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Unit I Fundamental Of Decision Making For Quantitative Techniques For Business Decisions Mcom Sem 2 Delhi University Notes

Unit I Fundamental Of Decision Making For Quantitative Techniques For Business Decisions Mcom Sem 2 Delhi University Notes

Unit I Fundamental Of Decision Making For Quantitative Techniques For Business Decisions MCOM Sem 2 Delhi University :  Here our cakart team members provides you Unit I Fundamental Of Decision Making For Quantitative Techniques For Business Decisions MCOM Sem 2 Delhi University  Complete Notes in pdf format. Download here  Unit I Fundamental Of Decision Making For Quantitative Techniques For Business Decisions MCOM Sem 2 Delhi University Notes and read well.

The area of Quantitative Methods for Decision-Making is based on the scientific method for investigating and helping to take decisions about complex problems in modern organisations. Public and private organisations today face complex management problems in which their managers have to take a decision. Decision-making is the process by means of which the choice is made between the alternatives or ways to resolve various situations or problems. These decisions have a very significant effect on organisations’ competitiveness and survival. The increasing availability of communication and information systems also provide enable decision-makers with access to a great deal of data and computer systems that can help them to take decisions. Quantitative Methods for Decision-Making, also known as Operations Research, is a science that provides decision-makers, managers and directors in an organisation with the methodologies and techniques that enable them to assess several alternatives and choose the best one for their organisation. This science is based on the scientific method and has countless successful applications. The methodology uses mathematical models, databases and computer programmes to help in decision-making. On this course, we will be focusing on the application of Quantitative Methods to decision-making in problems in the field of Business Management and Administration that involve quantitative factors.

Download here Unit I Fundamental Of Decision Making For Quantitative Techniques For Business Decisions Mcom Semester 2 Delhi University Notes in pdf format 

Unit I Fundamental Of Decision Making For Quantitative Techniques For Business Decisions Mcom Sem 2 Delhi University Notes

Unit I Fundamental Of Decision Making For Quantitative Techniques For Business Decisions MCOM Sem 2 Delhi University :  The objective of this course is to provide the essential concepts, quantitative models, solutions and latest techniques in solving management and administration problems in complex systems, with special emphasis on decision-making. In the classes, the essential topics will be discussed, as well as case studies of these models and methodologies in various areas of business administration. The application of models will be emphasised, as well as an explanation of how they can help in decision-making for the problems that appear in any organisation.

Competences to be attained

The objective of this course is to provide the essential concepts, quantitative models, solutions and latest techniques in solving management and administration problems in complex systems, with special emphasis on decision-making.  

General skills

Specific skills

 

Instrumental

• Organisation and planning capability.

• Knowledge of computer programmes.

• Problem-solving.

• Search for the appropriate information from various sources.

 

Interpersonal

• Oral communication in public.

• Teamwork.

• Written communication.

 

Systematics

• Critical reasoning in reading and in written work and in oral communication.

• Analysis and synthesis of qualitative and quantitative information.

• Adaptation to new situations.

 

Academic and professional

• Appreciate the importance and power of Quantitative Methods in decision-making in organisations of the present and future.

• Be able to recognise when this methodology and these techniques can be applied, and when they cannot.

• Learn how to apply the main techniques of the Quantitative Methods to analysis and solving of managerial problems.

• Be able to use analytical tools and methodologies based on mathematical models to help in decision-making in business environments.

• Be able to use information systems and computer programs to help in decision-making in business environments.

• Develop an understanding of the interpretation of the results of a study based on the methodology of Quantitative Methods.

Contents  

1.  Decisions under risk conditions

Alternatives, consequences, objectives, choice.

Uncertainty and risk: the concept of the lottery and probabilities.

Decisions under certainty conditions: preferences, usefulness function, maximisation.

Decisions under risk conditions.

The expected value.

The theory of expected usefulness.

2.  Risk aversion

Risk aversion.

Risk premium.

Risk aversion measures.

Assurance: insurance contracts and market

Diversification of assets portfolio.

3.  Sequential decisions and information

Sequential decisions.

Decision trees.

Perfect information.

Strategies and backward induction.

Value of information.

Conditional probabilities and updating information.

4.  Strategic interaction: decisions and games

Strategic interaction.

Players, rules, results, payments.

Extensive-form games.

Strategies and backward induction.

Normal-form games.

Conflicts, cooperation, efficiency.

5.  Games solution and equilibrium

Imperfect information.

Equilibrium in dominant strategies.

Iterated elimination of dominated strategies.

Rationalisable strategies.

Nash equilibrium.

6.  Infinite and sequential games and mixed strategies

Infinite series of strategies and reaction functions.

Applications: oligopoly and public goods.

Mixed strategies.

Sequential games.

Unit I Fundamental Of Decision Making For Quantitative Techniques For Business Decisions Mcom Sem 2 Delhi University Notes

Unit I Fundamental Of Decision Making For Quantitative Techniques For Business Decisions MCOM Sem 2 Delhi University :  Decision making is crucial for survival of business. Businesses have to make decision considering the limited amount of information. Decision making problems are divided into two types deterministic and probabilistic.

