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.
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
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.
Quantitative techniques are statistical and programming methods that help decision makers analyze problems, especially business problems, using quantitative data. They have evolved from early applications in the 19th century to today where they are used widely. They can be classified into statistical techniques, which analyze collected data, and programming techniques, like linear programming, that model relationships to find optimal solutions. Quantitative techniques help businesses with tasks like resource allocation, strategy selection, and decision making. However, they have limitations like not accounting for intangible human factors.
This document provides an introduction to quantitative techniques for management. It discusses the historical development of quantitative techniques from scientific management principles to their modern applications. The methodology of quantitative techniques involves formulating the problem, defining decision variables and constraints, developing a suitable mathematical model, acquiring input data, solving the model, validating the model, and implementing results. Quantitative techniques help managers make faster and more accurate decisions using a scientific approach. They allow for complex problems to be solved with greater ease and accuracy.
This document provides an overview of operational research techniques. It defines operational research as a scientific approach to problem solving for management. It describes several types of models used in operational research, including iconic, analogue, deterministic, symbolic, combined, heuristic, and probabilistic models. It also discusses common approaches and the significance of operational research for areas like profit maximization, cost minimization, and resource allocation. Specific techniques are outlined, such as linear programming, game theory, queuing theory, and the simplex method. Finally, potential applications and limitations of operational research are mentioned.
Quantitative techniques may be defined as those techniques which provide the decision makes a systematic and powerful means of analysis, based on quantitative data. It is a scientific method employed for problem solving and decision making by the management
This document discusses management as both an art and a science. It explains that while management has long been practiced as an art, modern industrialization has increased complexity, requiring management to become more scientific with a focus on training professional managers. It also discusses how statistical data and techniques are essential for modern managerial decision making given increasing uncertainty and complexity in business environments. Managers must make data-driven decisions using statistical methods to analyze relationships in data and gain insights. Overall, the document contrasts historical views of management as an art with the modern need for a scientific approach using quantitative and statistical 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.
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
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.
Quantitative techniques are statistical and programming methods that help decision makers analyze problems, especially business problems, using quantitative data. They have evolved from early applications in the 19th century to today where they are used widely. They can be classified into statistical techniques, which analyze collected data, and programming techniques, like linear programming, that model relationships to find optimal solutions. Quantitative techniques help businesses with tasks like resource allocation, strategy selection, and decision making. However, they have limitations like not accounting for intangible human factors.
This document provides an introduction to quantitative techniques for management. It discusses the historical development of quantitative techniques from scientific management principles to their modern applications. The methodology of quantitative techniques involves formulating the problem, defining decision variables and constraints, developing a suitable mathematical model, acquiring input data, solving the model, validating the model, and implementing results. Quantitative techniques help managers make faster and more accurate decisions using a scientific approach. They allow for complex problems to be solved with greater ease and accuracy.
This document provides an overview of operational research techniques. It defines operational research as a scientific approach to problem solving for management. It describes several types of models used in operational research, including iconic, analogue, deterministic, symbolic, combined, heuristic, and probabilistic models. It also discusses common approaches and the significance of operational research for areas like profit maximization, cost minimization, and resource allocation. Specific techniques are outlined, such as linear programming, game theory, queuing theory, and the simplex method. Finally, potential applications and limitations of operational research are mentioned.
Quantitative techniques may be defined as those techniques which provide the decision makes a systematic and powerful means of analysis, based on quantitative data. It is a scientific method employed for problem solving and decision making by the management
This document discusses management as both an art and a science. It explains that while management has long been practiced as an art, modern industrialization has increased complexity, requiring management to become more scientific with a focus on training professional managers. It also discusses how statistical data and techniques are essential for modern managerial decision making given increasing uncertainty and complexity in business environments. Managers must make data-driven decisions using statistical methods to analyze relationships in data and gain insights. Overall, the document contrasts historical views of management as an art with the modern need for a scientific approach using quantitative and statistical analysis.
The document discusses using the linear programming technique to aid in decision making for marketing and finance problems. It provides an example of using linear programming to determine the optimal allocation of advertising budgets across multiple media (television, radio, newspaper) to maximize total audience reach given budget constraints. Linear programming can be applied to problems in marketing mix determination, financial decision making, production scheduling, and more. It also briefly describes the simplex method for solving linear programming problems.
1. Managers use quantitative techniques like variance and regression analysis to make sales, customer performance, and futuristic predictions and decisions for organizations.
2. An educational institution in Jaipur uses mathematics to determine dimensions of spaces in its building like classrooms, stairs, gates, and wires to optimize use of space.
3. The institution analyzes applicant and student data like admission forms, cut-offs, performance in written tests, assignments, internships, and backgrounds to select and evaluate students.
