The document provides an introduction to statistical decision theory. It discusses what decisions are, why they must be made, and different classifications of decisions. It then covers the phases and steps involved in decision making. Different types of decision making environments are described, including decision making under certainty, risk, and uncertainty. Several decision making criteria are then explained, including Laplace criterion, maximin criterion, Hurwicz criterion, Savage criterion, and expected monetary value. Examples are provided to illustrate how to apply the Hurwicz, Savage, and expected monetary value criteria to make optimal decisions.
This document discusses decision making environments and techniques. It describes three types of decision making environments: decision making under certainty, uncertainty, and risk. It also discusses decision trees, Bayesian analysis, and utility theory as tools for decision making under uncertainty and risk. The key techniques covered are expected value analysis, maximax/maximin criteria, and expected opportunity loss criterion for decision making under risk.
Here are the key points about Quantitative Techniques:
- Quantitative Techniques adopt a scientific approach to decision-making using past data and constructing suitable models.
- Some important Quantitative Techniques used in business include linear programming, transportation models, assignment models, network models, inventory models, simulation, probability, decision trees, etc.
- These techniques help managers make explicit decisions and provide additional information to select optimal decisions.
- Quantitative Techniques were developed during World War II to assist with military operations and were later adopted by industry for managerial decision-making.
- They provide a systematic, data-driven approach to decision-making and help increase the probability of good decisions. Quantitative Techniques are widely used today across
The document provides an overview of operations research techniques. It discusses:
- Operations research aims to improve decision-making through methods like simulation, optimization, and data analysis.
- Major applications include production scheduling, inventory control, transportation planning, and more.
- The techniques were developed in World War II and are now used widely in business for problems like resource allocation, forecasting, and process improvement.
Operations Research - Meaning, Origin & CharacteristicsSundar B N
Operations research (OR) is a scientific approach to problem solving that uses quantitative analysis. It originated during World War II when the British military used empirical data and basic statistics to develop tactics. OR is characterized by its use of decision making, information technology, quantitative solutions, simulation, optimization, and interdisciplinary team-based work. It aims to uncover new problems and provide the best performance under given circumstances through mathematical modeling.
Managerial economics ppt baba @ mba 2009Babasab Patil
Managerial economics involves applying economic principles to business management problems in order to facilitate optimal decision-making. It integrates economic theory with business practices. Managerial economics helps managers understand concepts like opportunity costs, marginal analysis, and incremental costs to make decisions around pricing, production levels, investment, and more. It draws on both microeconomics, which examines individual markets and industries, and macroeconomics, which analyzes the overall economy and external business environment.
Operations research (OR) is an analytical method used to solve problems and make decisions by breaking problems down into basic components and solving them through defined mathematical steps. OR has many applications in fields like transportation, manufacturing, the military, and government for problems involving scheduling, facility location, health services, and more. It uses techniques like simulation, network analysis, and game theory to develop potential solutions, analyze alternatives, and test solutions in real-world situations.
The document provides an introduction to statistical decision theory. It discusses what decisions are, why they must be made, and different classifications of decisions. It then covers the phases and steps involved in decision making. Different types of decision making environments are described, including decision making under certainty, risk, and uncertainty. Several decision making criteria are then explained, including Laplace criterion, maximin criterion, Hurwicz criterion, Savage criterion, and expected monetary value. Examples are provided to illustrate how to apply the Hurwicz, Savage, and expected monetary value criteria to make optimal decisions.
This document discusses decision making environments and techniques. It describes three types of decision making environments: decision making under certainty, uncertainty, and risk. It also discusses decision trees, Bayesian analysis, and utility theory as tools for decision making under uncertainty and risk. The key techniques covered are expected value analysis, maximax/maximin criteria, and expected opportunity loss criterion for decision making under risk.
Here are the key points about Quantitative Techniques:
- Quantitative Techniques adopt a scientific approach to decision-making using past data and constructing suitable models.
- Some important Quantitative Techniques used in business include linear programming, transportation models, assignment models, network models, inventory models, simulation, probability, decision trees, etc.
- These techniques help managers make explicit decisions and provide additional information to select optimal decisions.
- Quantitative Techniques were developed during World War II to assist with military operations and were later adopted by industry for managerial decision-making.
- They provide a systematic, data-driven approach to decision-making and help increase the probability of good decisions. Quantitative Techniques are widely used today across
The document provides an overview of operations research techniques. It discusses:
- Operations research aims to improve decision-making through methods like simulation, optimization, and data analysis.
- Major applications include production scheduling, inventory control, transportation planning, and more.
- The techniques were developed in World War II and are now used widely in business for problems like resource allocation, forecasting, and process improvement.
Operations Research - Meaning, Origin & CharacteristicsSundar B N
Operations research (OR) is a scientific approach to problem solving that uses quantitative analysis. It originated during World War II when the British military used empirical data and basic statistics to develop tactics. OR is characterized by its use of decision making, information technology, quantitative solutions, simulation, optimization, and interdisciplinary team-based work. It aims to uncover new problems and provide the best performance under given circumstances through mathematical modeling.
Managerial economics ppt baba @ mba 2009Babasab Patil
Managerial economics involves applying economic principles to business management problems in order to facilitate optimal decision-making. It integrates economic theory with business practices. Managerial economics helps managers understand concepts like opportunity costs, marginal analysis, and incremental costs to make decisions around pricing, production levels, investment, and more. It draws on both microeconomics, which examines individual markets and industries, and macroeconomics, which analyzes the overall economy and external business environment.
Operations research (OR) is an analytical method used to solve problems and make decisions by breaking problems down into basic components and solving them through defined mathematical steps. OR has many applications in fields like transportation, manufacturing, the military, and government for problems involving scheduling, facility location, health services, and more. It uses techniques like simulation, network analysis, and game theory to develop potential solutions, analyze alternatives, and test solutions in real-world situations.