Deterministic model of problem solving depends on the relationship between uncontrollable factors and continuing process of optimizing system performance. A model is developed in under assumption related to existing business condition. If the variables under assumption do not truly reflect the current business conditions, the model developed also will not reflect the reality.

Mathematical optimization utilizes mathematical equation to determine the business decision. The business decision derive is in a numerical form.

A business model for decision making is constructed by analyst based on inputs of a decision maker. A business model is developed over a period of time using a progressive approach method.

Optimization Modeling Process

Optimization model is developed in three steps, 1st step is describing the problem, 2nd step is elaborating the solution and 3rd step is controlling the problem.

The optimized problem of the 1st step can be classified into linear and non-linear depending upon on nature of variables. Optimization problem has three following aspects:

  • An objective function to maximize or minimize.
  • A set of variables which affect the value of the objective function.
  • A set of uncontrollable factors referred as parameters.

The solution of optimized problem satisfying all parameters and constraints is referred as feasible solution. The objective of an optimization process is to value of variables, which minimize or maximize objective giving out an optimal solution.

Linear Programming

Linear programming is a mathematical procedure of determining linear allocation of business variables. For constructing linear program following factors are essential:

  • The objective function needs to be linear.
  • The objective must be to either maximize or minimize a linear function.
  • The constraints in the program should also be linear.

In formulating a linear program certain variables are integer in nature, such as function with integer variable is known as integer programming.

Decision Tree

In a certain decision-making process, probability plays an important role. On the decision model based upon probability is decision trees.

Scenario modeling

Business environment is always unpredictable and can throw up unusual situation more than often. Thus, organizations find themselves in the middle of dynamic environment. Here model and methods like sensitivity analysis, stability analysis, what-if analysis, scenario modeling, etc. is utilized.

Therefore, model under used uncertainties are as follows:

  • Scenario Analysis: this model assumes a different scenario a business may find itself with certain value of parameters.
  • Worst Case Analysis: this model assumes an extreme case scenario in computing different variables.
  • Monte-Carlo Model: this model assumes uncertainty through statistical distribution.

Theory of Constraints

Theory of constraints is a management concept which helps organization deal with situation, which hampers its growth and march towards higher level of performance. Theory of constraint encourages an organization to deal one constraint at a time and consist of following steps:

  • Identifying constraints of the existing system.
  • Identifying was to potential extract more out of system constraints.
  • Exploiting constraints to its fullest potential should be made priority.
  • As the company overcomes 1st constraint, it should look forward to working upon other constraints.

Unit I Fundamental Of Decision Making For Quantitative Techniques For Business Decisions Mcom Sem 2 Delhi University Notes

Unit I Fundamental Of Decision Making For Quantitative Techniques For Business Decisions MCOM Sem 2 Delhi University :  Business managers and directors used to rely on their experience and instinct to make tough decisions. Increasingly, however, they want to know what the numbers say. In the era of big data, quantitative methods used by operations analysts and economists provide solid evidence to guide management decisions on production, distribution, marketing and personnel management. These methods also help managers project future business conditions, enabling them to adjust their strategies as needed.

Types

Many types of quantitative methods can help drive business decisions. Some of the most commonly used include regression analysis, linear programming, factor analysis and data mining.

Regression Analysis

A popular technique among economists and statisticians, regression analysis uses complex statistical equations to estimate the impact of one or more factors, known as predictors or independent variables, on an outcome of interest, known as a dependent variable. Economists have used regression analysis to estimate the effect of education and experience on workers’ annual earnings. A company manager or business economist could use regression analysis to estimate the effect of advertising expenses on the company’s profits. Analysis of the data using this technique can estimate whether a correlation exists between the two and whether that relationship is statistically significant.

Linear Programming

All businesses face limited resources, including facility space, production equipment, supplies and labor. This makes optimal allocation of these resources a challenge for any manager. Linear programming, a popular technique in operations research and management analysis, is a mathematical method for determining how to achieve an optimal outcome, such as highest profits or lowest operating costs, subject to certain constraints, such as limited labor and supplies. Operations researchers and analysts in manufacturing and transportation have used linear programming to analyze and resolve problems related to planning, scheduling and distribution, according to Barry Render and Ralph Stair, authors of “Quantitative Analysis for Management.”

Factor Analysis

Factor analysis is a data reduction and analysis technique often used with survey data, making the method popular among market researchers. Factor analysis explores correlations in available data to identify underlying, unmeasured factors that could explain those relationships. For example, market researchers can use factor analysis to analyze data on consumer spending habits to identify factors that may explain particular purchasing patterns, such as a preference for certain types of products.

Data Mining

A combination of statistical and computer programming skills, data mining has grown in popularity as the quantity and size of statistical data sets have grown. Data mining refers to a class of methods used for analyzing large data sets, uncovering patterns and correlations buried within masses of raw data, according to Yale University law professor Ian Ayres, author of “Super Crunchers.” Online retailers such as Amazon have used data mining techniques to develop profiles of customer buying habits and used the information to recommend particular products based on a customer’s past purchases, as well as those of customers who have bought the same items.

Unit I Fundamental Of Decision Making For Quantitative Techniques For Business Decisions Mcom Sem 2 Delhi University Notes

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