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Author: admin, 15.05.2015. Category: Positive Quote Of The Day

Through the Enlightenment era's struggle and much suffering, "the individual" finally appeared. A bad decision may force you to make another one, as Harry Truman said, "Whenever I make a bum decision, I go out and make another one." Remember, if the first button of one's coat is wrongly buttoned, all the rest will be crooked.
When decision making is too complex or the interests at stake are too important, quite often we do not know or are not sure what to decide.
The decision-maker's style and characteristics can be classified as: The thinker, the cowboy (snap and uncompromising), Machiavellian (ends justifies the means), the historian (how others did it), the cautious (even nervous), etc. As the title of this site indicates, it is applied which means it is concrete not abstract or "knowledge for the sake of knowledge". Find out the set of possible actions that you can take and then gather reliable information about each one of them.
The explicit information about the course of actions may also expand your set of alternatives. Any careful strategizing and policy-making cannot be easy tasks; however the methodologies and techniques presented here can be used for improving procedural rationality during the process of strategizing. Many people treat goal setting this way -- they dream about where they want to go, but they do not have a map to get there.
The logic of worldly success rests on a fallacy: the strange error that our perfection depends on the thoughts and opinions and applause of other men!
Good decisions are made with less stress, and it is easier to explain the reasons for the decision that was made.
Making good strategic decisions is learnable and teachable through an effective, efficient, and systematic process known as the decision-making process.
The simplest decision model with only two alternatives, is known as Manicheanism, which was adapted by Zarathustra (B.C. The Industrial Revolution of the 19th century probably did more to shape life in the modern industrialized world than any event in history. Foundations of Good Decision-Making Process: When one talks of "foundations", usually it includes historical, psychological, and logical aspects of the subject. Since some decision problems are so complicated and so important, the individuals who analyze the problem are not the same as the individuals who are responsible for making the final decision. Deterministic versus Probabilistic Models: Before going further, we distinguish between deterministic and probabilistic decision-making problems. Understanding the Problem: It is critical for a good decision maker to clearly understand the problem, the objective, and the constraints involved.
Constructing an Analytical Model: This step involves the "translation" of the problem into precise mathematical language in order to make calculations and comparison of the outcomes under different possible scenarios. Finding a Good Solution: It is important here to choose the proper solving technique, depending on the specific characteristics of the model. Since the strategic solution to any problem involves making certain assumptions, it is necessary to determine the extent to which the strategic solution changes when the assumptions change.
One must evaluate the various courses of actions within the controllable inputs, consider various scenarios for uncontrollable inputs, and then decide the best course of action. As indicated in the above diagram, perceiving the need to face the decision problem is a point of departure and no more. At the "what-if" analysis stage of modeling, the modeler and the owner of the problem must concentrate on what can happen rather that what would happen.
Preparation for management, whether it is related to technology, business, production, or services, requires knowledge of tools, which can aid in the determination of feasible, optimal policies.
There are also situations where some may feel that the decision-maker should rely on simply "do the right thing" and damn the analytical strategic thinking .
Crainer S., The 75 Management Decisions Ever Made and 21 of the Worst, American Management Association, New York, 1999.
Steiss A., Strategic Management and Organizational Decision Making, Lexington Books, 1985.
Until the end of the eighteenth century, nearly all products were manufactured by individual artisans and craftsmen. The large system is the result of the application of scientific techniques to manufacturing and persists as a fundamental characteristic of modern industry.
Due to the globalization of telecommunications markets, and to the general decline of monopolies, "other licenced operators" are starting to appear almost in every country. The complexity of today's business operations, aggressive competition, and government controls have made the job of the manager increasingly difficult. Decision Science (DS) known also as Operations Research (OR), Management Science (MS), and Success Science (SS) is the science of making decisions. Management Science (MS) often takes an analytical view of a decision before making a decision.
