Operational research (OR) is a discipline that deals with applying advanced analytical methods to help make better decisions. OR uses scientific methods and especially mathematical modeling to study complex problems. It is considered a subfield of applied mathematics. Some key applications of OR include scheduling, facility planning, planning and forecasting, credit scoring, marketing, and defense planning. OR takes a systems approach, uses interdisciplinary teams, and aims to optimize objectives subject to constraints through quantitative modeling and analysis.
This document contains answers to assignment questions on operations research. It defines operations research and describes types of operations research models including physical and mathematical models. It also outlines the phases of operations research including the judgment, research, and action phases. Additionally, it provides explanations and examples of linear programming problems and their graphical solution method, as well as addressing how to solve degeneracies in transportation problems and explaining the MODI optimality test procedure.
The document discusses operations research (OR), including its origins during WWII to optimize resource allocation, its goal of applying scientific principles to optimize complex business and organizational problems, and its use of quantitative modeling and analysis. OR aims to find the global optimum solution by analyzing relationships between system components. It uses interdisciplinary teams and scientific methods to develop mathematical and other models of real-world problems, which are then solved using techniques like linear programming. The models represent important variables and constraints. OR has wide applications in areas like the military, production, transportation, and resource allocation.
The document describes an Operations Research course. It includes 8 units covering topics like linear programming, transportation problems, queuing theory, PERT-CPM techniques, game theory, and integer programming. It provides details of each unit including the number of lecture hours and the topics to be covered. It also lists the textbooks and reference books for the course. The course aims to introduce students to various operations research techniques and their applications in decision making.
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.
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 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 presents 15 quantitative techniques and tools: Linear Programming, Queuing Theory, Inventory Control Method, Net Work Analysis, Replacement Problems, Sequencing, Integer Programming, Assignment Problems, Transportation Problems, Decision Theory and Game Theory, Markov Analysis, Simulation, Dynamic Programming, Goal Programming, and Symbolic Logic. It provides a brief overview of each technique, describing its purpose and typical applications.
Operational research is the scientific approach to problem solving and decision making. It involves formulating problems mathematically and using scientific techniques like simulation, optimization, and data analysis to solve complex real-world problems. Some key applications of operational research include supply chain management, transportation and logistics, production scheduling, and resource allocation in industries like airlines, manufacturing, and healthcare. The goal is to help decision makers identify optimal solutions and improve performance.
applications of operation research in businessraaz kumar
1) Operations research is a quantitative approach to decision making based on the scientific method of problem solving. It involves modeling real-life situations as mathematical problems to arrive at optimal or near-optimal solutions.
2) The key steps in operations research problem solving are defining the problem, determining alternative solutions, evaluating alternatives using criteria, choosing the best alternative, implementing the chosen alternative, and evaluating the results.
3) Common techniques used in operations research include linear programming, transportation modeling, assignment modeling, and simulation methods like PERT/CPM. These techniques help optimize objectives while satisfying constraints.
This document discusses operations research and the assignment problem. It defines operations research as applying scientific methods to optimize systems. The assignment problem aims to minimize the cost or time of assigning jobs to people. For maximization problems, the Hungarian method cannot be directly applied, so values are subtracted from the maximum to convert it to a minimization problem. An example shows assigning classes to professors, with the optimal solution being C1 to P2, C2 to P1, etc. for a total efficiency of 330.
This document discusses systems analysis and simulation. It defines a system as a collection of elements that work together to achieve a goal. There are two main types of systems: discrete systems where state variables change at separate points in time, and continuous systems where state variables change continuously over time. A model represents a system in order to study it, as experimenting directly with the real system may not be possible or wise. Simulation models can be static or dynamic, deterministic or stochastic, discrete or continuous. Discrete-event simulation specifically models systems as they progress through time as a series of instantaneous events.
This document provides an overview of various operations research (OR) models, including: linear programming, network flow programming, integer programming, nonlinear programming, dynamic programming, stochastic programming, combinatorial optimization, stochastic processes, discrete time Markov chains, continuous time Markov chains, queuing, and simulation. It describes the basic components and applications of each model type at a high level.
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Resource management techniques involve efficiently using an organization's limited resources such as employees, equipment, and finances. Some key techniques include:
1. Linear programming, which uses mathematical models to determine the optimal allocation of resources to meet objectives and constraints. An example is determining the optimal product mix.
2. Operations research, which applies scientific principles and quantitative analysis to help maximize efficiency. It has been widely used by militaries and businesses since World War II.
3. Modeling real-world problems mathematically and using algorithms to determine the best solutions while optimizing objectives under constraints. This allows organizations to best utilize their resources.
The document presents an introduction to operations research, defining it as applying mathematical modeling to complex problems in business, industry, and government. It discusses the history and development of operations research, its objectives like improving efficiency and decision making, and the scope and methods used in operations research modeling including analytical, trial and error, and simulation approaches. The presentation provides an overview of operations research including its definition, objectives, modeling approaches, and applications in various fields.
