Applied Management Science:
Making Good Strategic Decisions

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Decision-Making is central to human activity. Thus, we are all decision-makers. However, "good" decision-making starts with a consecutive, purposeful, strategic-thinking process. This site offers practical information on this success science process because nothing succeeds sweeter than yet another success.

Professor Hossein Arsham   

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  1. Introduction and Summary
  2. The Science of Making Decisions
  3. Multi-perspective Structured Modeling
  4. Appendex: A Collection of Keywords and Phrases

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  • Introduction and Summary

    The Science of Making Decisions

    1. Introduction and Summary
    2. Operations Research, Management Science,
      Decision Science, and Success Science (OR/MS/DS/SS)
    3. What Is OR/MS/DS/SS?
    4. Historical Needs for OR/MS/DS/SS
    5. The Nature and Meaning of OR/MS/DS/SS
    6. The Methodology of OR/MS/DS/SS
    7. The Prototype Applications
    8. Flexibility and Variety of Careers in OR/MS/DS/SS
    9. The Multidisciplinary and Interdisciplinary Nature OR/MS/DS/SS:

    Multi-perspective Structured Modeling:
    Reflections before Action

    1. Introduction and Summary
    2. Multi-perspective Modeling Process
    3. Classifications of Models: Mechanical, Mental/Verbal, Analytical, and Simulation Models
    4. From Mental Modeling to Analytical Modeling
    5. Decision-Maker's Environment
    6. Modeling Is At the Heart of Decision-Making Process
    7. Analytical Modeling Process for Decision-Making
    8. Decision-Making Process in Organizations: Dynamic Strategic Plan
    9. The Difficulties of Analytical Modeling Process
    10. Modeling Validation Process
    11. Cost Considerations and Time Discounting Rate Factor
    12. Why Analytical Modeling?
    13. A Guide to Carrying Out the Modeling Process
    14. The Gaps between Modeling and Implementation
    15. The Becoming of a Management Scientist

    Introduction and Summary

    Many people still remain in the bondage of self-incurred tutelage. Tutelage is a person's inability to make his/her own decisions. 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. Sapere aude! "Have courage to use your own reason!"- was the motto of the Enlightenment era. During this period, Francisco Goya created his well-known "The sleep of reason produces monsters" masterpiece.

    Through the Enlightenment era's struggle and much suffering, "the individual" finally appeared. Eventually human beings gained their natural freedom to think for themselves. However, this has been too heavy a responsibility for many people to carry. There has been an excess of failure. They easily give up their natural freedom to any cult in exchange for an easy life. The difficulty in life is the choice. They do not even have the courage to repeat the very phrases which our founding fathers used in the struggle for independence. What an ironic phenomenon it is that you can get men to die for the liberty of the world who will not make the little sacrifice that it takes to free themselves from their own individual bondage.

    Good decision-making brings about a better life. It gives you some control over your life. 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.

    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.

    A good decision is never an accident; it is always the result of high intention, sincere effort, intelligent direction and skillful execution; it represents the wise choice of many alternatives. One must appreciate the difference between a decision and an objective. A good decision is the process of optimally achieving a given objective.

    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. In many instances, we resort to informal decision support techniques such as tossing a coin, asking an oracle, visiting an astrologer, etc. However formal decision support from an expert has many advantages. 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. Such techniques are now part of our everyday life. For example, when a bank must decide whether a given client will obtain credit or not, a technique, called credit scoring, is often used.

    Rational decisions are often made unwillingly, perhaps unconsciously. We may start the process of consideration. It is best to learn the decision-making process for complex, important and critical decisions. Critical decisions are those that cannot and must not be wrong. Ask yourself the objective: What is the most important thing that I am trying to achieve here?

    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. For example, political thinking consists in deciding upon the conclusion first and then finding good arguments for it.

    As the title of this site indicates, it is applied which means it is concrete not abstract or "knowledge for the sake of knowledge". It is axiomatic that if learning occurs, there is change in you. Change might occur in your attitude, thinking, beliefs and/or behavior. Something will have changed or else learning simply did not occur. This course changes your life for the better. The aim of this site is to make you a better decision maker by learning the decision-making process:

    1. What is the goal you wish to achieve? Select the goal that satisfies your "values". Everyone (including organizations) has a system of values by which one lives one's life. The values must be expressed on a numerical and measurable scale. This is needed in order to find what is your values' rank. The question "what do I want?" can be unbearably difficult (because of the conflicts among our desires) that we often can hardly bear to ask it. Winning a big-money lottery has left most people wishing they had never bought the successful ticket. Goals follow from the values, and from our capacity (i.e., our personal abilities, and physical resources) to achieve goals. On the other hand, if there were no conflict among our desires, each desire would be unchecked and we would go careening without limit from one direction to another. Abraham Maslow formalized general human desires into a hierarchy of wants, with the biological-genetic needs at the bottom and "self- realization" for creativity at the top.

    2. Find out the set of possible actions that you can take and then gather reliable information about each one of them. Information can be classified as explicit and tacit forms. The explicit information can be explained in structured form, while tacit information is inconsistent and fuzzy to explain.

      The explicit information about the course of actions may also expand your set of alternatives. The more alternatives you develop the better decisions you may make. Creativity in the decision-making process resides in the capacity for evaluating uncertain, hazardous, and conflicting information. You must become a creative person to expand your set of alternatives. Creativity, arises out of thinking hard (i.e., becoming of a thinker) rather than working hard (i.e., becoming of a workaholic). A bulldozer must work hard, a human being must think hard.

      A deep immersion in your decision-making process makes you more creative. The roots of creativity lie in consciousness incubation, and in the unconscious aesthetic selection of ideas that thereby pass into consciousness, by the usage of mental images, symbols, words, and logic. Saturation or too Narrow thinking; Inability to incubate (this, one must learn from cows); and the Fear of standing alone doing something new; block creativity . Most people treat knowledge as a liquid to be swallowed easily rather than as a solid to be chewed, and then wonder why it provides so little nourishment. Aristotle noted, "We call in others to aid us in deliberation on important questions, distrusting ourselves as not being equal to deciding."

      Be objective about yourself and your business. More than half of my students, semester after semester, raise their hands when I ask, "Is your judgment better than that of the average person?" It is important to identify your weaknesses as well as your strengths.

      There is no such thing as a creative/non-creative person. It is the creative process which make you more creative. Pablo Picasso realized this fact and said about himself: "All human beings are born with the same creative potential. Most people squander theirs away on a million superfluous things. I expend mine on one thing and one thing only: my art." Creative decision alternatives are original, relevant, and practical.

    3. Predict the outcome for each individual course of action by looking into the future.

    4. Choose the best alternative with the least risk in achieving your goal.

    5. Implement your decision. Your decision means nothing unless you put it into action. A decision without a plan of action is a daydream.

    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. The efficiency and effectiveness of such applications depends on the selection of strategizing process.

    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. What is a map? In essence, the written words and careful planning. Decision-making is a complicated process. This complication arises from the fact that your present goal (including wants, resources, and abilities) dictates your choices, however, your choices will change your goals. This influential-cycle keeps the decision-maker busy all the time. Selecting your goals and your criteria for success is a dynamic process and changes over time. The following flowchart depicts the goal as the foundation of decision-making process. This is true in almost all cases dealing with personal growth or organizational growth:

    Foundation of Decision-Making

    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! 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!

    On a daily basis a manager has to make many decisions. Some of these decisions are routine and inconsequential, while others have drastic impacts on the operations of the firm for which he/she works. 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. In our increasingly complex world, the tasks of decision-makers are becoming more challenging with each passing day. The decision-maker (i.e., the responsible manager) must respond quickly to events that seem to take place at an ever-increasing pace. In addition, a decision-maker must incorporate a sometimes-bewildering array of choices and consequences into his or her decision. Routine decisions are often made quickly, perhaps unconsciously without the need for a detailed process of consideration. However, for complex, critical or important managerial decisions it is necessary to take time to decide systematically. Being a manager means making critical decisions that cannot and must not be wrong or fail. One must trust one's judgement and accept responsibility. There is a tendency to look for scapegoats or to shift responsibility.

    Decisions are at the heart of any organization. At times there are critical moments when these decisions can be difficult, perplexing and nerve-wracking. Making decisions can be hard for a variety of structural, emotional, and organizational reasons. Doubling the difficulties are factors such as uncertainties, having multiple objectives, interactive complexity, and anxiety.

    Strategic decisions are purposeful actions. The future of your organization and the progress of your career might be profoundly affected by what you decide.

    Good decisions are made with less stress, and it is easier to explain the reasons for the decision that was made. Decisions should be made strategically. That is, one should make decisions skillfully in a way that is adapted to the end one wishes to achieve. To make strategic decisions requires that one takes a structured approach following a formal decision making process. Otherwise, it will be difficult to be sure that one has considered all the key aspects of the decision.

    Making good strategic decisions is learnable and teachable through an effective, efficient, and systematic process known as the decision-making process. This structured and well-focused approach to decision-making is achieved by the modeling process, which helps in reflecting on the decisions before taking any actions. Remember that: one must not only be conscious of his/her purposeful decisions, one must also find out the causes for which they are made. There is no such thing as "free-will". Those who believe in their free wills are in fact ignorant to the causes that impel them to their decisions. There is no such thing as arbitrary in any activity of man, least of all in his decision-making. Just as he has learned to be guided by objective criteria in making his physical tools, so he is guided by unconscious objective criteria in forming his decision in most cases.

    The simplest decision model with only two alternatives, is known as Manicheanism, which was adapted by Zarathustra (B.C. 628-551), and then taken by all other organized religions. Manicheanism is the duality concept, which divides everything in the world into discrete either/or and opposite polar, such as good and evil, black and white, night and day, mind (or soul) and body, etc. This duality concept was a sufficient model of reality for those old days in order to make their world manageable and calculable. However, nowadays we very well know that everything is becoming and has a wide continuous spectrum. There are no real opposites in nature. We have to see the world through our colorful mind's eyes; otherwise we do not understand complex ideas well.

    The Industrial Revolution of the 19th century probably did more to shape life in the modern industrialized world than any event in history. Large factories with mass production created a need for managing them effectively and efficiently. The field of Decision Science (DS) also known as Management Science (MS), Operations Research (OR) in a more general sense, started with the publication of The Principles of Scientific Management in 1911 by Frederick W. Taylor. His approach relied on the measurement of industrial productivity and on time /movement studies in the factories. The goal of his scientific management was to determine the best method for performing tasks in the least amount of time, while unfortunately using the stopwatch in an inhumane manner.

    A basic education in OR/MS/DS/SS for managers is essential. They are responsible for leading the business system and the lives in that system. The business system is dynamic in nature and will respond as such to disturbances internally and externally.

    The OR/MS/DS/SS approach to decision making includes the diagnosis of current decision making and the specification of changes in the decision process. 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. Specification of changes in decision process involves choosing what specific improvements in decision behavior are to be achieved and thus defining the objectives.

    Nowadays, the OR/MS/DS/SS approach has been providing assistance to managers in developing the expertise and tools necessary to understand the decision problems, put them in analytical terms and then solve them. The OR/MS/DS/SS analysts are, e.g., "chiefs of staff for the president", "advisors", "R&D modelers" "systems analysts", etc. Applied Management Science is the science of solving business problems. The major reason that MS/OR has evolved as quickly as it has is due to the evolution in computing power.

    Foundations of Good Decision-Making Process: When one talks of "foundations", usually it includes historical, psychological, and logical aspects of the subject. The foundation of OR/MS/DS/SS is built on the philosophy of knowledge, science, logic, and above all creativity. In this web site the decision "problem", does not refer to prefabricated exercises or puzzles with which most educators continually confront students, such as the problem of finding a solution to a system of equations, without giving any motivation for its need-to-know.

    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. Therefore, this site distingushes between a management scientist, someone who studies what decision to make, and a decision maker, someone responsible for making the decision.

    This site is about how to make good decisions when confronted with decision problems. It means real problems, the effective handling of which can make a significant difference. Almost all decision problems have environments with similar components as follows:

    1. The decision-maker. The term decision-maker refers to an individual, not a group.
    2. The analyst who models the problem in order to help the decision maker,
    3. Controllable factors (including your personal abilities and physical resources),
    4. Uncontrollable factors,
    5. The possible outcomes of the decision,
    6. The environment/structural constraints
    7. Dynamic interactions among these components.

    Deterministic versus Probabilistic Models: Before going further, we distinguish between deterministic and probabilistic decision-making problems. All the decision models can be classified as either deterministic or probabilistic models. In deterministic models your good decisions bring about good outcomes. You get that which you expect, therefore the outcome is deterministic (i.e., risk-free). However, in probabilistic decision models, the outcome is uncertain, therefore making good decisions may not produce good outcomes. Unlike deterministic models where good decisions are judged by the outcome alone, in probabilistic models, the decision maker is concerned with both the outcome value and the amount of risk each decision carries. When the outcome of your decision is rather certain and all the important consequences occur within a single period, then your decision problem is classified as a deterministic decision. However, in many instances, these types of models are encumbered with the two most difficult factors — uncertainty and delayed effects. Both difficulties can be overcome by probabilistic modeling which includes the time discounting factor. We will cover both deterministic and probabilistic decision-making models.

    After recognizing this no-nonsense classification of decision-making components, the OR/MS/DS/SS analyst performs the following sequence with some possible feedback loops between its steps:

    1. Understanding the Problem: It is critical for a good decision maker to clearly understand the problem, the objective, and the constraints involved.
    2. 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.
    3. Finding a Good Solution: It is important here to choose the proper solving technique, depending on the specific characteristics of the model. After the model is solved, validation of the obtained results must be done in order to avoid an unrealistic solution.
    4. Communicating the Results with the Decision-Maker: The results obtained by the OR/MS/DS/SS analyst have to be properly communicated to the decision-maker. This is the "sale" part. If the decision-maker does not buy the OR/MS/DS/SS analyst recommendations, he/she will not implement any of them.
    Problem understanding encompasses a problem structure, and a diagnostic process to assist us in problem formulation (i.e., giving a Form to a complex situation) and representation. This stage is the most important aspect of the decision-making process. Problem understanding is an interactive process between the decision maker and the OR/MS/DS/SS analyst. The decision maker may be unfamiliar with the analytic details of the problem formulation such as what elements to include in the model, and how to include them as variables, constraints, indexes, etc.

    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. You will learn this by performing the "what-if" scenarios and the necessary sensitivity analysis. Ensure that both plan and dispositions are flexible, adaptable to circumstances. Your plan should foresee and provide for a next step in case of success or failure.

    Gathering reliable information at the right time is a component of good decisions. It is helpful to understand the nature of the problem by asking "who?", "what?", "why?", "when", "where" and "how?". Finally, break them into three input groups, namely: Parameters, Controllable, and Uncontrollable inputs. Uncontrollable factors are the main components of decision-making which must be dealt with, by, e.g., forecasting. In making conscious decisions, we all make forecasts. We may not think that we are forecasting, but our choices will be directed by our anticipation of results of our actions or omissions.

    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 you know, the whole process of managerial decision-making is synonymous with the practice of management. Decision-making is at the core of all managerial functions. Planning, for example, involves the following decisions: What should be done? When? How? Where? By whom? As shown in the following diagram:

    Questions Relevant to the Stages of Decision-Making Process

    As indicated in the above diagram, perceiving the need to face the decision problem is a point of departure and no more. As soon as you elaborate, it becomes transformed by thought process to a mental model. The decision-making process contains a few well-defined stages, including describing, prescribing, and controlling the problem, each of these stages requires a set of relevant questions to be asked. Moreover, this process is never ending since the problem keeps changing, therefore there is a always need for feedback to measure the effect of your decision. Moreover, each decision problem that you make successfully became a rule, which served afterwards to make other decisions. This happens when your are facing a sequential decision-making problem.

    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. 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. The "what-if" analysis provides "look ahead" management. The management can use a dynamic model to experiment with future consequences of new policies. It provides information on what is likely to happen, not what necessarily will 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. 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. The strategic assessment at this stage must recognize both the internal analysis such as the strengths and weaknesses, and the external analysis such as threats and opportunities.

    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 . Whereas many agree that for defensible and responsible decisions one should at least know the balance of the analytical approach as well as the human-side of the decision which includes the ethical elements.

    All OR/MS/DS/SS concepts focus on communication of the results and recommended courses of actions (strategies). This helps all involved to build a consensus concerning the possible outcomes and recommended course of action. The decision-maker might incorporate some other perspectives of the problem, such as cultural, political, psychological, etc., into the management scientist's recommendations.

    Successful OR/MS/DS/SS modeling approach to decision-making demands a proper attitude as well as an understanding of more technical matters. Although both the OR/MS/DS/SS analyst and decision-maker should understand problem identification, model building, and solution techniques, the attitudes of both are probably the most important elements of successful application. Although proper attitude is not sufficient for successful application, it is necessary. An analyst who focuses more on techniques for solution than on model formulation will not be successful. 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. A decision maker who thinks that she or he can turn the analyst loose without guidance and expect to get relevant information back that can be applied directly to the problem and then forgotten will not make the best use of quantitative inputs. Instead, the interaction between the decision-maker and OR/MS/DS/SS analyst must be open, interactive, and focused on the ultimate goal of the effort: to develop and make the best use of the quantitative input to a decision problem.

