than one address, the address is dependent upon the name. When provided a name and the list the address can be uniquely determined; however, the inverse does not hold – when given an address and the list, a name cannot be uniquely determined because multiple people can reside at an address. Because an address is determined by a name, an address is considered dependent on a name.

(NOTE: A common misconception is that the relational model is so called because of the stating of relationships between data elements therein. This is not true. The relational model is so named because it is based upon the mathematical structures known as relations.)

Logically structuring data

Once the relationships and dependencies amongst the various pieces of information have been determined, it is possible to arrange the data into a logical structure which can then be mapped into the storage objects supported by the database management system. In the case of relational databases the storage objects are tables which store data in rows and columns. In an Object database the storage objects correspond directly to the objects used by the Object-oriented programming language used to write the applications that will manage and access the data. The relationships may be defined as attributes of the object classes involved or as methods that operate on the object classes.

The way this mapping is generally performed is such that each set of related data which depends upon a single object, whether real or abstract, is placed in a table. Relationships between these dependent objects is then stored as links between the various objects.

Each table may represent an implementation of either a logical object or a relationship joining one or more instances of one or more logical objects. Relationships between tables may then be stored as links connecting child tables with parents. Since complex logical relationships are themselves tables they will probably have links to more than one parent.

Management Notes

Relational Database Management System (RDBMS)

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A relational database management system (RDBMS) is a collection of programs and capabilities that enable IT teams and others to create, update, administer and otherwise interact with a relational database. RDBMS store data in the form of tables, with most commercial relational database management systems using Structured Query Language (SQL) to access the database. However, since SQL was invented after the initial development of the relational model, it is not necessary for RDBMS use.

The RDBMS is the most popular database system among organizations across the world. It provides a dependable method of storing and retrieving large amounts of data while offering a combination of system performance and ease of implementation.

RDBMS vs. DBMS

In general, databases store sets of data that can be queried for use in other applications. A database management system supports the development, administration and use of database platforms.

An RDBMS is a type of database management system (DBMS) that stores data in a row-based table structure which connects related data elements. An RDBMS includes functions that maintain the security, accuracy, integrity and consistency of the data. This is different than the file storage used in a DBMS.

Other differences between database management systems and relational database management systems include:

  • Number of allowed users. While a DBMS can only accept one user at a time, an RDBMS can operate with multiple users.
  • Hardware and software requirements. A DBMS needs less software and hardware than an RDBMS.
  • Amount of data. RDBMSes can handle any amount of data, from small to large, while a DBMS can only manage small amounts.
  • Database structure. In a DBMS, data is kept in a hierarchical form, whereas an RDBMS utilizes a table where the headers are used as column names and the rows contain the corresponding values.
  • ACID implementation. DBMSes do not use the atomicity, consistency, isolation and durability (ACID) model for storing data. On the other hand, RDBMSes base the structure of their data on the ACID model to ensure consistency.
  • Distributed databases. While an RDBMS offers complete support for distributed databases, a DBMS will not provide support.
  • Types of programs managed. While an RDBMS helps manage the relationships between its incorporated tables of data, a DBMS focuses on maintaining databases that are present within the computer network and system hard disks.
  • Support of database normalization. An RDBMS can be normalized, but a DBMS cannot.

Features of relational database management systems

Elements of the relational database management system that overarch the basic relational database are so intrinsic to operations that it is hard to dissociate the two in practice.

The most basic RDBMS functions are related to create, read, update and delete operations — collectively known as CRUD. They form the foundation of a well-organized system that promotes consistent treatment of data.

The RDBMS typically provides data dictionaries and metadata collections that are useful in data handling. These programmatically support well-defined data structures and relationships. Data storage management is a common capability of the RDBMS, and this has come to be defined by data objects that range from binary large object — or blob — strings to stored procedures. Data objects like this extend the scope of basic relational database operations and can be handled in a variety of ways in different RDBMS.