This document discusses three main methods for solving operational research (OR) models: analytical methods, iterative methods, and the Monte-Carlo method. Analytical methods use tools like calculus and graphs to find closed-form solutions. Iterative methods are used when analytical methods are too complex; they start with a trial solution and iteratively improve it until optimal. The Monte-Carlo method experiments on a model by inserting random variable values and observing their effects on the criterion over time.
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.
Approaches to gather business requirements, defining problem statements, business requirements for
use case development, Assets for development of IoT 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.
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.
This document discusses green marketing and green power. It defines green marketing as using environmental claims to promote products. Common claims include biodegradable, compostable, and recyclable. Research shows consumers prefer and will pay more for environmentally friendly products. However, some claims can be deceptive. The FTC issued guidelines for environmental marketing claims covering labeling, advertising, and qualifications for claims. The document also discusses green power options like green pricing programs and renewable energy certificates that allow consumers to support renewable energy. Consumer protection issues in green power marketing are addressed through voluntary certification programs and state standards.
Kerala State Drugs and Pharmaceuticals Ltd (KSDP) is a public sector company located in Kalavoor, Alappuzha that manufactures and supplies life-saving drugs. It has 9 departments including production, marketing, finance, quality control, and research & development. KSDP produces various tablets, liquids, powders and capsules. It has strengths like experienced employees and high-quality products, but also faces weaknesses such as high production costs and a lack of research capabilities. The organizational study helped familiarize with KSDP's business operations and relate academic theory to practical functions.
Enterprise wide systems are large-scale software packages that support business processes, information flows, reporting, and data analytics in complex organizations. They include enterprise resource planning systems, supply chain management systems, and customer relationship management software. While data warehousing systems do not directly support business processes, enterprise systems are built on software platforms and databases to help with decision making, automation, and product quality for complex organizations, though they can be costly to customize.
The document welcomes the reader and then discusses the time value of money concept. It states that the time value of money is the central concept in finance theory and refers to the value of money over time with interest or inflation. It notes that money today is worth more than the same amount in the future due to opportunities to earn interest and because inflation increases prices over time. The document then provides examples of how the time value of money applies to loans and compounding versus discounting value concepts.
The marketing environment consists of internal and external forces that influence marketing opportunities and strategies. The micro or internal environment includes factors within the company like suppliers, customers, and publics. The macro or external environment comprises the PESTLE forces - political, economic, socio-cultural, technological, legal, and environmental factors. These forces shape opportunities and threats in the broader society. Analyzing the marketing environment involves auditing influences, assessing trends, identifying key forces, evaluating competitive position, and determining strengths, weaknesses, opportunities, and threats.
Kerala State Drugs and Pharmaceuticals Ltd. (KSDP) is a public sector pharmaceutical company located in Kerala, India. It was established in 1974 with the mission of providing affordable, quality medicines. KSDP manufactures and supplies essential drugs and formulations to government institutions in Kerala. It produces over 100 products, primarily tablets, capsules and powders. While it once had a vitamin division, KSDP now focuses solely on its core formulation business. The company aims to manufacture and market quality medicines at economical prices to improve access and healthcare for the people of Kerala.
This document provides an overview of different production systems, including job shops, batch production, and mass production. It also discusses flexible manufacturing systems (FMS) and how they provide increased flexibility over mass production while still enabling higher volumes than a job shop. Programmable automation, specifically computer numerical control (CNC), is also introduced as a way to increase flexibility through computer-controlled machine tools. CNC has allowed for numerical control of machining processes like milling, drilling, boring, turning, grinding, sawing, and electrical discharge machining (EDM).
This document discusses production planning and control. It describes production planning as determining required facilities, layout, and rate of production. Planning occurs over long, medium, and short term horizons. Production control monitors plan execution by starting operations on time, recording progress, identifying deviations, and improving future plans. The document also defines types of production systems like job shops, batches, and mass production based on order patterns and standardization.
This presentations covers Definition of Operations Research , Models, Scope,Phases ,advantages,limitations, tools and techniques in OR and Characteristics of Operations research
Why Operations Research?
Introduction
Origin of operations research
Definition of operations research
Characteristics of operations research
Role of operations research in decision-making
Methods of solving operations research problem
Phases in solving operations research problems
Typical problems in operations research
Scope of operations research
Why to study operations research
Nita H.Shah Ravi M. Gor Hardik Soni
Operational research (OR) is the application of advanced analytical techniques to improve decision making. It involves using tools from mathematics like algorithms, statistics, and modeling techniques to find optimal solutions to complex problems. Some common OR techniques include linear programming, network flow programming, integer programming, nonlinear programming, dynamic programming, and stochastic programming. OR has many applications in business for issues like inventory planning, production scheduling, financial management, and risk management. It helps organizations make better decisions around areas like sequencing jobs, production scheduling, and introducing new products/facilities. OR allows for more systematic and analytical decision making with less risk of errors.