    Today's business decisions are driven by data. In all aspects of our lives, and importantly in the business context, an amazing diversity of data is available for inspection and given insights. Moreover, business managers and decision makers are increasingly encouraged to justify decisions on the basis of data. Taking this course gives you an edge. Graduates with strong quantitative skills are in demand. This phenomenon will grow as the impetus for data-based decisions strengthens and the amount and availability of data increases. The quantitative toolkit can be developed and enhanced at all stages of your career.

    Further Readings:
    Balachandran S., Decision Making: An Information Sourcebook, Oryx Press, 1987.
    Browne N., and S. Keeley, Asking the Right Questions: A Guide to Critical Thinking , Prentice Hall, 2000.
    Bouyssou D., et al., Evaluation and Decision Models: A Critical Perspective, Kluwer Academic Publishers, 2001.
    Buckangham M., and C. Coffman, First, Break All the Rules: What the World's Greatest Managers Do Differently, Simon & Schuster Trade, 1999.
    Carroll B., The Biases of Management, Routledge, 1993.
    Crainer S., The 75 Management Decisions Ever Made and 21 of the Worst, American Management Association, New York, 1999. Davis M., The Art of Decision-Making, Springer-Verlag, 1986.
    Driver M., K. Brousseau, and Ph. Hunsaker, The Dynamic Decision Maker: Five Decision Styles for Executive and Business Success, Harper & Row, 1990.
    Eiser J., Attitudes and Decisions, Routledge, 1988.
    Forman E., and M. Selly, Decision by Objectives: How to Convince Others That You Are Right, World Scientific, 2001.
    Fromm E., Escape From Freedom, Henry Holt, 1995.
    Gore Ch., K. Murray, and B. Richardson, Strategic Decision-Making, Cassell, 1992.
    Harrington J., G. Hoffherr, and R. Reid, The Creativity Toolkit: Provoking Creativity in Individuals and Organizations, McGraw-Hill, 1998.
    Jennings D., and S. Wattam, Decision Making: An Integrated Approach, Pitman Pub., 1998
    Kaplan M., Decision Theory as Philosophy, Cambridge University Press, 1996.
    Klein G., et al., (Ed.), Decision Making in Action: Models and Methods, Ablex Pub., 1993
    Shapira Z., Organizational Decision Making, Cambridge Univ Pr., 1997. A study of organizational aspects such as conflict, incentives, power and ambiguity, on the other. It draws mainly from the tradition of Herbert Simon, who studied organizational decision making process.
    Simon J., Developing Decision-Making Skills for Business, M.E. Sharpe, Inc., 2000.
    Spender j., and H. Kijne, Scientific Management: Frederick Winslow Taylor's gift to the world?, Kluwer Academic Publishers, 1996.
    Steiss A., Strategic Management and Organizational Decision Making, Lexington Books, 1985.
    Taylor F., The Principles of Scientific Management; and Shop Management, Routledge/Thoemmes Press, 1993.
    Thompson C. (ed.), Scientific Management: A Collection of the More Significant Articles Describing the Taylor System of Management, Routledge/Thoemmes, 1993.
    Wegner D., The Illusion of Conscious Will, MIT Press, 2002. The brain conducts business on its own. Even though there is no one in charge of its operations, the mind struggles in generating a strong personal self-identity called Motif.
    Wickham Ph., Strategic Entrepreneurship: A Decision-making Approach to New Venture Creation and Management, Pitman, 1998.

    Introduction and Summary

    Until the end of the eighteenth century, nearly all products were manufactured by individual artisans and craftsmen. With the advent of new manufacturing technology in the late eighteenth and early nineteenth centuries came the Industrial Revolution. Early advances occurred in England and spread quickly throughout Europe. While technological breakthroughs led to more efficient production processes, the cost of associated manufacturing equipment was beyond the capital resources of individual craftsmen. 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. Today there are many large man-made systems besides factories, such as hospitals, airports, and telecommunication systems.

    The large system is the result of the application of scientific techniques to manufacturing and persists as a fundamental characteristic of modern industry. Today larger companies employ thousands of workers, deal in billions of dollars, manufacture hundreds of products, and service a multitude of markets. 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.

    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. 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. A problem is a chance for you to do your best.

    The complexity of today's business operations, aggressive competition, and government controls have made the job of the manager increasingly difficult. 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. 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. As a result, effective decision-making often requires the availability of information analyzed and summarized in a timely fashion.

    An effective and proven process has been developed over the last 70 years and is known as Operations Research/Management Science/Decision Science/Success Science (OR/MS/DS/SS).

    Operations Research, Management Science, Decision Science, and Success Science (OR/MS/DS/SS)

    Decision Science (DS) known also as Operations Research (OR), Management Science (MS), and Success Science (SS) is the science of making decisions. Let us ask ourselves first "What is in the name Management Science?" To manage means to utilize what is controllable, and to be able to predict what is uncontrollable in order to achieve a specific objective. Science is a continuing search; it is a continuing generation of theories, models, concepts, and categories. Therefore, Management Science is the science for managing and usually involves decision-making. Science is a continuing search; it is a continuing generation of theories, models, concepts, and categories. Therefore, Management Science is the science for managing and almost always involves decision-making.

    In search of the genealogy of OR/MS, we might ask ourselves a more general question "What is OR/MS/DS/SS?" First let's find out what we mean by "Is" in general.

    "Is" As Definition: Literally, the question "What Is OR/MS/DS/SS?" calls for a "definition" of OR/MS.

    "Is" As Invitation: The situation is different when we look up the word OR/MS/DS/SS in an encyclopedia rather than in a dictionary.

    "Is" As Cop-out: The question "What Is OR/MS/DS/SS?" is often asked when the questioner has little or no acquaintance with OR/MS, and wants to discharge his or her duty to learn something about OR/MS, hoping for a short answer.

    "Is" As Escape: Students confronted with the task of learning OR/MS/DS/SS rarely feel the need to ask the preliminary question, "What Is OR/MS/DS/SS?". They are more likely to ask specific questions such as "What is linear programming?", "What is a constraint?", or "What is a decision tree?".

    "Is" As Summing Up: Some OR/MS/DS/SS analysts, who are reaching the end of their careers, feel the need to answer the question "What Is OR/MS/DS/SS?" in these circumstances, the question "What Is OR/MS/DS/SS?" is to get into the history and philosophy of OR/MS/DS/SS.

    "IS" As Wonder: Are we to conclude that the question "What Is OR/MS/DS/SS?" should be dismissed as meaningless? The question "What Is OR/MS/DS/SS?" is posed here to express a feeling of wonder, to signify the excitement that possesses us at the beginning of this course. He who can no longer pause to wonder and stand rapt in awe, is as good as dead; his eyes are closed.

    What Is OR/MS/DS/SS

    Management Science (MS) often takes an analytical view of a decision before making a decision. That is, reflection before action, as a Chinese proverb says, "To chop a tree quickly, spend twice the time sharpening the ax." Carpenters say, "Measure twice, cut once." It's no delay to stop to edge the tool.

    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. Analytical methods are applied to planning and management problems in areas such as production and operations, inventory management, and scheduling. Techniques, often using powerful computer programs, are available to solve problems ranging from real-time control of specific business, industrial, agricultural, and administrative operations to long-term planning models for corporations and public sector agencies.

    It is ironic that the idea of utilizing knowledge from a variety of disciplines was a central tenet of the early days in OR/MS. From the beginning, practical problems did not fit into neat disciplinary boundaries. OR/MS/DS/SS became established in organizations and interdisciplinary teams and positions included mathematicians, statistician, psychologists, economists, sociologists, etc. However, over the years the interdisciplinary teams were broken up and new recruits into OR/MS/DS/SS tended to come from applied mathematical and statistical backgrounds. Academically, OR/MS/DS/SS became increasingly focused on mathematical models and strategic solution algorithms. OR/MS/DS/SS was locked into a hard, technical shell. In recent years, however, this situation is changing with the arrival of "soft" methodologies and critical systems strategic thinking .

    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. A good model does not solve the problem in itself, but allows us to experiment with different systems variables to come up with new ideas about how to tackle the decision problem.

    The typical OR/MS/DS/SS approach is to build a model for the problem being studied. Such a model is often (but not always) mathematical. Practical problems are often unstructured and the definition and clarification of problems, as well as the building of models, is an important part of the OR/MS/DS/SS methodology. Most people discover that the understanding created by building a model is a very valuable part of an OR/MS/DS/SS project. Once a model is built, algorithms often have to be used to solve it. An algorithm is a series of steps that will accomplish a certain task. The study, understanding, and invention of such algorithms is also an important part of OR/MS/DS/SS modeling for decision-making. The decision maker might incorporate some other perspectives of the problem such as cultural, psychological, etc., into the management scientist's recommendations. Finally, communicative and political skills are needed in implementing the results of an OR/MS/DS/SS model in a real-life situation. OR/MS/DS/SS models are aimed at assisting the decision-maker in his/her decision-making process.

    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. Therefore, there is no such thing as "someone who is born as a business person"; rather, one becomes a business person. If a successful business person is also a management scientist, then he/she can transfer management knowledge to another person. This is because ideas are communicated using analytical language. If an approach is used where no conscious thought (i.e., knowing what you know) was present, then the rationale of the strategic solution cannot be explained nor defended to another person. Unfortunately, the evidence on rational decision-making is largely negative evidence, evidence of what people do not do.

    You may ask, "Why must we learn the decision-making process?" Here are a few motivating reasons:

    You must also master the exact meanings of the Keywords and Phrases used in OR/MS/DS/SS professions because if your vocabulary is limited your thoughts are limited and vice versa. You have to know the business side of your profession. What is important for you is to learn the language of the managers. More management-oriented decision-makers are saying "your language seems so far removed from mine." These decision makers are simply left to sink or swim in an environment which seems to marginalize them simply through the use of OR/MS/DS/SS jargons. For example, industrial engineers (i.e., OR/MS/DS/SS practitioners in factories) must learn how to translate "precision" into extra dollars in terms of earnings/savings. This is the only language managers know.

    The field of OR/MS/DS/SS is always changing. Its changes are driven by the technology it uses and that it extends, and the applications that it affects.

    Overcoming the Communication Barriers: Knowledge is what we know. Information is the communication of knowledge. In every knowledge exchange, there is a sender and a receiver. The sender makes common what is private, does the informing, the communicating. The receiver takes in the information turns it into knowledge. Depending on the audience of the report, the OR/MS/DS/SS model may or may not be included. It is the task of the management science team to write a report that is understandable by all that will read it. In short, without the ability to effectively translate the models and resulting calculations back into the real-world situation from which they were derived in an understandable way, the OR/MS/DS/SS professional is not able to accomplish his/her purpose. Communication is a chain of events in which the message serves as the basic link, as is depicted in the following flowchart, known as the Shannon's communication model. Feedback/acknowledgment provide assurance of consistency in the encoding and decoding processes.

    Shannon's communication model

    Communication, which is a basic human activity, is not always accomplished successfully. Effective communication requires clarity of mind, clarity of purposeful signals, and a meeting of the minds. It is common that people are looking for hidden meaning! How often we have heard somebody saying, "That's not quite what I meant"?

    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. The analyst is to assist the decision-maker in his/her decision-making process. Therefore, the analyst must be equipped with more than a set of analytical methods.

    Specialists in model building are often tempted to study a problem, and then go off in isolation to develop an elaborate mathematical model for use by the manager (i.e., the decision-maker). Unfortunately the manager may not understand this model and may either use it blindly or reject it entirely. The specialist may feel that the manager is too ignorant and unsophisticated to appreciate the model, while the manager may feel that the specialist lives in a dream world of unrealistic assumptions and irrelevant mathematical language.

    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. After the manager has built up confidence in this model, additional detail and sophistication can be added, perhaps progressively only a bit at a time. This process requires an investment of time on the part of the manager and sincere interest on the part of the specialist in solving the manager's real problem, rather than in creating and trying to explain sophisticated models. This progressive model building is often referred to as the bootstrapping approach and is the most important factor in determining successful implementation of a decision model. Moreover the bootstrapping approach simplifies otherwise the difficult task of model validating and verification processes.

    The OR/MS/DS/SS modeling process is more than a set of analytical methods. OR/MS/DS/SS models are aimed at assisting the decision-maker in his/her decision-making process. A fundamental part of OR/MS/DS/SS modeling is the "systems approach" to problem solving. This approach indicates that the context of organizational problems is as important as the stated problem. Defining a problem, collecting data, consulting with people involved in the solution, and implementing change are all aspects of the OR/MS/DS/SS education and training. As it is easier to make plans than to carry them out, models that are not to be implemented are ones that were not drawn up correctly and taken seriously from the start.

    The OR/MS/DS/SS modelling process helps to improve operations in business and government through the use of scientific methods and the development of specialized techniques. Operations Research is not "research"; it is the process cycle of re-searching for an optimal (or desirable) strategic solution to the existing decision problem/situation.

    The cycle of decision making

    The Cycle of Decision-Making

    OR/MS/DS/SS modeling process provides systematic and general approaches to problem solving for decision-making, regardless of the nature of the system, product, or service. The approaches and tools used in OR/MS/DS/SS models are based on one or more of the following analytical methods, simulation, and qualitative or logical reasoning. Many of these tools and approaches depend on computer-based methodologies for implementation.

    In summary, the OR/MS/DS/SS modeling process is the application of scientific methods to complex organizational decision problems/opportunities. The OR/MS/DS/SS models are aimed at assisting the decision-maker in his/her decision-making process. 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. Improvement of an existing system and good designs for new systems are the aims of OR/MS.

    The OR/MS/DS/SS modeling process is one of the greatest innovative decision-making tools of the twentieth century.

    Further Readings:
    Gharajedaghi J., Systems Thinking - Managing Chaos and Complexity: A Platform for Designing Business Architecture, Butterworth-Heinemann, 1999.
    Kauffman S., At Home in the Universe: The Search for Laws of Self-Organization and Complexity, Oxford Univ. Press, 1996.
    Kim D., Introduction to Systems Thinking , Pegasus Communications, 1999. The author provides an interesting metaphor for strategic resources as water being contained in tank, flowing through pipes into and out of different parts of the business system and, most importantly, from and to competitors.
    Mingers J., and A. Gill (editors), Multi-methodology: The Theory and Practice of Combining Management Science Methodologies, Wiley, 1997.

    Historical Needs for OR/MS

    Until the middle of the 19th century, most industrial enterprises only employed a few workers. However, as companies expanded, it became less and less feasible for one person to manage all of the new managerial functions of the business effectively. New scientific methodologies were developed to provide assistance to each new type of managerial function as it appeared. As more specialized forms of management emerged, more specialized subfunctions, such as statistical quality control, equipment maintenance, marketing research, and inventory control emerged. Whenever a managerial function is broken down into a set of different subfunctions, a new task, called the executive function of management, is created to integrate the diverse subfunctions so that they efficiently serve the interests of the business as a whole. The executive function evolved gradually with organizations themselves. However, increasing demands were made on the manager who, in turn, sought aid outside the organization. This gave rise to management consultants. What we call OR/MS/DS/SS today is, in fact, the use of scientific tools to aid the executive.

    OR originated in Great Britain during World War II to bring mathematical or quantitative approaches to bear on military operations. Since then OR/MS/DS/SS has evolved to be applicable to the management of all aspects of a system, product, or service, and hence is often referred to as Systems Science or Management Science. It has now become recognized as an important input to decision-making in a wide variety of applications in business, industry, and government.

    The term OR arose in the 1940's when research was carried out on the design and analysis of mathematical models for military operations. 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. The growing complexity of management has necessitated the development of sophisticated mathematical techniques for planning and decision-making, and the OR/MS/DS/SS features prominently in this structured decision-making process cycle by providing a quantitative evaluation of alternative policies, plans, and decisions. The mathematical disciplines most widely used in OR/MS/DS/SS modeling process include mathematical programming, probability and statistics, and computer science. 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.

    Military organizations had gone through the same type of evolution as other businesses and industries. This organizational evolution took place in the twenty year gap between the end of World War I and the beginning of World War II when the military leadership had to turn to teams of scientists for aid. These teams of scientists were usually assigned to the executive in charge of operations; hence their work came to be known as Operational Research in the United Kingdom and by a variety of names in the United States: Operation Research, Decision Science, Operational Analysis, System analysis, Success Science, and Management Science. The name Operations Research is the most widely used.

    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. The emerging search for assistance was accelerated by the outbreak of the Korean War. This vigorous growth of OR in the military continued to provide rapid applicability to other industries and sectors.

    The Nature and Meaning of OR/MS/DS/SS

    Many definitions of OR/MS/DS/SS have been offered, as well as many arguments as to why it cannot be defined. The following definitions provide a useful basis for an initial understanding of the nature of OR/MS:

    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).

    Scientific approach to problem solving for executive management (Wagner 1969).