The most common means of data access for the RDBMS is SQL. Its main language components comprise data manipulation language and data definition language statements. Extensions are available for development efforts that pair SQL use with common programming languages, such as the Common Business-Oriented Language (COBOL), Java and .NET.

RDBMS use complex algorithms that support multiple concurrent user access to the database while maintaining data integrity. Security management, which enforces policy-based access, is yet another overlay service that the RDBMS provides for the basic database as it is used in enterprise settings.

RDBMS support the work of database administrators (DBAs) who must manage and monitor database activity. Utilities help automate data loading and database backup. RDBMS manage log files that track system performance based on selected operational parameters. This enables measurement of database usage, capacity and performance, particularly query performance. RDBMS provide graphical interfaces that help DBAs visualize database activity.

While not limited solely to the RDBMS, ACID compliance is an attribute of relational technology that has proved important in enterprise computing. These capabilities have particularly suited RDBMS for handling business transactions.

As RDBMS have matured, they have achieved increasingly higher levels of query optimization, and they have become key parts of reporting, analytics and data warehousing applications for businesses as well. RDBMS are intrinsic to operations of a variety of enterprise applications and are at the center of most master data management systems.

How RDBMS works?

As mentioned before, an RDBMS will store data in the form of a table. Each system will have varying numbers of tables with each table possessing its own unique primary key. The primary key is then used to identify each table.

Within the table are rows and columns. The rows are known as records or horizontal entities; they contain the information for the individual entry. The columns are known as vertical entities and possess information about the specific field.

Before creating these tables, the RDBMS must check the following constraints:

  • Primary keys: This identifies each row in the table. One table can only contain one primary key. The key must be unique and without null values.
  • Foreign keys: This is used to link two tables. The foreign key is kept in one table and refers to the primary key associated with another table.
  • Not null: This ensures that every column does not have a null value, such as an empty cell.
  • Check: This confirms that each entry in a column or row satisfies a precise condition and that every column holds unique data.
  • Data integrity: The integrity of the data must be confirmed before the data is created.

Assuring the integrity of data includes several specific tests, including entity, domain, referential and user-defined integrity. Entity integrity confirms that the rows are not duplicated in the table. Domain integrity makes sure that data is entered into the table based on specific conditions, such as file format or range of values. Referential integrity ensures that any row that is re-linked to a different table cannot be deleted. Finally, user-defined integrity confirms that the table will satisfy all user-defined conditions.

Advantages of relational database management system

The use of an RDBMS can be beneficial to most organizations; the systematic view of raw data helps companies better understand and execute the information while enhancing the decision-making process. The use of tables to store data also improves the security of information stored in the databases. Users are able to customize access and set barriers to limit the content that is made available. This feature makes the RDBMS particularly useful to companies in which the manager decides what data is provided to employees and customers.

Furthermore, RDBMS make it easy to add new data to the system or alter existing tables while ensuring consistency with the previously available content.

Other advantages of the RDBMS include:

  • Flexibility: Updating data is more efficient since the changes only need to be made in one place.
  • Maintenance: Database administrators can easily maintain, control and update data in the database. Backups also become easier since automation tools included in the RDBMS automate these tasks.
  • Data structure: The table format used in RDBMS is easy to understand and provides an organized and structural manner through which entries are matched by firing queries.

On the other hand, relational database management systems do not come without their disadvantages. For example, in order to implement an RDBMS, special software must be purchased. This introduces an additional cost for execution. Once the software is obtained, the setup process can be tedious since it requires millions of lines of content to be transferred into the RDBMS tables. This process may require the additional help of a programmer or a team of data entry specialists. Special attention must be paid to the data during entry to ensure sensitive information is not placed into the wrong hands.

Some other drawbacks of the RDBMS include the character limit placed on certain fields in the tables and the inability to fully understand new forms of data — such as complex numbers, designs and images.

Furthermore, while isolated databases can be created using an RDBMS, the process requires large chunks of information to be separated from each other. Connecting these large amounts of data to form the isolated database can be very complicated.