The document provides an overview of operations research (OR), including its history, methodology, tools and techniques, and applications. It discusses how OR began during World War II to analyze military operations and optimize resource allocation. The seven main steps of the OR methodology are described. Common OR tools include linear programming, game theory, decision theory, queuing theory, inventory models, simulation, and dynamic programming. Finally, the document outlines some example applications of OR in fields like accounting, construction, and facilities planning.
Operations research is a quantitative approach to solving real-world problems. It originated during World War I when military operations were analyzed quantitatively. It has since been applied to areas like transportation, logistics, and business management. Models are important in operations research as they allow complex real-world problems to be abstracted and analyzed mathematically. There are different types of models including iconic, analog, and symbolic models. Symbolic models using mathematical symbols are most commonly used in operations research. Well-designed models should be adaptable, have few assumptions, and involve limited variables. Models provide a systematic approach to problem-solving but must be tested and their assumptions validated.
Operational research (OR) is defined as a systematic and analytical approach to decision-making and problem-solving. It uses techniques from mathematics, statistics, and other fields to arrive at optimal or near-optimal solutions to complex problems. Some key points made in the document include: OR aims to help executives make better decisions; it follows a scientific approach and uses interdisciplinary teams; it considers the system as a whole and aims to find the best objective solution. OR has wide applications in fields like national planning, defense, industry, R&D, and agriculture. The OR modeling process typically involves 7 phases: problem formulation, system observation, model formulation, model verification, alternative selection, presentation of results, and implementation/evaluation.
This document provides information about obtaining fully solved assignments from an assignment help service. Students are instructed to send their semester, specialization, and contact details to the provided email address or call the phone number to receive help with their assignments. The document includes sample assignments covering topics in quantitative management, with questions regarding linear programming, inventory management, queuing theory, simulation, game theory, and dynamic programming.
Operational research (OR) is the scientific approach to problem solving and decision making. It involves modeling complex real-world situations and using analytical methods to evaluate solutions and help decision makers choose optimal alternatives. Some key OR techniques include linear programming, simulation, and data analysis. OR has been successfully applied in many fields like transportation, manufacturing, healthcare, and the airline industry to improve efficiency, maximize profits, and aid strategic planning. The document provides an overview of OR methodology, history, applications, and examples of its use.
The document provides an overview of the history and applications of operations research (OR). It discusses:
- OR originated in the UK during World War II when scientists were called upon to apply a scientific approach to military operations and allocate scarce resources effectively.
- The success of OR in the military spread its use to other government departments and industries.
- Today, OR uses quantitative techniques like mathematical modeling, computer analysis and simulation to help organizations like the military, businesses, transportation and more make optimal decisions. It breaks problems down and finds the best solutions.
The operation research book that involves all units including the lpp problems, integer programming problem, queuing theory, simulation Monte Carlo and more is covered in this digital material.
Operation research history and overview application limitationBalaji P
This document provides an overview of operation research (OR). It discusses OR topics like quantitative approaches to decision making, the history and definition of OR, common OR models like linear programming and network flow programming, and applications of OR. It also explains problem solving, decision making, and quantitative analysis approaches. OR aims to apply analytical methods to help make optimal decisions for complex systems and problems.
This document discusses optimization problems in engineering applications. It begins by defining optimization and describing how it can be applied to engineering problems to minimize costs or maximize benefits. Some examples of engineering applications that can be optimized are described, such as designing structures for minimum cost or maximum efficiency. The document then discusses procedures for solving optimization problems, including recognizing and defining the problem, constructing a model, and implementing solutions. It also describes different types of optimization problems and methods for solving linear programming problems, including the graphical and simplex methods.
The document provides an overview of quantitative analysis. It discusses that quantitative analysis is the systematic study of an organization's structure, characteristics, functions, and relationships to provide executives with a quantitative basis for decision making. The characteristics of quantitative analysis include a focus on decision making, applying a scientific approach, using an interdisciplinary team, and applying formal mathematical models. The quantitative analysis process involves defining the problem, developing a model, acquiring data, developing a solution, testing the solution, and validating the model. Common tools used in quantitative analysis include linear programming, statistical techniques, decision tables, decision trees, game theory, forecasting, and mathematical programming.
1) The document discusses definitions and characteristics of operations research (OR). It provides definitions of OR from several leaders and pioneers in the field that describe OR as applying scientific methods to optimize complex systems.
2) Key characteristics of OR mentioned are that it takes a team approach using quantitative techniques, aims to help executives make optimal decisions, relies on mathematical models, and uses computers to analyze models.
3) Limitations of OR discussed include that it is time-consuming, practitioners may lack industrial experience, and solutions can be difficult to communicate to non-technical executives. Linear programming is introduced as a prominent OR technique.
Operations research is a subfield of applied mathematics that uses advanced analytical tools to help businesses make more informed decisions. It involves using data, statistical analysis, and mathematical modeling to formulate solutions to a variety of business problems. Key aspects of operations research include taking a systems-oriented approach, using interdisciplinary teams, and applying the scientific method. Some common applications are resource allocation, linear programming, inventory control, replacement and maintenance issues, queuing problems, and job shop sequencing.
