# Economic Tools for Management Decision Making

Managerial decision-making draws on economic concepts as well as tools and techniques of analysis provided by decision sciences. The major categories of these tools and techniques are optimization, statistical estimation and forecasting. Most of these methodologies are technical. These methods are briefly explained below to illustrate how tools of decision sciences are used in managerial decision making.

## 1. 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 resolve the optimization problem, he or she may make use of the results of mathematical analysis. In the profit maximization example, the profit maximizing condition requires that the firm select the production level at which marginal revenue equals marginal cost. This condition is obtained from an optimization model/technique. The techniques of optimization employed depend on the problem a manager is trying to solve.

## 2. 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 complex and advanced. Thus, 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 desire to estimate its cost function i.e. the relationship between cost concept and the level of output. A firm may also wish to the demand function of its product that is the relationship between the demand for its product and 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 engender a mathematical model showing how a set of variables are related. This mathematical relationship can also be used to generate forecasts.

An example from the automobile industry is befitting for illustrating the forecasting method that employs simple regression analysis. Let us assume that 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 data (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 rigorous mathematical technique is used to locate the straight line that most accurately represents the relationship between the time series on auto sales and disposable income.

## 3. Forecasting

It is a method or a technique 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. Numerous forecasting techniques 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 technical, planning for the future is a critical aspect of managing any organisation or a business. The long-term success of any organisation has close association with the propensity of the management of the organisation to foresee its future and develop appropriate strategies to deal with the likely future scenarios. Intuition, good judgment and knowledge of economic conditions enables the manager to ‘feel’ or perhaps anticipate the likelihood in the future. It is not easy, however, to metamorphose a feeling about the future outcome into concrete data 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 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 he attempt at preparing the quarterly sales volume forecasts? Reviewing the actual sales data for the product in question for past periods will give a good start. Suppose that the forecaster has access to actual sales data for each quarter during the 25-year period the firm has been in business. Employing this historical data, the forecaster can identify the general trend 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. An in depth review of the data may unearth some type of seasonal pattern, such as, peak sales occurring around the holiday season. Thus, by reviewing historical data, there is a high probability that the forecaster develops a good understanding of the pattern of sales in the past periods. Understanding such patterns can result in 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. Take for example 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. The other pertinent variable may be 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. This category of forecasting technique utilizes time series data 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).

Credit: Managerial Economics-CU