This document provides an introduction to financial analytics and its key capabilities and applications. It discusses how financial analytics can provide insights into predictive sales, cash flow, and product profitability to improve business performance and decision making. Financial analytics leverages data from across the organization to give leadership a unified view of financial health, which products are most profitable, and what the future holds for key metrics like sales and cash flow. The document also notes how financial analytics can streamline processes, boost forecast accuracy, and help optimize resource allocation.
Decision theory as the name would imply is concerned with the process of making decisions. The extension to statistical decision theory includes decision making in the presence of statistical knowledge which provides some information where there is uncertainty. The elements of decision theory are quite logical and even perhaps intuitive. The classical approach to decision theory facilitates the use of sample information in making inferences about the unknown quantities. Other relevant information includes that of the possible consequences which is quantified by loss and the prior information which arises from statistical investigation. The use of Bayesian analysis in statistical decision theory is natural. Their unification provides a foundational framework for building and solving decision problems. The basic ideas of decision theory and of decision theoretic methods lend themselves to a variety of applications and computational and analytic advances.
This document provides an overview of game theory and two-person zero-sum games. It defines key concepts such as players, strategies, payoffs, and classifications of games. It also describes the assumptions and solutions for pure strategy and mixed strategy games. Pure strategy games have a saddle point solution found using minimax and maximin rules. Mixed strategy games do not have a saddle point and require determining the optimal probabilities that players select each strategy.
Strategic choice is the process of selecting a strategy from among alternatives to best meet an organization's objectives. The process involves four steps: 1) focusing on strategic alternatives, 2) analyzing the alternatives, 3) evaluating the alternatives using objective and subjective factors, and 4) choosing among the alternatives. Subjective factors that influence strategic choice include a strategist's commitment to past actions, decision-making style, consideration of internal politics, government policies, and corporate social responsibility obligations.
Executive support systems (ESS) are software tools that allow executive managers to access summarized reports from across an organization to help with strategic decision making. An ESS aggregates both internal and external data, enables online analysis like trend and scenario analysis, and has a user interface to quickly retrieve relevant information. The system's hardware, software, communication networks, and user interface work together to provide executives with timely summarized data and the ability to drill down into more detail if needed. ESS tools are intended to support strategic decision making by streamlining access to organizational data.
FORECASTING TECHNIQUES OR MODELS : BUSINESS ANALYTICSNavya Jayakumar
This document provides an overview of forecasting model building and types of forecasting models. It discusses qualitative and quantitative forecasting models. Qualitative models include consumer surveys, sales force opinions, Delphi method, past analogies, executive opinions, and nominal group techniques. Quantitative models include time series analysis, linear regression, trend projection, and econometric models. The document also covers aggregate planning techniques like trial-and-error, linear programming, and make-or-buy decisions.
This document discusses key concepts in decision theory and decision making under uncertainty. It begins by defining decision theory and describing the degree of certainty in decision making problems. It then outlines elements of decision analysis like states of nature, chance occurrences governed by probabilities, and payoff matrices. An example involving production decisions for a dairy product is provided. The document also discusses criteria for decision making under uncertainty like Laplace, maximin, maximax, Hurwicz, and regret. It concludes by covering expected monetary value, expected opportunity loss, expected value of perfect information, and decision trees as approaches to decision making under risk.
The document provides an overview of operations research, including:
- Operations research developed during World War II to allocate scarce military resources efficiently. It has since been applied to optimize operations in many industries.
- Mathematical modeling involves representing real-world problems mathematically to enable analysis and optimization using tools like linear programming.
- Linear programming formulates problems as mathematical models to find optimal solutions while respecting constraints, and was pioneered in the 1930s-40s. It has become one of the most important developments in 20th century mathematics.
Game theory is the study of how optimal strategies are formulated in conflict situations involving two or more rational opponents with competing interests. It considers how the strategies of one player will impact the outcomes for others. Game theory models classify games based on the number of players, whether the total payoff is zero-sum, and the types of strategies used. The minimax-maximin principle provides a way to determine optimal strategies without knowing the opponent's strategy by having each player maximize their minimum payoff or minimize their maximum loss. A saddle point exists when the maximin and minimax values are equal, indicating optimal strategies for both players.
Strategic Business Unit Defined
A strategic business unit is a fully functional and distinct unit of a business that develops its own strategic vision and direction.
The document discusses the capabilities of information technology and its organizational impact. It describes three levels of IT-based capabilities - strategic, system, and technology functionality. IT plays an important role across different organizational sectors such as marketing, human resources, and financial management. Traditional offices were designed for people and paper, but more recent offices are built for information systems and computer infrastructure.
Decision theory deals with determining the optimal course of action when alternatives have uncertain consequences. There are several key concepts: decision alternatives are available options; states of nature are uncontrollable events; and payoff is the numerical outcome of alternatives and states. The decision process involves defining the problem, listing states, identifying alternatives, expressing payoffs, and applying a model to select the optimal alternative based on criteria. Decision making can occur under certainty, risk, or uncertainty depending on what is known about states and payoffs. Different techniques are used depending on the environment.
This document provides an overview of game theory concepts. It defines game theory as analyzing situations of conflict and competition involving decision making by two or more participants. Some key points:
- Game theory was developed in the 20th century, with a seminal 1944 book discussing its application to business strategy.
- Basic concepts include players, pure and mixed strategies, zero-sum vs. non-zero-sum games, and payoff matrices to represent outcomes.
- Solutions include finding equilibrium points using minimax and maximin principles for pure strategies or solving systems of equations for mixed strategies when no equilibrium exists.
- Dominance rules can reduce game matrices, and graphical or algebraic methods solve for mixed strategies without saddles
1) The document discusses several criteria for decision making under uncertainty including maximax, maximin, minimax, minimin, and Laplace.
2) The maximax criterion is optimistic and chooses the alternative with the highest possible payoff. Maximin is pessimistic and chooses the alternative with the highest minimum payoff.