Quantitative analysis for business decision (QABD)- Linear programming probl...Chandra Shekar Immani
Linear programming is an optimization technique for allocating limited resources to achieve the greatest benefit. It can be used to solve problems in various industries and fields. Some common applications include determining optimal product mixes, production schedules, transportation routes, and portfolio selections. The document provides examples of linear programming applications in industries like oil refining, transportation, manufacturing, and more. It also discusses the advantages of linear programming in improving decision quality and using resources efficiently with a scientific approach.
Liner programming on Management ScienceAbdul Motaleb
The document discusses management science and linear programming. It provides details on:
1) Management science uses various scientific principles and analytical methods to help organizations make rational decisions to maximize profit or minimize expenses.
2) Management science research can be done on fundamental, modeling, and application levels.
3) Linear programming is a method to achieve the optimal outcome given linear constraints and can be used to solve production planning, marketing mix, product distribution, and staff scheduling problems in business.
4) The key characteristics of linear programming problems are that they involve optimization with an objective function and constraints, and have linear relationships between variables.
This document provides an overview of the history and objectives of operations research. It discusses how operations research originated during World War II to help optimize the use of limited military resources. After the war, operations research techniques were applied to industrial problems to maximize profits and minimize costs. The document outlines the key objectives of operations research as providing a scientific basis for management decision making and helping managers make better decisions through the use of mathematical modeling and analysis.
Introduction to Business Analytics and Simulation
http://nguyenngocbinhphuong.com/course/mo-phong-trong-kinh-doanh/
1) What is Business Analytics?
2) Types of Business Analytics: Descriptive, Predictive & Prescriptive
3) Data for Business Analytics: Structured & Unstructured or Semi-Structured
4) Models in Business Analytics: Logic-Driven Models & Data-Driven Models
5) Types of Business Simulation: Monte Carlo Simulation & System Simulation
Forecasting :- Introduction & its ApplicationsDeepam Goyal
This document discusses forecasting, including its introduction, characteristics, principles, need, process, areas of application, advantages, and disadvantages. It provides examples of forecasting in supply chain management, economics, earthquakes, buildings, land use, sports, politics, transportation, telecommunications, products, sales, and technology. The document also presents a case study of Henkel, a manufacturing company that improved sales forecasting accuracy from 69.3% to 85.3% by implementing social forecasting with incentives for top forecasters.
The document provides an introduction to industrial management and engineering. It discusses key concepts like the functions of management, productivity improvement, and the roles of an industrial engineer. It also covers topics such as applications of industrial engineering, production management, the relationship between production management and industrial engineering, and tools used in management science.
This chapter introduces spreadsheet modeling and decision analysis as a field of management science that uses computers, statistics, and mathematics to solve business problems. It discusses how spreadsheet models represent real-world phenomena with mathematical relationships and can help analyze decisions by evaluating potential outcomes. Examples are given of companies that achieved significant cost savings and efficiency gains by developing spreadsheet and other mathematical models to optimize areas like procurement, logistics, inventory management, and operations. The chapter also covers characteristics of models, benefits of modeling approaches, categories of mathematical models, and cognitive biases that can influence decision-making.
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.
Unilever uses a state-of-the-art customer demand planning system to forecast demand. It blends historical shipment data, promotional data, and current order data to generate statistical forecasts, which are then adjusted based on planned promotion predictions and point of sale data. This approach has helped Unilever reduce inventory levels and improve customer service. The document also discusses different forecasting techniques like time series analysis, causal methods, and judgmental forecasts, and how to measure forecast accuracy.
Demand management involves understanding customer needs, planning products and services, and fulfilling demands across a business and its partners. Effective demand management relies on demand forecasting to predict future needs and inform resource planning. Forecasting considers factors like historical data, demand variability, and required accuracy. Accurate forecasts allow businesses to optimize inventory, capacity, and costs to best meet customer demands over different time horizons.
Business forecasting uses qualitative and quantitative methods to predict future business conditions and trends. Qualitative methods gather opinions through surveys and focus groups, while quantitative analyzes statistical data to identify trends. Some alternative methods like astrology are unlikely to be more effective than traditional qualitative and quantitative approaches. Forecasting provides benefits like informed decision-making but also costs as data may be unreliable, outdated, or unable to account for unexpected external changes.
Chapter I-Intro to Quantitative Analysismeladariel
This chapter introduces quantitative analysis and outlines the typical steps in the quantitative analysis approach: defining the problem, developing a model, acquiring input data, developing a solution, testing the solution, analyzing results, and implementing results. It also discusses examples of quantitative analysis, advantages of modeling, types of models, and potential problems that can arise.
This document discusses different methods of forecasting. It begins by defining forecasting as a planning tool used to predict future events that will influence an organization. It then outlines the key advantages of forecasting such as helping managers plan ahead and identify weaknesses.