    Optimal decision-making in, and modeling of, deterministic and probabilistic systems that originate from real life. These applications, which occur in government, business, engineering, economics, and the natural and social sciences, are largely characterized by the need to allocate limited resources. In these situations, considerable insight can be obtained from scientific analysis, such as that provided by OR/MS/DS/SS (Hiller-Lieberman 1974).

    A branch of applied mathematics wherein the application is to the decision-making process (Gross 1979).

    Comparing definitions given by More-Kimball and Gross, the divergence is notable after almost 40 years: in one case, OR/MS/DS/SS is defined as scientific method, while in the other it is seen as a branch of mathematics.

    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.

    The Methodology of OR/MS

    OR/MS/DS/SS is the scientific method of decision-making. In most discussions of the general scientific method you would find certain stages and essential processes, as depicted in the following flowchart:

    The General Scientific Approach

    Although these phases of an OR/MS/DS/SS project are normally initiated in the order listed, they usually do not terminate in this order. In fact, each stage usually continues until the project is completed and continuously interacts with the others.

    Among key tasks of the scientific enterprise, perhaps none is more fundamental than that of making parts of the world understandable. Such understanding is typically seen as involving certain sorts of empirically supported explanations. That seemingly simple idea pitches us headlong into a variety of philosophical and historical thickets, but of present interest are apparent connections to human subjectivity, social structures, philosophical presuppositions, and other such matters.

    Understanding something involves removal of at least some of its mystery. The process of coming to understand something involves a transition from mystery to sense making. It is a coming to see an answer to a particular sort of "Why?" question. 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. All those matters are structured in terms of human concepts, human experience, and the human condition generally.

    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. What seems to make sense is also notoriously dependent upon psychological circumstances, mental condition, levels of various substances in the brain, and so forth. Both batches of factors provide some potential for subjective, human intrusion into the process.

    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. And we cannot get behind or underneath it to examine its credentials.

    Things that make intense sense in dreams, or to the intoxicated, or to the mad, are often utterly indescribable in ordinary discourse. Not only is this "sense" faculty thus not infallible, but there is apparently no noncircular procedure for justifying reliance upon it. 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. And there is, obviously, no hope whatever for an empirically based case of the required, noncircular sort.

    Thus, one of the foundational aims of science may not even be definable in human-free terms. Ultimately, we are unable to avoid taking the deliverance of some human cognitive capacity or function as reliably given, and we simply go from there. There is no other alternative. And neither rigor, the empirical, nor formalisms will get us out of that. Something has to be fundamental in even the most rigid axiom system along with the given some notion of proof, rigor, etc. Even mere coherence still demands reliance on some ultimate identifying of coherence and upon some principle linking coherence to the relevant characteristics aimed at, upon some value assignment to that characteristic, etc. So the whole idea of understanding rests upon an involuntary endorsement of the objective legitimacy of specific human inner phenomenal experiences associated with particular things having a genuinely sense-making appearance.

    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. But not only is that "seeing" itself mediated by its embedding conceptual context, the relevant sense of seeing something is deeply experientially psychological, involving hard-to-define cognitive connections that may simply be causal results of our human cognitive structure. And the conditions of that experience seem nearly unmanageably rich. We have only the spottiest ideas of what go into it, which may be why our references in this whole area are almost always metaphorical - "see," "light," "grasp," and so on.

    Explanations are what supply the materials that allow us to see. 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. Different sorts of explanations may do that in different ways in different contexts. Very generally, explanations supply such materials by formally, narrative, or otherwise displaying a field of background causal webs, patterns, events, conditions, law, and/or historical developments within which the phenomena in question fit organically, so that the phenomena become integrated, constituent parts of some larger pattern or flow.

    Like other scientists, OR/MS/DS/SS thinkers formulate theories and models, usually in mathematical terms. "Realism" in its philosophical sense is the tendency to identify concepts and quantities in those theories and models with real features of the external world.

    Further Readings:
    Engel A., Problem-Solving Strategies, Springer Verlag, 1998.
    Proctor T., Creative Problem Solving for Managers, Routledge, London, 1999.
    Starfield A., K. Smith, and A. Bleloch , How to Model It: Problem Solving for the Computer Age, Burgess Intl. Group, 1994.

    The Prototype Applications

    An important consequence of the application of OR/MS/DS/SS to a wide variety of problems is that a small set of problem types have been identified which account for most problems. Because of the frequent recurrence of these problems, prototype techniques have been developed for modeling them and for deriving solutions from these models. Prototype applications include:

    Forecasting: Using time series analysis to answer typical questions such as: How big will demand for products be? What are the sales patterns? How will this affect profits?

    Finance and Investment: How much capital do we need? Where can we get this? How much will it cost?

    Manpower planning and Assignment: How many employees do we need? What skills should they have? How long will they stay with us?

    Sequencing and Scheduling: What job is most important? In what order should we do jobs?

    Location, Allocation, Distribution and Transportation: Where is the best location for an operation? How big should facilities be? What resources are needed? Are there shortages? How can we set priorities?

    Reliability and Replacement Policy: How well is equipment working? How reliable is it? When should we replace it?

    Inventory Control and Stockout: How much stock should we hold? When do we order more? How much should we order?

    Project planning and control: How long will a project take? What activities are most important? How should resources be used?

    Queuing and Congestion: How long are queues? How many servers should we use? What service level are we giving?

    This broad range of potential applications and wide variety of OR/MS/DS/SS modeling process techniques, which can be selected and combined for a multi-disciplinary approach, work together to make the profession a dynamic and exciting one.

    Flexibility and Variety of Careers in OR/MS/DS/SS

    Completion of OR/MS/DS/SS enables graduates to find employment as OR/MS/DS/SS analysts, academicians or managers. It is a fact that education and work in OR/MS/DS/SS can lead to the executive suite where decisions are made. Career opportunities in the following areas of business are excellent:

    Manufacturing, Insurance, Planning, Systems analysis, Marketing, Budgeting, Finance, Program evaluation, Banking, Services (non-profit).

    The OR/MS/DS/SS profession should be particularly considered by persons who are attracted to the use of mathematics, statistics, and other branches of science, in general, for solving decision-making problems of practical significance.

    Some individuals believe that OR/MS/DS/SS is viewed as a "young person's" profession. Given the fact that analytical modeling is at the heart of OR/MS/DS/SS activity, such an assertion might be relevant. This belief originally came from the mathematical community. Some mathematicians believe that mathematics is a mind game, therefore like any other game, young persons engage in them more fully. However, youth is not a time of life -- it is a state of mind. Therefore, as long as your mind is active, you are young and indeed well-suited for the excitement of the OR/MS/DS/SS profession. No one is too old if they have a passion to learn. To absorb new ideas is to live anew and to see the world with fresh eyes. Henry Ford said "Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young. The greatest thing in life is to keep your mind young."

    Also visit the following collection:
    Decision Making Resources

    The Multidisciplinary and Interdisciplinary Nature OR/MS/DS/SS

    The growing trends in interventional managerial decision-making increasingly utilize applications of more than one technique and involve individuals from other disciplines. 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. A creative thinking must look in detail at how those from disciplines outside of OR/MS/DS/SS can come to work in the organizations on multi-disciplinary studies. Those who have come from such backgrounds, clearly share their perspectives and experiences.

    OR/MS/DS/SS as Systems Sciences

    Today the word "Engineering" has a broader meaning and scope than merely dealing with physical engines. The word engineering in phrases such as re-engineering business activities has a much wider scope. For example, economists like to think of themselves as something like 'engineers' trying to keep the 'train' of state on track. 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. 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.

    Systems engineering exists as a discipline because the complexity of large scale systems tends to defy effective design of the whole. 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. Systems engineers learn to model, simulate, optimize, integrate, and evaluate systems. They participate in group projects in such systems application areas as environmental control, telecommunications, transportation, project/construction management, and manufacturing.

    OR/MS/DS/SS as Industrial Engineering

    Industrial Engineers design systems that enable people and society to improve productivity, efficiency, effectiveness, and quality of the work environment. All engineers work at planning, designing, implementing, and controlling the systems that represent the way people use technology. 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. The following show the range of problems: 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. Systems that facilitate effective decision-making and implementation in areas such as scheduling, inventory, and quality control are typical of industrial engineering.

    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. 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.

    Design and development of these systems require the unique background of the Industrial Engineer. The process of engineering always starts with measurement. Where other engineers might measure temperatures, pressures, or wind loads, the Industrial Engineer measures the time of a work cycle, dollar values of expenditures, rates of machine failures, or demand processes for finished goods. Usually the mathematical analysis must take into account risk and uncertainty to a larger extent than in other engineering fields. Computer simulation and optimization are often required. 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.

    Also visit the following collection:
    Decision Making Resources.

    Further Reading:
    Haviv M., and R. Hassin, To Queue or Not to Queue: Equilibrium Behavior in Queueing Systems, Kluwer Academic Publishers, 2003. Focuses on the practical viewpoint of customer behavior and its effect on the system performance measure.

    OR/MS/DS/SS as Modern Manufacturing Systems

    Rapid progress in the area of modern manufacturing is probably most evident through the developments in intelligent manufacturing systems. The same fast advancements have made the objective of achieving a balanced technical program a challenging task.

    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. 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.

    Further Reading:
    Gershwin S., (ed.), Analysis and Modeling of Manufacturing Systems, Kluwer Academic Publishers, 2003.

    OR/MS/DS/SS as Management Information Systems

    There is much overlap between the OR/MS/DS/SS and Information Systems fields. Many business operations require intensive knowledge of computing and information systems. Similarly, management of computing and information facilities often require a deep understanding of issues such as scheduling, replacement strategy, and policies on the development and adaptation of new technology.

    The business world is becoming more computer and information intensive; therefore, specialists in OR/MS/DS/SS and Information Systems combine a background in OR/MS/DS/SS modeling process and a good knowledge of current computing technologies. They design and manage computerized systems that control the production and distribution of a firm's goods and services. Career opportunities exist in most industries and government organizations in the areas of systems analysis and design.

    OR/MS/DS/SS as Production and Operations Management

    Operations Management is the functional area of business that is concerned with the production of 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).

    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. Issues in the management of operations include: forecasting of the demand for the organization's products and/or services; development of efficient manufacturing processes; Inventory planning and control; work force scheduling; and design and management of distribution and transportation networks.

    The study of Operations Management embraces the disciplines of OR/MS/DS/SS, Statistics, and Computing and Information Systems. This field is a blend of field studies and the use of computerized models to analyze and simulate the operation of real systems.

    Operations are at the heart of most organizations, and opportunities are found in the area of forecasting, inventory management, the design of production facilities, work force scheduling, and the location and layout of distribution networks. 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.

    For more information, visit the following collection:
    Decision Making Resources.

    OR/MS/DS/SS as System Dynamics, and System Design Engineering

    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. For example, any air traffic control system would have SDE early in the design process.

    Their similarities are that both tend to use models at a higher level of aggregation, and deliberately scope the system boundaries widely. Both are likely to start out conceptual and end up mathematical model, and indeed sometimes both have well articulated modeling processes.

    Their differences depend on their different application and purposes. SD tends to be doing just one OR/MS/DS/SS model at a time, and always the continuous time. SDE may well use many models, each in a different form and drawing on different disciplines for different parts of the system to be designed. In terms of who does it and what they know, in SDE technical engineering knowledge is the main focus, and knowledge of the client situation is an input to the process. In SD, the OR/MS/DS/SS model developer is expected to be extremely familiar with all the moving pieces in the client's world. That is because a major task of the SD model developer is to represent that world. The SDE model developer task is to only to design something that will work as specified within that world.

    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.

    Introduction and Summary

    What processes allow us to make a good decision? At what point does the thought process begin? Is there any structure in making a decision? This is where Applied Management Science and more specifically multi-perspective structured decision-making processes create their mark.

    One needs to understand that reality is paramount to our logical reasoning process in making a model. One might ask what is a model? Models are different things to different people. Ask the kitchen chef what is a model and he might respond, why the recipe, of course. Here is a structured way to prepare that delicious dish or sumptuous dinner.

    Models are categorized according to their distinctiveness such as kind, evolution in time, as well as accessibility of records. Models can be static in nature (Iconic), or act like reality but often not appear like reality (Analog). Mathematical and computer models are known as symbolic models. Here we see algebraic, numerical, and logical modeling. These mathematical models are designed to offer understanding to some aspect of said reality. Simulation models can be classified as computerized duplications of real systems. The computer performs these mathematical functions with precision and speed. Dynamic modeling in organizations is the collective ability to understand the implications of change over time. This skill lies at the heart of successful strategic decision process. The availability of effective visual modeling and simulation enables the analyst and the decision-maker to boost their dynamic decision by rehearsing strategy to avoid hidden pitfalls.

    No human inquiry can be called science unless it pursues its path through mathematical exposition and demonstration, which is mathematical modeling. In creating a mathematical model, first is a mental model. Second, when written down to add clarity and dimension. And third, when you translate it into the language of science, that is mathematical model. Other managerial functions, such as organizing, implementing, and controlling, rely heavily on decision-making.

    Reporting or communicating one's conclusions to the decision-maker is a vital aspect of the modeling process. After you have investigated and reasoned through the problem at hand, your results could prove disastrous if you were unable to relate your findings to the proper individual(s). It is therefore necessary to make every effort to create reports and provide information that is understandable for all parties involved.

    The analytical decision-making process is an assessment based on the choice of alternatives. That is, choosing the alternative that fits the need of the person or organization. In order to provide solutions through modeling one must obtain the facts, eliminate things that are not relative to understand the real decision problem/opportunity.

    There is a three-stage structure to the systems analysis, design, and control process. The prescription of a solution stage allows for the identification of a strategic solution in the implementation stage. These solutions depend upon budgets, time, and other considerations. For every managerial decision there are several possible solutions. Good decisions are based on considering all the solutions and choosing the one that fits the operation. Utilizing software to solve complex business problems in today's world has become commonplace. Analysis of this data can often be challenging. As the OR/MS/DS/SS person assigned to the problem, it is your job to provide solution information to the decision-maker. These Managerial Interpretations must be presented in a manner to which the decision-maker is accustomed. Presenting technical data to one that is not familiar with the "language" will render your report useless. Post-prescription monitoring activities include updating the strategic solution in order to control the problem. Change is ever present. The information to solve today's problems must remain current. Therefore, in order to maintain relevancy models must contain the latest information. This in turn provides the decision-maker with the best possible analysis to make quality decisions.

    Validation is the process of comparing the model's output with the behavior of the phenomenon. This is to say that confirmation of the model's behavior is essential. How else can one determine if the proper model has been built. Then there is always the question of cost. Modeling can be very expensive. The more complicated the model, the greater the cost. Inputs and constraints added to existing problems create extra costs. Plus, there is the matter of timely decisions.

    A Multi-perspective structured decision modeling process consists of reflections before action. According to Sun Tzu "Victory is achieved before, not during, battle."

    Much of the analysis of OR/MS/DS/SS is through system models that are synthetic representations of physical/operational 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. By experimenting with the model, the effects of various management decisions can be explored.

    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. However, an analysis of the system through the use of a reasonable model usually provides valuable input to managerial decisions. 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. These relationships are frequently expressed in a mathematical form. 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.

    The OR/MS/DS/SS approach to the decision-making process is mostly through mathematical models. The use of mathematical modeling spreads to the public and private sector and has grown rapidly since the availability of PC. By definition the mathematical modeling process of reality is the mathematization of reality as we perceive it. Mathematizing could be in the forms of quantifying, graphical visualizing, tabular coordinating and/or symbols notation systems to develop mathematical descriptions and explanations that make heavy demands on modelers' reality representational capabilities.

    Practitioners in OR/MS/DS/SS have long held fast to the tenet that the system under study, and its operational characteristics, should dictate the modeling approach, and not that the modeling familiarity of the analyst should dictate his/her description of the system. This is an easy thing to state but quite another to accomplish, regardless of how true it may be. 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. In the modeling process one must consider the following facts:

    1. You must see, but that is not enough; you must then take time to observe.
    2. You must think, but that is not enough; you must then take time to reason.
    3. 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.
    4. You must also plan well your actions, but that is not enough; you must then take time to implement, and perhaps adapt, your plans.
    5. 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.

    There are essentially two polar points of view regarding analytical modeling process in OR/MS:

    1. If used properly methods will give the one "correct" answer to a decision problem and will prescribe the course of action for an executive to take; or

    2. The methods are native and essentially useless, the proverbial "will o' the wisp," and therefore "practical" people should not waste time studying them.

    The truth lies somewhere between these two extreme opinions. Quantitative Methods can be useful if their proper place in the analysis of decision problems is clearly seen.

    Since, abstraction is the most powerful tool that we have in strategic thinking about decision problems. Parts of this Web site are "philosophical" because a model is an abstraction of reality that we hope to use to understand reality: it must delicately weigh and balance the feature of reality that is important in a decision situation. Oversimplifying can lead to poor decisions. Making a model too complex can lead to untimely decisions as well as decision recommendations that are really not understood by anyone. A well-balanced model can provide important and useful information at low cost. Models do not simply appear; they are built and required extensive work.