3) Minimax regret considers the maximum regret for each alternative and chooses the one with the minimum maximum regret. This accounts for opportunity loss across states of nature.
The “Blue Ocean” approach is a strategic tool that helps innovation strategists’ asses current and desired future strategic states whereas..Red Ocean is a current state.
The assignment problem is a special case of transportation problem in which the objective is to assign ‘m’ jobs or workers to ‘n’ machines such that the cost incurred is minimized.
Operational research is a systematic approach to decision-making that uses analytical and statistical techniques to arrive at optimal or near-optimal solutions to complex problems. The document provides an overview of operational research, including its history, scope, methodologies, tools and techniques, and applications in various fields such as national planning, defense, and industry. It also discusses some limitations of operational research related to incorporating non-quantifiable factors, computational complexity, and challenges in implementation.
MBA 201 (BUSINESS ENVIRONMENT)
Q-1. What is public debt? Describe its role in the economy.
Q-2. What is corporate intelligence?
Q-3. Define the role of the RBI in enforcing FEMA.
Q-4. Describe the social responsibility of business.
Q-5. Explain the economic role of government in business environment.
MBA 202 (RESEARCH METHODOLOGY)
Q-1. What do you mean by research? Explain its significance in modern times.
Q-2. Explain in detail techniques involved in defining a research problem.
Q-3. What is questionnaire? What are different types of questionnaire?
Q-4. What are the precautions one should take while administering “Data Collection”.
Q-5. Write short note on methods of business forecasting.
MBA 203 (FINANCIAL MANAGEMENT)
Q-1. What is “Return on capital employees?”
Q-2. What are the factors on which risk involved in investment depends?
Q-3. What are the advantages of cash planning? How does cash budget help in planning the firms cash flows?
Q-4. Explain the various approaches for computing the cost of equity capital.
Q-5. Explain various method of financial statement analysis.
MBA 204 (CORPORATE & BUSINESS LAWS)
Q-1. Explain how a case is brought before the courts, and describe the court process.
Q-2. Describe tort law and compare it to criminal law.
Q-3. What do you understand by Corporate & Business Laws? Explain.
Q-4. What is a non-profit corporation?
Q-5. State the various classes of companies that can be formed under the act. Explain the characteristics of each.
MBA 205 (OPERATIONS RESEARCH)
Q-1. Explain characteristics and classification of queuing model.
Q-2. Explain degenerate transportation problem.
Q-3. Write at least five application areas of linear programming.
Q-4. What do you understand by modified distribution method?
Q-5. What is the role of decision making in OR. Explain its scope.
MBA 206 (MANAGERIAL EFFECTIVENESS)
Q-1. “Decision-making is a critical activity in the lives of managers”. Define
Q-2. What are the requirements of an effective control system?
Q-3. Risk can always be associated with loss. Analyse the statement.
Q-4. Time management is more than just managing our time. Comment.
Q-5. Managers should concentrate on results, not on being busy. Describe.
This document provides an overview of key legal issues for entrepreneurs including intellectual property rights, patents, trademarks, copyrights, trade secrets, licensing, product safety and liability, insurance, and contracts. It defines these legal concepts and terms, provides examples, and outlines important considerations for entrepreneurs in managing legal risks and protecting their innovations, brands, and businesses.
Quantitative techniques are statistical and operations research methods that help with decision making, especially for business and industry. They provide tools for scientific analysis, help solve business problems, and enable optimal resource allocation. Some examples of quantitative techniques include linear programming, inventory planning, and statistical quality control. While quantitative techniques provide benefits, they also have limitations such as not accounting for intangible human factors and high costs. Quantitative analysis should be seen as a supplement to, not a substitute for, subjective managerial judgment in decision making.
Quantitative techniques are statistical and mathematical methods used to support decision making, especially related to business. They help quantify planning factors and alternatives using tools like linear programming, break-even analysis, and simulation. Quantitative techniques are goal-oriented and aim to find optimal solutions under constraints using quantitative data and models. They provide a systematic approach to managerial decision making and help improve quality of solutions.
This document provides an introduction to financial analytics and its key capabilities and applications. It discusses how financial analytics can provide insights into predictive sales, cash flow, and product profitability to improve business performance and decision making. Financial analytics leverages data from across the organization to give leadership a unified view of financial health, which products are most profitable, and what the future holds for key metrics like sales and cash flow. The document also notes how financial analytics can streamline processes, boost forecast accuracy, and help optimize resource allocation.
Decision theory as the name would imply is concerned with the process of making decisions. The extension to statistical decision theory includes decision making in the presence of statistical knowledge which provides some information where there is uncertainty. The elements of decision theory are quite logical and even perhaps intuitive. The classical approach to decision theory facilitates the use of sample information in making inferences about the unknown quantities. Other relevant information includes that of the possible consequences which is quantified by loss and the prior information which arises from statistical investigation. The use of Bayesian analysis in statistical decision theory is natural. Their unification provides a foundational framework for building and solving decision problems. The basic ideas of decision theory and of decision theoretic methods lend themselves to a variety of applications and computational and analytic advances.
This document provides an overview of game theory and two-person zero-sum games. It defines key concepts such as players, strategies, payoffs, and classifications of games. It also describes the assumptions and solutions for pure strategy and mixed strategy games. Pure strategy games have a saddle point solution found using minimax and maximin rules. Mixed strategy games do not have a saddle point and require determining the optimal probabilities that players select each strategy.
Strategic choice is the process of selecting a strategy from among alternatives to best meet an organization's objectives. The process involves four steps: 1) focusing on strategic alternatives, 2) analyzing the alternatives, 3) evaluating the alternatives using objective and subjective factors, and 4) choosing among the alternatives. Subjective factors that influence strategic choice include a strategist's commitment to past actions, decision-making style, consideration of internal politics, government policies, and corporate social responsibility obligations.
