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.

Read More:

• Demand Forecasting in Managerial Economics
• Steps in Demand Forecasting
• Objectives of Demand Forecasting

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.

Read More:

• Techniques of Demand Forecasting
• Time Horizon in Forecasting
• Requirements of a Good Forecast
• Managers and Selection of Proper Forecasting Technique
• Expert Opinion Method of Demand Forecasting

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).