"Systems Analysis and Project Management" by D. Cleland and W. King, 1968
The application of systems concepts to management functions related to planning came to be known as systems analysis. It should be noted that, unfortunately, in this area (and indeed not only in this one), there also exists a terminological jungle: different people use different terms to define the same phenomenon or concept. For instance, many executives and those who analyze management processes use as synonyms the terms "systems analysis," "operations research," "operational analysis," "cost-effectiveness analysis," or "cost-benefit analysis," and so on. Others attempt to delineate specialized areas of study defined by these terms.
If one directs one's interest toward understanding the meaning and substance of the systems approach rather than toward clarifying the content of the activities defined by one or another of these terms, such semantic confusion will not cause great difficulty. All the activities denoted by these terms share common elements and differ primarily in their domain of application and the goal of the analysis. Here, as we shall discover, it is more useful to focus on the process of analysis — that is, on its methodology — rather than on the goals and the domain of its application. It follows that we can consider the fundamental elements of operational analysis, systems analysis, cost-effectiveness analysis, and others simultaneously, for they are in fact identical in character. We can then leave the search for precise definitions to those who work under specific conditions and find it useful to try to define the content of these terms precisely.
To understand the substance of the methodology known as "systems analysis," one must first grasp the character of the problems being analyzed, for the very purpose of systems analysis lies precisely in analyzing the problems to be solved in the course of planning.
By its character, systems analysis is a scientific process or methodology. It can be defined in terms of its basic elements. The systems analysis approach presupposes:
- a systematic investigation and mutual comparison of those alternative courses of action that lead to the achievement of desired goals;
- a comparison of alternatives on the basis of the cost of resources expended and the benefits achieved by each alternative;
- thorough accounting for and analysis of uncertainties.
To understand the essence of systems analysis, its role and place in strategic decision-making, it is first necessary to have a clear understanding of the problem's fundamental content, its most important elements, and the principles for solving it.
Systems analysis is a methodology for analyzing and solving problems that employs a systematic investigation and comparison of alternatives, based on the ratio between the estimated cost of resources expended on their implementation and the resulting benefits. As part of this investigation, a thorough analysis of the uncertainties inherent in future decisions is mandatory.
Although logical and mathematical methods are frequently used in systems analysis for solving problems, one should not assume that there is a necessary connection between systems analysis and complex mathematical methods. Extremely complex problems are very often analyzed and solved without recourse to anything more complex than high-school mathematics. The role of human judgment in systems analysis is often completely misunderstood. Systems analysis serves only as a supplement to the decision-maker's judgment, based on experience and intuition. Moreover, in many cases, judgments are used much more effectively in systems analysis than the decision-maker could use them personally.
Let us consider the role of systems analysis in practice and its inherent limitations. Is a scientific analysis of every problem necessary? Should it be used in solving every problem? The answer to this question is as follows. Systems analysis can, in principle, be useful for solving the strategic problems encountered in management at the planning stage, the tactical problems related to implementation, and the problems we encounter in everyday life; however, applying it everywhere and always would be unreasonable.
Some problems are inherently simple and do not require thorough analysis. A decision-maker who is standing in the middle of the road with a car bearing down on him at 60 miles per hour is clearly in a situation requiring a decision. His ardent desire is to avoid injury; to achieve this, he has at least two alternatives — to run to the left or to the right side of the road. Any prolonged deliberation or analysis will clearly lead to an outcome entirely inconsistent with his wishes. This situation demands a fast and precise decision. Such decisions are, by their character, typical of the traditional manager, and the ability to make them is the strength of such a manager. Decisions of this kind underlie the management of combat, where the battle may be won by the one who seizes the initiative rather than by the one who selects the best course of action. For such time-critical tactical and military problems, the speed of decision-making is a critical parameter, since the ultimate outcome of the situation depends more on how quickly an action is taken than on which particular alternative is chosen. In such situations, the tactical commander must, of necessity, rely on experience and intuition to solve problems.