Executive support systems (ESS) are software tools that allow executive managers to access summarized reports from across an organization to help with strategic decision making. An ESS aggregates both internal and external data, enables online analysis like trend and scenario analysis, and has a user interface to quickly retrieve relevant information. The system's hardware, software, communication networks, and user interface work together to provide executives with timely summarized data and the ability to drill down into more detail if needed. ESS tools are intended to support strategic decision making by streamlining access to organizational data.
FORECASTING TECHNIQUES OR MODELS : BUSINESS ANALYTICSNavya Jayakumar
This document provides an overview of forecasting model building and types of forecasting models. It discusses qualitative and quantitative forecasting models. Qualitative models include consumer surveys, sales force opinions, Delphi method, past analogies, executive opinions, and nominal group techniques. Quantitative models include time series analysis, linear regression, trend projection, and econometric models. The document also covers aggregate planning techniques like trial-and-error, linear programming, and make-or-buy decisions.
This document discusses key concepts in decision theory and decision making under uncertainty. It begins by defining decision theory and describing the degree of certainty in decision making problems. It then outlines elements of decision analysis like states of nature, chance occurrences governed by probabilities, and payoff matrices. An example involving production decisions for a dairy product is provided. The document also discusses criteria for decision making under uncertainty like Laplace, maximin, maximax, Hurwicz, and regret. It concludes by covering expected monetary value, expected opportunity loss, expected value of perfect information, and decision trees as approaches to decision making under risk.
The document provides an overview of operations research, including:
- Operations research developed during World War II to allocate scarce military resources efficiently. It has since been applied to optimize operations in many industries.
- Mathematical modeling involves representing real-world problems mathematically to enable analysis and optimization using tools like linear programming.
- Linear programming formulates problems as mathematical models to find optimal solutions while respecting constraints, and was pioneered in the 1930s-40s. It has become one of the most important developments in 20th century mathematics.
Game theory is the study of how optimal strategies are formulated in conflict situations involving two or more rational opponents with competing interests. It considers how the strategies of one player will impact the outcomes for others. Game theory models classify games based on the number of players, whether the total payoff is zero-sum, and the types of strategies used. The minimax-maximin principle provides a way to determine optimal strategies without knowing the opponent's strategy by having each player maximize their minimum payoff or minimize their maximum loss. A saddle point exists when the maximin and minimax values are equal, indicating optimal strategies for both players.
Strategic Business Unit Defined
A strategic business unit is a fully functional and distinct unit of a business that develops its own strategic vision and direction.
The document discusses the capabilities of information technology and its organizational impact. It describes three levels of IT-based capabilities - strategic, system, and technology functionality. IT plays an important role across different organizational sectors such as marketing, human resources, and financial management. Traditional offices were designed for people and paper, but more recent offices are built for information systems and computer infrastructure.
Decision theory deals with determining the optimal course of action when alternatives have uncertain consequences. There are several key concepts: decision alternatives are available options; states of nature are uncontrollable events; and payoff is the numerical outcome of alternatives and states. The decision process involves defining the problem, listing states, identifying alternatives, expressing payoffs, and applying a model to select the optimal alternative based on criteria. Decision making can occur under certainty, risk, or uncertainty depending on what is known about states and payoffs. Different techniques are used depending on the environment.
This document provides an overview of game theory concepts. It defines game theory as analyzing situations of conflict and competition involving decision making by two or more participants. Some key points:
- Game theory was developed in the 20th century, with a seminal 1944 book discussing its application to business strategy.
- Basic concepts include players, pure and mixed strategies, zero-sum vs. non-zero-sum games, and payoff matrices to represent outcomes.
- Solutions include finding equilibrium points using minimax and maximin principles for pure strategies or solving systems of equations for mixed strategies when no equilibrium exists.
- Dominance rules can reduce game matrices, and graphical or algebraic methods solve for mixed strategies without saddles
1) The document discusses several criteria for decision making under uncertainty including maximax, maximin, minimax, minimin, and Laplace.
2) The maximax criterion is optimistic and chooses the alternative with the highest possible payoff. Maximin is pessimistic and chooses the alternative with the highest minimum payoff.
3) Minimax regret considers the maximum regret for each alternative and chooses the one with the minimum maximum regret. This accounts for opportunity loss across states of nature.
The “Blue Ocean” approach is a strategic tool that helps innovation strategists’ asses current and desired future strategic states whereas..Red Ocean is a current state.
The assignment problem is a special case of transportation problem in which the objective is to assign ‘m’ jobs or workers to ‘n’ machines such that the cost incurred is minimized.
Operational research is a systematic approach to decision-making that uses analytical and statistical techniques to arrive at optimal or near-optimal solutions to complex problems. The document provides an overview of operational research, including its history, scope, methodologies, tools and techniques, and applications in various fields such as national planning, defense, and industry. It also discusses some limitations of operational research related to incorporating non-quantifiable factors, computational complexity, and challenges in implementation.
MBA 201 (BUSINESS ENVIRONMENT)
Q-1. What is public debt? Describe its role in the economy.
Q-2. What is corporate intelligence?
Q-3. Define the role of the RBI in enforcing FEMA.
Q-4. Describe the social responsibility of business.
Q-5. Explain the economic role of government in business environment.
MBA 202 (RESEARCH METHODOLOGY)
Q-1. What do you mean by research? Explain its significance in modern times.
Q-2. Explain in detail techniques involved in defining a research problem.
Q-3. What is questionnaire? What are different types of questionnaire?
Q-4. What are the precautions one should take while administering “Data Collection”.
Q-5. Write short note on methods of business forecasting.
MBA 203 (FINANCIAL MANAGEMENT)
Q-1. What is “Return on capital employees?”
Q-2. What are the factors on which risk involved in investment depends?
Q-3. What are the advantages of cash planning? How does cash budget help in planning the firms cash flows?
Q-4. Explain the various approaches for computing the cost of equity capital.
Q-5. Explain various method of financial statement analysis.