This analytical approach is known by several different names: Operations Research (OR), Operational Research (a UK-ism), Decision Sciences (DS), Systems Science, Mathematical Modeling, Industrial Engineering, Critical Systems strategic thinking, Success Science(SS), and Systems Analysis and Design.
Systems modeling process depict a complex problem, with its many, interconnected variables, in a way that amplifies and clarifies our understanding of the decision problem.
The idea that the rational decision-making process can be studied, learned, and taught makes the decision-making process a scientific approach that is based on logical principles.
Progressive Approach to Modeling: Modeling for decision making involves two distinct parties, one is the decision-maker and the other is the model-builder known as the analyst.
Such miscommunication can be avoided if the manager works with the specialist to develop first a simple model that provides a crude but understandable analysis. Until the middle of the 19th century, most industrial enterprises only employed a few workers. OR originated in Great Britain during World War II to bring mathematical or quantitative approaches to bear on military operations.
The term OR arose in the 1940's when research was carried out on the design and analysis of mathematical models for military operations.
Military organizations had gone through the same type of evolution as other businesses and industries. The potential of computer and information systems as new tools for management forced the non-technically trained executives to begin to look for help in the utilization of the computer. A scientific method of providing executive management with a quantitative base for decisions regarding operations under their control (Mores-Kimball 1943). The application of the scientific method by inter-disciplinary teams to problems involving the control of organized (man-machine) systems so as to provide solutions which best serve the purpose of the organization as a whole (Ackoff- Sasieni 1968). Optimal decision-making in, and modeling of, deterministic and probabilistic systems that originate from real life.
A branch of applied mathematics wherein the application is to the decision-making process (Gross 1979).
In examining these definitions, it should be noted that neither the old and well-established scientific discipline nor science itself has ever been defined in a way that is acceptable to most practitioners. Among key tasks of the scientific enterprise, perhaps none is more fundamental than that of making parts of the world understandable.
What seems to make sense is, of course, tightly connected to such important factors as background beliefs, conceptual matrix, theory commitments, paradigms, and even worldviews. There is an internal phenomenal, experiential dimension to things appearing sense-making, and the presence of that feel, that seeming, that seeing, may be the most fundamental component of something's making sense to us. Things that make intense sense in dreams, or to the intoxicated, or to the mad, are often utterly indescribable in ordinary discourse.
Thus, one of the foundational aims of science may not even be definable in human-free terms. Explanation of something is to help us in experiencing what is known as "making-sense." Something makes sense when we see how and why it occurs, or why it is as it is, what meaning (if any) it has, what role it plays in some contextual setting, and so forth.
Forecasting: Using time series analysis to answer typical questions such as: How big will demand for products be?
Location, Allocation, Distribution and Transportation: Where is the best location for an operation?
Manufacturing, Insurance, Planning, Systems analysis, Marketing, Budgeting, Finance, Program evaluation, Banking, Services (non-profit).


The growing trends in interventional managerial decision-making increasingly utilize applications of more than one technique and involve individuals from other disciplines.
Today the word "Engineering" has a broader meaning and scope than merely dealing with physical engines.
For example, economists like to think of themselves as something like 'engineers' trying to keep the 'train' of state on track. Systems engineering exists as a discipline because the complexity of large scale systems tends to defy effective design of the whole.
Industrial Engineers design systems that enable people and society to improve productivity, efficiency, effectiveness, and quality of the work environment. The idea of a factory is also extended to include health care systems, municipal systems, and transportation systems; in fact, all of the systems that are essential to the functioning of modern society are included.
Human behavior and capabilities are key elements in the systems with which Industrial Engineers work. In designing the layout of a production line for an automobile manufacturer, the checkout counter for a supermarket, the organization of office work flow for a bank, a materials handling system, or a steel plant, the engineer must consider physical requirements, cost parameters, and the physiological and behavioral performance of the human operators.