Operational research (OR) is an analytical method that uses mathematical modeling to help organizations make optimal decisions. It breaks problems down into components and solves them systematically using defined steps. OR aims to help executives obtain the best solution using techniques like modeling interrelationships between subsystems. It applies scientific methods without personal bias to handle complex problems requiring interdisciplinary teamwork and computer modeling. The OR process involves 7 steps: formulating the problem, observing the system, modeling the problem mathematically, verifying the model, selecting alternatives, presenting results, and implementing and evaluating recommendations. OR has wide applications in fields like national planning, defense, industry, research, business, agriculture, education, transportation, and home management.
Operations research materials are for businesses and other organizations in any sector, like manufacturing, as well as services. therefore, I contribute this interesting material to this community. The nature of this study focused on investment policy and promotion and gave special consideration to investment in the manufacturing sector. This chapter sets the scene for the study by discussing in broad terms the background of the study, the problem statement, and the objective of the study. It also defines the study's scope and its significance. Governments need to perform various functions in the field of political, social, and economic activities to maximize social and economic welfare. To perform these duties and functions governments require large amounts of resources called public revenues. Public revenue consists of taxes, and revenue from administrative activities like fines, fees, gifts, and grants. However, taxes are the first and foremost important sources of public revenue which are central to the current economic growth and development agenda. The importance of taxation as a veritable tool of economic growth and development depends on a proper tax system that can generate revenue through tax. While fulfilling the revenue function, taxes also have a pervasive influence on the economic decisions of individuals and businesses, and social equity. (SADC, 2004). Likewise, there is a general agreement that, the process of economic growth and investment\capital formation is closely interconnected. According to the World Bank (2013), GDP growth is higher for those countries, that have relatively higher investment/GDP ratios.
Virtually governments are keen to attract potential investment. Investment can generate new jobs, bring in new technologies, and, more generally, promote growth and employment. The resulting net increase in domestic income is shared with the government through taxation of wages and profits and possibly other taxes on business. Given the above-mentioned potential benefits, policymakers continually re-examine their tax rules to ensure they are attractive to investment. At the same time, governments continually balance the desire to offer a competitive tax environment for the Investment sector, with the need to ensure that an appropriate share of domestic tax is collected from this investment (Ibid). Tax policy shapes the environment in which international trade and investment take place. Thus, a core challenge is finding the optimal balance between a tax regime that is business and investment-friendly and one that can leverage enough revenue for public service delivery to enhance the attractiveness of the economy (Ibid).
Tax concessions represent perhaps the most widely adopted measures in developing countries to promote economic development. Today virtually all developing countries and many developed countries too offer inducements to approved enterprises in the form of reductions in or exemptions from import duties and inc
This document provides an overview of a project report on simulating a single server queuing problem. The report includes an introduction to operations research, simulation, and the queuing problem. It discusses the research methodology, which involves defining the problem, developing a simulation model, validating the model, analyzing the data, and presenting findings and recommendations. The goal is to use simulation to provide optimal solutions to the queuing problem under study.
This document provides an overview of operational research. It defines operational research as applying scientific methods to optimize systems. The key approaches include orientation, problem definition, data collection, model formulation, model solution, validation, and implementation. Common techniques are linear programming, dynamic programming, queueing theory, inventory control, decision theory, network analysis, and simulation. These have been used across industries, defense, planning, agriculture, and public utilities. Overall, operational research is a tool that can improve productivity by optimizing systems through scientific problem solving.
The document defines operations research and discusses its history and applications. It originated during World War II to optimize limited military resources. Operations research uses mathematical modeling to aid decision making. It defines problems, formulates models, derives solutions, and implements recommendations. Some key applications include allocating scarce resources optimally and minimizing costs and wait times.
The document discusses projects and production. It notes that projects such as constructing a hospital or developing a new product involve doing something once, while production involves repetitive activities like treating patients or manufacturing multiples of a product. Projects and production are interwoven, with projects helping to solve production problems. The aim of production is to provide goods and services for people in the right quantities, locations, times and at reasonable costs while meeting quality standards. Value is added through various transformations in the production process.
MRP stands for materials requirements planning. It is a system used to plan for and schedule the manufacturing and purchasing of materials and components. MRP takes the master production schedule for end items and generates detailed schedules for all raw materials, components, and sub-assemblies required. It offsets requirements by lead times and nets requirements against inventory and scheduled receipts to determine precise ordering needs. MRP provides a systematic approach to ensure the right materials are available at the right time to efficiently manufacture products to meet demand.
Operations management interacts with other functional areas by managing the transformation process and providing information to support other business functions. As the most diverse organizational function, operations management operates under many names and faces like VP of operations or manufacturing manager. Other functions like marketing, finance, human resources, and accounting rely on operations management to understand production capabilities, capital investment needs, job requirements, and inventory/capacity details in order to coordinate activities and achieve organizational goals.
Work study techniques like method study and work measurement can be used to improve productivity by reducing excess work content and ineffective time. The key steps involve observing current work processes, analyzing for unnecessary tasks or motions, developing more efficient methods, and setting standard times to measure productivity. Implementing work study can lead to greater output, better quality, and higher productivity through systematic analysis and improvement of work methods.