MBA 204 (CORPORATE & BUSINESS LAWS)
Q-1. Explain how a case is brought before the courts, and describe the court process.
Q-2. Describe tort law and compare it to criminal law.
Q-3. What do you understand by Corporate & Business Laws? Explain.
Q-4. What is a non-profit corporation?
Q-5. State the various classes of companies that can be formed under the act. Explain the characteristics of each.
MBA 205 (OPERATIONS RESEARCH)
Q-1. Explain characteristics and classification of queuing model.
Q-2. Explain degenerate transportation problem.
Q-3. Write at least five application areas of linear programming.
Q-4. What do you understand by modified distribution method?
Q-5. What is the role of decision making in OR. Explain its scope.
MBA 206 (MANAGERIAL EFFECTIVENESS)
Q-1. “Decision-making is a critical activity in the lives of managers”. Define
Q-2. What are the requirements of an effective control system?
Q-3. Risk can always be associated with loss. Analyse the statement.
Q-4. Time management is more than just managing our time. Comment.
Q-5. Managers should concentrate on results, not on being busy. Describe.
This document provides an overview of key legal issues for entrepreneurs including intellectual property rights, patents, trademarks, copyrights, trade secrets, licensing, product safety and liability, insurance, and contracts. It defines these legal concepts and terms, provides examples, and outlines important considerations for entrepreneurs in managing legal risks and protecting their innovations, brands, and businesses.
Quantitative techniques are statistical and operations research methods that help with decision making, especially for business and industry. They provide tools for scientific analysis, help solve business problems, and enable optimal resource allocation. Some examples of quantitative techniques include linear programming, inventory planning, and statistical quality control. While quantitative techniques provide benefits, they also have limitations such as not accounting for intangible human factors and high costs. Quantitative analysis should be seen as a supplement to, not a substitute for, subjective managerial judgment in decision making.
Quantitative techniques are statistical and mathematical methods used to support decision making, especially related to business. They help quantify planning factors and alternatives using tools like linear programming, break-even analysis, and simulation. Quantitative techniques are goal-oriented and aim to find optimal solutions under constraints using quantitative data and models. They provide a systematic approach to managerial decision making and help improve quality of solutions.
The document discusses quantitative techniques and assignment problems. It begins by defining quantitative techniques as the scientific approach to managerial decision making that involves manipulating raw data into meaningful information. It then discusses assignment problems specifically, which aim to assign a number of origins to destinations at minimum cost, with each origin and destination receiving only one assignment. The document provides an example assignment problem and solves it step-by-step using the Hungarian method, subtracting minimum row and column values to reach an optimal solution.
Introduction, Meaning and Characteristics of Operations Research Background of Operations Research, Operations Research, Scope of Operations Research, Finance department, Personnel Management, applications of operations research in business, applications of operations research, Hewlett-Packard, CHARACTERISTICS OF OPERATIONS RESEARCH, are addressed.
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Mba i qt unit-1_basic quantitative techniquesRai University
Quantitative techniques help business managers make optimal decisions by using mathematical and statistical methods. They allow managers to analyze problems scientifically, deploy resources efficiently, and choose the best strategies. Some key quantitative techniques include linear programming, simulation, and queuing theory. While useful for optimization, quantitative techniques also have limitations like not accounting for human factors and high implementation costs. Overall, they provide systematic and powerful analytical tools to supplement managerial judgment.
Operations Research - An Analytic Tool for a Researcher.pptLadallaRajKumar
The document discusses operations research and its applications in various fields. It begins by introducing operations research and listing some common problems that can be analyzed using operations research tools. It then discusses important operations research tools like linear programming, simulation, and network analysis. It also outlines opportunities for operations research in fields like finance, consulting, and as analysts. Finally, it provides some examples of operations research applications in biology, pharmacy, and oil/gas industries.
Importance of quantitative techniques in managerial decisionsAman Sinha
The document discusses the importance of quantitative techniques in managerial decision making. It describes how quantitative techniques involve applying mathematics and statistics to solve problems. The document also provides examples of how quantitative methods can be applied in various business functions like marketing, production, human resources, and finance. Specific quantitative models and tools are discussed for tasks like facility location, project management, performance appraisal, and financial analysis.
Importance of quantitative techniques in managerial decisionsAman Sinha
The document discusses the importance of quantitative techniques in managerial decision making. It describes how quantitative techniques involve applying mathematics and statistics to solve problems. The use of quantitative methods grew after World War II with developments like linear programming and computer technology. Quantitative methods are important across business functions like marketing, production, human resources, and finance for tasks like facility location, data mining, performance appraisal, and financial analysis. The document provides examples of how quantitative problems in areas like logistics and probability can be solved.
Study of Managing Business at Reliance Industries Limited”shah kunal
The document is a project report on studying the business management of Reliance Industries Limited. It contains an introduction to Reliance Industries which discusses its size and operations. The rest of the report is divided into groups that cover quantitative analysis, management information systems, marketing management, human resource management, cost and management accounting, financial management, and production and operations management as they relate to Reliance Industries. For each topic, there are sections that discuss concepts and practices used by Reliance Industries.
Quantitative management is not a modern business idea but a management theory that came into existence after World War II. Business owners initially used it in Japan to pick up the pieces of the devastation caused by the war and started taking baby steps toward reconstruction. It focuses on the following elements of business operations:
Customer satisfaction
Business value enhancement
Empowerment of employees
Creating synergy among teams
Creating quality products
Preventing defects
Being responsible for quality
Focusing on continuous improvement
Leveraging statistical measurement
Remaining focused on the processes
Commitment to refinement and learning
Quantitative techniques in management as a collection of mathematical and statistical tools. They’re known by different names, such as management science or operation research. In modern business methods, statistical techniques are also viewed as a part of quantitative management techniques.