Rapid progress in the area of modern manufacturing is probably most evident through the developments in intelligent manufacturing systems. Modern manufacturing is the capability of surviving and prospering in a competitive environment of continuous and unpredictable change by reacting quickly and effectively to changing markets, driven by customer-designed products and services. Operations Management is the functional area of business that is concerned with the production of goods and services.
Operations refers to the production of goods and services -- the set of value-added activities that transforms inputs into outputs. Operations Management is concerned with management of the production and distribution of the goods and services of a firm or government organization. The general purpose of System Dynamics (SD) is enabling a correct choice of policy or strategy in a complex setting; while the purpose of System Design Engineering (SDE), is coming up with a workable architecture and overall design for a complex device, or physical system, or man-made system, such as am airport. Their similarities are that both tend to use models at a higher level of aggregation, and deliberately scope the system boundaries widely.
The above comparison is useful to a point, just to know what goes on in other fields and perhaps extract isolated learning points as part of some other endeavor. One needs to understand that reality is paramount to our logical reasoning process in making a model. Models are categorized according to their distinctiveness such as kind, evolution in time, as well as accessibility of records. No human inquiry can be called science unless it pursues its path through mathematical exposition and demonstration, which is mathematical modeling. Reporting or communicating one's conclusions to the decision-maker is a vital aspect of the modeling process. The analytical decision-making process is an assessment based on the choice of alternatives.
Validation is the process of comparing the model's output with the behavior of the phenomenon.
A Multi-perspective structured decision modeling process consists of reflections before action.
The analytical results obtained from a model must always be tempered with experienced judgment, since there usually exist factors that cannot be accounted for in the model. You must realize what needs to be done, but that is not enough; you must then take time to understand "how and why" and the consequences.
You must also plan well your actions, but that is not enough; you must then take time to implement, and perhaps adapt, your plans. You must now communicate with the decision maker what you have done, but that is not enough; you must then take time to interpret what you have accomplished, its meaning and consequences so that others may also see. The methods are native and essentially useless, the proverbial "will o' the wisp," and therefore "practical" people should not waste time studying them.
Since, abstraction is the most powerful tool that we have in strategic thinking about decision problems. The modeling process is a well-focused strategic thinking while following some logical sequences.
In many instances, we resort to informal decision support techniques such as tossing a coin, asking an oracle, visiting an astrologer, etc. A weird life it is, indeed, to be living always in somebody else's imagination, as if that were the only place in which one could at last become real! Diagnosis is the identification of problems (or opportunities for improvement) in current decision behavior; it involves determining how decisions are currently made, specifying how decisions should be made, and understanding why decisions are not made as they should be. After the model is solved, validation of the obtained results must be done in order to avoid an unrealistic solution.
You will learn this by performing the "what-if" scenarios and the necessary sensitivity analysis. Most of the management activity is a "rear view." That is, no manager can ever have any information other than what has happened in the past, hence managing is done by looking in the rear view mirror. In addition to skills related to communication and qualitative reasoning, enterprises wishing to remain competitively viable in the future, need model-driven decision support systems to help them understand the complex interactions between all components of a given organization's system, both internal, and external situations. In all aspects of our lives, and importantly in the business context, an amazing diversity of data is available for inspection and given insights. Coffman, First, Break All the Rules: What the World's Greatest Managers Do Differently, Simon & Schuster Trade, 1999. Kijne, Scientific Management: Frederick Winslow Taylor's gift to the world?, Kluwer Academic Publishers, 1996. With the advent of new manufacturing technology in the late eighteenth and early nineteenth centuries came the Industrial Revolution. Today larger companies employ thousands of workers, deal in billions of dollars, manufacture hundreds of products, and service a multitude of markets. A new company entering a competitive market where a first, and even a second, operators already exist has to face several problems, and can analyze the opportunities the situation may offer.