The document describes the main types of forecasting methods: judgmental/qualitative methods which rely on subjective estimates, extrapolative/time series methods which analyze past trends to predict the future, and causal/explanatory methods which use statistical models and variables to forecast. Specific judgmental, extrapolative, and causal techniques are defined such as the Delphi technique, moving averages, and regression analysis.
All companies need to be more effectively than ever before. In the current financial climate, every dollar invested is important and know that your business is operating efficiently is an imperative need, but as a Manager not always easy to know if the decisions are really the best for your company.
Forecasting methods by Neeraj Bhandari ( Surkhet.Nepal )Neeraj Bhandari
This document discusses various forecasting methods used to predict future outcomes when historical data is available or not. It describes subjective qualitative methods like sales force composites, customer surveys, and Delphi techniques that rely on expert opinions. Objective quantitative methods include causal models that examine factors influencing outcomes and time series analysis of historical trends, seasonality, and levels. The document also outlines short, medium, and long-term forecasting horizons and the appropriate techniques for each.
This document provides an overview of forecasting and decision making. It defines forecasting as predicting future events based on past and present data to help managers make decisions. Various forecasting techniques are discussed, including qualitative methods like executive opinion and quantitative time series models. Decision making is defined as selecting an action from alternatives to achieve objectives. The document outlines characteristics, types, and advantages of decision making. It also discusses limitations of forecasting like costs and uncertainty.
Management science uses quantitative methods like mathematical modeling, statistical analysis, and optimization techniques to analyze complex business problems and improve decision-making. It can be applied to various functional areas of management, including production, marketing, finance, and human resources. For each functional area, operations research techniques are used to make data-driven decisions that optimize processes, minimize costs, maximize profits/revenue, and improve quality/productivity while considering the limitations of the models.
The document discusses the development of a business intelligence system. It describes the key phases as analysis, design, planning, development, and implementation/control. The analysis phase identifies business needs. The design phase derives an overall architecture. Planning defines the functions and assesses data sources. Development includes building prototypes and defining data warehouses/marts. Implementation deploys the system through developing warehouses/marts, metadata, ETL tools, and applications.
The document discusses using the linear programming technique to aid in decision making for marketing and finance problems. It provides an example of using linear programming to determine the optimal allocation of advertising budgets across multiple media (television, radio, newspaper) to maximize total audience reach given budget constraints. Linear programming can be applied to problems in marketing mix determination, financial decision making, production scheduling, and more. It also briefly describes the simplex method for solving linear programming problems.
1. Managers use quantitative techniques like variance and regression analysis to make sales, customer performance, and futuristic predictions and decisions for organizations.
2. An educational institution in Jaipur uses mathematics to determine dimensions of spaces in its building like classrooms, stairs, gates, and wires to optimize use of space.
3. The institution analyzes applicant and student data like admission forms, cut-offs, performance in written tests, assignments, internships, and backgrounds to select and evaluate students.
Quantitative analysis for business decision (QABD)- Linear programming probl...Chandra Shekar Immani
Linear programming is an optimization technique for allocating limited resources to achieve the greatest benefit. It can be used to solve problems in various industries and fields. Some common applications include determining optimal product mixes, production schedules, transportation routes, and portfolio selections. The document provides examples of linear programming applications in industries like oil refining, transportation, manufacturing, and more. It also discusses the advantages of linear programming in improving decision quality and using resources efficiently with a scientific approach.
Liner programming on Management ScienceAbdul Motaleb
The document discusses management science and linear programming. It provides details on:
1) Management science uses various scientific principles and analytical methods to help organizations make rational decisions to maximize profit or minimize expenses.
2) Management science research can be done on fundamental, modeling, and application levels.
3) Linear programming is a method to achieve the optimal outcome given linear constraints and can be used to solve production planning, marketing mix, product distribution, and staff scheduling problems in business.
4) The key characteristics of linear programming problems are that they involve optimization with an objective function and constraints, and have linear relationships between variables.
This document provides an overview of the history and objectives of operations research. It discusses how operations research originated during World War II to help optimize the use of limited military resources. After the war, operations research techniques were applied to industrial problems to maximize profits and minimize costs. The document outlines the key objectives of operations research as providing a scientific basis for management decision making and helping managers make better decisions through the use of mathematical modeling and analysis.
Introduction to Business Analytics and Simulation
http://nguyenngocbinhphuong.com/course/mo-phong-trong-kinh-doanh/
1) What is Business Analytics?