When appropriately used, quantitative approaches to management can become a powerful means of analysis, leading to effective decision-making. These techniques help resolve complex business problems by leveraging systematic and scientific methods.
i. It is the application of scientific methods, techniques and tools to problems involving the operations of a system so as to provide those in the control of the system with optimum solutions to the problems.
ii. Operation Research is a tool for taking decisions which searches for the optimum results in parity with the overall objectives and constraints of the organization.
iii. Operations research (OR) is an analytical method of problem-solving and decision-making that is useful in the management of organizations. In operations research, problems are broken down into basic components and then solved indefined steps by mathematical analysis.
The document discusses three topics:
1. Human Resource Management - How HR analytics can help resolve challenges in HR by making it more data-driven.
2. Water Management - New digital technologies can monitor water usage and help optimize water resource management.
3. Manufacturing Industry - Advanced analytics in manufacturing can help with predictive maintenance, quality testing, supply chain optimization, and product optimization to reduce costs and improve processes.
Keys to Succeed in Implementing Total Preventive Maintenance (TPM) and Lean S...IJMTST Journal
Competition is global and it continues to get more intense, with changes in technology, introduction of new and differentiated products and techniques. These changes are faster than what can be implemented. Profits are no longer driven by prices but with costs.[1] Customers have access to just about anything at their finger tips. The expectation like quick response, lower prices, flexible orders and quality products, is increasing every day from the customers. Our OEM’s (Original Equipment Manufacturers) are searching for new methods of doing business and they expect their suppliers, like us to do the same. The challenge in front of us is how we respond effectively to these changing trends in the industry for our survival & growth. Change is the only certainty and the above is very much applicable to any business to achieve and sustain competitive edge. It is evident that organizations, which are innovative and visionary, have successfully implemented the change, realizing its business strategies would lead to their long term survival
Business Application of Operation ResearchAshim Roy
This document discusses a project on the business applications of operations research. It begins with an acknowledgment section thanking teachers and parents for their support. The main body provides an abstract, introduction and overview of operations research. It discusses the early history and development of OR, and provides examples of its applications in business such as optimizing supply chain management and power grid operations. The document outlines the various techniques, methods, and areas where OR is applied to improve decision making and efficiency.
The document discusses operations research (OR), which uses analytical methods to help organizations make better decisions. OR involves decomposing problems, developing mathematical models, and using techniques like simulation, optimization, and data analysis to evaluate alternatives and identify optimal solutions. The document provides examples of how OR has been applied in various sectors to improve efficiency and reduce costs. It also outlines the typical phases of an OR project, including problem identification, mathematical modeling, and ensuring available data can support the model.
LITERATURE REVIEW OF OPTIMIZATION TECHNIQUE BUSINESS: BASED ON CASEIAEME Publication
In today’s complex business world, decision making plays a vital role in the
success of any business. The simplex method, an operation research technique is
widely used to finding solutions in many real world problems. This paper is an attempt
to get an insight about the various application of optimization techniques in business.
It is a conceptual research based on various literatures available. This study is based
on different cases applied on selected sectors, viz., industrial, financial, resource
allocation, agriculture, marketing and personnel management area.
This presentation provides an overview of management science and linear programming. It introduces management science as an interdisciplinary field using mathematical modeling, engineering, statistics, and algorithms to help organizations make rational decisions. Linear programming is presented as a key tool in management science used to optimize objectives subject to constraints. Examples are given of how linear programming can be applied to production planning, marketing mixes, product distribution, and personnel assignments. The characteristics, advantages, and limitations of the linear programming approach are also summarized.
This document provides an introduction to operational research. It discusses the definition, history, scope, methodologies, tools, and limitations of operational research. Operational research uses analytical and quantitative approaches to help organizations make better decisions and solve complex problems. It originated during World War II to support military planning and operations and has since been applied across various domains including manufacturing, transportation, healthcare, and government.
Application of operation tools in recent retail revolutionnashango
The document discusses various operations tools that can be used to improve efficiency in retail businesses. It describes five major tools: [1] the transportation model, which helps optimize distribution and minimize transportation costs; [2] the assignment model, which allocates resources to activities to minimize costs or maximize profits; [3] the queuing model, which analyzes wait times and resource utilization; [4] network models like PERT and CPM that help plan and schedule complex projects; and [5] inventory models that aim to minimize inventory costs while maintaining sufficient stock. These tools provide quantitative techniques to help retailers optimize operations, solve problems efficiently and maximize profits in today's dynamic retail environment.
Operational research (OR) uses analytical techniques to improve decision-making and efficiency. It encompasses problem-solving methods applied to optimize performance. OR analyzes systems through mathematical modeling, simulation, and other techniques. It aims to make the best use of resources by carefully planning and analyzing processes. Examples of OR applications include scheduling, facility planning, forecasting, yield management, and defense logistics. The field originated from military planning in World War II and has since expanded to business, industry, and public policy problems.
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This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
2. QUANTITATIVE TECHNIQUES
UNIT 1: SYLLABUS (THEORY)
Quantitative Techniques: Introduction
(as per University of Rajasthan)
Statistical & Operations Research Techniques
Scope & Application of Quantitative Techniques
Scientific Approach in Decision Making
Limitation of these Techniques
3. Quantitative Techniques defined as those techniques which provide the
decision maker with a systematic and powerful means of analysis and help,
based on quantitative data, in exploring policies for achieving pre-
determined goals.
Quantitative techniques are those statistical and programming techniques:
which support the decision making process especially related to industry and
business.
MEANING
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF QUANTITATIVE TECHNIQUES
4. ROLE
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF QUANTITATIVE TECHNIQUES
It enables proper deployment of resources.
It offers solutions for various business
problems.
It provides a tool for scientific analysis.
It enables proper deployment of resources.
It enables proper deployment of resources.
It supports in minimising waiting and
servicing costs.
It helps the management to decide when to
buy and what is the procedure of buying.
It helps in reducing the total processing
time necessary for performing a set of jobs.
5. STATISTICAL RESEARCH
Statistical Techniques are those techniques which are used to
conduct statistical analysis on a certain phenomenon.