It is no longer possible for one individual to be aware of the details of every characteristic of the firm or to make all decisions regarding its operation. Therefore, there is no such thing as "someone who is born as a business person"; rather, one becomes a business person. Its changes are driven by the technology it uses and that it extends, and the applications that it affects. Gill (editors), Multi-methodology: The Theory and Practice of Combining Management Science Methodologies, Wiley, 1997.
Since that time the scope of OR has expanded to include economics (known as econometrics), psychology (psychometrics), sociology (sociametrics), marketing (marketing research and marketing science), astrology (astronomy), and corporate planning problems.
Not only is this "sense" faculty thus not infallible, but there is apparently no noncircular procedure for justifying reliance upon it.
And a good explanation must supply the sort of materials that, in the complicated human cognitive context in question, will trigger that shift from mystery to sense. Moreover, they involve a blend of "hard" and "soft" as well as a mixing of different "hard" or "soft" techniques with the increasing use of multiple methods within one piece of analysis.
Building upon foundations in mathematics, statistics, operations research, and economics, Systems Engineering involves the design, control, and management of complex systems arising in manufacturing, transportation, telecommunications, and the environment.
The core of the discipline focuses on certain areas of mathematics and methodology, rather than on particular physical sciences, as is typical of other engineering specialties. All engineers work at planning, designing, implementing, and controlling the systems that represent the way people use technology. Systems that facilitate effective decision-making and implementation in areas such as scheduling, inventory, and quality control are typical of industrial engineering.
The Industrial Engineer has a dual role to extend human capability to operate, manage and control the overall production system and to ensure the safety and well being of those working in the system.
Usually the mathematical analysis must take into account risk and uncertainty to a larger extent than in other engineering fields.
The same fast advancements have made the objective of achieving a balanced technical program a challenging task.


Critical to successfully accomplishing in modern manufacturing systems are making quick but good decisions concerning the standard for the exchange of products, concurrent engineering, virtual manufacturing, component-based hierarchical shop floor control system, information and communication infrastructure. They design and manage computerized systems that control the production and distribution of a firm's goods and services. In conjunction with other functional areas, it also deals with the management of resources (inputs) and the distribution of finished goods and services to customers (outputs). Specialization in Operations Management is particularly useful when combined with the study of another functional area of business such as marketing, finance, or management information systems. A system model relates those variables which affect the performance of the system to a measure (or indicators) of systems performance in a logical manner. However, an analysis of the system through the use of a reasonable model usually provides valuable input to managerial decisions. This is an easy thing to state but quite another to accomplish, regardless of how true it may be. Doerr, Symbolizing, Communicating, and Mathematizing: Key Concepts of Models and Modeling, in P. It presents the Black-Scholes theory of options as well as introducing such topics in finance as the time value of money, mean variance analysis, optimal portfolio selection, and the capital assets pricing model. Kiss, (eds.), Rethinking the Process of Operational Research and Systems Analysis, Pergamon Press, 1984.
Self-incurred is this tutelage when its cause lies not in lack of reason but in lack of resolution and courage to use it without wishing to have been told what to do by something or somebody else. In fact, many frustrations with oneself are caused by not being able to use one's own mind to understand the decision problem, and the courage to act upon it. The explicit information can be explained in structured form, while tacit information is inconsistent and fuzzy to explain. Pablo Picasso realized this fact and said about himself: "All human beings are born with the same creative potential. Some of these decisions could involve large sums of money being gained or lost, or could involve whether or not the firm accomplishes its mission and its goals. Specification of changes in decision process involves choosing what specific improvements in decision behavior are to be achieved and thus defining the objectives. Hunsaker, The Dynamic Decision Maker: Five Decision Styles for Executive and Business Success, Harper & Row, 1990.