Forecasting is making predictions about future events or outcomes based on past data and trends. It can involve both formal statistical methods using quantitative data as well as less formal judgment-based qualitative techniques. Key aspects of forecasting include dealing with risk and uncertainty, keeping data up-to-date, and comparing predictions to actual outcomes. There are various categories of forecasting methods, including qualitative techniques based on expert opinion for long-term predictions and quantitative models using historical data for short-term forecasts.
This document discusses various forecasting methods used for different planning horizons. It outlines long, medium, and short term planning decisions and notes that demand forecasting is essential for planning. The document then describes several subjective and objective forecasting methods, including opinion polls, moving averages, regression models, and time series analysis. It also discusses normal and abnormal demand patterns and delves deeper into opinion polls and the Delphi method.
This document discusses various aspects of strategic facility location decisions. It begins by explaining that location decisions are strategic, long-term, capital-intensive, and difficult to reverse. It then outlines a hierarchy of location problems from the plant level down to individual workstations. Important factors in choosing a location include market access, raw materials, transportation, and costs. The document analyzes a case study of choosing a new plant location and later deciding whether to shut down an existing plant in response to changing demand.
This document discusses factors to consider when selecting a facility location. It identifies primary factors like material, labor, and existing facilities that drive industrialization in an area. Secondary factors include available financing, infrastructure, and insurance. Location selection errors can be behavioral if personal factors outweigh business success, or non-behavioral from a lack of analysis or ignoring key industry characteristics. Developing a location strategy helps companies determine product offerings, demand forecasts, optimal manufacturing/service locations, and how to best access customers at minimum cost. Proximity to customers, available skilled labor, business-friendly policies, and supplier networks are also important location selection criteria.
There are three basic types of facility layouts: process, product, and fixed-position. Process layouts group similar activities together by department, while product layouts arrange activities in line by production sequence. Fixed-position layouts are used when products are too large to move. Effective layouts consider factors like material flow, space utilization, bottlenecks, and flexibility.
This document discusses facilities planning and location analysis. It summarizes BMW's decision to locate a new plant at an existing facility in Regensburg, Germany. Reasons for this selection included the facility's track record, flexibility to shift production, agility of the production team, saving time, developing skills of new recruits, and accessibility along with cost effectiveness and political climate. It defines plant layout as arranging machines, equipment and facilities to ensure efficient material flow from receipt to output. An optimal layout impacts production optimization, minimizing material handling time and costs, flexibility, flow, land and building use, effective labor utilization and improved worker quality of life. Product, production process and production volume determine layout.
This document discusses inventory functions and costs. It describes five categories of stock: pipeline, cycle, seasonal, safety, and other. Inventory costs include procurement costs which are generally fixed per order, inventory holding costs which are the cost per unit per time period, and stockout costs when demand exceeds supply. The document advocates for selective inventory control using ABC analysis to prioritize items based on annual consumption value and tailor inventory policies accordingly in order to minimize total inventory costs.
Methods used in production are classified as either analytic, synthetic, continuous, or intermittent. An analytic system breaks down raw materials into components, while a synthetic system combines materials into a finished product. Continuous production runs for extended periods without stopping, like steel production, while intermittent production stops frequently to change products or fulfill orders. Most services traditionally used intermittent production but are moving toward more standardized continuous models.
Work sampling is a technique used to determine the percentage of time workers or machines spend in different states like working, idle, etc. This is done by taking a large number of instantaneous random observations over time. The ratio of observations where a worker/machine is engaged in a particular activity to the total observations gives the percentage of time spent on that activity. Taking a large number of random samples improves the accuracy of the results.
Job analysis is a systematic exploration of the tasks, duties, and responsibilities involved in a job. It identifies the key aspects of the job and provides essential information for human resource functions like recruitment, selection, training, and performance appraisal. The common methods used for job analysis include observation, interviews, questionnaires, technical conferences, and diaries. The key outputs of job analysis are the job description, job specification, and job evaluation. The job description outlines the key purpose, duties and responsibilities of the role, while the job specification defines the minimum qualifications required. Job evaluation determines the relative worth of different jobs in an organization.
There are three main sources of finance: 1) Security financing or external financing through ownership securities like equity shares and preference shares, and creditorship securities like debentures and bonds. 2) Internal financing through retained earnings and depreciation. 3) Loan financing, which includes short term loans from traders, banks, and commercial paper, as well as medium and long term loans from specialized financial institutions.
This document discusses different types of leverage:
1. Operating leverage refers to how operating profits vary with sales. Higher fixed costs lead to higher operating leverage and risk.
2. Financial leverage refers to using debt financing. Higher debt leads to higher financial leverage and risk but can also increase returns.
3. Composite leverage is the combined effect of operating and financial leverage on overall risk and returns. The document provides examples of calculating operating, financial, and composite leverage.