Uses of RDBMS

Relational database management systems are frequently used in disciplines such as manufacturing, human resources and banking. The system is also useful for airlines that need to store ticket service and passenger documentation information as well as universities maintaining student databases.

Some examples of specific systems that use RDBMS include IBM, Oracle, MySQL, Microsoft SQL Server and Postgre SQL.

RDBMS product history

Many vying relational database management systems arose as news spread in the early 1970s of the relational data model. This and related methods were originally theorized by IBM researcher E.F. Codd, who proposed a database schema, or logical organization, that was not directly associated with physical organization, as was common at the time.

Codd’s work was based around a concept of data normalization, which saved file space on storage disk drives at a time when such machinery could be prohibitively expensive for businesses.

File systems and database management systems preceded what could be called the RDBMS era. Such systems ran primarily on mainframe computers. While RDBMS also ran on mainframes — IBM’s DB2 being a pointed example — much of their ascendance in the enterprise was in UNIX midrange computer deployments. The RDBMS was a linchpin in the distributed architecture of client-server computing, which connected pools of stand-alone personal computers to file and database servers.

Numerous RDBMS arose along with the use of client-server computing. Among the competitors were Oracle, Ingres, Informix, Sybase, Unify, Progress and others. Over time, three RDBMS came to dominate in commercial implementations. Oracle, IBM’s DB2 and Microsoft’s SQL Server, which was based on a design originally licensed from Sybase, found considerable favor throughout the client-server computing era, despite repeated challenges by competing technologies.

As the 20th century drew to an end, lower-cost, open source versions of RDBMS began to find use, particularly in web applications.

Eventually, as distributed computing took greater hold, and as cloud architecture became more prominently employed, RDBMS met competition in the form of No SQL systems. Such systems were often specifically designed for massive distribution and high scalability in the cloud, sometimes forgoing SQL-style full consistency for so-called eventual consistency of data. But, even in the most diverse and complex cloud systems, the need for some guaranteed data consistency requires RDBMS to appear in some way, shape or form. Moreover, versions of RDBMS have been significantly restructured for cloud parallelization and replication.

Management Notes

ORACLE

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Oracle database (Oracle DB) is a relational database management system (RDBMS) from the Oracle Corporation. Originally developed in 1977 by Lawrence Ellison and other developers, Oracle DB is one of the most trusted and widely-used relational database engines.

The system is built around a relational database framework in which data objects may be directly accessed by users (or an application front end) through structured query language (SQL). Oracle is a fully scalable relational database architecture and is often used by global enterprises, which manage and process data across wide and local area networks. The Oracle database has its own network component to allow communications across networks.

Oracle DB rivals Microsoft’s SQL Server in the enterprise database market. There are other database offerings, but most of these command a tiny market share compared to Oracle DB and SQL Server. Fortunately, the structures of Oracle DB and SQL Server are quite similar, which is a benefit when learning database administration.

Oracle DB runs on most major platforms, including Windows, UNIX, Linux and Mac OS. Different software versions are available, based on requirements and budget. Oracle DB editions are hierarchically broken down as follows:

  • Enterprise Edition: Offers all features, including superior performance and security, and is the most robust
  • Standard Edition: Contains base functionality for users that do not require Enterprise Edition’s robust package
  • Express Edition (XE): The lightweight, free and limited Windows and Linux edition
  • Oracle Lite: For mobile devices

A key feature of Oracle is that its architecture is split between the logical and the physical. This structure means that for large-scale distributed computing, also known as grid computing, the data location is irrelevant and transparent to the user, allowing for a more modular physical structure that can be added to and altered without affecting the activity of the database, its data or users. The sharing of resources in this way allows for very flexible data networks whose capacity can be adjusted up or down to suit demand, without degradation of service. It also allows for a robust system to be devised as there is no single point at which a failure can bring down the database, as the networked schema of the storage resources means that any failure would be local only.

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