    Further Readings
    Evans J., and D. Olson, Introduction to Simulation and Risk Analysis, Prentice Hall, 2002.
    Harrington J. , and K. Tumay, Simulation Modeling Methods: To Reduce Risks and Increase Performance, McGraw-Hill, 2000. CD-ROM included.
    Jennings D., and S. Wattam, Decision Making: An Integrated Approach, Pitman Pub., 1998
    Kaplan M., Decision Theory as Philosophy, Cambridge University Press, 1996.
    Klein G., et al., (Ed.), Decision Making in Action: Models and Methods, Ablex Pub., 1993
    Lesh R., and H. Doerr, Symbolizing, Communicating, and Mathematizing: Key Concepts of Models and Modeling, in P. Cobb, E. Yackel, and K. McClain (Eds.), Symbolizing and Communicating in Mathematics Classrooms: Perspectives on Discourse, Tools, and Instructional Design, Lawrence Erlbaum Associates, N.J., 361-383, 2000.
    Petroski H., Invention by Design: How Engineers Get from Thought to Thing, Harvard University Press, 1998.
    Ross Sh., Simulation, Academic Press, 2001
    Ross Sh., Introduction to Probability Models, Academic Press, 2002.
    Ross Sh., An Elementary Introduction to Mathematical Finance: Options and other Topics, Cambridge University Press, 2002. 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.
    Ross Sh., Stochastic Processes, Wiley, 1995.
    Tomlinson R., and I. Kiss, (eds.), Rethinking the Process of Operational Research and Systems Analysis, Pergamon Press, 1984.
    Walker W., S. Rahman, J. Cave, Adaptive policies, policy analysis, and policy-making, European Journal of Operational Research, 128, 282-289, 2001.

    Multi-perspective Modeling Process

    The modeling process is a well-focused strategic thinking while following some logical sequences. Therefore, in this sense, strategic thinking has to be learned in the way dancing has to be learned. One can dance with logic.

    The most widely used models are spoken languages. Consciousness and language are closely related. We report our conscious experiences using language, and these verbal reports are the central model for human consciousness. We consciously experience linguistic stimuli such as words and sentences, and also process them unconsciously. Our language helps to structure our conscious experience, by shaping their mental model. Language is a model consisting of a sequence of metaphors to convey our feelings, desires, passions, etc. to other persons. Language is a system of encoding thoughts, moreover we think because we have words and the more words we have, the better able we are to think conceptually.

    It is the impossible attempt to step outside our human condition - the ordinary language and its linguistic rules in particular its grammar, within which we do our strategic thinking , put limitations on our strategic thinking and its communication to others. We think in "words" and moreover the "grammar" is a major barrier for our strategic thinking . Consider the statement "nothing" does exist. It seems a meaningless statememt, however 3 - 3 = 0. As another example, we hear in the evening news that "Nobody was hurt in that car crash" is it necessary to state it? Grammar too puts limitations on our strategic thinking . For example every verb must have a subject. When we say, "It thunder," we know well that there is no such "it." One must rather say "Thunder is going on." Our false strategic thinking is incorporated in our whole language; we cannot reason without, so to speak, reasoning wrongly. We overlook the fact that speaking, no matter of what, is itself a model.

    Because of all the above limitations is our spoken language, in many historical instances the scientists have to create new kinds of mathematics and logic to enable them to express and therefore communicate effectively their ideas. Mathematical development, being the language of science is an ever evolving and expanding process.

    Why are fashion models called models? The answer is that they try to represent a reality of how you will look, for example, in the same clothes.

    The external world presents itself to us through our sense perceptions collectively are only a set of interfaces mediums to our brains. By analyzing these raw data, we are in fact engaged in constructing of a mental model of the reality, however we see only what the mind is prepared to comprehend. Models are re-presentations of reality which may or may not represent reality accurately. Essentially all models are inexact, however some are more useful than the others are. For example, we make models about people inside our head. When we act, we find out if the model is accurate! Many people make the mistake of assuming their model is reality; they blame others and say "something is wrong." A "validated" model is the one which re-presents reality.

    Anyone can perceive the outside world. A thinker can not only see the outside world, a thinker can re-present those perceptions as models, as depicted in the following figure:

    The World As We Know It

    The above figure represents the following steps:

    1. One perceives the outside world through his/her physical sense of perceptions.
    2. The thinker in the above figure processes and analyzes the information through mental activities to form an interpretation.
    3. The thinker re-presents the interpretation (now called understanding) back "as if" it is indeed the reality itself.
    4. The outside world appearances to the mind are of four kinds. Things either are what they appear to be; or they neither are, nor appear to be; or they are, and do not appear to be; or they are not, and yet appear to be. Rightly to aim in all these cases is the thinkers task.

    You may ask "How does understanding come to a thinker?" The answer is, we understand the world in this way through knowledge. We become a little piece of the world by becoming conscious of it. However, we might be overconfident about our understanding and think we are better at guessing or estimating than we actually are. This happens because we perceive the world through 'our' senses and interpret what we perceive based on our experiences and trained ways of strategic thinking. The point is that there are many traps that we humans fall into when making important decisions.

    Doing something does not imply understanding what we are doing or being conscious of how we are doing it. Talking, remembering, and making decisions are examples of activities in which we continuously engage in blissful ignorance of the process and procedures involved.

    Unlike mathematics which requires only the consistency, mathematical models need interfaces between symbolic representation and the representations of external reality. Those interfaces are provided by observation and/or experiments. But what is observation? The word corresponds to the Latin verb "observe" which means to attend to in practice (e.g., to observe a custom), and also to watch attentively. Here we are interested in the second meaning i. e., watching attentively, deliberately, and explicitly.

    To understand what we observe, we may perform some experiment on it. An n-order experiment is an experiment where the experimenter is permitted to change n conditions or parameters of the system under study. A set of observation is a zero-order experiment. While in the ordinary sensitivity analysis in optimization decision problems is 1-order experimentation.

    The external world is conceived as actual because it exists in relation to a certain time and space. Understanding the external world is achieved by a chain of explanation that is logical in form and therefore free from the time's dominion. Understanding gives you a clear conception of what you think. Moreover it makes you the cause of what you think. The ideas become yours. These in turn, secure the joy, independence and serenity that we call "freedom". The grand aim of all science, including management science, is to cover the greatest number of empirical facts by logical deduction from the smallest number of hypotheses.

    A model is a representation of reality from the modeler's perspective. Therefore, you must develop a multi-perspective model of the problem on hand to understand the problem. Friedrich Nietzsche's theory of knowledge comes to mind: "There is only a perspective seeing, only a perspective knowing, and the more affects we allow to think about one thing, the more complete will our concept of this thing, our objectivity, be." You must look at the problem from many angles and consider how the pieces fit together, to see the "whole" of the decision problem. Immanuel Kant and Arthur Schopenhauer, among others, called this model "the World as a representation" of our understanding through Time, Space, and Causality.

    The representation of external world is a perspective modeling, which is a complex process involving combinations and interactions among perception, motivation, memory, learning and development, emotions, consciousness, language, rationality, sociality, personality, and psychopathology.

    Scientific research is based on the idea that everything that takes place is determined by laws of nature, and it is a useful tool to explain the physical world. This this holds also for the action of people as Spinoza introduced the concept of Motivation to explain humans' actions, and stated that "there is no more dangerous error than that of mistaking the effect for the cause: I call it the real corruption of reason." Moreover, one must affirm differences by "ethics of letting be" and a "delight in differences" while committing to our own perspective.

    The process of observing the system is a learning activity. Therefore, this is a tripartite concept, namely, Thinker-and-Learning-and-System. There are a variety of orders in which the three concepts could be arranged. For example, "systems for learning" which is mainly our educational institutions.

    Modeling is the science of making an optimal judgment that requires a combination of many disciplines. Decision-making is a central human activity. Thus, we are all decision makers, and a "good" decision-making process encompasses many disciplines of study. Appreciation of decision making is wonderful: it makes what is excellent in this strategic thinking belongs to you as well. OR/MS/DS/SS modeling approach to decision-making is aimed at understanding the decision problem (or opportunity) and assisting the decision-maker in his/her decision-making process. Models explain the problems and provide solutions. As Ludwig Wittgenstein said, "the riddle does not exist. If a question can be put at all, then it can also be answered."

    Why is the OR/MS/DS/SS a science? What is science? Science is the subject of thought. Thought itself is a sequence of internal symbolic activities that leads to novel, productive ideas or conclusions about a decision problem. However, strategic thinking is performed on a version of the external world called a " mental model". Therefore, modeling is the process that occurs in the neural networks of your brain, i.e., chains of thought when starting the structured consecutive-focused-strategic thinking. Modeling includes perceiving, formulating our experience, processing, and re-presenting information from the external world. The result of these structured processes is called a model. Managerial problem solving requires mental modeling, which is a process of resolving stress (i.e., competition of forces) until our experience of the problem is formulated.

    By analyzing (i.e., a structured consecutive-focused-strategic thinking) we process this information to under-stand reality (i.e., to see it beneath ourselves). The result is a "model." By describing a model of reality you become conscious of reality. Therefore, a model is a re-presentation of reality. To achieve an accurate model one must use a mathematical modeling process cycle.

    Mathematics was invented by humans in an attempt to define life in their own terms. Mathematics has been used in all branches of physics. For example, on modeling our universe, Galileo Galilei said:

    " this grand book -- I mean the universe -- which stands continually open to our gaze, but it cannot be understood unless one first learns to comprehend the language and interpret the characters in which it is written. It is written in the language of mathematics"

    The essential fact is that all the pictures, which science now draws of nature, and which alone seem capable of according with observational facts, are mathematical models.

    It would be wonderful to be able to learn such an awe-inspiring language and to master the underlying principles of mathematical modeling, even if you have never thought of yourself as mathematically inclined. Mathematical modeling (i.e., mathematical strategic thinking ) is the process of contemplating on the decision problem. In mathematical modeling, mathematics is used as a language to describe, and as a tool to prescribe, and control the decision-making process. Therefore mathematical models process aims at describing, prescribing, and controlling our decision-making process in all areas of human activities. The cardinal aim of mathematical modeling process is to make our world measurable, calculable, predictable, and thus more manageable.

    Primarily the use of mathematics in decision making is that of language called mathematical modeling by which we discuss those parts of the decision problem which can be described by numbers or by similar relations of order. Mathematical models can describe complicated decision problems including the interactions among its components that are too complicated to be expressed everyday language. Therefore, mathematical modeling is the science of skillful operations with concepts and rules invented just for this purpose.

    The decision-making process is contemplating on the elements of the decision. By definition of esthetics, the longer you contemplate on anything the more beautiful that thing is. With respect to beauty of the mathematical modeling process, we distinguish it from other mental manifestations; this process is the result of the perfect apprehension of relations formed by a complexity of elements of the model.

    Our high school curriculum should put more emphasis on mathematical modeling rather than maths which in most cases are merely "puzzle solving" which has nothing to do with students lives. This will bring excitement in learning the math language and its applications.

    In concluding this section, the main question for us is "how people make sense of each other and the world they live in?" Making sense is the activity of fitting decisions into a coherent pattern of mental representations that include concepts, beliefs, goals, and actions. Much of human strategic thinking can be understood in terms of coherence as constraint satisfaction, and many decision problems can be given coherence-based solutions. The main difficulty is how coherently can one integrate the strategic thinking with emotions. Emotions are bodily conditions by which our energy is increased or decreased, aided or restrained, and at the same time the idea of those conditions. Emotions which are under your control are good, because after all passions and compassions are related.

    Further Readings:
    Baron J., Thinking and Deciding, Cambridge University Press, 1994.
    Churchman C., The Design of Inquiring Systems, Basic Books, New York, 1971. Early in the book he stated that knowledge can be considered as a collection of information, or as an activity, or as a potential. He also noted that knowledge resides in the user and not in the collection.
    Jackson M., Critical systems thinking and practice, European Journal of Operational Research, 128, 233-244, 2001.
    McCall M., and R. Kaplan, Whatever It Takes: The Realities of Managerial Decision Making, Prentice Hall, 2001.
    Mingers J., and and A. Gill, (Eds.), Multi-methodology: The Theory and Practice of Integrating Management Science Methodologies, Wiley & Sons, 1997.
    Newson M., and V. Cook, Chomsky's Universal Grammar: An Introduction, Blackwell Pub., 1996.
    Pidd M., Tools for Thinking : Modelling in Management Science, Wiley, 1997.
    Rowland G., A Tripartite Seed: The Future Creating Capacity of Designing, Learning, and Systems, Hampton Press, 1999.
    Wittgenstein L., Philosophical Investigations, Prentice Hall, 1999.

    From Mental Modeling to Analytical Modeling

    Our interpretation of objects, events, and processes relationships in creating a mental model is partly learnt and partly the result of deeper cognitive-psychological responses.

    Mental models shape the firms' actions because they affect what decision-makers see and pay attention to. In other words, mental models determine which information receives the attention of decision-makers and which is ignored. Decisions are the result of applying a decision rule or policy to information about the world, as we perceive it. The policies are themselves conditioned by institutional structures, organizational strategies, and cultural norms. Therefore, an appreciation of the hygiene of mental models is important for the decision-maker.

    All mental models have a few key characteristics that the thinker must be aware of:

    1. Mental models include what a person thinks is true, not necessarily what is actually true.
    2. Mental models are similar, but not the same, in structure to the thing or concept they represent.
    3. Mental models are simpler than the reality they represent. They include only enough information that the thinker needs in making decision.

    Reflecting on the Philosophy of Knowledge, there are two extreme schools of thought: Empirical and Theoretical. The Empirical (i.e., a Greek word for experience) approach to knowledge relies on experimentation, observations, and data analysis. Again, empirical knowledge is gathered from data from some area of experience and then conclusions are drawn from the data about the area of experience. Examples of empirical models are those you used to verify results in the physics labs by experimentation. Utilitarian schools of thought is based on the fact that: No man's knowledge can go beyond his experience. A fact in itself is nothing. It is valuable only for the idea attached to it, or for the proof which it furnishes.

    The theoretical approach, on the other hand, relies on mental models and pure thoughts without any reference to the external world. Examples of theoretical models are the chemistry structure of molecules.

    Theoretical-models are condensed, and abstract while applied-models are descriptive and concrete. The distinction between pure empirical and pure theoretical knowledge is expressed by Francis Bacon in the following analogies: "The men of experiment are like the ant, they only collect and use; the reasoners resemble spiders, who make cobwebs out of their own substance. But the bee takes the middle course; it gathers its material from the flowers of the garden and field, but transforms and digests it by a power of its own."

    Modeling is at the core of OR/MS/DS/SS activity. It is situated between theory and experiment and utilizes both. OR/MS/DS/SS models are aimed at assisting the decision-maker in his/her decision-making process.

    The OR/MS/DS/SS descriptive modeling process contains mostly "How" rather than "Why" questions. This is always the case in any scientific approach to problem solving, including OR/MS/DS/SS field of investigation with the views on underling relationships by the mean of causality (for physical entities) and motivation (for human actions). For example, Newton's theory of gravity says something very clear about how planets move, proposing a force that acting in a certain way, accounts very well for the observed phenomena. But why? To move from the how to the why you have to go from a theoretical entity (a force called gravity) to a real entity. If you ask 'why' the answer is 'because of gravity'.

    The Critical Realism (CR) school of thought distinguishes between the empirical, the actual and the real. The first corresponds to sense data concerning events, the second to what happens whether we sense it or not, and the third to what causes the events to occur. CR argues that bodies or entities (people or material things or even abstract entities such as societies) have causal/motivational powers that exist whether they are brought into action or not. An event is caused as a result of the powers of various bodies acting in contingent ways. These causal powers are the things that we have to try to understand and model though they are not necessarily visible. The main fact is that in any modeling process one has to make assumptions about the world (ontology) and how we might find out about it (epistemology).

    Pure scientific theories, in general, are judged on the basis of:

    Scientific theories, the Simplex Method for optimization, as an example, may not be attainable by most branches of natural, life and social sciences, or any empirical methods. Theory construction in a particular scientific domain is constrained by the demands and possibilities imposed by the experiential data and the method of observing them. In some abstract sense, OR/MS/DS/SS scientists working in the 'non-exact' fields seek a compromise between the empirical basis of scientific knowledge, on one hand, and the systematic coherence and structure of scientific understanding, on the other. Fallible critique and self-correction is the nature of science. However, this is not the case in art, morality, and religion for example.

    In scientific strategic-thinking process one must have an open-mind for new ideas, to be able to think differently, to see thing from many perspectives. The classroom universities and scientific journals are the environments for debates, and exchange of ideas. Open-mindedness is the main condition to achieve the ultimate aim of education: Being able to think for yourself.

    In each class I teach, a few students enroll in the course, unfortunately with preconceiving ideas, and beliefs. That is why sometimes it is hard for them to rethink and re-evaluate those ideas.

    Two Alternative Theories of Decision-Making: There are two basic theories of human decision-making. The first is called normative theory because it proposes to present guidelines and techniques for accomplishing predetermined goals. It tells you what decision you ought to make. The other is positive theory (or behavioral theory) and seeks only to describe and explain how decisions are made. The positive theory is based on: reproducibility, refutation, reductionism, and objectivity without any detachments.

