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Management Science is a methodology to decision making based on Scientific Methods. Management science refers to mathematical science that focuses on the effective use of technology by organizations. In contrast, many other science & engineering disciplines focus on technology giving secondary considerations to its use. Management Science is the discipline of applying advanced analytical methods to help make better decisions for the organization.
The management science methodology helps businesses to improve their operations through the use of scientific methods and the development of specialized techniques. It is the process of searching an optimal solution to the existing problem. Management science methodology provides systematic, analytical and general alternatives to the problem solving for decision-making, regardless of the nature of the system, product, or service. Management science methodology is the application of scientific methods to complex organizational problems. Models are aimed at helping the decision-maker in decision-making process. Management science methodology is one of the innovative decision making tool of the twentieth century.
Management science techniques are used on a wide variety of problems from a vast array of applications. For example, integer programming has been used by baseball fans to allocate season tickets in a fair manner. When seven baseball fans purchased a pair of season tickets for the Seattle Mariners, the Mariners turned to management science and a computer program to assign games to each group member based on member priorities. By using such operational research techniques, management science gives executives the power to make more effective decisions and build more productive systems.
Advantages of Using Management Science:
Management Science is a powerful technique to decision making. By using techniques such as mathematical modeling, integer programming & linear programming to analyze complex situations, operations research gives executives the power to make more effective decisions and build more productive systems. The main advantages of using management science or operational research are:
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(a) Management science or operational research models help in making improved decisions and reduce the risk of making flawed decisions. It gives managers an improved insight into decisions are made.
(b) An operation research oriented planning model helps in coordinating different divisions of a company.
(c) Management science helps in implementing better control in the organization.
(d) An operational research approach may provide the executive an analytical and quantitative basis to identify the problem area.
(e) Lastly, management science helps in analyzing a particular problem in decision making.
Business Challenges: Operational Research
Operations research methods and technologies have conventionally played an important role in business areas such as supply chain planning and logistics network design and operation. In the future, management science will help companies deal with a broad range of new business challenges.
The increased complexity of running a successful business:
Many large companies with complex business processes have used management science for years to help executives and managers make good strategic and operational decisions. American Airlines and IBM have amazingly complex operations in logistics, customer service and resource allocations that are built on management science technologies. As the trend of increased business complexity moves to smaller enterprises, management science will play vital operational and strategic roles.
New opportunities in the electronic economy:
The Internet and telecommunications revolutions will increase the requirement for management science logical decision tools. The idea of a static supply chain - materials supply, product manufacturing, multichannel distribution and product sales - will give way to customized, rarity, built-on-the-fly virtual supply chains. The necessary components will be arranged and assembled as needed, the configuration, functionality and price dictated by application requirements and the market.
Lots of information, but no decisions:
Enterprise resource planning systems and the Web have contributed to a persistent information environment; decision-makers have total access to every piece of data in the organization. The problem is that most people need a way to transform this wealth of data into actionable information that helps them make good tactical and strategic decisions. The role of management science decision methods is to help influence a company's investment in information technology infrastructure by providing a way to convert data into actions.
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Operations research and management science involve using mathematical modeling and statistical methods to help people understand & analyze complicated business processes and make good decisions. These techniques are especially useful for helping understand and deal with business complexity and uncertainty. In the 21st century business climate, complexity and uncertainty are at an all-time high: the electronic economy requires managers and executives to make better and faster operational and strategic decisions; globalization and the Internet are shifting and redefining relationships with customers, suppliers, partners and competitors; and the lowest unemployment rate in two generations is taxing the ability and imagination of companies to find, train and retain workers and managers.
Management Science Techniques:
Management science is the science for managing and involves decision making. It utilizes what is controllable, and tries to predict what is uncontrollable in order to archive a specific objective. Science is a continuous search; it is a continuing generation of theories, models, concepts, and categories. Management science uses analytical methods to solve problems in areas such as production and operations, inventory management, and scheduling. Typical management science approach is to build a model for the problem being studied, such a model is often a mathematical model. Practical problems are often unstructured and lack clarification in definition of problem which makes mathematical modeling a challenge. Therefore modeling of a problem is important phase in problem solving technique. Once model is built, algorithms are used to solve problem. Various techniques are devised to model problem and solve it for possible solutions. Some of them are:
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Linear programming consists of a single objective function, representing either a profit to be maximized or a cost to be minimized, and a set of constraints that circumscribe the decision variables. The objective function and constraints are all linear functions of the decision variables.
