Minimizing The Complexity of the Decision Process

One way of simplifying the decision process is to establish objectives, policies, and procedures that will provide guidelines for managers to follow when making routine and repetitive decisions. Professional codes, rules, and policy manuals are steps in this direction, since they guide decision making by outlining acceptable behavior. Thus, problems that are well structured and not complex can be solved with programmed decisions. For example, consider the supervisor who needs to specify the wage rate for a new employee. Basically, this is a routine and repetitive decision that allows the supervisor to use the company's job specification to classify and determine the wage rate for the employee. If the job was new and ill-defined, then the decisional problem would be unprogrammed, requiring greater innovative responses. As will be noted later in this chapter, mathematical models along with the use of computers have made programmed decisions more available for routine problem-solving situations. Also, it is fortunate that the capacity to make better decisions can be developed through training, experience, and an expanded knowledge of the total decision-making process.

Although the decisional process implies a rational way of thinking about making a choice. Herbert A. Simon states that human beings are not always rational in making decisions. He  suggests that it is impossible for the behavior of an individual, regarding personal or organizational decisions, to reach a high degree of rationality. Four basic reasons for such a lack of rationality are listed below:

1. Individuals have incomplete knowledge of the facts surrounding a problem and are limited in their ability to foresee future events. At any given time, limited human and material resources restrict the amount of information that can be obtained about a given situation. Thus, for any situation, knowledge is fragmented, particularly that related to the possible consequences of various course of action. As a result, it may be useless to search for the optimum (best possible) decision.
2. Identifying all alternative solutions for a problem is normally impossible. As noted earlier, time and cost constraints require that only the most viable alternatives be considered. By eliminating alternatives that are not feasible, the list of possible course of action can be narrowed. Since the number of alternatives must be limited, it is extremely important that the true problem be identified. The more accurate the statement of the problem, the more likely the selected alternative will produce a satisfactory solution.
3. The anticipated consequences of various alternatives often differ from those that are actually realized. Such differences result from changes in factors beyond a manager's sphere of influence. Assume that the loan officer of a bank is considering a $7,500 loan to a costumer who wants to purchase a new sailboat with complete rigging. Since the banker is not certain about changing conditions, it is impossible to determine the actual consequences that may occur if the loan is made. Some perceived consequences might include (a) the loan could overextended the costumer's financial status and possibly result in a repossession of the boat; (b) a repossession could be viewed by top management as an exercise of poor judgment by the officer who granted the loan; or (c) loan payments might be late and, therefore, require additional time and money to collect them. In any event, the banker is not certain that the loan will be repaid. Yet, regardless of the difficulty associated with determining possible outcomes, predictions must be made and evaluated according to some scale of desirability. In terms of evaluating the effects of each alternative, there is no simple approach that is acceptable in all situations. In some cases, the alternatives could be evaluated in terms of profit. In others where there are multiple evaluation criteria, the process is much more complicated. This is particularly true when the problem involves uncertain or risky elements.
4. A decision by one manager may have an adverse effect on objectives in another area of the organization. Specifically, organizations have multiple goals, and decisions that optimize results in one department can cause lower levels of achievement (sub optimization) in other departments. For example, a production efficiency objective to mass produce high-volume goods would conflict with a marketing goal to sell distinctive quality products with high profit margins. As a result, an optimization of both goals may not be realistically possible. Also, since knowledge of the future is limited, a decision that produces optimal results in the short run may be sub optimal at a later date. Remember that the automobile was first viewed as preserving the environment since horses would not have to be on city streets. Air pollution today, however, is a major concern surrounding automobile emissions.

The effects of sub optimization can be lessened by following a systems approach for every decision. This allows each alternative to be evaluated in conjunction with decision on every phase of the organization, however, is complicated and time consuming. Regardless of this difficulty, the decision maker must make an attempt to specify how various alternatives will actually affect the system. Possibly, future advancements in computerized decision making will allow managers to evaluate systematically the impact of each alternative on the entire organization. Even then, the degree of rationality found in the decision making process will still be influenced by the quantity of information available, the number of alternatives that can be examined, and the pressures exerted by internal and external environments. An organization must, therefore, be concerned with the limits of rationality, how the limits can be overcome, and the impact of organizational structure on the decision maker.