    Sensible decisions are always based on facts. Normative modeling is not about knowing the reality of the world, but rather having a sense of idealism of how the world should, or could, be. For example, an economist with positive views relies on facts to describe any slowdown in the economy while another economist with normative views sees it as an unavoidable cycle in economy, merely based on some ideologies; i.e., idols to worship. One of the well known myths of Normative economics is that there is always a recession in the US economy every 5 years. Here is another example of normative strategic thinking : Does history repeat itself, OR do historians repeat each other?

    Further Readings:
    Albach H. , and B. Bloch, Management as a science: Emerging trends in economic and managerial theory, Journal of Management History, 6(3), 138-158, 2000.
    Archer M., et al. (Eds.), Critical Realism: Essential Readings, Routledge, 2000.
    Badaracco, Jr., J., Defining Moments: When Managers Must Choose Right and Right, Harvard Business School Press, 1997.
    Bailey M., Studies in Positive and Normative Economics, Edward Elgar Pub., 1992.
    Bell D., H. Raiffa, and A. Tversky, (Ed.), Decision Making: Descriptive, Normative, and Prescriptive Interactions, Cambridge University Press, 1988.
    Beroggi G., Decision Modeling in Policy Management: An Introduction to the Analytic Concepts, Boston, Kluwer Academic Publishers, 1999.
    Buskrirk R., Modern Management & Machiavelli, New American Library, 1974.
    Casti J., and A. Karlqvist, (eds.), Beyound Belief: Randomness, Prediction and Explanation in Science, CRC Press, 1991.
    Christensen, C., The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail, 1997, Harvard Business School Publishing.
    Connolly T., H. Arkes, and K. Hammond (eds), Judgment and Decision Making: An Interdisciplinary Reader, Cambridge University Press, 2000.
    Dawson R., The Confident Decision Maker: How to Make the Right Business and Personal Decisions Every Time, Morrow William & Co., 1995.
    Findler N., Contributions to a Computer-Based Theory of Strategies, Springer-Verlag, 1990.
    Gärdenfors P., and N-E. Sahlin, (Eds.), Decision, Probability and Utility, Cambridge Univ Pr., 1999. It covers the foundations of decision theory, the conceptualization of probability and utility, philosophical difficulties with the rules of rationality and with the assessment of probability, and causal decision theory.
    Joyce J., The Foundations of Causal Decision Theory, Cambridge Univ Pr., 1999. It offers a ‘representation theorem' that shows that both causal decision theory and its main rival, Richard Jeffrey's logic of decision, are both instances of a more general conditional decision theory.
    Goldratt E., and J. Cox, Goal : A Process of Ongoing Improvement, 1988, North River.
    Rivett P., The Craft of Decision Modelling, 1994, Wiley & Sons.
    Sterman J., Business Dynamics: Systems Thinking and Modeling for a Complex World, Irwin Pub., 2000.
    Sutherland S., Irrationality: Why We Don’t Think Straight!, Rutgers University Press, 1994.

    Classifications of Models:
    Mechanical, Mental/Verbal, Analytical, and Simulation Models

    The decision analyst must identify which type of model best suits the decision problem. This is why we will discuss a classification of modeling the systems before getting into the process of model building. Although OR/MS/DS/SS mostly concentrates on mathematical models the other model types are also prevalent in practice.

    What is a System: Systems are formed with parts put together in a particular manner in order to pursuit an objective. The relationship between the parts determines what the system does and how it functions as a whole. Therefore, the relationship in a system are often more important than the individual parts. In general, systems that are building blocks for other systems are called subsystems

    The Dynamics of a System: A system that does not change is a static system. Many of the systems we are part of are dynamic systems, which are they change over time. We refer to the way a system changes over time as the system's behavior. And when the system's development follows a typical pattern we say the system has a behavior pattern. Whether a system is static or dynamic depends on which time horizon you choose and which variables you concentrate on. The time horizon is the time period within which you study the system. The variables are changeable values on the system.

    Models can be classified according to their characteristics such as types, evolution in time, and availability of information, as shown in the following figure.

    A Classification of Models

    Iconic models are usually static in nature, such as a dollar bill. Analog models are physical, however they are designed to act like reality but usually do not look like reality. They are mostly mechanical models. However, business activities are dynamic processes. Business is a process that follows mathematical patterns. Therefore, it can be represented by symbolic (i.e. algebraic, numerical, logical) models. Symbolic models include a large class of models known as mathematical and computer simulation models.

    Mechanical Models: A model that takes on the physical appearance of the object is called a physical model. This type of model is used to display or test the design of items ranging from new buildings to new products. In the aircraft industry, scale models of new aircraft are built and tested in wind tunnels to record the aerodynamics of design. An automobile-parts manufacturer may have a three-dimensional scale model of the plant floor, complete with miniature machines and equipment, so that a new layout of the plant can be analyzed. The machines in the model can be rearranged and new layouts studied in order to improve the material flow.

    Mechanical models have the advantage of being usable for experimentation. In the aircraft example, the testing of a different design may mean that a completely new model must be built. In addition to offering the advantage of experimentation, mechanical models lucidly describe the problem or system under study; this is helpful in generating innovative design alternatives for solving the decision problem. Nevertheless, only a relatively small class of problems can be solved with mechanical models. Problems such as portfolio selection, media selection, and production scheduling are examples of problems that cannot be analyzed with a mechanical model. Basically, mechanical models are useful only in design problems and even some of these can be analyzed more efficiently and completely with mathematical models that can be computerized. Besides this, mechanical models do not contain explicit relationships between the decision alternatives and dependent variables or objectives and, trial-and-error methods of problem solving must be used. Although this in itself is not a major drawback, the trial-and-error process, coupled with a need to rebuild the model for each design change, can lead to a very time-consuming and costly process in some cases.

    Mental/Verbal Models:A verbal model is a translation of the mental model. Therefore, a mental/verbal model expresses all of the functional relationships between the variables in a word passage. For example, consider the advertising manager of a company that manufactures breakfast cereal who makes the following statement concerning television commercials on Saturday morning: "a 20-second spot has much more impact on our target audience than a 15-second spot." In this example, the different time durations of the commercial are the decision alternatives; its "impact" which, we could infer, relates to the propensity of the viewers' parents to purchase the company's cereal, is the dependent variable. Thus, we have a relationship between decision alternatives and a dependent variable that relates to company objectives. Such models are used extensively in the business world and have the advantage of being easy to understand. Often they are an outcropping of many years of managerial experience and are useful for summarizing this experience in understandable language.

    Mental/verbal models, however, have a number of shortcomings. The decision-maker cannot experiment with them, nor do they indicate specifically how the outcomes or measures (or indicators) of effectiveness change with the decision alternatives. In the preceding mental/verbal model, we do not know how much more impact a 20-second commercial has over a 15-second commercial. The other drawback is that it is not easy to show how the relationships change with the decision alternative. If we constructed a mental/verbal model that answered such questions for all possible commercial lengths, we would have a very lengthy mental/verbal model that would be difficult to understand and not useful for experimentation. Nevertheless, mental/verbal models can play an important role in the decision process. They can be used to verbalize decision strategies for more sophisticated models.

    Analytical Models: Analytical models are mathematical models aimed at simplification, abstraction of real systems in order to provide insight, and understanding some interested aspect of the reality. However, modeling the reality by abstraction should be connected with real problems, domains, and practice by means of verification and or validation. An abstraction may be looked at from one side as a compression of many instances into one generality or from the other side as a special purpose power tool that yields the solution for many problems. These types of models are applied mostly to the static and/or deterministic systems.

    Compared with mechanical models, mathematical models facilitate experimentation because all dependent variables, independent variables, constants, and parameters are explicitly related through the language of mathematics. The decision-maker can test the effects of different decision alternatives, constants, and parameter values on the dependant variables much more easily than with any other type of model. Furthermore, mathematical models can represent many complex problems efficiently and concisely and, in many cases, provide the cheapest way to analyze these problems. It is for these reasons that we address the various mathematical models and solution techniques most often used in practice.

    Solution procedures can be either single pass or iterative. A single-pass solution procedure is one in which the final values of all the decision variables are determined simultaneously according to some well-defined procedure. An iterative solution technique, on the other hand, is one in which a number of steps are required to arrive at a final solution and where partial or complete solutions are entertained at each step. Discrete or continuous variables are frequently necessary for a particular problem. Finally, an optimal solution is one that can be shown to be at least as good as any other given the assumptions of the model, whereas a satisfactory solution is one that is considered "good" with respect to the objectives and constraints, yet cannot be shown to be the best. Thus, if in our previous example of the normative-static-deterministic model the decision variables are continuous, the relationships linear, and an optimal solution is desired, the list of potential solution techniques for the model can be reduced to just one-linear programming. In this way, one or more viable alternatives for the solution methodology can be identified, and the formulation of the model can begin.

    Simulation Models: The degree of abstraction in mathematical models is a definite impediment to the managerial acceptance of such models. It is not surprising to encounter resistance from managers who have not had sufficient training in and exposure to these models, and also from managers who have training but do not have the time to pay close attention to the model. Mathematical models use symbolic language of mathematics which has its own limitations. Analog models are also physical however they are designed to act like reality but usually do not look like reality. The models may be too complex (such as, an international airport) to solve efficiently, requiring gross oversimplifications of the real problem in order to get a good strategic solution. Under these circumstances, the problem that is "solved" no longer resembles the original problem, and, if the solution is implemented, disastrous organizational effects could result. Proper selection of model type and solution technique should minimize this type of mistake. The simulation models are the computerized duplications of real systems and are by far more realistic, especially for the modeling of dynamic and/or probabilistic systems such as an international airport. Dynamic modeling in organizations is the collective ability to understand the implications of change over time. This skill lies at the heart of successful strategic decision process. The availability of effective visual modeling and simulation enables the analyst and the decision-maker to boost their dynamic decision by rehearsing strategy to avoid hidden pitfalls.

    All decision-making models can be classified as either Deterministic Models or Probabilistic Models. This depends largely on how influential the uncontrollable factors are in determining the outcome of a decision. Unlike deterministic models, the probabilistic models are viewed with the awareness of theory of games and expected outcome. The center of interest moves from the deterministic to stochastic using statistical estimations and predictions. Unlike deterministic models where a good decision is judged by its outcome alone, probabilistic models the decision maker is concerned with both the outcome value and the amount of risk each decision carries. We will cover both deterministic and probabilistic decision-making processes.

    Further readings:
    Chomsky N., On Language, New Press, 1998.
    Harrington J. , and K. Tumay, Simulation Modeling Methods: To Reduce Risks and Increase Performance, McGraw-Hill, 2000. CD-ROM included.
    Pidd M., Tools for Thinking : Modelling in Management Science, Wiley, 1997.

    Decision-Maker's Environment

    Since a model of a system is a re-presentation of the system that contains those elements that affect the objective of our decision, it is important to identify the most important elements and categorize them. The problem understanding requires criteria for grouping together entities of the decision model in a same category.

    The desired output usually determines the controllable inputs, which includes your personal abilities and physical resources. The input into a system can be classified either as controllable or uncontrollable, as the figure below illustrates. Time-horizons for the modeling review must be selected that are short enough so that the uncontrollable inputs (or probabilistic knowledge of them) will not change significantly. Even for short time-horizons one might consider the time discounting factor for future periods. The output is often called performance measure (or indicators) for the system.

    It is a fact that in any organization where performance is measured by some indicators, then performance improves. Moreover, when performance is measured and reported, the rate of improvement accelerates. In 1980, US Steel employed 120,000 people in steel making. Now (in 2001) it turns out the same tonnage with 20,000 workers.

    The following Figure depicts the OR/MS/DS/SS structured decision-making process approach. Remember that, when structures and systems are aligned, they facilitate empowerment. When they are not, they work against it.


    Structured Decision-Making Process and Its Components:
    Analysis, Design, and Control

    OR/MS/DS/SS models are aimed at assisting the decision-maker in his/her decision-making process. In the decision-making modeling process we investigate the effect of presenting different decisions retrospectively; that is, "as if" the decision has already been made under a different course of action. That is why the sequence of steps in the modeling process must be considered in reverse order. For example, the output (which is the result of our action) must be considered first.

    It is helpful to understand the nature of the problem by asking "who?" "what?" "why? " "where? " "when?". Finally break them into three groups: Uncontrollable, Controllable, and Parameters (that defines the problem). As indicated in the above activity chart, the decision-making process has the following components:

    1. Performance measure (or indicators): Measuring business performance is at the top of the management decision making. The development of effective performance measurement (or indicator) is seen as increasingly important by many organizations. However, the challenges of achieving this in the public sector and for non-profit organizations are, arguably, considerable. Provides the desirable level of outcome (objective of your decision). Objective is important in identifying the problem. The major task for the decision-maker is the solution to the problem of "values" among different objectives, and the selection of a single objective that has the "highest value." Then if needed, all other objectives should be included in the set of constraints to be satisfied. The following table provides a few examples of performance measure (or indicator) for different levels of organizational:

      Performance Measure
      Strategic  Financial, Growth, and Innovations
      Tactical  Cost, Quantity, and Customer's satisfaction
      Operational  Target setting, and Conformance with standard

      For the chief executive officer, development of the organizational culture is the top responsibility, and creating balanced decision processes is a major aspect of culture building. Clearly, if you are seeking to improve a system's performance, an operational view is what you are after. Such a view gets at how a system really works - not simply what correlation its past output behaviors have generated. It is essential to understand how a system currently is working if you want to change how it will work in the future. Financial measures and indicators are often criticized for their narrow focus, because they often encourage short term decisions. Performance management is a cyclical process. It start with effective and efficient planning and end in compensation of individuals for their performance, as shown in the following process:

      Cyclical process of performance

    2. Uncontrollable inputs: These come from the decision maker's environment. Uncontrollable inputs often create the problem and constrain the actions.
    3. Parameters: Parameters are the constant elements that do not change during the time horizon of the decision review. These are the factors partially defining the problem. Strategic decisions usually have longer time horizons than both the Tactical and the Operational decisions.
    4. Controllable Inputs: The collection of all possible courses of action you might take.
    5. Interactions Among These Components: These are logical, mathematical functions representing the cause-and-effect relationships among inputs, parameters, and outcome.
    6. There are also a set of constraints which apply to each of these. Therefore, they do not need to be treated separately.

    7. Actions: Action is the ultimate decision and is the best course of strategy to achieve the desirable goal.
    8. Decision-making involves the selection of a course of action (means) in the pursuit of one's objective (ends). The way that our action impact the outcome of a decision depends on how the inputs and parameter of the problem are interrelated and how they relate to the outcome.

      Functionality is the most important type of relationship involved in the decision-making process interactions. When the outcome of a decision depends on our course of action, then we must consider all actions, one by one. This is to bring about a desirable change in some other aspect of our action. We succeed if we have knowledge about the interaction among the components of the problem.

      "Oh, my mind, please grant me the serenity to know what input is controllable, and what is not. To choose the best of the first kind, and the ability to perform probability and statistical analysis to predict and then react to any change in the second kind, and the wisdom to know the difference!" Wisdom is the accurate application of accurate knowledge.

    9. Controlling the Problem: Few problems in life, once solved, stay that way. Changing conditions tends to un-solve problems that were previously solved, and their solutions create new problems. One must identify and anticipate these new problems.

    The Difficulties of Objective Function Determination: It is not always an easy task to define a clear and measurable objective function in many organizations. This is a dilemma for almost all non-profit organizations. The term not-for-profit often brings to mind the idea of an altruistic organization serving society. The reality is that the not-for-profit is a far more complex organization that is responsible to diverse groups of stakeholders. Unlike for-profit organizations, not-for-profit organizations have to focus on dual, and often conflicting, goals relating to fulfilling their overall mission while also generating enough revenue to maintain their operating structures. Therefore, one must analyze the effects of both the economic and mission related orientations operationalized through the service encountered.

    Remember: if you cannot control it, measure it in order to predict it.

    For example in a marketing system, the inputs are shown in the following table:

    - Classification -
    Type of Input
    Controllable Input
    Uncontrollable input
    Market Area
    Number of Competitors
    Number of Salesman
    Season of the year
    Total Demand

    A Marketing System

    Strategic Thinking: Successful strategies are usually developed as a result of strategic thinking, not as a result of formal planning processes. Why? Strategy is not about plans but about insights into how to create value; the process of developing insights should not be confused with planning, which is about turning insights into action. Often, companies fail to distinguish between the purpose of a business (why the business exists) and constraints (what the business must do in order to survive). For example, satisfying stakeholders is constraint, it is not the purpose or reason the business exists. The following are a sample of useful outcomes from an effective strategic-thinking process:

    Further Readings:
    Ashton R., and A. Ashton, Judgment and Decision-Making Research in Accounting and Auditing, Cambridge Univ Pr., 1995. Important factors such as the pricing of products and services, evaluating corporate performance, granting credit to prospective borrowers, and investing in financial securities are considered.
    Bond T., and Ch. Fox, Applying the Rasch Model: Fundamental Measurement in the Human Sciences, Lawrence Erlbaum Associates, Inc., 2001.
    Jackson M., Systems Methodology for the Management Sciences, Kluwer Academic Publishers, 1992.
    Joyce J., The Foundations of Causal Decision Theory, Cambridge Univ Pr., 1999. It offers a ‘representation theorem' that shows that both causal decision theory and its main rival, Richard Jeffrey's logic of decision, are both instances of a more general conditional decision theory.
    Gable C., Strategic Action Planning Now: A Guide for Setting and Meeting Your Goals, CRC Press, 1999.
    Keys P., Operational Research and Systems: The Systemic Nature of the Operational Research, Kluwer Academic Publishers, 1991.
    Monahan G., Management Decision Making, Cambridge Univ Pr., 2000. A spreadsheet-based introduction to the tools and techniques of modern managerial decision making.
    Neely A., Measuring Business Performance, Economist Books, 2002.
    Sloma R., How to Measure Managerial Performance, Beard Group, 1999.
    Weinberg G., An Introduction to General Systems Thinking , Dorset House, 2001.