This document discusses inventory management. It defines inventory as the stock of goods that an enterprise needs to run its production and distribution processes smoothly. The main objectives of inventory management are to ensure continuous supply while avoiding overstocking or understocking, maintaining optimal investment levels, and minimizing costs and losses. There are different types of inventories - raw materials, work in progress, and finished goods. Key tools and techniques for managing inventory include determining stock levels, safety stocks, economic order quantities, and analyzing inventory turnover and age.
Financial management involves obtaining and utilizing funds for efficient business operations. It includes investment, financing, and dividend decisions. The key objectives of financial management are profit maximization and wealth maximization. Profit maximization ensures economic efficiency and social welfare but has disadvantages like ambiguity regarding timing and quality of benefits. Risk management methods include risk avoidance, prevention, transfer, and retention such as through insurance. Capital budgeting evaluates long-term investment projects using traditional methods like payback period and discounted cash flow methods like net present value, internal rate of return, and profitability index. The cost of capital considers the expected rates of return on different sources of funds like debt, preference shares, and equity. The weighted average cost of capital is used to
This document discusses cash management and the objectives, motives, and basic problems of holding cash for businesses. It notes that there are three main reasons firms hold cash: to meet daily transaction needs, protect against uncertainties, and take advantage of opportunities. The objectives of cash management are to meet cash disbursements while minimizing idle cash balances. Firms must control cash inflows and outflows, determine optimal cash levels through cash budgeting, and invest any surplus cash. The key motives for holding cash are transactional needs, precautionary needs to address contingencies, and speculative opportunities.
The document discusses capital structure, which refers to the mix of long-term financing sources like debt and equity. It notes that capital structure should maximize returns to shareholders without adding undue risk. It then covers various factors that affect capital structure decisions, including return, risk, flexibility, debt capacity, and control considerations. Specific factors discussed include trading on equity, retaining control, enterprise nature, legal regulations, financing purpose and period, investor requirements, company size, and government policy.
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Jacquard Fabric Explained: Origins, Characteristics, and Usesldtexsolbl
In this presentation, we’ll dive into the fascinating world of Jacquard fabric. We start by exploring what makes Jacquard fabric so special. It’s known for its beautiful, complex patterns that are woven into the fabric thanks to a clever machine called the Jacquard loom, invented by Joseph Marie Jacquard back in 1804. This loom uses either punched cards or modern digital controls to handle each thread separately, allowing for intricate designs that were once impossible to create by hand.
Next, we’ll look at the unique characteristics of Jacquard fabric and the different types you might encounter. From the luxurious brocade, often used in fancy clothing and home décor, to the elegant damask with its reversible patterns, and the artistic tapestry, each type of Jacquard fabric has its own special qualities. We’ll show you how these fabrics are used in everyday items like curtains, cushions, and even artworks, making them both functional and stylish.
Moving on, we’ll discuss how technology has changed Jacquard fabric production. Here, LD Texsol takes center stage. As a leading manufacturer and exporter of electronic Jacquard looms, LD Texsol is helping to modernize the weaving process. Their advanced technology makes it easier to create even more precise and complex patterns, and also helps make the production process more efficient and environmentally friendly.
Finally, we’ll wrap up by summarizing the key points and highlighting the exciting future of Jacquard fabric. Thanks to innovations from companies like LD Texsol, Jacquard fabric continues to evolve and impress, blending traditional techniques with cutting-edge technology. We hope this presentation gives you a clear picture of how Jacquard fabric has developed and where it’s headed in the future.
DefCamp_2016_Chemerkin_Yury-publish.pdf - Presentation by Yury Chemerkin at DefCamp 2016 discussing mobile app vulnerabilities, data protection issues, and analysis of security levels across different types of mobile applications.
2. is a discipline that deals with the application of advanced
analytical methods to help make better decisions. It is
often considered to be a sub-field of mathematics.
3. The application of scientific and especially
mathematical methods to the study and
analysis of problems involving complex
systems
4. In fact that many experts consider the start of
Operational Research in the III century B.C., during
the II Punic War
Thomas Edison made use of Operational Research
Second World War by England to solve their complex
war problems.
England made OR teams. These teams included
expert
mathematicians, statisticians, scientists, engineers, et
c.
Therefore, United States of America (USA) also started
using OR to solve their war problems. After the
war, soon industries and businesses also started
using OR to solve their complex management
problems.
5. Scheduling: of aircrews and the fleet for
airlines, of vehicles in supply chains, of
orders in a factory and of operating theatres
in a hospital.
Facility planning: computer simulations of
airports for the rapid and safe processing of
travellers, improving appointments systems
for medical practice.
6. Planning and forecasting: identifying possible
future developments in
telecommunications, deciding how much
capacity is needed in a holiday business.
Credit scoring: deciding which customers
offer the best prospects for credit companies.
7. Marketing: evaluating the value of sale
promotions, developing customer profiles
and computing the life-time value of a
customer.
Defence and peace keeping: finding ways to
deploy troops rapidly.
