In this article we will discuss about the quantitative way of decision-making.
The administration of a modern business enterprise has become an enormously complex exercise. There has been an increasing tendency to turn to quantitative techniques and models as a potential means for solving many of the problems that arise in such an enterprise.
Management in action is decision-making. We consider decision-making in business to be a process whereby management, when confronted by a problem, selects a specific course of action or solution from a set of possible courses of actions. Since there is generally some uncertainty about the future, we cannot be sure of the consequences of a decision made. The process of making decisions in a business has the same essential characteristics as problem-solving behaviour in general.
The business manager wants to choose the course of action that is most effective in attaining the goals of the organisation. In judging the effectiveness of different possible decisions, we must use some measuring unit.
The most commonly used measure in making decisions is the amount of profit in monetary terms but for our purpose here, we will take only a few of these:
1. Decisions under certainty or uncertainty.
2. Decisions made for one time-period only or a sequence of inter-related decisions over several time-periods.
3. Decisions where the opponent is nature (a family planning, a picnic) or a thinking opponent (setting the price of a product after considering the actions of the competitors).
The following general process of solution is adopted for all types of decision situations:
1. Establish the criteria that will be utilized. One of the criteria may be maximization of profit. In a capital budgeting decision, we choose the project with the highest pay off.
2. Select a set of alternatives for consideration.
3. Determine the model which will be used and the values of the parameter of the process, e.g., we may decide that the algebraic expression of the model of total expenses is:
Total Expenses = a+b units sold.
The parameters are “a” and “b” and their values would have to be determined in order to use the model.
4. Determine that alternative which optimizes or falls in line with the criterion that has been chosen in item 1 above.
Real life problems are very complicated in nature. In empirical situation, there is a large number of inherent “facts,” Moreover, every potential course of action triggers off a chain reactions— of course an effect and interaction—and there is no end to this process. Consider the problem of erection of a factory building.
Much time is spent on gathering factual information about the project, e.g., the exact location, the physical features of the building ; a minute study of the climatic conditions of the potential sites and their bearing on most of the construction; the raising of finance and the cost of finance raised.
By far the most important decision is in respect of the alternative uses to which these funds can be put in the present and future periods. If the manager as a decision-maker prefers to collect all the facts before he acts, it follows that he will never act. It is to be appreciated that it is beyond the comprehension of human mind to consider every aspect and dimension of an empirical problem.
Some characteristics of the problem must be ignored if at all a decision is to be made. In other words, it is for the decision-maker to abstract from the empirical situation those factors which he considers to be the most relevant to the problem he faces. In this way, abstraction initiates the solution of many a human problem.
Once the selection of the critical factors or variables has been made by the decision-maker, the next step is to have their combination in a logical manner so as to form a counter-path or model of the empirical situation; ideally, it strips a natural phenomenon of its complexity.
It, therefore, duplicates the essential behaviour of the natural phenomenon with a few variables, simply related. The more the simplicity of the model, .the better it is for the decision-maker, provided the model serves as a reasonably reliable counter-path of the empirical order.
The advantages of a simple model are:
1. It economizes on time as well as on thought.
2. It is within the reach of comprehension and ability of the decision-maker.
3. If occasion arises, the model can be modified quickly and effectively.
The aim of the decision-maker in constructing a model is to approximate reality as far as possible. In other words, a model is a de facto approximation of reality. Replication of reality seems to be a lofty aim and meeting it would consume an infinite length of time. Besides, such an elaborate model would be beyond the reach of human comprehension. Therefore, the manager as a decision-maker wants the simplest possible model that predicts outcomes reasonably well and consistent with effective action on his part.
Having constructed the model, it is possible to draw certain conclusions about its behaviour by means of a logical analysis. The decision-maker bases his action or solution on these conclusions. The effectiveness of a model depends upon the logical analysis used in drawing conclusions and the abstraction of critical variables from our example.
The decision-maker may decide that an interest rate of 12% matches the annual opportunity cost of money for his firm. He can make his decisions on the construction of the factory premises by calculating the present value of the cash flows and would not have to consider the alternative uses of which his funds can be put to in detail.
Generally, there are two possible types of errors in decision-making to start with. He can error in applying logic to the process of reasoning from premises to conclusions to solutions. The concern may be able to obtain funds at the cost of 12% but management may have decided not to raise any new capital. The premise that one can use the interest rate to represent an opportunity cost is valid, but the conclusion that the use of interest rate applies to all investments is erroneous.
Secondly, there may be a mistake in selecting the variables or the variables selected are not adequate for the construction of the model in our example. The decision-maker has taken into account the time value of money but has ignored the risk element that is associated with the use of money.
It is not possible to eliminate errors of this type altogether because it would amount to a consideration of all conceivable pertinent variables and would preclude decisive action Abstraction does violate reality to some extent but it is a necessary condition for problem-solving. This is one reason why decision-making carries with it the possibility of errors.
There are several ways of representing the models. Common place repetitive problems as those of eating, walking and opening doors are a matter of thinking in the mind of the decision-maker m an informal and intuitive manner. Such problems are resolved without the aid of a formal model if the problem is somewhat more complex or unusual; we spend more time on it.
It is possible to express to the extent of selecting the important elements of the problem and proceeding to examine and experiment with them. The nature of variables determines the technique of describing and relating selected variables. If the variables are amenable to a quantitative representation then there are strong reasons for selecting a mathematical representation of the model.
Mathematics has a theoretical rigour of its own, and so it ensures a certain orderly procedure on the part of the investigator. It demands specificity in respect of the variables that have been abstracted and the relationships assume to be existing amongst them. For example, it is more difficult to make implicit assumptions in a mathematical model than in a literary model.
Secondly, mathematics is a potent tool for relating variables and for deriving logical conclusions from the given premises Mathematics facilitates the solution of problems of bewildering complexities and also facilitates the decision-making process where quantitative analysis is applicable.
In the recent past, especially since World War II, a host of business problems have been quantified with some degree of success, leading to a general approach which has been designated as operations research. Undoubtedly, the quantitative representation of business problems is much older than operations research, considering the practice of accountancy. However, recently the use of quantitative techniques has covered all the areas of modern business.
A word of caution is necessary for those businessmen who are found to employ quantitative techniques for business decisions. The conclusion derived from a mathematical model contains some degree of error because of the abstraction process. It is a matter of judgment as to when to modify the conclusion in view of the magnitude of error. Operations research supplements business judgment; it does not supplant it.
Moreover, there are many business problems which cannot be given a quantitative representation and so they require the use of qualitative models and solutions. Within the constraints mentioned here, quantitative analysis can become an extremely productive technique for managerial decision-making. Problems which would perplex the initiation of the most experienced executives may, on some occasions, be resolved with relative ease.