    Modeling Is At the Heart of the Decision-Making Process

    The structured modeling process is at the heart of OR/MS/DS/SS activities. The main question then becomes, "How close is the model to the real world?" Know that a model is not reality, but it does contain some parts of reality. The question is: "Does it contain the important parts relevant to the decision problem?"

    Modeling and our reasoning reflect our desire to grasp reality. Modeling is a structured consecutive-focused-strategic thinking for understanding reality for utilitarian purposes. The connection between partitioning a circle into 360 degrees and a year into a number of days is an interesting example. This desire for a mathematical model of the universe and its processing difficulties is apparent. Some analogous ones existed in music, architecture, etc. These mathematical models to represent reality required fitting between small integer numbers (for ease of representation), and complex phenomena whose numerical parameters did not exactly fit in the integer-based scheme. It is credible that the 360-system and the 6-8-9-12 scheme in music were the results of this conflict; these examples are mathematically suitable models and semantically justified. As Bill Gates said, "If you're any good at math at all, you understand business. It's not its own deep, deep subject."

    What is Mathematics? Mathematics is the science of patterns and orders, as well as the language of science. Mathematics is the only language shared by all human beings regardless of culture, religion, or gender. Moreover, no human investigation can be called real science if it cannot be demonstrated mathematically.

    With mathematics as a language we can explain the mysteries of the universe or the secrets of DNA. We can understand the forces of planetary motion, or discover cures for catastrophic diseases. Mathematics is not just for calculus majors. It's for all of us. And it's not just about pondering imaginary numbers or calculating difficult equations. It's about making good strategic decisions.

    Mathematics is part of human culture because it does not exist outside of the human mind. Symbolic reasoning and calculations with symbols are central to analytical (i.e. mathematical) modeling. Therefore, like any foreign language, you must develop an understanding of mathematics, which is the language of all sciences, including the OR/MS/DS/SS modeling process aimed at assisting the decision-maker. Here is an example of the usefulness of mathematical symbols: Suppose you wish to buy a shirt for $50 (tax included), the tax is 5%, what is to original price of your shirt? Let x and y be the amount of the tax and the original price respectively, therefore, the mathematical model is 50 = x + y, and x = 0.05y. This gives, 50 = 0.05y + y = 1.05y. Therefore, the original price is 50/1.05 = $47.62. How much is the tax? How do you generalize this result? You may ask, what is this x, in mathematics? Well, whatever we do not know we call it x (or any other letter from the end of alphabet series). X also has a political significance as in Malcom X.

    A mental model is a representation of your thoughts about reality. Therefore, it is an objectification of reality, which in turn means the subjective begetting of the reality. Mathematical models employ symbols and notations, including numbers. Thus, there are three distinct concepts: the reality, the mental model, and the representation. In its many different forms, analytical modeling is a procedure that recognizes and verbalizes a problem and then quantifies it by turning the words into mathematical expressions. Modeling is a structured consecutive-focused-strategic thinking for understanding the decision problems, and actions.

    In all high schools around the world mathematics is used to translate Word or Story Problems into symbolic representations (i.e., mathematical models). After solving, the results are translated back into the original language in which the problem was stated.

    OR/MS/DS/SS is a systematic approach to problem solving in that it considers the context of the problem as important as the problem itself. It utilizes a team approach by capitalizing on the talent of an OR/MS/DS/SS analyst to assess, coordinate, and incorporate knowledge relevant to solving a certain decision problem from experts in other fields, (known also as think-tank approach). The difficulties in clear communication among the team members in any OR/MS/DS/SS project can increase with the size of the team. Span of management refers to the number of employees supervised by a single person. The term itself has nothing to do with a desired size of the span. In other words, whether the one supervises two employees or one hundred, span of management is the term applied to the number. In the three-person group (i.e., one supervisor and two employees), the six possible relationships or interactions may exist.

    By applying a scientific approach, managers are also able to make accurate predictions for what is not under their control. OR/MS/DS/SS modeling process is a scientific approach in that it uses measurable and numerical scales to translate observed phenomena. If 'God geometrizes' as Plato says, man certainly arithmetizes. The world is qualitative. However, humans can understand, compare, and manipulate numbers only. Qualitative information may be characterized and processed by assigning numbers. Therefore, we use some measurable, numerical scales to quantify the world. This enables us to understand the world by finding any relationship, and using manipulation, comparison, calculation, etc. Then we use the same scale to qualify it back to the world. This is the essence of the "human understanding structured process." As Tom Peters, an international authority on business management says, "If you can't measure it, you can't manage it".

    There has been recognition that some knowledge cannot be quantified and cannot be captured, codified or stored. However, the predominant approach to the decision making based on this knowledge is to try to convert it to a form that can be handled using the quantitative analysis.

    Quantitative analysis tends to drive out qualitative analysis, even in the Liberal Arts areas of study, such as organization science, sociometrics, and psychometrics. The "fuzzy set theory" has even been developed to quantify qualitative terms that we use to express our feelings. However, it is questionable whether the internal world of one's own experience can also be subjected to analytical modeling, just like the external world. The following is a paraphrase of what Adam Smith said about the main difficulty in representation of feelings: "It is not an easy task to construct analytical models for feelings, because our senses will never inform us of what, e.g., somebody is in suffering as long as we ourselves are at our eases." However, in the medical professions it is common to be questioned, "on a scale of one to ten, one being the worst, how do you feel?" This elicits subjective answers from the patients.

    Mathematical modeling can claim to be the most original creation of mankind. The originality of modeling lies in the fact that in model building connections between things are exhibited which, apart from the agency of human reason, are extremely unobvious. Thus the ideas, now in the minds of modelers, lie very remote from any notions that can be immediately derived by perception through the senses; unless it is perception stimulated and guided by an antecedent modeling process.

    Advantages of using the modeling approach to problem solving: A question for you: "When a management scientist goes to work, does he/she wait for problems to be assigned or does he/she go find problems?" Do not create problems for yourself and others. Wait for the problem to be assigned to you. The problem owner(s) and the management scientist consultant are two different parties.

    Unfortunately, when you graduate from your MBA program and are equipped with many problem solving techniques, you may have a temptation to use these techniques by looking for any kind of problems. (Do you recall when you were young and first held a hammer? Didn't everything start to look like a nail?) This may create unnecessary difficulties for the organization you are supposed to help. Remember that Problems come first and then the solutions, not the other way around!

    A management scientist provides aid and/or facts to the decision maker in order to make a better decision. The management scientist should not attempt to make these decisions or to influence the decisions. As such, the management scientist and the decision maker should not be the same person. The management scientist, therefore, is to serve as an objective voice to interpret a managerial decision problem that cannot be solved internally because of proximity or bias.

    A mathematical (i.e., analytical) model is the one whose relationships are expressed in the rigorous language of mathematics. In this way, a mathematical model is abstract because one cannot visualize the system it is supposed to portray by merely looking at it.

    Defining the system boundaries: Often, in modeling process the analysts do not model "systems" -- rather, they model specific problems that the decision makers (i.e., the managers) wish to understand. It is important and necessary to clearly define the boundaries of the system's decision problem under investigation. In this context a system is the restricted portion of the universe under consideration and its boundaries are the limits that separate the system from the remainder of the universe. Often it may turn out that the initial choice of boundaries is too restrictive. Therefore, to fully analyze a given system, it may be necessary to expand the system boundaries to include other subsystems that strongly affect the decision strategy. Suppose you are to study and make a descriptive model of an international airport, what are the boundaries for such a large system?

    Components of Analytical Modeling Process
    Classification of Knowledge:   Knowledge about Objects, Events, Processes, Relations
    Types of Comprehension:   Understand, Interpret, Relate, Select, Recall, Compare
    Types of Analysis:   Relate, Compare, Interpolate, Extrapolate, Generalize, Specify
    Results of Model Evaluations:   Accept, Reject, Possible, Irrelevant

    Know that analytical modeling is more than a collection of concepts and skills to be mastered; it includes methods of investigation and reasoning, and the means of communications (i.e., making common what is individually experienced). Depending on the audience of the report, the mathematical model may or may not be included. It is the task of the management science team to write a report that is understandable by all that will read it.

    Finally, collaboration between the decision maker an the analyst is needed for success in achieving effective and efficient model-based decision system, as depicted in the following flow chart.

    In above process the levels of influence and involvement at various levels are stressed. The empowerment modeling requires substitutive changes and adaptations the way both analyst and decision-maker function in the decision process.

    The following flow chart depicts the involvement at various levels of influence between the decision-maker and the OR/MS/DS/SS analyst. Empowerment modeling requires substitutive changes and adaptations the way both analyst and decision-maker function in the decision process.

    Further Readings:
    Buckley J., and T. Feuring, Fuzzy and Neural: Interactions and Applications, Springer Verlag, Heidelberg, 1999.
    Cast J., and L. Casti, Alternate Realities: Mathematical Models of Nature and Man, Wiley, 1989.
    Williams H., Model Building in Mathematical Programming, Wiley, 1999.
    Giere R., Understanding Scientific Reasoning, Holt Rinehart & Winston, 1998.
    Marcja A., and C. Toffalori, A Guide to Classical and Modern Model Theory, Kluwer Academic Publishers, 2003. A beginner's book on mathematical modeling.
    Nozick R., Invariances: The Structure of the Objective World, Belknap Pr., 2001.
    Newman J. (Editor), The World of Mathematics, Simon & Schuster, 2000.
    Rowley Ch., and F. Schneider, (eds.), Encyclopedia of Public Choice, Kluwer Academic Publishers, 2003.
    Sawyer D., Getting It Right: Avoiding the High Cost of Wrong Decisions, Lucie Press, Boca Raton, FL, 1998.
    Yager R., Essentials of Fuzzy Modeling and Control, Wiley, 1994.

    Analytical Modeling Process for Decision-Making

    A decision is a reasoned choice among alternatives. Making a decision is part of the broader subject of problem solving. Although the management science approach can be used to construct a mathematical model, it is useless if the result is too complex to be communicated to the decision maker. Regarding the importance of communication in the OR/MS/DS/SS modeling process, I have found that people tend to overcomplicate an issue. The worst offense seems to be in written reports. There is a general "fear" of appearing unsophisticated or even unintelligent if one writes in a straightforward, simplistic manner. The end result is a product that is incomprehensible to the decision maker. To avoid such an outcome, the analysis should be done in stages. You must overcome the communication barriers. Depending on the audience of the report, the mathematical model may or may not be included. It is the task of the management science team to write a report that is understandable by all that will read it.

    Decisions deserve appropriate time. As a decision scientist, you want the opportunity to see a decision unfold, revealing opportunities for study and assessment. The general procedure that can be used in the process cycle of decision-making contains the following similar steps: (1) describe the problem, (2) prescribe a solution, and (3) control the problem by assessing/updating the strategic solution continuously in the face of changing business conditions. Clearly, there are always feedback loops among these general steps.

    The general steps in this process are analogous to the structured process of treating an illness. When a patient has a health problem, the patient goes to see the doctor to solve the problem. In order to do so, the doctor, with the participation of the patient, describes the problem by taking a blood test or x-ray, to diagnose the illness. Then the doctor prescribes medications (prescribing medicine). There are also follow-up visits to make sure the prescription action is effective in curing the patient; otherwise the doctor changes the medications. That is possibly why what the doctors do they call it "practice". Remember that, here there are two distinct parties because if patients wanted to talk diagnosis, they talk drugs. If they wanted to talk symptoms, they talk drugs. They talk about solutions before understanding the problem. In this analogy, the doctor is the management scientist while the patient is the decision maker (the owner of the problems).

    Descriptive modeling process is using OR/MS/DS/SS techniques to describe how people see their worlds. A good descriptive model comes from good observation and representation that is validated and verified against evidence. This increases confidence in the descriptive model, and then could be used for prescriptive purposes.

    Description of the Problem: As soon as you detect a problem, think about and understand it in order to adequately describe the problem in writing. Develop a mathematical model or framework to re-present reality in order to devise possible solutions to the problem. The model must be validated before you offer a solution. Clearly, one needs to be skilled at having many different perspectives to get closer to reality. When different models are combined using different perspectives, we get a better understanding of reality. That's why OR/MS/DS/SS modeling process utilizes a team approach by capitalizing on the talent of individuals to assess, coordinate, and incorporate knowledge relevant to solving a certain decision problem from experts of other fields, (known also as think-tank approach). Describing all components of a problem is also called inverse-engineering in the field of cognitive science.

    You must also use the Occam's Razor in model-building process when describing your decision problem. A good model is both inclusive (i.e., it includes what belongs to the problem) and exclusive (i.e., shaved-off what does not belong to the problem). To use an old cliché, it should not be hard to see the forest through all the trees. What good strategic thinking is all about: The ability to look at complex situations and strip away things that do not count. William of Ockham is the first person in introducing descriptive modeling as a formal and rigorous process.

    Be concrete rather than abstract. Observe, and identify the factors influencing your decision, and find out what is and what is not under your control, and recognize the political realities. Unless the problem is clearly formulated by the management scientist and accepted by the owner of the problem as the "same," it is likely that the owner of the problem will reject the recommended strategic solution. Make sure want the decision maker is looking for, identify the constraints, and seek continuous feedback. In some cases, a strategic solution to the existing problem may even create new problems. The OR/MS/DS/SS mathematical modeling process will not solve a decision problem, nor is it intended to. Its main purpose is to produce insight and promote creativity to help decision makers make a "good" decision.

    The most important part of decision-making is to understand the problem. An excellent example is, "name a former president of the United States who is not buried in the USA." This is a wonderful example of the need to understand the question before attempting to answer. Remember that the formulation of a problem is often more essential than its solution. In fact, if you understand the problem, it usually tells you how to solve the problem. Here is another example for problem understanding: give the number of automobiles produced in America during the year of your choice.

    Prescription of a Solution: This is an identification of a strategic solution and its implementation stage. Search for a strategic solution using OR/MS/DS/SS modeling process solution techniques. Any given managerial decision problem has several solutions. A satisfactory strategic solution, also called a "good decision", is desired. There is no such thing as the solution for real-life problems. Choose an appropriate solution. One size does not fit all. Solutions depend on budget, time, and many other constraints and conditions. Think of the design process as involving first the generation of alternatives and then the testing of these alternatives against a whole array of requirements and constraints. Here is a question for you: does a good decision always result in a good outcome? Why not? Give an example.

    Managerial Interpretations and Communication: The decision problem is often stated by the decision maker in non-technical terms. When you think over the problem and find out what module of the software to use, you will use the software to get the solution. The strategic solution should also be presented to the decision maker in the same style of language which is understandable by the decision maker. Therefore, do not just give her/him the computer printout. You must also provide managerial interpretations of the strategic solution in some non-technical terms while preparing a business report or presentation.

    We already presented the elements of thinking process within an individual analyst and the role of communication among the analysts and an individual decision-maker taking the ultimately accountability. The aim is to reduce the pitfalls and resistance by decision-makers (e.g., managers) for successful implementation of analysts' recommendations. This contributes to fill the wide exiting gaps between these two parties. This can be achieved by analyzing its components namely: the Individual Choice, Communication Tools and Barriers, and the Group Feedback Information systems, and Their Interactions.

    Post-prescription: Change is the norm in most organizations. Business cycles and management philosophies change, demographic factors shift, sales and profits increase or decrease, employees come and go, technology is introduced, and technology becomes obsolete. some changes occur quickly, whereas others are almost imperceptible. The speed and duration of change may vary considerably, but nevertheless change in continuous. Therefore you must allow for revising the model as necessary. This means constantly updating the prescribed solution. This stage of problem solving is practiced in the free-based economy societies in contrast to the programmed-based economy societies where the model (i.e. the program) is taken more seriously than reality itself!

    The model is in the service to reality, not the other way around. George Bernard Shaw said "The only man who behaved sensibly was my tailor; he took my measurements anew every time he saw me, while all the rest went on with their old measurements and expected them to fit me."