8. SYSTEM ORIENTATION:-OR studiestheproblemas
awhole.itemphasisesonoverallapproachtothesystem.any
activityinonepartofanorganisationhaseffectontheother
partoftheorganisation.ORtriestoidentifyallpossible
interactionsintheactivitiesofanorganisationandstudies
theirimpact
INTERDISCIPLINARY TEAM APPROACH:-OR
is interdisplinaryinnature.Itisperformedbyateamof
scientistsdrawnfromdifferentfacultiessuchas
mathematicals,statistics,economics,engineering,managemen
t,physicsetc.
9. SCIENTIFIC APPROACH:-OR uses
scientific methods to solve complex
problems.OR is a formalised process of
reasoning where problems are defined and
analysed scientifically
DECISION MAKING:-OR is a decision
science which helps management to take better
decision.
10. OPTIMISATION OBJECTIVE:-OR
attempts to find the best and optimal solution
to a problem using OR techniques.it tries to
optimise a well defined function subject to
given constraints.
MATHEMATICAL MODELS AND
QUANTITATIVE SOLUTION:-OR uses
models built by quantitative measurement of
variables concerning a given problem and
derives a quantitative solution from the model.
11. BAD ANSWERS TO THE PROBLEM:-OR
cannot give perfect answers or solutions to the
problems.it merely helps to get bad answers to
the problems which otherwise have worse
answers.
USE OF COMPUTERS:-OR often requires a
computer to solve the complex mathematical
method.
12. O.R PROVIDES A TOOL FOR SCIENTIFIC
ANALYSER:-OR provides the executives with a
more precise description of the cause and effect
relationship and risk underlying the business
operations in measurable terms.
OR PROVIDES SOLUTION FOR VARIOUS
BUSINESS PROBLEMS:-OR techniques are being
used in the field of
production,procurement, marketing,finance and other
allied field.
13. OR HELPS IN MINIMISING WAITING AND
SERVICING COSTS:- OR enables the
management to decide when to buy and how much to
buy.the main object of inventory planning is to
achieve balance between the cost of holding stocks
and the benefits from stock holding.
OR ASSIST IN CHOOSING AN OPTIMUM
STRATEGY:-Linear programming technique is used
to allocate resources in an optimum manner in
problems of scheduling,product mix and so on.
14. OR ENABLES PROPER DEVELOPMENT OF
RESOURCES:-the OR technique namely PERT
enables us to determine the earliest and the latest
times for each of the events and activities and helps
in identification of critical path.
OR FACILITATES THE PROCESS OF
DECISION MAKING:-
Decision theory,decision tree technique,simulation
method are used to imitate an operation or process
prior to actual performance.
16. To coordinate the function of airforce, navy
and army in a warfare or techniques is
applied for their best performance
17. To co-ordinate the functions in an industry
such as ,production ,advertisment and
marketing. The industry depends on OR
technique.
18. To coordinate to the development process of
countries. They depend on OR techniques for
future economics social policies
19. To increase the agricultural production they
can easily be solved by the application of OR
techniques
20. OR techniques like monte carlo technique
technique and queuing and linear programing
are of grate use in transportation activities
21. OR is useful to directing authority
It is useful to production management
Which is useful to marketing management
It is useful to personal management
It is very useful to the financial management
22. Models play a very important in O.R. They are
representation of reality. Models provide descriptions
and explanations of the operations of the system that
they represent.
“A model in O.R may be defined as an idealized
representation of a real life system”
Properties of a good model
It should be a simple
It should be capable of adjustments with new
formulations without having any significant change in
its frame
It should contain very few variables
A model should not take much time in its
construction
23. It describes problems more concisely
It provides some logical and systematic approach
to the problem
It indicates limitation and scope of the problem
It tends to make the over all structure of the
problem more comprehensible
It facilitates dealing with the problem in its
entirety
It enables the use of high-powered mathematical
techniques to analyse the problem
It helps in finding avenues new research and
improvements in a system
24. Models are only an attempt to understand an
operations and should never be considered as
absolute in any sense
The validity of any model can only be varified
by carrying on experiment and relevant data
characteristics
25. There are three types of models that are used in O.R
Iconic Models (physical models)
Analogue Models(physical models)
Symbolic Models (Mathematical models)
Iconic model
• Iconic models are obtained by enlarging or reducing the size of
the system
• They are specified and concrete
• They are easy to construct
• Eg: photographs,globes.maps etc
• It is difficult to manipulate for experimental purpose.It cannot be
used to study the changes in operation of a system.
• Difficult to make any modifications,improvements in these
models also adjustments in the changing situation cannot be
done.
26. In this models one set of properties is used to
represent another set of properties
This models are easier to mainpulate than
iconic models
They are less concrete and less specific
Eg: lines on a map are analogues of elevation
as they represent the rise and fall of heights.
27. This type of models are used to represent
variables and the relationship between them
They are some kind of mathematical equations .
Eg : Inventory models,Allocation
models,Repacement models etc
They are most abstract and most general
These models are easier to manipulate
experimentally
These models yield more accurate results under
manipulation
Thus in O.R. symbolic models are used whenever
possible.