This include all statistical steps i.e from collection of data till
analysis and interpretation of collected data.
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF QUANTITATIVE TECHNIQUES
6. Operations Research (O.R) is an analytical method of problem-solving
and decision-making that is useful in the management of
organizations.
In operations research, problems are broken down into basic
components and then solved in defined steps by mathematical
analysis.
According to H.M Wagner
"Operations Research(O.R) is a scientific approach to problem solving
for executive management."
OPERATIONS RESEARCH
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF QUANTITATIVE TECHNIQUES
7. CHARACTERISTICS
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF OPERATIONS RESEARCH IN QUANTITATIVE TECHNIQUES
O.R. often requires a computer to solve the complex
mathematical model or to perform a large number of
computations that ae involved. Use of digital
computer has become an integral part of the
operations research approach to decision making.
USE OF INFORMATION TECHNOLOGY (I.T)
O.R provides the managers a quantitative base for
decision making. OR attempts to provide a systematic
and rational approach for quantitative solution to the
various managerial problems.
QUANTITATE SOLUTION
study of the OR is incomplete without a study of
human factors. In deriving quantitative solution we do
not consider human factors, which doubtlessly plays a
great role in the problems.
TAKING HUMAN FACTORS
O.R. is performed by a team of scientists whose
individual members have been drawn from different
scientific and engineering disciplines.
For example, one may find a mathematician,
statistician, physicist, psychologist, economist and an
engineer working together on an O.R problem.
USE OF INFORMATION TECHNOLOGY (I.T)
8. TECHNIQUES
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF OPERATIONS RESEARCH IN QUANTITATIVE TECHNIQUES
It refers to finding the optimum solution for supply of
uncertain demand in stores.
Its goal is to achieve economic balance between the
cost of providing services and cost associated with
the wait received for that service.
QUEUING THEORY / WAITING LINE
Simplex Method is also a mathematical technique in
which limited resources are divided into two or more
than two competitive programmes in such a way that
an optimum solution is obtained.
SIMPLEX METHOD
It is used by business to take decisions and improving
decision under the conditions of risk, uncertainty and
uncertainty. It helps decision maker to analyse and
interpret a set of complex situations with many
different possible consequences.
DECISION THEORY
It is a technique which is used to find solution of
decision making by designing, constructing and
manipulating a model of real system.
This technique is used in analysing a number of
complex problems where analytical methods are
difficult or unknown.
SIMULATION
9. TECHNIQUES
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF OPERATIONS RESEARCH IN QUANTITATIVE TECHNIQUES
Sequencing System is used by industries to reduce
the cost and production time.
By applying Sequencing System, the production
programme can be made effective and applied in a
systematic way.
SEQUENCING SYSTEM
Allocation Model is used for the purpose of decision-
making. With the help of allocation model, decision
regarding the satisfaction of unlimited requirements
of business with the limited resources available can be
taken. It reduces the risk and provides optimum
solutions of objectives
ALLOCATION MODEL
Competition Model is used where two or more than
two uniform industries encounter similar type of
competition.
This model is used by industries to improve the
management and workers relationship to make it
more efficient.
COMPETITION MODEL
It is a technique which is used to find solution of
decision making by designing, constructing and
manipulating a model of real system.
This technique is used in analysing a number of
complex problems where analytical methods are
difficult or unknown.
SIMULATION
10. TECHNIQUES
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF OPERATIONS RESEARCH IN QUANTITATIVE TECHNIQUES
Markov Process is designed to find the solution of
decision-making problems.
It is a managerial tool used to estimate production by
examining and predicting the customer behaviour i.e
their loyalty towards one brand and habit of switching
to other brands.
MARKOV PROCESS/ANALYSIS
Inventory system is followed for proper storing of
inventory to maintain the flow of supply and demand
of product. It helps the management to ascertain
when to buy and how much to buy an item of
inventory so that carrying cost, ordering cost and
outage of stock can be minimum.
INVENTORY SYSTEM
Competition Model is used where two or more than
two uniform industries encounter similar type of
competition.
This model is used by industries to improve the
management and workers relationship to make it
more efficient.
COMPETITION MODEL
It is a technique which is used to find solution of
decision making by designing, constructing and
manipulating a model of real system.
This technique is used in analysing a number of
complex problems where analytical methods are
difficult or unknown.
SIMULATION
11. TECHNIQUES
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF OPERATIONS RESEARCH IN QUANTITATIVE TECHNIQUES
It enables the management of manufacturing or
business concern to make optimum use of available
resources.
Linear means straight like while Programming refers
to exploration of various alternatives towards solution
of problem.
LINEAR PROGRAMMING
The game theory enables a person in taking decisions
when two or more intelligent and rational opponents
are involved in conflict or competition.
It was developed during world war to by John Von
Neumann.
Efforts are made to determine a rival's most profitable
counter strategy and to formulate the appropriate
defensive measure rather than drawing conclusions
from past behaviour of opponent.
GAME THEORY
12. The following are the scope of quantitative techniques in different
areas :
- Scope of Quantitative Techniques in Industry
- Scope of Quantitative Techniques in Developing Economies
- Scope of Quantitative Techniques in Agriculture Industry
- Scope of Quantitative Techniques in Organisation
- Scope of Quantitative Techniques in Business and Society
SCOPE
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF QUANTITATIVE TECHNIQUES
13. Industrial management deals with a series of problems, starting right from
the purchase of raw materials till the dispatch of final products. The
management is ultimately interested in overall understanding of the
alternative methods, of optimising profits.
Many industries have gained immensely by applying Operations Research in
various tasks.
For example: operations research can be used in the fields of manufacturing
and production, blending and product mix, inventory management, for
forecasting demand, sale and purchase, for repair and maintenance jobs, for
scheduling and sequencing planning, and also for scheduling and control of
projects.