Reid, The Creativity Toolkit: Provoking Creativity in Individuals and Organizations, McGraw-Hill, 1998. These service industries, including banks, hospitals, insurance companies, consulting firms, and governments, are faced with operational complexities similar to those noted for the manufacturing industry. Even within a manager's relatively small span of control the factors affecting his decisions are often so numerous and their effects so pervasive that "seat of the pants" decisions are no longer acceptable. This modeling process is now widely used in the manufacturing industry, least cost distribution of goods, and finance functions as well as in service industries, and the health and education sectors. It has now become recognized as an important input to decision-making in a wide variety of applications in business, industry, and government. Any such case, to have any chance of being convincing, would have to employ resources and procedures and justification for employment of which would ultimately track back at least in part of the faculty itself. By considering the system as a whole, rather than as individual components, Systems Sciences provide direction as to the optimal design of the business systems, as well as their on-going operations and maintenance. The systems that are the subject of Industrial Engineering design are broad and are characterized by a need to integrate both the physical and decision-making capabilities of humans together with all other aspects of the system design.
Career opportunities exist in most industries and government organizations in the areas of systems analysis and design.
By experimenting with the model, the effects of various management decisions can be explored. While a system model may take many forms, it usually includes the logical relationships between the variables affecting system performance and some measure (or indicators) of system performance.
It is our belief that a conceptually oriented, interpretive perspective is of definite utility to the analyst in the quest for a model that, as accurately as possible, describes the system under study.
Tumay, Simulation Modeling Methods: To Reduce Risks and Increase Performance, McGraw-Hill, 2000. Cave, Adaptive policies, policy analysis, and policy-making, European Journal of Operational Research, 128, 282-289, 2001. This web site focuses on the formal model-driven decision support techniques such as mathematical programs for optimization, and decision tree analysis for risky decisions. In our increasingly complex world, the tasks of decision-makers are becoming more challenging with each passing day. An analyst who focuses more on techniques for solution than on model formulation will not be successful. While technological breakthroughs led to more efficient production processes, the cost of associated manufacturing equipment was beyond the capital resources of individual craftsmen. As a result, effective decision-making often requires the availability of information analyzed and summarized in a timely fashion.
The concepts and techniques found in Industrial Engineering are to assist you in developing the skills that meet the specific challenges of systems which involve managerial activities. This duality concept was a sufficient model of reality for those old days in order to make their world manageable and calculable. The analyst's main interest should be in providing assistance in decision-making and not in finding methods of solution that are more elegant or marginally faster than existing methods. To take advantage of the mass production available through the application of new technology, and the concomitant penetration of massive markets for the goods produced, enterprises possessing sufficient capital organized men and machines into what has become known as the factory system.
Some areas of OR, such as inventory control, production control, and scheduling theory, have grown into sub-disciplines of their own right and have become largely indispensable in the modern world.
Of course, what kinds of things strike us as mysterious, and why, involves all sorts of deep roots in, for example, human nature, cognition, specific context, and perspective. By altering values of the values of the variables in these relationships, the manager or analyst can determine the effect of the variety of the conditions on the operational effectiveness of the system described by the model. Ask yourself the objective: What is the most important thing that I am trying to achieve here?
In addition, a decision-maker must incorporate a sometimes-bewildering array of choices and consequences into his or her decision.
However, nowadays we very well know that everything is becoming and has a wide continuous spectrum. Other managerial functions, such as organizing, implementing, and controlling, rely heavily on decision-making. Routine decisions are often made quickly, perhaps unconsciously without the need for a detailed process of consideration.
This phenomenon will grow as the impetus for data-based decisions strengthens and the amount and availability of data increases. However, for complex, critical or important managerial decisions it is necessary to take time to decide systematically. Utilizing software to solve complex business problems in today's world has become commonplace.
They do not even have the courage to repeat the very phrases which our founding fathers used in the struggle for independence.
Selecting your goals and your criteria for success is a dynamic process and changes over time. Being a manager means making critical decisions that cannot and must not be wrong or fail. The decision maker might incorporate some other perspectives of the problem such as cultural, psychological, etc., into the management scientist's recommendations.



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