2) Types of Business Analytics: Descriptive, Predictive & Prescriptive
3) Data for Business Analytics: Structured & Unstructured or Semi-Structured
4) Models in Business Analytics: Logic-Driven Models & Data-Driven Models
5) Types of Business Simulation: Monte Carlo Simulation & System Simulation
Forecasting :- Introduction & its ApplicationsDeepam Goyal
This document discusses forecasting, including its introduction, characteristics, principles, need, process, areas of application, advantages, and disadvantages. It provides examples of forecasting in supply chain management, economics, earthquakes, buildings, land use, sports, politics, transportation, telecommunications, products, sales, and technology. The document also presents a case study of Henkel, a manufacturing company that improved sales forecasting accuracy from 69.3% to 85.3% by implementing social forecasting with incentives for top forecasters.
The document provides an introduction to industrial management and engineering. It discusses key concepts like the functions of management, productivity improvement, and the roles of an industrial engineer. It also covers topics such as applications of industrial engineering, production management, the relationship between production management and industrial engineering, and tools used in management science.
This chapter introduces spreadsheet modeling and decision analysis as a field of management science that uses computers, statistics, and mathematics to solve business problems. It discusses how spreadsheet models represent real-world phenomena with mathematical relationships and can help analyze decisions by evaluating potential outcomes. Examples are given of companies that achieved significant cost savings and efficiency gains by developing spreadsheet and other mathematical models to optimize areas like procurement, logistics, inventory management, and operations. The chapter also covers characteristics of models, benefits of modeling approaches, categories of mathematical models, and cognitive biases that can influence decision-making.
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.
Unilever uses a state-of-the-art customer demand planning system to forecast demand. It blends historical shipment data, promotional data, and current order data to generate statistical forecasts, which are then adjusted based on planned promotion predictions and point of sale data. This approach has helped Unilever reduce inventory levels and improve customer service. The document also discusses different forecasting techniques like time series analysis, causal methods, and judgmental forecasts, and how to measure forecast accuracy.
Demand management involves understanding customer needs, planning products and services, and fulfilling demands across a business and its partners. Effective demand management relies on demand forecasting to predict future needs and inform resource planning. Forecasting considers factors like historical data, demand variability, and required accuracy. Accurate forecasts allow businesses to optimize inventory, capacity, and costs to best meet customer demands over different time horizons.
Business forecasting uses qualitative and quantitative methods to predict future business conditions and trends. Qualitative methods gather opinions through surveys and focus groups, while quantitative analyzes statistical data to identify trends. Some alternative methods like astrology are unlikely to be more effective than traditional qualitative and quantitative approaches. Forecasting provides benefits like informed decision-making but also costs as data may be unreliable, outdated, or unable to account for unexpected external changes.
Chapter I-Intro to Quantitative Analysismeladariel
This chapter introduces quantitative analysis and outlines the typical steps in the quantitative analysis approach: defining the problem, developing a model, acquiring input data, developing a solution, testing the solution, analyzing results, and implementing results. It also discusses examples of quantitative analysis, advantages of modeling, types of models, and potential problems that can arise.
This document discusses different methods of forecasting. It begins by defining forecasting as a planning tool used to predict future events that will influence an organization. It then outlines the key advantages of forecasting such as helping managers plan ahead and identify weaknesses.
The document describes the main types of forecasting methods: judgmental/qualitative methods which rely on subjective estimates, extrapolative/time series methods which analyze past trends to predict the future, and causal/explanatory methods which use statistical models and variables to forecast. Specific judgmental, extrapolative, and causal techniques are defined such as the Delphi technique, moving averages, and regression analysis.
All companies need to be more effectively than ever before. In the current financial climate, every dollar invested is important and know that your business is operating efficiently is an imperative need, but as a Manager not always easy to know if the decisions are really the best for your company.
Forecasting methods by Neeraj Bhandari ( Surkhet.Nepal )Neeraj Bhandari
This document discusses various forecasting methods used to predict future outcomes when historical data is available or not. It describes subjective qualitative methods like sales force composites, customer surveys, and Delphi techniques that rely on expert opinions. Objective quantitative methods include causal models that examine factors influencing outcomes and time series analysis of historical trends, seasonality, and levels. The document also outlines short, medium, and long-term forecasting horizons and the appropriate techniques for each.
This document provides an overview of forecasting and decision making. It defines forecasting as predicting future events based on past and present data to help managers make decisions. Various forecasting techniques are discussed, including qualitative methods like executive opinion and quantitative time series models. Decision making is defined as selecting an action from alternatives to achieve objectives. The document outlines characteristics, types, and advantages of decision making. It also discusses limitations of forecasting like costs and uncertainty.
Management science uses quantitative methods like mathematical modeling, statistical analysis, and optimization techniques to analyze complex business problems and improve decision-making. It can be applied to various functional areas of management, including production, marketing, finance, and human resources. For each functional area, operations research techniques are used to make data-driven decisions that optimize processes, minimize costs, maximize profits/revenue, and improve quality/productivity while considering the limitations of the models.
