**Introduction**

The administration of a modem business enterprise has become an enormouslycomplex exercise. There has been an increasing tendency to tum to quantitativetechniques and models as a potential means for solving many of the problems that arisein such an enterprise. Management in action is decision-making. We consider decisionmakingin business to be a process whereby management, when confronted by aproblem, selects a specific course of action or solution from a set of possible courses ofactions. Since there is generally some uncertainty about the future, we cannot be sure ofthe consequences of a decision made. The process of making decisions in a business hasthe same essential characteristics as problem-solving behavior in general.

**Business Decisions**

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The business manager wants to choose the course of action that is most effectivein attaining the goals of the organisation. In judging the effectiveness of differentpossible decisions, we must use some measuring unit. The most commonly usedmeasure in making decisions is the amount of profit in monetary terms but for ourpurpose 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-relateddecisions over several time-periods.

3. Decisions where the opponent is nature (a family planning, a picnic) or athinking opponent (setting the price of a product after considering theactions of the competitors).

The following general process of solution is adopted for all types of decisionsituations:

1. Establish the criteria that will be utilized. One of the criteria may bemaximization of profit. In a capital budgeting decision, we choose the project with thehighest pay off.

2. Select a set of alternatives for consideration.

3. Determine the model which will be used and the values of the parameterof the process, e.g., we may decide that the algebraic expression of the model of totalexpenses is:

Total Expenses= a+ b units sold.

The parameters are “a” and “b” and their values would have to be determined inorder to use the model.

4. Determine that alternative which optimizes or falls m line with thecriterion that has been chosen in item 1 above.

**Abstraction**

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Real life problems are very complicated in nature. In empirical situation, there isa large number of inherent “facts,” Moreover, every potential course of action triggersoff a chain reactions – of course an effect and interaction – and there is no end to thisprocess. Consider the problem of erection of a factory building. Much time is spent ongathering factual information about the project, e.g., the exact location, the physicalfeatures of the building ; a minute study of the climatic conditions of the potential sitesand their bearing on most of the construction; the raising of finance and the cost offinance raised. By far the most important decision is in respect of the alternative uses towhich these funds can be put in the present and future periods. If the manager as adecision-maker prefers to collect all the facts before he acts, it follows that he will neveract. It is to be appreciated that it is beyond the comprehension of human mind toconsider every aspect and dimension of an empirical problem. Some characteristics ofthe problem must be ignored if at all a decision is to be made. In other words, it is forthe decision-maker to abstract from the empirical situation those factors which heconsiders to be the most relevant to the problem he faces. In this way, abstractioninitiates the solution of many a human problem.

**Model Building**

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Once the selection of the critical factors or variables has been made by thedecision-maker, the next step is to have their combination in a logical manner so as toform a counter-path or model of the empirical situation; ideally, it strips a naturalphenomenon of its complexity. It, therefore, duplicates the essential behavior of thenatural phenomenon with a few variables, simply related. The more the simplicity of themodel, the better it is for the decision-maker, provided the model serves as a reasonablyreliable 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 aspossible. In other words, a model is a de facto approximation of reality. Replication ofreality 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 thatpredicts outcomes reasonably well and consistent with effective action on his part.

**Solutions**

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Having constructed the model, it is possible to draw certain conclusions about itsbehavior by means of a logical analysis. The decision-maker bases his action or solutionon these conclusions. The effectiveness of a model depends upon the logical analysisused 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 annualopportunity cost of money for his firm. He can make his decisions on the construction ofthe factory premises by calculating the present value of the cash flows and would nothave to consider the alternative uses of which his funds can be put to in detail.

**Errors**

Generally, there are two possible types of errors in decision-making to start with.He can err in applying logic to the process of reasoning from premises to conclusions tosolutions. The concern may be able to obtain funds at the cost of 12% but managementmay have decided not to raise any new capital. The premise that one can use the interestrate to represent an opportunity cost is valid, but the conclusion that the use of interestrate applies to all investments is erroneous.

Secondly, there may be a mistake in selecting the variables or the variablesselected are not adequate for the construction of the model in our example. Thedecision-maker has taken into account the time value of money but has ignored the riskelement that is associated with the us~ of money. It is not possible to eliminate errors ofthis type altogether because it would amount to a consideration of all conceivablepertinent variables and would preclude decisive action. Abstraction does violate realityto some extent but it is a necessary condition for problem-solving. This is one reasonwhy decision-making carries with it the possibility of errors.

**Model-Building Techniques**

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There are several ways of representing the models. Common place repetitiveproblems as those of eating, walking and opening doors are a matter of thinking in themind of the decision-maker in an informal and intuitive manner. Such problems areresolved without the aid of a formal model. If the problem is somewhat more complexor unusual, we spend more time on it. It is possible to express to the extent of selectingthe important elements of the problem and proceeding to examine and experiment withthem. The nature of variables determines the technique of describing and relatingselected variables. If the variables are amenable to a quantitative representation, thenthere are strong reasons for selecting a mathematical representation of the model.Mathematics has a theoretical rigor of its own, and so it ensures a certain orderlyprocedure on the part of the investigator. It demands specificity in respect of thevariables that have been abstracted and the relationships assume to be existing amongstthem. For example, it is more difficult to make implicit assumptions in a mathematicalmodel than in a literary model. Secondly, mathematics is a potent tool for relatingvariables and for deriving logical conclusions from the given premises. Mathematicsfacilitates the solution of problems of bewildering complexities and also facilitates thedecision-making process where quantitative analysis is applicable.

In the recent past, especially since World War IT, a host of business problemshave been quantified with some degree of success, leading to a general approach whichhas been designated as operations research. Undoubtedly, the quantitative representationof business problems is much older than operations research, considering the practice ofaccountancy. However, recently, the use of quantitative techniques has covered all theareas of modem business.

A word of caution is necessary for those businessmen who are found to employquantitative techniques for business decisions. The conclusion derived from amathematical model contains some degree of error because of the abstraction process. Itis a matter of judgment as to when to modify the conclusion in view of the magnitude oferror. Operations research supplements business judgment; it does not supplant it.Moreover, there are many business problems which cannot be given a quantitativerepresentation and so they require the use of qualitative models and solutions. Withinthe constraints mentioned here, quantitative analysis can become an extremelyproductive technique for managerial decision-making. Problems which would perplexthe initiation of the most experienced -executives may, on some occasions, be resolvedwith relative ease.