In solving strategic problems — whether in the military or civilian sphere — the conditions are such that the decision's outcome critically depends on the degree of understanding of the situation. In other words, the final result of the action is far more influenced by the character of the chosen alternative than by the speed of action. Decisions in this category are characterized by uncertainties, an unlimited number of possible alternatives, and the fact that resource expenditure will take place in the future, often quite a distant one.
There are, naturally, types of decisions that occupy an intermediate position between those critical in terms of time and those critical in terms of information about the situation, and that do not require a thorough analysis. Routine decisions made daily within the framework of established general policy are examples that require neither great haste nor the cost of serious analysis.
Systems analysis is an evolution of traditional methods of scientific analysis. Since number and measure constitute the foundation of the natural sciences, systems analysis came to be regarded as a quantitative method. Systems analysis indeed often employs mathematical tools to formulate and solve problems. It is therefore necessary to define the role that qualitative and quantitative methods of analysis play in it. Since methods of qualitative analysis of problems were in use long before most quantitative methods were conceived, there is a clear need to demonstrate the legitimacy of using quantitative methods in decision-making.
To justify the necessity of applying quantitative analysis to aid the decision-maker and to demonstrate that it is far preferable to fortune-telling or coin-tossing, the subject of discussion must be placed on a basis different from asserting the innate virtues of science. Science, like everything else in our complex world, has both positive and negative aspects, and the acceptability of its methods cannot be justified solely on the grounds that it exists. One can, of course, point to the quite successful application of scientific methods in decision-making in the military and industrial domains and show the likelihood of further successes as scientific knowledge advances. It should be acknowledged that the benefits obtained in the past as a direct result of scientific analysis have been extremely impressive, and perhaps this alone will be sufficient proof to justify further attention to this area.
To advance further in our reasoning, one should imagine that the same volume of input information can be available to analysts equipped with scientific methods, and to a fortune-teller, and to a gambler, and to the person who makes decisions on the basis of intuitive analysis. But the scientific approach to developing decisions offers an additional advantage, for it guarantees the logic of the analysis and the soundness of the decision. A fully subjective approach provides no such guarantees. One might add that if scientific processes are viewed as a supplement to the subjective processes employed by the decision-maker, then nothing is lost, and something may be gained from their use.
Scientific analysis is a logical process in which pencil and paper, and sometimes a slide rule or a computer, are used. In any case, scientific analysis requires a clear formulation (and documentation, which is very important) of initial assumptions, the logic of reasoning, and conclusions. This means that the process of scientific analysis can always be reproduced and verified, even after the decision has been made and the result of its implementation is known. This makes it possible to evaluate the quality of analytical procedures. If the utility and value of the analysis have been demonstrated, then the given procedure can be used in the future with confidence in the quality of the final result. A purely qualitative approach does not have such advantages, unless, of course, one takes into account that the decision-maker learns from trial and error.
However, the potential advantages of scientific analysis by no means diminish the role of subjective judgment in the decision-making process. One of the most characteristic features of the scientific approach is its abstraction — that is, the exclusion from consideration of certain aspects of the real problem facing the decision-maker. The removal of a number of aspects from the systems analysis means that only part of the real problem is investigated in a strictly scientific manner. The decision-maker, if he wants the decision to be the best possible, must then combine the results of the scientific analysis with the significant but unquantifiable factors that could not be part of the formal analysis. In carrying out this part of the decision-making process, he must use his judgment, intuition, and experience at the same level as a manager of the traditional type. The difference between the two approaches is that the person making a decision using scientific methods draws a clear line between quantitative and qualitative analysis and uses each where it will bring the greatest benefit.
A good example of factors that are difficult to account for in formal analysis is morale. In any decision about a problem, the effect of a given action on people's morale is of the same, if not greater, importance for the effectiveness of an organization as the effect of any quantitatively characterized factor. Yet the role of morale is difficult to measure and assess quantitatively. In this case, the analyst can first formulate and solve the problem without taking the organization's morale into account. The task of the decision-maker will then be to combine in his decision both the data of the formal analysis obtained previously and his own assessment of the impact of the formal alternatives on the organization's morale. This will enable him to identify the alternative that, from his point of view, is the best in real life. This alternative may or may not coincide with the one obtained on the basis of formal analysis. The value of formal analysis in this example lies in the fact that it has accounted for all factors except morale, and this allows the decision-maker to focus his judgment on the element that requires his personal assessment.