The document discusses the development of a business intelligence system. It describes the key phases as analysis, design, planning, development, and implementation/control. The analysis phase identifies business needs. The design phase derives an overall architecture. Planning defines the functions and assesses data sources. Development includes building prototypes and defining data warehouses/marts. Implementation deploys the system through developing warehouses/marts, metadata, ETL tools, and applications.
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.
The document discusses the evolution of management information systems (MIS) over time from early transaction processing systems to modern intelligent systems, business analytics, and big data. It provides definitions of MIS and related concepts. It also outlines several frameworks for understanding MIS and how it relates to other disciplines like management, computer science, and accounting. Key factors in MIS design include opportunities/risks, company strategy/structure, decision-making processes, and available technology/information sources.
Quantitative Data Analytics And Its Applications In Business(1)[1]arindam1108
This document provides information about a 3-day quantitative data analytics program taking place from April 10-12, 2012 at the Indian Institute of Management in Ahmedabad, India. The program aims to teach participants how to use data analytics to support business decision making, with a focus on applications in marketing and finance. It will cover topics such as connecting analytics to business problems, statistical tools for data analysis, and case studies. The program is intended for senior managers who use business information and research for decision making. Participants will learn how to extract useful insights from data to improve initiatives like marketing, demand forecasting, and communications budget optimization.
Business analytics combines data, information technology, statistical analysis, quantitative methods, and computer-based models to provide decision makers with possible scenarios to make well-informed decisions. It refers to the skills, technologies, and practices for developing new insights into business performance based on data analysis and statistics. Business analytics has evolved over time from operations research during WWII to management science, business intelligence, decision support systems, and software tools today. It impacts organizations by improving profitability, market share, costs, and key performance indicators. Descriptive analytics describes past data, predictive analytics forecasts future events, and prescriptive analytics recommends optimal solutions.
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.
Business Analysis using Machine LearningIRJET Journal
The document discusses using machine learning techniques like linear regression, random forest, and decision trees to analyze transaction data from a confectionery business in order to forecast product demand and sales. It applies these machine learning algorithms to a dataset containing over 20,000 transactions to analyze factors like product sales over time. The results can help the business optimize product offerings based on demand and improve profitability.
This paper illustrates the similarities between the problems of customer churn and employee turnover. An example of employee turnover prediction model leveraging classical machine learning techniques is developed. Model outputs are then discussed to design \& test employee retention policies. This type of retention discussion is, to our knowledge, innovative and constitutes the main value of this paper.
Business analytics uses statistical methods and technologies to analyze historical data and gain new insights to improve strategic decision-making. It refers to skills, technologies, and practices for continuously developing new understandings of business performance based on data analysis. Business analytics is commonly used to analyze various data sources, find patterns within datasets to predict trends and access new consumer insights, monitor key performance indicators in real-time, and support decisions with current information. It provides companies the ability to interpret large volumes of data to make informed decisions supporting organizational growth.
This document discusses using business intelligence (BI) strategies and tools to improve human resource (HR) management. It proposes separating personnel data from business data and analyzing employment trends to better screen candidates and improve productivity. BI involves collecting and analyzing large amounts of employee data (profiles, appraisals, compensation) to gain insights for strategic HR decisions. Implementing a BI approach for HR could help translate existing employee data into future-focused actions around candidate screening, cost management, and productivity enhancements.
This document discusses the quantitative theory of management. It describes three branches of quantitative management: management science, operations management, and management information systems. Management science uses mathematical models to increase decision effectiveness. Operations management uses quantitative techniques like forecasting and quality control for inventory management and production planning. Management information systems design computer systems for management use. The document also outlines the assumptions, evaluation, attributes, and limitations of the quantitative management theory.
Discuss the complexity of problem definition and the importance of a.pdfaroramobiles1
Discuss the complexity of problem definition and the importance of accurate data to successfully
apply quantitaive analysis in management.
Solution
The quantitative methods contain two component parts, the quantitative and method, with
asymmetrical attention to the quantitative term.
Speaking about method, interest is focused upon the so- called Scientific Method. Science is the
mastering of things of the real world, by knowledge about the truth. The term method drives to
dialogue on methodology in science which is clouded, as the phrase scientific method is used in
two different ways. The one is very general, as a process of improving understanding. Although
vague, it is considered as a powerful definition, since it leaves room for criticizing dogmatic
clinging to beliefs and prejudices, or appreciating careful and systematic reasoning about
empirical evidence. The other is the traditional sense, and supports that there is a unique standard
method, which is central to identity of the science. In effect, scientific progress requires many
methods, so there is not a unique standard method, though taught as a straightforward testing
hypotheses derived from theories in order to test those theories. The more acceptable definition
of scientific method is a process by which scientists, collectively and over time, endeavour to
construct an accurate (that is reliable, consistent and non-arbitrary) representation of the real
world. The popular hypothetic-deductive standard method is excluding consideration of the
process of discovery in science. Rather, research is defined as a penetrating process of learning
and understanding the substance of actual things and facts, by use of different methods. The
research process incorporates formulation of a research issue and construction of a conceptual
framework, by using all available information sources.