Network flow programming is a generalized form of linear programming. The class of network flow programs includes such problems as the transportation problem, the assignment problem, the shortest path problem, the maximum flow problem, the pure minimum cost flow problem, and the generalized minimum cost flow problem. When a situation can be entirely modeled as a network, very efficient algorithms exist for the solution of the optimization problem, many times more efficient than linear programming in the utilization of computer time and space resources.
Integer programming is concerned with optimization problems in which some of the variables are required to take on discrete values. Rather than allow a variable to assume all real values in a given range, only predetermined discrete values within the range are permitted. In most cases, these values are the integers, giving rise to the name of this class of models.
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Dynamic programming models are represented in a different way than other mathematical programming models. Rather than an objective function and constraints, a Dynamic Programming model describes a process in terms of states, decisions, transitions and returns. The process begins in some initial state where a decision is made. The decision causes a transition to a new state. Based on the starting state, ending state and decision a return is realized. The process continues through a sequence of states until finally a final state is reached. The problem is to find the sequence that maximizes the total return.
Stochastic programming clearly recognizes uncertainty by using random variables for some aspects of the problem. With probability distributions assigned to the random variables, an expression can be written for the expected value of the objective to be optimized. Then a variety of computational methods can be used to maximize or minimize the expected value. This page provides a brief introduction to the modeling process.
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Simulation method is a technique where the goal is to develop simulators that provide the decision-maker with the ability to conduct sensitivity studies to (1) search for improvements, and (2) to test and standard the improvement ideas that are being made.
Probabilistic techniques are another class of modeling approach for problem solving. It is based on application of statistics for probability of uncontrollable events as well as risk assessment of decision. In this technique risk means uncertainty for which the probability of distribution is know. Therefore risk assessment involves study of the outcomes of decisions along with their probabilities. Probability assessment tries to fill gap between what is know and what need to be known for an optimal solution. Therefore, probabilistic models are used to prevent events happening due to adverse uncertainty. Decision analysis and queuing systems are example of probabilistic techniques.
Management science is concerned with developing and applying models and concepts that may prove useful in helping to enlighten management issues and solve managerial problems, as well as designing and developing new and better models of organizational excellence. Optimization techniques are probably the most crucial to managerial decision making. Given that alternative courses of action are available, the manager attempts to produce the most optimal decision, consistent with stated managerial objectives. Thus, an optimization problem can be stated as maximizing an objective (called the objective function by mathematicians) subject to specified constraints. In determining the output level consistent with the maximum profit, the firm maximizes profits, constrained by cost and capacity considerations. While a manager does not solve the optimization problem, he or she may use the results of mathematical analysis. In the profit maximization example, the profit maximizing condition requires that the firm choose the production level at which marginal revenue equals marginal cost. This condition is obtained from an optimization exercise. Depending on the problem a manager is trying to solve, the conditions for the optimal decision may be different.
Management science describes the discipline that is focused on the application of information technology for informed decision-making. In other words, management science represents the study of optimal resource allocation. The goal of management science is to provide rational bases for decision making by seeking to understand and structure complex situations, and to utilize this understanding to predict system behavior and improve system performance. Much of the actual work is conducted by using analytical and numerical techniques to develop and manipulate mathematical models of organizational systems that are composed of people, machines, and procedures. This article introduces some of the methods and application that are affiliated with management science, and elaborates on some of the benefits that may be gained by incorporating management science into the actual business framework.
By utilizing management science methods, the objective is to apply to any given project the most suitable scientific techniques selected from mathematics, any of the sciences including the social and management sciences, and any branch of engineering, respectively Moreover, utilizing management science methods allow to develop and implement software, systems, services, and products related to a clients methods and applications. The systems may include strategic decision-support systems, which play a vital role in many organizations today.
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