Managerial decision making uses both economic concepts and tools, and techniques of analysis provided by decision sciences. The major categories of these tools and techniques are: optimization, statistical estimation, forecasting, numerical analysis, and game theory. While most of these methodologies are fairly technical, the first three are briefly explained below to illustrate how tools of decision sciences are used in managerial decision making.
OPTIMIZATION.
Optimization techniques are probably the most crucial to managerial decision making. Given that alternative courses of action are available, the manager attempts to produce the most optimal decision, consistent with stated managerial objectives. Thus, an optimization problem can be stated as maximizing an objective (called the objective function by mathematicians) subject to specified constraints. In determining the output level consistent with the maximum profit, the firm maximizes profits, constrained by cost and capacity considerations. While a manager does not solve the optimization problem, he or she may use the results of mathematical analysis. In the profit maximization example, the profit maximizing condition requires that the firm choose the production level at which marginal revenue equals marginal cost. This condition is obtained from an optimization exercise. Depending on the problem a manager is trying to solve, the conditions for the optimal decision may be different.
STATISTICAL ESTIMATION.
A number of statistical techniques are used to estimate economic variables of interest to a manager. In some cases, statistical estimation techniques employed are simple. In other cases, they are much more advanced. Thus, a manager may want to know the average price received by his competitors in the industry, as well as the standard deviation (a measure of variation across units) of the product price under consideration. In this case, the simple statistical concepts of mean (average) and standard deviation are used.
Estimating a relationship among variables requires a more advanced statistical technique. For example, a firm may want to estimate its cost function, the relationship between a cost concept and the level of output. A firm may also want to know the demand function of its product, that is, the relationship between the demand for its product and different factors that influence it. The estimates of costs and demand are usually based on data supplied by the firm. The statistical estimation technique employed is called regression analysis, and is used to develop a mathematical model showing how a set of variables are related. This mathematical relationship can also be used to generate forecasts.
An automobile industry example can be used for the purpose of illustrating the forecasting method that employs simple regression analysis. Suppose a statistician has data on sales of American-made automobiles in the United States for the last 25 years. He or she has also determined that the sale of automobiles is related to the real disposable income of individuals. The statistician also has available the time series (for the last 25 years) on real disposable income. Assume that the relationship between the time series on sales of American-made automobiles and the real disposable income of consumers is actually linear and it can thus be represented by a straight line. A fairly rigorous mathematical technique is used to find the straight line that most accurately represents the relationship between the time series on auto sales and disposable income.
FORECASTING.
Forecasting is a method or a technique used to predict many future aspects of a business or any other operation. For example, a retailing firm that has been in business for the last 25 years may be interested in forecasting the likely sales volume for the coming year. There are numerous forecasting techniques that can be used to accomplish this goal. A forecasting technique, for example, can provide such a projection based on the experience of the firm during the last 25 years; that is, this forecasting technique bases the future forecast on the past data.
While the term "forecasting" may appear to be rather technical, planning for the future is a critical aspect of managing any organization—business, nonprofit, or otherwise. In fact, the long-term success of any organization is closely tied to how well the management of the organization is able to foresee its future and develop appropriate strategies to deal with the likely future scenarios. Intuition, good judgment, and an awareness of how well the economy is doing may give the manager of a business firm a rough idea (or "feeling") of what is likely to happen in the future. It is not easy, however, to convert a feeling about the future outcome into a precise number that can be used, for instance, as a projection for next year's sales volume. Forecasting methods can help predict many future aspects of a business operation, such as forthcoming years' sales volume projections.
Suppose that a forecast expert has been asked to provide quarterly estimates of the sales volume for a particular product for the next four quarters. How should one go about preparing the quarterly sales volume forecasts? One will certainly want to review the actual sales data for the product in question for past periods. Suppose that the forecaster has access to actual sales data for each quarter during the 25-year period the firm has been in business. Using these historical data, the forecaster can identify the general level of sales. He or she can also determine whether there is a pattern or trend, such as an increase or decrease in sales volume over time. A further review of the data may reveal some type of seasonal pattern, such as, peak sales occurring around the holiday season. Thus by reviewing historical data, the forecaster can often develop a good understanding of the pattern of sales in the past periods. Understanding such a pattern can often lead to better forecasts of future sales of the product. In addition, if the forecaster is able to identify the factors that influence sales, historical data on these factors (variables) can also be used to generate forecasts of future sales.
There are many forecasting techniques available to the person assisting the business in planning its sales. For illustration, consider a forecasting method in which a statistician forecasting future values of a variable of business interest—sales, for example—examines the cause-and-effect relationships of this variable with other relevant variables, such as the level of consumer confidence, changes in consumers' disposable incomes, the interest rate at which consumers can finance their excess spending through borrowing, and the state of the economy represented by the percentage of the labor force unemployed. Thus, this category of forecasting techniques uses past time series on many relevant variables to forecast the volume of sales in the future. Under this forecasting technique, a regression equation is estimated to generate future forecasts (based on the past relationship among variables).
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