The quantitative methods have a number of attributes, such as: they employ measurable data to
reach comparable and useful results, assume alternative plans for achieving objectives, plan data,
concerning observations collection, configuration and elaboration by statistical and econometric
stochastic methods, check data reliability, choose appropriate sampling method, use carefully the
estimates of the parameters for forecasting and planning purposes, etc. since they derive from ex-
post data concerning past.
In an increasingly complex business environment managers have to grapple with a problems and
issues which range from the relatively trivial to the strategic. In such an environment the
quantitative techniques have an important role. It is obvious that life for any manager in any
organization is becoming increasingly difficult and complex. Although there are many factors
contributing to this, figure 1 illustrates some of the major pressures making decision making
increasingly problematic. Organizations find them selves operating in an increasingly complex
environment. Changes in government policy, privatiz.
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.
This document provides an overview of decision making. It defines decision making as selecting a preferred course of action from two or more alternatives. The document outlines the characteristics of operations decisions and the framework for decision making, which involves defining the problem, establishing criteria, generating alternatives, evaluating alternatives, and implementing and monitoring the decision. It also discusses using decision models, including computer-aided models and economic models like break-even analysis, in decision making.
The document discusses the Applied Marketing Research course taken by the author. It explains that the course teaches important marketing analytics skills like conjoint analysis, multidimensional scaling, experimental design, Latin square design, and structural equation modeling. These skills help turn data into actionable business insights. The author chose this course to gain experience applying statistical techniques and presenting results in a clear way to help decision making. Data-driven marketing analytics can help with tasks like product development and positioning by providing a structured understanding of customer preferences.
BUSINESS ANALYTICS, BACKBONE OF ORGANIZATIONS - A LITERATURE REVIEW.pdfAdheer A. Goyal
Business analytics is the process by which businesses use statistical methods and technologies based on historical data in order to attain organizational goals and make profit. Analytics are now regularly used in multiple areas of life. It should come as no surprise that business analytics is one of the fastest growing markets in enterprise software landscape. This article discusses about history and terminology of analytics. There is also a brief discussion about how business analytics gives opportunities not only to large scale and multinational companies but also to small and medium enterprises. In this conceptual paper major types of business analytics i.e., decision analytics, descriptive analytics, predictive analytics and prescriptive analytics are included. We also noted how business analytics can help you in supply chain management, analyze the key performance indicators which further helps in decision making, boost relationship with consumers and improve efficiency in the basis of product data. Then it consists of brief description about advantages and disadvantages of business analytics, difference between business analytics and business intelligence. This paper concludes with challenges in business analytics posed by the big data analytics, data scientists, business organization etc. and thoroughly researched the impact of business analytics on innovation.
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Importance of quantitative techniques in managerial decisions
1. AMET Journal of Management 71 Jan – June 2011
IMPORTANCE OF QUANTITATIVE TECHNIQUES
IN MANAGERIAL DECISIONS
Abstract
The term ‘Quantitative techniques’
refers to the methods used to quantify
the variables in any discipline. It means
the application of subjects like
mathematics and statistics, econometrics
and operations research to understand
and solve problems.. It is a study of the
application of differential calculus,
integral calculus and matrix algebra,
measures of central tendencies,
measures of averages, correlation and
regression etc. It also includes the
application of the techniques of
management science such as Linear
programming, Game theory, CPM and
PERT analyses to business problems.
The relevance and usefulness of
Quantitative Techniques in seven
functional areas of Management are
discussed in this paper.
Introduction:
Truly, the importance of Quantitative
proficiency cannot be over emphasized
to Management Professionals! This
body of knowledge involving
quantitative approaches has been given
various names like- Business
Mathematics, Business Statistics,
Operations Research, Decision Science
and Management Science. All are
concerned with rational approaches to
decision making based on the scientific
method.
*P.Murugesan
For example, consider the following
simple mathematical problem:
A) “A Mayor of a town wants
to improve the bus services
between 2 destinations falling
within his district. The
destinations are 1 hour
journey apart and he wants the
bus services in such a way that
a traveler need not wait for
more than 20 minutes time, at
either side. So, how many
buses are totally required ?