28. Solving a model consists of finding the values of the controlled
variables that optimize the measures of performance, or estimating
them approximately. O.R. model solve the following methods.
1. Analytic methods: In these methods all the tools of classical
mathematics such as differential calculus and finite difference are
available for the solution of a model.
2. Iterative method: whenever the classical method fail, we use
iterative procedure. The classical method fail because of the
complexity of the constraints or of the number of variables. In this
procedure we start with a trail solution and set of rules for
improving it.
3. Monte Carlo technique of simulation: the basis of Monte Carlo
technique is random sampling of a variable possible values. For this
technique, some random numbers are required which may be
converted into random variates whose behavior is known from past
experience. In short, Monte Carlo technique is concerned with
experiments on random numbers and it provides solutions to
complicated O.R. problems.
29. 1. Allocation models: allocation models involves the allocation of
resources to activates in such a manner that some measures of
effectiveness is optimized. Allocation problem can be solved by Linear
and Non linear programming techniques. Linear programming
technique is used in finding a solution for optimizing a given objective
such as maximizing profit or minimizing cost under certain constrains.
It is a technique used to allocate scarce resources in an optimum
manner in problems of scheduling product mix and so on.
2. Sequencing : These are concerned with placing items in a certain
sequence or order for service.
3. Waiting or Queuing theory: These are models that involve waiting for
services. In business world several types of interruption occur.
Facilitates may break down and therefore repairs may be required.
Power failure occur, workers or the needed materials do not show up
where and when expected. These cause waiting line problems.
4. Inventory models: these are models with regard to holding resources.
The decision required generally entail the determination of how much of
resources are to be acquired or when to acquire them. Inventory
controls aims at optimum inventory level. Inventory planning is meant
to take optimum decision about how much to buy and when to buy.
5. Competitive strategy model(Game model): These are models which
arise when two or more people are competing for a certain resources.
30. 6.Decision theory: Decision theory concerned with making sound
decisions under conditions of certainty, risk and uncertainty.
7.Net work analysis: Net work model involve the determination of an
optimum sequence of performing certain operations concerning
some jobs in order to minimize over all time and cost. PERT,CPM and
other net work technique such as Gantt chart come under net work
model.
8.Simulation: simulation is a technique of testing of model which
resembles a real life situation. This technique is used to imitate an
operation prior to actual performance.
9.Search models: This model concern itself with search problems.
Examples of search problems are 1)Advertising agencies search for
customers. 2)Personnel department search for good executives.
10.Replacement theory: These are models concerned with situation
that arise when some items (such as machine,electricals etc)need
replacement because the same deteriorate with time or may break
down completely or may become out of date due to new
developments.
31. Formulating the problem
Constructing the model
Deriving the solution
Testing the validity
Controlling the solution
Implementing the result
32. Formulating the problem:-
It consists in
identifying, defining and specifying the
measures of the components of a decision
model. This should yield a statement of the
problem’s element that include the
controllable variables, the uncontrollable
parameters , the restrictions or constraints of
the variables and the objectives for defining
an improved solution.
33. Constructing the model:-
This phase is concerned
with the choice of proper data inputs and
design of the appropriate information output .
It requires the representation of
interrelationship among the elements in terms
of mathematical formulae.one or more
equations or inequalities is required to express
the fact that some or all of the controlled
variables can only be manipulated within limits.
34. Deriving the solution:-
The phase deals with
mathematical calculation for obtaining solution
to the model. A solution to the model means
those values of the decision variables that
optimise the measures of effectiveness in a
model. These are various methods available for
obtaining the solution like analytical
method, numerical method , and simulation
method.
35. Testing the validity:-
A model is said to be
valid if it can give a reliable prediction of the
system’s performance. A good practitioner of
OR realises that his model must have a longer
life and must be a good representation of the
system and must correspond to reality .In
effect,performance of the model must be
compared with the policy or procedure that it is
meant to replace.
36. Controlling the solution:-
This phase of the
study establishes control over the solution of
proper feed back of the information
variables which deviated significantly. When
one or more variables change significantly
, the solution goes out of control. In such
situations the model be modified accordingly.
37. Implementing the result:-
This phase would
basically involve a careful examination of the
solution to be adopted and its realities . This
phase of OR is primarily executed through the
cooperation of both OR experts and those who
are responsible for managing and operating the
system.
38. OR tries to find out the optimal solution , taking all
the factors into account . when there are large
number of factors involved , a study of all of them
difficult or impossible.
The solution in a problem can be obtained by OR
techniques , only if the problem can be quantified. It
is not easy or impossible to quantify all elements
particularly when they are intangible.
OR is specialist’s job.It requires the effort of
Mathematicians and managers put together. When
Mathematicians fail to understand the business
problem or when the manager fails to understand
the working of OR there is gap between who provides
solution and who uses the solution.
39. Greatest difficulty is created by time factor . A
solution at the right time will be more useful
than a perfect solution arrived late. Therefore
there is the problem of choosing between
best solution and timely solution.