    Monitoring Activities: These activities include updating the strategic solution in order to control the problem. A dictionary tells us that "to manage" means "to control." On the other hand, "everything changes" except the fact that "everything changes." Everything flows; nothing remains unchanged. In this ever-changing world of ours, it is crucial to periodically update solutions to any given problem. Good decision-making process is a creative idea; it can only be effective in changing forms of creative ideas. Monitor the progress of the implementation. A model that heretofore was valid may lose validity due to changing conditions, thus becoming an inaccurate representation of reality and adversely affecting the ability of the decision-maker to make good decisions. The model you create should be able to cope with changes. Unlike mathematical puzzles (e.g., solving equation 2X - 6 = 0 where there is one and only one correct solution), real life problems do not have a single, correct solution. They cannot be "solved once and forever." One must learn to live with dynamic nature, that is, to update the solutions. Therefore, in this sense, the OR/MS/DS/SS modeling process to problem solving is not an exact science such as Mathematics, but one where decisions must ultimately be made by the decision maker.

    The Importance of Feedback and Control: It is necessary to emphasize more on the importance of strategic thinking about the feedback and control aspects of a decision problem. It would be a mistake in discussing the context of the OR/MS/DS/SS decision process to ignore the fact that one can never expect to find a never-changing, immutable solution to a business decision problem. The very nature of the environment in which business decision-making takes place is change, and therefore feedback and control are an important part of the context of the OR/MS/DS/SS modeling process.

    The above focused and structured process is depicted as the Systems Analysis, Design, and Control stages, respectively, as shown in the following flow chart, including the validation and verification activities:

    Validate the Model: Validation is the process of comparing the model's output with the behavior of the phenomenon; i.e., comparing model execution to reality. Validation is concerned with the question, "are we building the right model?" Validation can only be demonstrated relative to some intended use for the model. This is clearly true, as no model can ever capture perfectly all of the details of a real system (nor would we want one to). In fact, we typically wouldn't even want to capture all of the parts of reality in a single model (not parsimonious). One can only decide how much and what kind of deviation between model and reality is acceptable relative to some framework for which the model is intended to be used.

    During the validation the management scientist asks the question, "What does this model have to do with the real world?" Finally, as it is easier to make plans than to carry them out, models that are not to be implemented are not drawn up correctly and not taken seriously from the start. Here is a question for you: "Why does a dead fish weigh more than when it was alive?"

    An excellent example is the presentation of the above question, "Why does a dead fish weigh more than when it was alive?" to members of the Royal Society. This provoked extensive and often ingenious attempted explanations. Unfortunately, the critical fact that the statement was false was never considered. In our headlong rush to figure out the solution, we forget to think about the problem itself. We must carefully think about information and its validity as it is presented to us.

    Verify the Model: Verification is the process of comparing the computer code with the model to ensure that the code is a correct implementation of the model. During verification, one checks the computer implementation of the model.

    An effective way to learn about a good strategic decision-making process is to have a computer-supported capability that assures the user that the Systems Analysis, Design, and Control processes to make good strategic decisions, it does not matter whether the user is a novice, or an expert about his/her organizational environment.

    Documentation: There are many important reasons for documenting of decision-making process stages with complete and accurate information. The OR/MS/DS/SS analyst will delight in explaining verbally in great detail how the project works. If the person has left the company or consultant long since gone did it, then the documentation is useless, unless of course they documented what it meant.

    OR/MS/DS/SS and Strategic Planning: Strategic development is fundamentally concerned with organizational development over the longer term, moving an organization in the direction of a desirable goal or objective. Thus deciding where you want to go, examining what may lie ahead, choosing between options, setting targets, planning how to move in the direction you want to and checking progress along the way are all important components of any process designed to support strategic development.

    Strategic decisions, such as launching a new product or choosing between sites for a new building program are characterized by their difficulty to reverse since they often have enduring consequences. Helping managers to understand the nature of the issues they face and to have confidence that decisions made have been supported by appropriate analysis of knowledge, information and data are some of the contributions that OR/MS/DS/SS Techniques can make to strategic development.

    The that OR/MS/DS/SS Techniques & Strategy stream intends to promote discussion of the contribution that OR makes to strategic issues in organizations and to increase awareness of the potential for that OR/MS/DS/SS Techniques to support strategic development and strategic level analysis. For example, the operations must be carried out by a unit within the organization with a view to determining the long run viability of the unit and scenario planning helps decision makers in devising strategies and thinking about future possible scenarios.

    For more information, visit the following collection:
    Decision Making Resources.

    Further Readings:
    David A., Models implementation: A state of the art, European Journal of Operational Research, 134, 459-480, 2001.
    Harrington J., et al., Business Process Improvement Workbook: Documentation, Analysis, Design, and Management of Business Process Improvement, McGraw-Hill, 1997.
    Hunt D., Process Mapping: How to Reengineer Your Business Processes, Wiley & Sons, 1996.

    Modeling Validation Process

    The model-validation step is given the least attention by novice model builders. In this step the assumptions and the logic in the relationships are tested to see if they conform to reality. Model validation is a two-step process. The first step is two determine whether the model is internally correct in a logical sense. Even though tests for this would depend on the type of model being validated, several suggestions can be made.

    1. Compute some outcomes with the model that can be verified with hand calculations when computers must be used to solve the model.
    2. Run separate segments of complicated models alone so that the results can be verified.
    3. Eliminate random elements from stochastic models to ease verification of essential logic.
    4. Replace complex probability functions with elementary ones so that results are more easily verified.
    5. Construct simple test situations that test as many combinations of circumstances in the model as feasible.

    The second step in the model validation phase is to compare the model outputs against actual data from the real situation. If the model outputs are in the form of a time series by the statistical data analysis techniques, such as the t-test.

    When the output is in the form of mean values, variances, proportions, or probability distributions, various statistical tests can be used to test the hypothesis that these output elements differ significantly from the actual mean values, variances, proportions, or probability distributions. However, when the model has been constructed for a problem in which there are no past data with which to compare, the model builder must rely on a thorough, logical check and a careful study of the model results for any discrepancies or unusual circumstances. This approach to model validation is used, for example, when a normative model is being validated or when a model has been built to propose a solution to a problem never before faced by the decision maker. In addition to checking the logic and studying the results, there are several questions that could help in assessing the validity of a model. These questions include: How many previously known theorems or results does the model bring to bear on the problem? How obvious is the interpretation of the model? How sensitive is the model to changes in the assumptions that characterize it?

    If the model contains some previously known theorems or results, or if the model has much intuitive appeal, the model builder can be more confident in the model. However, a model whose outcomes are quite sensitive to changes in the assumptions should be studied further with regard to the nature of the assumptions made in the problem-definition step.

    There could be a number of reasons why a model would fail the validation step. Sometimes the model is intractable or too complex to work with and thus cannot be adequately verified. Morris provides some suggestions for simplifying complex models.

    1. Make some variables into constants
    2. Eliminate some variables entirely.
    3. Use linear relationships in lieu of nonlinear relationships.
    4. Add stronger assumptions and restrictions.
    5. Suppress randomness.

    Of course once a valid model is obtained, the model is put to work as an aid to decision-making process. Although this task may sound easy, it should not be taken for granted. A model builder may arrive at a model that can be shown to save thousands of dollars per year, yet it is worth nothing if the person who is to use it does not accept it. This can happen because the decision-maker does not understand the model or the techniques used to solve it. The model builder does not understand the manager and the coalition with which the manager associates. A coalition is made up of the person, books, journals, and other communication devices in the manager's environment. They are the sources the manager consults; the basis of the language he or she uses; and the sources of criteria for what is and is not important. This emphasizes that if the model builder and the decision-maker are not the same person; the model builder is well advised to include the decision-maker in every step of the model-building process. In this way, the chances of having a successful model implemented are greatly increased.

    Model construction required valid data: Definitions of any attributes used in the model including calculation or summarization rules must be clearly explained. This ensures that the intended use is evident. The data must be complete and accurate, approved by the decision maker, and fully reviewed using statistical data analysis by the OR/MS/DS/SS analyst.

    Further Readings:
    Bodily S., Modern Decision Making: A Guide to Modeling with Decision Support Systems, McGraw-Hill, 1985.
    Davis R., Business Process Modelling with ARIS: A Practical Guide, Springer, London, 2001.
    Eilon S., The Art of Reckoning: Analysis of Performance Criteria, Academic Press, 1984.
    Smith C. Sr., Computer-supported Decision Making: Meeting the Decision Demands of Modern Organization, Alex Publishing Co., Connecticut, 1998.
    Ólafsson S., Teaching mathematical modelling to business students, Annals of Operations Research, 82, 1998, 49-58
    Rivett P., The Craft of Decision Modelling, Wiley & Sons, 1994.

    Cost Considerations

    Model construction can be very expensive. It may not be prudent to spend $500,000 to develop a model that may return $50,000. The reason these models are expensive to build is that they can become very complicated as inputs and constraints are added. It is time consuming to identify and correctly correlate these items to a mathematical model, and this model must be a correct interpretation of a complex system. Unfortunately, this complexity fosters a wealth of things that can go wrong or be misinterpreted, and that results in the entire model not responding as an accurate mathematical representation of reality. An even worse case scenario is that the model may respond with total falseness. In this case, the results can be disastrous to the decision-making process and to the firm itself. Accuracy is paramount in the problem definition as well as the mathematical expression.

    As an example, the F-22 is being recognized as one of the most successful, best managed new generation weapons systems. It is a model for the weapons development program. However, as demand continues to fall in other defense programs served by the F-22 team, a portion of the fixed and overhead costs formerly supported by those programs automatically shift over to the F-22. The danger is that this model program could ultimately become unaffordable because of the growing overhead and fixed cost burdens.

    The amount of time it will take to complete the decision modeling process mostly determines the costs the data gathering and processing. The more data (i.e., information) that is collected the more it costs. Of course, the more relevant the data collected the closer the model is in representing reality. Some companies are satisfied with "ball park" results to save on the cost and get the results quicker. The "ball park" results use less data and more assumptions, (assume = ass-u-&-me), which may not be accurate. Enormous energy is wasted and wrong decisions are made because people make assumptions contrary to the facts and proceed with their deductive theorizing on the basis of those assumed (but wrong) facts.

    Many solution algorithms are designed to solve their targeted mathematical models perfectly. However, considering imperfect models, uncertain data, and the limited numerical accuracy of computer hardware, it is sensible and justifiable to use a fast solution algorithm that produces a solution which is less than optimal. For example, in solving an Integer Linear Program (ILP), one may have to relax (i.e., remove) the integrality condition. It may be necessary to compromise the quality of solution to obtain a "good" solution within a reasonable amount of computational time.

    A topic usually discussed in relation to cost-effectiveness analysis by relevant to all decision-making activities are the perspective of the decision. That is, whose costs and outcomes should be measured? In terms of for-non-profit business decisions, for example, it seems obvious that the perspective should be that of the persons who would be affected by the decision. However, in many situations the customer is not the only one who is affected. Models have evolved from simple trees with "arbitrary" (nonutility-based) outcome measures to increasingly sophisticated models including systems simulation a more sophisticated modeling and evaluation methodology. This has been made possible largely through the development of sophisticated microcomputer software that facilitates model construction, permits spreading of modeling efforts over multiple work sessions, saving differing versions of models, and automating evaluation. Enhanced computing power has made complex simulations possible, and advances such as parallel computing will provide even greater computational speed in the future. Innovations that permit application of models by nonexperts and interaction with decision models over the Internet are already beginning to appear and will continue to evolve.

    Time Discounting Rate Factor: Discounted rate consideration in decision-making is the choice between outcomes that occur at different times into the future. Therefore, the (absolute) value of future outcomes should be discounted by a factor known as a "discount rate". The discount rate is the percentage increase in value needed to offset a given delay. For example, if $100 now were just as attractive at $110 one year from now, then the annual discount rate would be 10%. The discount rate should be a constant positive rate, and most decision analyses use a low rate such as 3%.

    Moderate changes in the discount rates applied to a decision analysis can substantially alter the results. If cost considerations were used as the basis for decision analytic discount rates, the results would depend heavily on the circumstances under which subjective discount rates were elicited.

    Subjective discount rates are influenced by several, for example, subjective discount rates are higher for gains than for losses, higher for small-magnitude than for large-magnitude outcomes, and higher for short delays than for long delays.

    For more information, visit the following collection:
    Decision Making Resources.

    Further Readings:
    Bodily S., Modern Decision Making: A Guide to Modeling with Decision Support Systems, McGraw-Hill, 1985.
    Dasgupta P., and D. Pearce, Cost-benefit Analysis: Theory and Practice, Macmillan, 1972.
    Klein D., Decision-Analytic Intelligent Systems: Automated Explanation and Knowledge Acquisition, Lawrence Erlbaum Pub., 1994.
    Smith C. Sr., Computer-supported Decision Making: Meeting the Decision Demands of Modern Organization, Alex Publishing Co., Connecticut, 1998.

    Decision-Making Process in Organizations:
    Dynamic Strategic Plan

    Decisions are at the heart of success of any organization. At time there are critical moments when these decisions can be difficult, perplexing and nerve-wracking. Making decisions can be hard for a variety of structural, emotional, and organizational reasons. Adding to these, doubling the difficulties are factors such as uncertainties, having multiple objectives, interactive complexity, and the anxiety. The organizational decisions at different levels are depicted in the following chart:

    Organizational Decision-Making Process

    The critical component in the above chart is organization's values and the ranks among values. The final product will necessarily shape the mission, the purpose, and the vision of the organization. The organization's values and the ranks among values can be achieved by the following major sequence of activities: Identification, Evaluation, Option Development, Options Evaluation, Selection, Communications, and Implementation.

    Strategic decisions are purposeful actions and have longer a time horizon than both the tactical and the operational decisions. At each level in the above chart, the OR/MS/DS/SS analyst and the decision maker must agree about the performance measure (or indicators) about that level. This can be done by asking questions such as: What is the performance measure (or indicators)?, Why should we measure it?, How do we measure it?

    In recent years there has been an increased focus on organizational learning, the learning organization, and the knowledge creating company. Broadly, this stream of thought suggests that organizations should focus on better utilizing their intellectual capacity and improve knowledge flows among their members to achieve a competitive advantage. The influence of the dynamic global environment and rapid advances in information technology caused the recognition that knowledge and the capacity to develop knowledge are the only resources for a sustainable competitive advantage.

    Knowledge is broadly defined as credible information that is of potential value to an organization. More specifically:

    A number of rigorous research methodologies are still needed to address problems in each of the above areas and other areas related to knowledge management. Among potential approaches are: analytical modeling approaches, theory driven empirical studies, case studies, and field research.

    A dynamic strategic plan is to assist manager in his/her decision-making process. The context is very specific: the organization. The objective may remain the same but the strategy needs to be updated in the light of new information and needs to be modified/adapted to take account of other factors left out.

    The dynamic nature of strategic plan

    OR/MS/DS/SS is all about identifying scope, boundaries, and outcomes of decisions. The above chart depicts the dynamic nature of fact-based analysis and the relevant synthesis in strategy decision making in an integrated parts.

    Remember that your model is only a model among many other models and no amount of fact-based analysis will change the fact that any model a best only represents one view of the world. For this reason a multiple perspectives approach is needed in producing a model, which gave the right answer. The process of producing a model is much more important than the model itself. By definition a model only allows one to explore a limited set of parameters and then onto to the next model. That is, what Operation Re-search is all about-- searching and re-searching.

    Further Readings:
    Ackoff R., Re-creating the Corporation: A Design of Organizations for the 21st Century, Oxford University Press, 1999.
    Bushev M., Synergetics: Chaos, Order, Self-Organization, London, World Scientific, 1994.
    Harrington J, G. Hoffherr, and R. Reid, The Creativity Toolkit: Provoking Creativity in Individuals and Organizations, McGraw-Hill, 1998.
    Forrester J., Systems dynamics, systems thinking, and soft OR, System Dynamics Review, Vol. 10, No. 2, 1994.
    Hannon B., M. Ruth, Dynamic Modeling: Modeling Dynamic System, Springer Verlag, 2000. Provides models for the optimal use of natural resources.
    Harrison F., and M. Pelletier, Foundations of strategic decision effectiveness, Management Decision, 36(3), 147-159, 1998.
    March J., Decisions and Organizations, Basil Blackwell Inc, 1989.
    Owen J., Program Evaluation: Forms and Approaches, Sage Publications, 1999.
    Rollett H., Knowledge Management: Processes and Technologies, Kluwer Academic Publishers, 2003. Points out all the issues that need to be taken into account to make knowledge management a success.
    Ryan M., The role of social process in participative decision making in an international context, Participation and Empowerment: An International Journal, 7(2), 33-42, 1999.
    Shapira Z., Organizational Decision Making, Cambridge Univ Pr., 1997. A study of organizational aspects such as conflict, incentives, power and ambiguity, on the other. It draws mainly from the tradition of Herbert Simon, who studied organizational decision making process.
    Steiss A., Strategic Management and Organizational Decision Making, Lexington Books, 1985.
    Sterman J., Business Dynamics, McGraw-Hill, 2000. Effective decision making in a world of growing dynamic complexity requires us to become systems thinkers.
    Sutherland J., Towards a Strategic Management and Decision Technology: Modern Approaches to Organizational Planning and Positioning, Kluwer Academic Publishers, 1989.
    Fedor D., and S. Ghosh, (Eds.), Advances in the Management of Organizational Quality, Elsevier, 2000.
    Wade D., Corporate Performance Management: How to Build a Better Organization Through Measurement-driven Strategic Alignment, Butterworth-Heinemann, Boston, 2001.