For convenience sake assume
that the buses will be
operational for 24 hours
continuously and also ignore
lunch breaks, tea breaks etc”
B) 10 black colour cards (both
sides black) and 20 green
colour cards (both sides green)
are available in a box.
Assuming you close your eyes
and pick up the cards one by
one, how many cards you
should pick up, before you
have 2 cards of the same
colour ?
(Answers are given at the end )
2. AMET Journal of Management 72 Jan – June 2011
Quantitative Techniques P. Murugesan
While a trained person will
solve this puzzle within a few
minutes, a person who is not
equipped with Quantitative
inputs, will indeed struggle for
a long time.
Two developments that occurred during
the post-world war II period, led to the
growth and use of quantitative methods
in nonmilitary applications.
• One, the most significant
development was the discovery
of Linear programming by
G.Danzig in 1947.
• Secondly, the computer
technology explosion.
It has been widely accepted that a
manager should, to a certain extent be
familiar with techniques to deal with
numbers in order to make right decision
at the right time. Students of
management are advised to have a
working knowledge of mathematics and
statistics applied to business problems
for a successful career as a manager of
multinational companies. Decisions can
be called scientific only when they are
backed by facts expressed numerically.
In modern times, the managers are
exposed to different software such as
SPSS, EXCEL, SAS, SAP, EViews so
that the decision making process
becomes not only scientific but also
reliable.
Computers simplify the monotony of
doing calculations.
Management science (MS) is an inter-
disciplinary branch of applied
mathematics devoted to optimal decision
planning with strong links with
economics, business, engineering and
other sciences. It uses various scientific
research-based principles, strategies and
analytical methods including
mathematical modelling, statistics and
numerical algorithms to improve an
organisation's ability to enact rational
and meaningful management decisions
by arriving at optimal or near optimal
solutions to complex decision problems.
In short, management sciences help
businesses to achieve their goals using
the scientific methods of operational
research. Management science is
concerned with developing and applying
models and concepts that may prove
useful in helping to elucidate
management issues and solve managerial
problems, as well as designing and
developing new and better models of
organisational excellence. The
application of these models within the
corporate sector became known as
management science. The problem
solving process involves the following
seven steps:
1.Identify and define the problem
2.Determine the set of alternative
solutions
3.Determine the criteria to
evaluate the alternatives
4. Evaluate the alternatives
3. AMET Journal of Management 73 Jan – June 2011
Quantitative Techniques P. Murugesan
5. Choose an alternative
6.Implement the selected
alternative
7.Evaluate the results.
Let us analyse the importance of
quantitative methods in seven functional
areas of management.
1. Marketing
Quantitative marketing is about data,
facts, information and knowledge. We
define quantitative marketing as the
utilization of facts and knowledge to
understand better the behavior of
consumers across the marketing
enterprise to maximize marketing
investment.
2. Business Analytics
Business Analytics is a specialized
domain that has been growing at an
annual rate of about 30 per cent.
Companies incur significant expenditure
on business intelligence. Besides the job
being challenging, diversified and
refreshing, the pay packet is quite
attractive. Analytics include complex
statistical analysis, computational
modeling and data mining. The domain
encompasses enterprise decision
management, predictive science, strategy
science, fraud analytics, credit risk
analysis, marketing analytics, and so
on. With the growing popularity of
Business Intelligence ) tools, the
business significance of analytics is
gaining greater acceptance in industry
3. Marketing Engineering
Marketing engineering is
computer assisted marketing analysis
and planning. Marketing managers must
make ongoing decisions about product
features, prices, distribution options and
sales compensation plans. When making
these decisions, managers choose from
among alternative courses of action in a
complex and uncertain world. Marketing
engineering provides managers with new
concepts, methods and technologies to
make decisions in increasingly data-
intensive marketing environments.
4. Data Mining
Knowledge of advanced data mining
techniques enables marketers to gather
and organize data and address key
business questions, to learn how to
leverage the growing volume of
customer data captured in the marketing
process. Multiple regression analysis,
logistic regression analysis, decision
trees, factor analysis, cluster analysis,
risk modeling, neural networks, Web log
analysis, and market basket analysis are
used to organize, analyse and summarise
the data and make relevant inferences
about the behaviour of different
segments of customers.
4. AMET Journal of Management 74 Jan – June 2011
Quantitative Techniques P. Murugesan
5. Production
5.1 Facility location
Plant expansion and new facility
construction are among the most far
reaching decisions an organisation
faces. Breakeven analysis can be done
for the selection of best location by
comparing alternative locations on an
economic basis. Factor rating is a
means of assigning quantitative
values to all the factors related to each
decision and deriving a composite
score that can be used for comparison.
Further linear programming can be
applied to find out the transportation
costs for raw materials and finished
goods so that they can decide the
location of a plant.