Without a prior formal analysis, the decision-maker could easily focus only on the obviously important factors and fail to give sufficient attention to the element — morale — that can only be properly assessed on the basis of subjective judgment.
Our assessment of strategic decisions and the role that formal methods of scientific analysis should play in them appears quite straightforward. We regard the process of analysis as a logical and substantive method, the essence of which is that a significant part of a complex problem is reduced to simple results that the manager can use in developing the best decision in conjunction with other factors. This allows him to concentrate analytical resources on those aspects of the problem where their use will be most effective. Such an approach enables the most effective combination of both formal and informal methods of analysis. A comprehensive approach is, in all likelihood, far better than a purely subjective approach to developing decisions.
The formal approach to solving problems necessarily includes determining which of the available alternatives is the best. The process of searching for the best solution is called optimization. Thus, the best alternatives will be the optimal alternatives. In solving complex problems involving major uncertainties, the level of development of systems analysis is such that optimization in the true sense cannot be achieved or even attempted. In other words, even if one thinks in terms of searching for the best alternatives, achieving this is very often not possible.
Global optimization in systems analysis is impossible if only because not all judgments can be expressed quantitatively. Only part of the problem can be quantified, and the person who is firmly convinced that a solution obtained for the abstract model of a problem necessarily applies to the real problem is doomed to errors and failures. Moreover, since the objects of systems analysis are, as a rule, complex, it is far from always possible to understand their structure well.
The systems analyst must clearly understand that the problem he has formulated and constructed on paper is no longer the real problem. It is an artificial problem that is merely closely related (one hopes) to the problem that actually exists. And one can only suppose that the solution of this artificial problem will be useful for the person making decisions about real problems. One should always bear in mind this distinction between problems, for the decision-maker or analyst who wishes to apply "paper solutions" directly to real problems may sometimes be bitterly disappointed.
Under real conditions, the process of abstraction just described often proves impossible. The analyst may discover that his understanding of the system's structure is so limited that any formal representation he is capable of producing will bear very little resemblance to reality. In this position, he is unable to select the best course of action. It is worth noting, however, that the alternative to formal analysis would be a fully subjective approach, and once this is recognized, concerns about the relative value of properly applied formal analysis begin to fade.
A person is by nature incapable of exhaustively comprehending complex problems that require accounting for many interacting factors. Any formal analysis, or even an attempt at formal analysis, is usually valuable in that it at the very least forces the decision-maker to think about the essentials and to move in the right direction. And although the systems analyst in his final analysis may not be able to tell the decision-maker infallibly what the right thing to do would be, the very fact of the analysis will require him to enumerate alternatives and to pose the question of what he is trying to achieve. Moreover, the decision-maker will be given a clear understanding of what he needs to know in order to make a rational decision. Even if he does not know everything he should know and does not have all the necessary information, the simple knowledge of what he needs generally provides a better basis for decision-making. This allows him, when making a decision, either to be cautious or to take a risk and hope for a positive outcome. It should be emphasized that if the necessary data are absent, the understanding of their significance may prompt a search for the missing information and its use, if not in the given problem, then in other similar problems in the future.
We have already mentioned the role of judgment in the decision-making process and shown its importance for solving any problems, even those that are solved analytically. The role of judgment, however, is not limited to the supplementary functions described above. The fundamental analytical framework known as "decision theory" includes judgment as an element of formal problem analysis.
Subjective human judgments are incorporated into the process of formal analysis in two ways: either as probabilistic judgments or as weighted assessments. Since most problems being solved involve future uncertainties, it becomes necessary to assess the probability of future events. Such assessments are best made on the basis of experience. Thus, the assessment of outcomes becomes a necessary element of the formal analysis of the problem being solved. One of the valuable qualities of systems analysis is that these two categories of judgment are examined and investigated independently of each other, whereas in the mind of the decision-maker, they often merge.