    The Difficulties of Analytical Modeling Process

    Most real world problems cannot be formulated adequately as mathematical models. Since the real world problems are usually of large-scale they must be stated in a very rigid algebraic form in order to be solved by computerized algorithms. The modeler must analyze the characteristics of problem situations in a legitimate way in order to formulate and represent the problem as a valid analytical model. Almost all-real world problems are characterized as having the following difficulties:
    1. Many conflicting "fuzzy" goals (objectives). For example, "Man fools himself. He prays for a long life, yet fears an old age."
    2. A lack of specificity as to what the decision variables (and hence those variables subject to control) and the fixed inputs (parameters) are.
    3. Uncertainty as to the bounds or restrictions on the decision variables and their functions.
    4. A lack of knowledge of cause-effect relationships.
    5. Stochastic (probabilities) elements.
    6. An underlying dynamic character that causes the goals, restrictions, and cause-effect relationships to vary over time.
    7. Unavailability of data necessary to specify the problem.
    8. A qualitative description of some of the data.
    9. The possibility of unforeseen consequences resulting from the alteration of existing conditions or the imposition of new ones.

    By representing the decision problem as an analytical model, these difficulties could be overcome by one or several of the strategies that follow:

    1. The creation of a single performance (or objective) function.
    2. Specification of the problem variables.
    3. Determination of the exact bounds on the problem variables (or, more generally, on functions of the problem variables).
    4. Determination of the functions forms and parameters describing the functional form.
    5. Resolution of the stochastic (uncertain) elements by creating a deterministic form or probability assessments.
    6. Reconciliation of the dynamic nature of the problem and conversion to a static mode, and revising the model periodically.
    7. Solution of the data collection problem.
    8. Quantification of the data.
    9. Inclusion of all-important elements and unforeseen consequences of changes in the problem variables.

    It is extremely important to understand the concepts and philosophy of modeling process in this part before embarking on the remainder of this site. If the models are to play a useful role in business decision-making, we must know what to expect from a model and from the process of model building. To understand the significance of the techniques used in OR/MS/DS/SS decision-making, one must have a firm grasp of the role of a model and the process of model building.

    A key to success in modeling is the recognition that the model is an abstraction. However, abstraction is the most powerful tool that we have in strategic thinking. Models are not constructed to provide the only answer to a decision problem. Instead, they provide information that is helpful in decision-making. A model should not have all the complexities of reality. If it did, it would be extremely difficult to solve and would probably provide the decision-maker with little insight or, information. Conversely, the model should not be so much of an over simplification that it bears little resemblance to the real world. A good model strikes a balance.

    All models including verbal, mental, and mathematical contain independent variables, dependent variables, parameters and constants. In verbal models, these elements are put together in a loose and often intuitive fashion, making understanding apparent and communication easy. As one moves from verbal to mental to mathematical models, the relationships between the variables and the parameters become more specific. The degree of specificity needed determines the type of model that will be used in a specific situation.

    There are a variety of model types within the classification of mathematical models. Classification of mathematical models as descriptive or normative, static or dynamic, deterministic or stochastic (i.e., risky and uncertainty) is not idle taxonomy. Since the decision model must be formulated to provide useful information in such a way that the model can be expeditiously solved, it is important for both decision-makers and model builders to be aware of the existing models and their essential features.

    The process of mathematical model building is an iterative process in the context of Systems Thinking. No one, even the most experience model builder, develops a usable model in a single, straightforward development. Instead, there is a process of tentative formulation and validation, followed by reformulation and revalidation until a degree of confidence in the usefulness of the model is developed. In all stages mathematics and logic are being used. Notice that, mathematics plays two distinct functions in the modeling process: At the formulation stage, mathematics is being used as a language, this includes probability as the language of risk in measuring the uncertainty to describe the problem. In all other stages, mathematics is being used as a tool in answering managerial questions.

    Perspectives on risk behavior and uncertainty: The key focus of risk management must be in exploring business perspectives on why managers engage in risk taking despite having an awareness of danger and the implications of their decision on others including the shareholders. The essential factors in the area of risk perception and risk management are in exploring risk as uncertainty, awareness of uncertainty, and debating how uncertainty is managed from the analysts and the decision maker perspectives.

    Further Readings:
    Beltrami E, Mathematics for Dynamic Modeling, Academic Press., 1987.
    Brady J., and E. Monk, Concepts in Enterprise Resource Planning, Course Technology, 2001. Increased productivity by bringing a company's many different systems together into one large integrated system.
    Kim D., Systems Thinking Tools: A User's Reference Guide, Pegasus Communications, 1994.
    Gershenfeld N., The Nature of Mathematical Modeling, Cambridge Univ. Pr., 1998.
    Pidd M., Tools Thinking : Modelling in Management Science, Wiley, 1997.
    Sterman J., Business Dynamics: Systems Thinking and Modeling for a Complex World, Irwin Professional Pub., 2000.

    Why Analytical Modeling?

    We are attempting to 'model' what the reality is so that we can predict it. The tools of applied OR/MS/DS/SS modeling process help to understand the decision problem at hand, determine logical results of the decision, and choose an optimal course of action. Much of this is done with modeling. A model is a representation of a situation. The change in the environment and variables surrounding the decision problem can be studied to determine the effects that they have on the decision problem. Modeling is a kind of simplification of reality intended to promote understanding of reality. The information learned from the model can then be applied to the real world decision problem. The tasks of the modeler are:
    1. To force the decision maker to make his assumptions explicit.

    2. To provide a well-defined statement of the problem. The logical processes utilized in OR/MS/DS/SS modeling process force the manager to clarify and define the problem under consideration.

    3. To give you the road map, compass, and guide posts to reach what is the most important objective for your company.

    4. To provide process support, to structures and automates a group of tasks about the review and to optimize business operations or to discover and develop new lines of business.

    5. To have separation of functional from the transactional applications. The application can function independently of an organization's core transactional applications, yet it can be dependent on such applications for data and might send results back to these applications

    6. To enforce the modeler to use time-dependent, integrated data from multiple sources.

    7. To provide a frame of reference for solving the central and related problems. OR/MS/DS/SS modeling process relies upon mathematical models of a problem that can be adapted to future problems similar in nature.

    8. To provide answers to ‘what-if' types of questions. In some cases a model is a description of some system intended to predict what happens if certain actions are taken. Decision-making models also provide a basis for performing sensitivity analyses. These analyses provide several benefits to decision-makers. First is the ability to determine the parameters on which an analysis is most dependent. Further re-search to better define the values of such critical values may be deemed worthwhile. Second is the ability to determine a range of conditions under which a given optimal strategy may be worthwhile. For example, the sensitivity range for the coefficient of the objective function is useful when the change in the coefficient is outside of a certain threshold value.

    9. To measure and interpret the results and their implications in dollars -- the only language manager can understand.

    10. To obtain an objective scientific evaluation that is able to be reproduced by others. Since it is a scientific approach based on facts (not belief or opinion), managers can convince others why they made a particular decision. This is called defensible decision-making. Business people deal with facts.

    11. To resolve the conflicts of interest among the components of the organization. The decision maker might incorporate some other perspectives of the problem such as cultural, political, psychological, etc., into the management scientist's recommendations.

    12. To find the source of disagreements. The structure of decision models provides a framework for discussing a decision problem or opportunity. By making choices, assumptions, and input parameters explicit, decision-maker(s) who come to differing conclusions can focus on the source of their disagreements. It is much easier, for example, to deal with a criticism such as " I disagree with your estimate of the sales forecast" than it is to deal with the less specific "I don't agree with your conclusion."

    13. To assist the manager (the decision maker or the person who has a problem). To provide guidance to managers in their decision-making. The OR/MS/DS/SS modeling process allows the manager to minimize the risk associated with an unknown decision outcome.

    The approaches and tools used in OR/MS/DS/SS models are based on one or more of the following analytical methods, simulation, and qualitative or logical reasoning. Many of these tools and approaches depend on computer-based methodologies for implementation.

    Logic (including dialogue logic, interrogative logic, informal logic, probability logic and artificial intelligence) is the vehicle (i.e., a container) for conveying (delivering) ideas and solutions to other people. You may have brilliant ideas, but if you are unable to use "strong" logic to get them across, your ideas won't get you anywhere. The word logistic is derived from logic with a similar but physical meaning. Unfortunately, not many people care to learn logic, because everybody already conceives himself or herself to be proficient enough in the science of reasoning. But I observe that this satisfaction is limited to one's own rationalization, and does not extend to that of other people. Many people use "for example, ..." to prove something, but, "for example" is not a proof. However, a good "counterexample" could be both necessary and sufficient for disproving a given statement.

    The main concern with respect to logic is the following question: Is translatability into the language of logic really the exclusive form of justification and rigor in mathematics. According to Poincaré there are varieties of formal logical theories. Therefore, the relations between syntax, semantics and pragmatics, between constructivist and classical positions, and the role of logic in foundations are to be challenged. Nevertheless, anthropological or sociological works have shown that logical activities of mind are universal.

    Logic is the hygiene of the strategic thinking in the decision-making modeling process. As I said already, it is also a strong container in which you put your ideas in order to deliver them to others. Logic by itself is nothing -- it is an empty container. Both good ideas and strong logic are needed to communicate the ideas. Having good ideas without strong logic for communication is like silver in the mine.

    Further Readings:
    Hacking I., Representing and Intervening: Introductory Topics in the Philosophy of Natural Science, Cambridge University Press, 1983.
    Nonaka I., and N. Konno, The concept of "Ba": Building a foundation for knowledge creation, California Management Review, 41, Spring 1998.

    A Guide to Carrying Out the Modeling Process

    Most OR/MS/DS/SS projects have impact on the important areas of quality, excellence and performance measurement and indicators, but frequently the analyst knows less about current initiatives in these areas than the decision maker, i.e., your client. The following steps, have ben proven to help you to change that balance and make an even greater impact.

    Step 1: Go and see your client. Find out:

    1. What he thinks the problem is; i.e., what kind of system is involved? What is it doing that is undesirable?
    2. Why is it undesirable?
    3. What decision(s) would the client like to make?
    4. How does the problem hinder the function of the client and his/her system in the company?
    5. What would your client like you to do for them?

    Step 2: Go and see the system concerned. Find out:

    1. How it works/behaves.
    2. If the complaint of behavior occurs as stated.
    3. How the people in the system view the problem.

    Step 3: Study the position of the client and his/her system in relation to the rest of the company/organization. Find out:

    1. What are the interaction relationships among the components of the system?
    2. What criteria do these relationships imply for assessing the performance of your client and his/her system?
    3. How might the project affect his/her relationships in the organization?

    Step 4: Review findings so far and decide, for the time being:

    1. If you can accept the client's account of the system's behavior.
    2. If you can accept the client's criteria for judging the system's behavior.
    3. If you have a sufficient understanding of the way the system works in order to identify and consider the effects of possible changes.
    4. What you think your client needs from you.
    5. Why you cannot immediately satisfy your client's need.
    6. If you have done as much as possible to help the client.

    Step 5: Plan further activities/investigations directed towards improving your answers to Step 4 in the light of the review.

    Step 6: Report and discuss current ideas with your client and reach joint agreement about your current plans for further study.

    Step 7: Carry out your planned investigations and return to Step 6.

    As a final word of caution, before expending any effort in OR/MS/DS/SS project, both the analyst and the decision-maker must consider the following essential questions:

    These are serious questions that must be confronted frankly and candidly by both parties before becoming irreversibly involved in carrying any OR/MS/DS/SS project.

    Dialog between the Consultant and the Decision Maker: A list of fruitful dialogs is outlined below. It should be emphasized that neither all questions here will be asked a decision-maker in every situation, nor will the questions be limited to these. The appropriate skills of tact, discretion and good listening as well as assertiveness are required from a successful consultant.

    Further Readings:
    Fortuin L., P. Van Beek, and L. Van Wassenhove, OR at Work: Practical Experiences of Operations Research, Taylor and Francis Publishers, London, 1996.
    Klein G., et al., (Ed.), Decision Making in Action: Models and Methods, Ablex Pub., 1993.
    Sandler T., Collective Action: Theory and Applications, University of Michigan Press, 1992.

    The Gaps between Modeling and Implementation

    There are decision-makers who force the world to suit their model, and those who correct their model to suit their realities of the world.

    Success is the ability to put into implementation phase what is in your decision model. In recent years, there has been increasing concern over the relevance of many aspects of the OR/MS/DS/SS modeling process. The predominant paradigm used is that of science aimed at explanation and prediction. However, all too often, management scientists use methods which are increasingly sophisticated and therefore increasingly difficult for the decision maker to relate the problem he/she is experiencing.

    In ample ways managing decision process is no different from managing other aspects of an organization. First, there must be a vision that links with the organization objectives. Second, people must be aligned with that vision and third, the alignment must be from the top down and all across the organization. In fact, it is the process, which organizations gain value through their decisions. Decision process is a multidimensional process. It requires consideration of consequences, culture, process and infrastructure.

    Unfortunately, gaps exist between the theory and the application of modeling for decision-making. This statement is, of course, from the perspective of the modelers who recognize the gap and not from the perspective of decision makers. Not all managers are aware of modeling concepts for decision-making or the practice of modeling for decisions.

    The evidence for the existence of this gap is the many papers published on modeling. Very few address actual situations and even fewer present validated solutions to recognized problems.

    Reason for the Gaps:

    There are a number of factors contributing to the existence and even growth of the gap. These include:

    1. The organization database contains a wealth of information, yet the decision technology group members tap a fraction of it. Employees waste time scouring multiple sources for information. The decision-makers are frustrated because they cannot get business-critical information exactly when they need it. Therefore, too many decisions are based on guesswork, not facts. Many opportunities are also missed, if they are even noticed at all.

    2. The real problems are tough to define and usually difficult to analyze and model.

    3. As it is easier to make plans than to carry them out, models that are not to be implemented are not drawn up correctly and not taken seriously from the start.

    4. Data are often scattered, incomplete, and lacking in accuracy. Some companies are satisfied with "ballpark" results in order to save on the cost and get the results quicker. The "ballpark" results use less data and more assumptions. This approach takes less time to collect and thus saves money as well.

    5. Close collaboration between the modeler and the problem owners is required. Collaboration is occurring infrequently since the organization does not see a direct and often immediate benefit. The organization also does not have the confidence in the ability to deliver without causing damage in some form. Case experience is helpful in establishing trust and the willingness to cooperate.

    6. There exists a need to influence the culture and attitude towards modeling within the business community and this requires a more capable and better-educated manager. Numerous cases exist where a company spends a large amount of dollars on a marketing promotion and a small amount of dollars in researching its effectiveness.

    7. In the modeling process, the legacy of culture cannot be ignored given its role in organization identity, attitudes and behavior. This is more evident in modeling international business decisions since the national cultures are firmly rooted in history, geography, religion, language, education, customs and myriad other influences ranging from climate to cuisine.

    8. Managers are poorly trained in the concepts and/or the use of analytical models.

    9. Modelers must especially address the issues labeled as important by the manager from a cost saving perspective.

    10. Most modeling approaches to decision making have assumed that the problem was well defined and the definition was stable. Increasing attention must be paid to "ill-structured" or "wicked" problems in which agreement about the problem definition cannot be assumed. Wicked problems require an approach, which emphasizes communication among diverse stakeholders and supports groups in making sense of the conflicting signals and priorities in their environment which combines three elements: one or more modeling frameworks (formalisms, notations), hypertext software projected on a shared display screen, and facilitation. The use of a facilitated shared display for collective construction of "soft models" efficiently create shared understanding and shared commitment on such decision problems.

    The Ten Natural Laws! (by Bob Bedow, DELEX Systems, Inc.):

    1. Ignore the problem and go immediately to the solution since that is where the profit lies.
    2. There are no small problems, only small budgets.
    3. Names are control variables.
    4. Clarity of presentation leads to aptness of critique.
    5. Invention of the wheel is always on the direct path of a cost plus contract.
    6. Undesirable results stem only from bad analyses.
    7. It is better to extend an error than to admit to a mistake.
    8. Progress is a function of the assumed reference system.
    9. Rigorous solutions to assumed problems are easier to sell than assumed solutions to rigorous problems.
    10. In desperation address the problems.

    Further Readings:
    David A., Models implementation: A state of the art, European Journal of Operational Research, 134, 459-480, 2001.

    The Becoming of a Management Scientist

    The teaching of an OR/MS/DS/SS course may need to be restructured to provide the greatest benefit to the students, but the content of the course should not be altered. The course has many valuable applications. OR/MS/DS/SS teaches basic quantitative reasoning skills, formal modeling skills, and the ability to understand and learn from models in other disciplines. The problem exits in communicating these applications to the students. Once the support of the student body is gained, the course should no longer experience such a decline.

    Further Readings:
    Jackson M., Towards coherent pluralism in management science, Journal of the Operational Research Society, 50(1), 12-22, 1999.

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    Professor Hossein Arsham   

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