5.2 Product design
It is the structuring of components/
parts or activities so that as a unit they
can provide a specified value.
Computer Aided Design(CAD),
Computer Aided
Manufacturing(CAM), Group
technology (GT), and Computer
Integrated Manufacturing system
(CIM) are designed to integrate
product design and manufacturing
activities with both the suppliers of
materials and components as well as
the customers of the firm’s products.
5.3 Process planning
It consists of designing and
implementing a work system to
produce the desired goods or services
in the required quantities at the
appropriate time and within
acceptable costs. Monte Carlo
Simulation can be done using
software such SIMSCRIPT,GPSS,
DYNAMO,SLAM,SIMAN etc.
Assembly and flow-process chart can
also be used
5.4 Project management
A project is a unique set of activities
that must be completed to achieve a
specific objective within a limited
time period by utilizing appropriate
resources. The network models
Critical –path method and program
evaluation and review technique used
for project scheduling
6. Human Resource
6.1 Performance Appraisal
Qualitative approaches like interviews
and questionnaires are not always
suitable. For example, if your aim is
to compare jobs for pay purposes, you
may need to say that, in effect, Job A
is twice as challenging as Job B and
so is worth twice the pay. To do this,
we must be able to assign quantitative
values to each job. The position
analysis questionnaire and the US
Department of Labor approach are
popular quantitative methods.
5. AMET Journal of Management 75 Jan – June 2011
Quantitative Techniques P. Murugesan
6.2 Position Analysis questionnaire
(PAQ):
A questionnaire used to collect
quantifiable data concerning the
duties and responsibilities of various
jobs.It is a very structured job
analysis questionnaire. The PAQ
contains 194 items, each of which
(such as written materials) represents
a basic element that may or may not
play an important role in the job. The
job analyst decides if each item plays
a role and if so to what extent. For
example, written materials received a
rating of 4, indicating that written
materials (like books, reports, and
office notes) play a considerable role
in this job. The analyst can do this
online.
The advantage of the PAQ is that it
provides a quantitative score or
profile of any job in terms of how that
job rates on five basic activities: (1)
having decision making /
communication / social
responsibilities, (2) performing
skilled activities, (3) being physically
active, (4) operating vehicles /
equipment, and (5) processing
information. The PAQ’s real strength
is thus in classifying jobs. In other
words, it lets you assign a quantitative
score to each job based on its decision
making, skilled activity, physical
activity, vehicle/equipment operation,
and information processing
characteristics. You can therefore use
the PAQ results to quantitatively
compare jobs and then assign
appropriate pay levels for each job.
US Department of Labor (DOL) Job
analysis procedure:
A standardized method by which
different jobs can be quantitatively rated,
classified, and compared based on data
people and things scored.The US
Department (DOL) job analysis
procedure also provides a standardized
method by which to quantitatively rate,
classify and compare different jobs.
7. Finance
Financial markets and others generate
vast amounts of data on asset returns,
their volatility, and other financial
variables in long and high-frequency
time series. The ability to analyse market
behaviour requires knowledge of the
properties of time series and appropriate
estimation methods. Since the early
1980s techniques for analysing time
series which exhibit auto-regression
have yielded important studies of
financial markets, increasing our
knowledge of financial variables'
volatility. It examines techniques for the
valuation of different classes of
securities, analyses criteria for guiding
investment decisions, considers the
measurement of asset risk and return and
discusses statistical techniques of
forecasting.
E - Views software is provided for
regression analysis and diagnostic
procedures. It improves the confidence
and skill in the use of the mathematical
and statistical methods used in the
analysis of financial instruments and
financial markets, including the
6. AMET Journal of Management 76 Jan – June 2011
Quantitative Techniques P. Murugesan
calculation of financial market yields
and prices, frequency distributions, risk
and probability, correlation and
regression analysis.
Answers to the puzzle:
A) A simple problem of Logistics: Total requirement- 6 buses. 3 buses to be positioned
on each side, in the beginning.
B) A simple problem of Probability :2 or 3 cards
About the author:
Prof. P. Murugesan has 40 years of
experience in teaching Quantitative
subjects, Research Methodology,
Production Management & allied
Subjects
Quotable Quotes
• The best exercise is to bend down in order to lift somebody.
• Happiness is like electricity; it is available everywhere, you have to know just
where to plug in.
• Worries are like babes; the longer you nurse them , the bigger they become.
• When you talk, you already know the contents; when you hear there could be
something new.
• Nothing big has ever been achieved, without being passionate about it.
• Fear & greed are generally credited to be the ultimate motivators of mankind. But
the desire to excel, achieve & serve others are as strong.
• No one has damaged his eyes by looking at the brighter side of life
• Old age is a question of mind over matter; if you don’t mind, it doesn’t matter