SCOPE IN INDUSTRY
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
IN QUANTITATIVE TECHNIQUES
14. The scope in Developing Economies focuses on planning to achieve
maximum growth per capital income in minimum time; considering the
goals and restrictions of the country.
Poverty and hunger are the core problems faced by many countries.
Therefore, people like statisticians, economists, technicians,
administrators, politicians and agriculture experts can work in
conjunction, to solve this problem with an Operations Research
approach in Quantitative Techniques.
SCOPE IN DEVELOPING ECONOMIES
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
IN QUANTITATIVE TECHNIQUES
15. Population explosion has led to scarcity of food. Optimum allocation of
land for various crops in accordance with climatic conditions is a
challenge for many countries.
Also, each developing country is facing the problem of optimal
distribution of water from several water bodies. These areas of concern
hold a great scope for Scientific Research
SCOPE IN AGRICULTURE INDUSTRY
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
IN QUANTITATIVE TECHNIQUES
16. Operational productivity of organisations have improved by using
quantitative techniques.
Techniques of Operations Research, can be applied to minimise cost,
and maximise benefit for decisions.
For example:
A departmental store faces problem like employing additional staff or
purchasing an additional asset for business like a business vehicle, etc.
SCOPE IN ORGANISATION
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
IN QUANTITATIVE TECHNIQUES
17. Businesses and society can directly be benefited from Operations
Research.
For example: hospitals, clinics etc. Operations research methods can be
applied directly to solve administrative problems such as minimising the
waiting time of outdoor patients.
Similarly, the business of transport can also be benefited by applying
simulation methods. Such methods, can help to regulate train arrivals
and their running timings.
Queuing theory, can be applied to minimise congestion and passengers
waiting time.
Industries such as petroleum, paper, chemical, metal processing, aircraft,
rubber, mining and textile have been extremely benefited by its use.
SCOPE IN BUSINESS AND SOCIETY
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
IN QUANTITATIVE TECHNIQUES
18. Applications of quantitative techniques in managerial decision-making
are as follows:
- Finance, Budgeting and Investment
- Marketing
- Physical Distribution
- Purchasing, Procurement and Exploration
- Personnel
- Production
- Research and Development
APPLICATION
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF QUANTITATIVE TECHNIQUES
19. A scientific approach improves precision — it reduces the odds of pursuing
projects with false positive returns and increases the odds of pursuing projects
with false negative returns.
Entrepreneurs who behave like scientists perform better, are more likely to
pivot to a different idea, and are not more likely to drop out than the control
group in the early stages of the startup.
In order to evaluate the alternatives, certain quantitative techniques have been
developed which facilitate in making objective decisions, those are:
- Marginal Analysis
- Co-effectiveness Analysis
- Operations Research
- Linear Programming
SCIENTIFIC APPROACH IN DECISION MAKING
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF QUANTITATIVE TECHNIQUES
20. SCIENTIFIC APPROACH IN DECISION MAKING
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF QUANTITATIVE TECHNIQUES
This technique is also known as ‘marginal costing’. In
this technique the additional revenues from additional
costs are compared.
The profits are considered maximum at the point
where marginal revenues and marginal costs are
equal.
This technique can also be used in comparing factors
other than costs and revenues.
MARGINAL ANALYSIS
This is a scientific method of analysis of decision
problems to provide the needed quantitative
information in making these decisions.
The important purpose of this is to provide the
managers with scientific basis for solving
organisational problems involving the interaction of
components of the organisation.
This seeks to replace the process by an analytic,
objective and quantitative basis based on information
supplied by the system in operation and possibly
without disturbing the operation.
OPERATIONS RESEARCH
21. SCIENTIFIC APPROACH IN DECISION MAKING
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF QUANTITATIVE TECHNIQUES
This analysis may be used for choosing among
alternatives to identify a preferred choice when
objectives are far less specific than those expressed
by such clear quantities as sales, costs or profits.
Koontz, O’Donnell and Weihrich have written that
“Cost models may be developed do show cost
estimates for each alternative and its effectiveness.
Social objective may be to reduce pollution of air and
water which lacks precision. Further, he has
emphasised for synthesising model i.e., combining
these results, may be made to show the relationships
of costs and effectiveness for each alternative.”
CO-EFFECTIVE ANALYSIS
It is a technique applicable in areas like production
planning, transportation, warehouse location and
utilisation of production and warehousing facilities at
an overall minimum cost.
It is based on the assumption that there exists a linear
relationship between variables and that the limits of
variations can be ascertained.
MARGINAL ANALYSIS
22. LIMITATION
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF QUANTITATIVE TECHNIQUES
Quantative Techniques is an costly affair.
An organisation needs to invest time, money and
effort into Quantitate Techniques such as Operations
Research to make it effective.
Professionals need to be hired to conduct constant
research. For better research outcomes, these
professionals must constantly review the rapidly
changing business scenarios.
COSTLY EXERCISE
Quantitative techniques can lead to misleading
results, not least if you use them incorrectly.
If, for example, you count the number of children
sitting an exam rather than the percentage of those
passing it or if you focus only on the past three
months when the yield was high and ignore the 21
months before where the yield was extremely low
CAN HAVE MISLEADING RESULTS
23. LIMITATION
QUANTITATIVE TECHNIQUE - UNIT 1: INTRODUCTION
OF QUANTITATIVE TECHNIQUES
Quantitative Techniques Approach is mathematical in
nature.
It tries to find out an optimal solution to a problem, by
taking all the factors into consideration.
The need of computers become unavoidable because
these factors are enormous and it requires huge
calculations to express them in quantity and to
establish relationships among them.
DEPENDENCE ON COMPUTERS
Quantitate Techniques are not good at capturing
feelings of human and its behaviour.
There are lots of complexities of human relations and
behaviour which must be taken into account while
implementing Quantitate Technique's decisions, as it
is a very delicate task and often it fails to generate
accurate results.
WEAK ANALYSIS ON HUMAN BEHAVIOUR