Forecasting Methods for Management
Managers frequently need to make decisions about the future of the organization. Forecasting is the science of estimating or predicting future trends to support managers in this process. Forecasting methods can be used to provide information to support decisions about many aspects of the business including buying, selling, production, and hiring. Many statistical techniques are available for use in forecasting. However, each is not equally applicable in every situation. In addition to quantitative methods for forecasting, there are also subjective or qualitative forecasting methods that are used by many managers. Experienced and insightful managers can take advantage of years of experience to extrapolate trends in ways that are still not possible through the use of quantitative techniques alone. As a result, quantitative and qualitative analyses are inseparable for most forecasts.
Every day, managers are faced with decisions that need to be made. Some of these are simple such as the routine reordering of supplies or approval of timesheets. Others are more complex, such as determining how to rate someone in an annual review or determining who should be included in the company's layoffs. Another category of complex decision making that managers face is forecasting. This is the science of estimating or predicting future trends. Forecasting is used to support managers in making decisions about many aspects of the business including buying, selling, production, and hiring. For example, managers need to be able to predict the demand for a product or service over a given time period. This will allow them make a number of other decisions. If there will be an increased demand for the organization's product, management can feel confident that they can meet their financial obligations for that time period. However, they may also need to hire additional workers, lease additional facilities, and acquire or store additional raw materials or components to meet the increased demand. Further, a reasonable forecast about demand can also enable the organization to make better strategic decisions about where to take the business line in the future, whether or not to invest in an additional product line, and so forth. On the other hand, if the organization knows that there will be a decreased demand for their products or services for the foreseeable future, they can make other decisions such as whether or not layoffs are called for, if the design of the product needs to be reconsidered, if the business needs to be taken in another direction, and other decisions regarding corporate strategy. The ability to forecast future events with some degree of accuracy is necessary not only for the operation of the organization itself but also for all the members of the supply chain. The same knowledge about the demand for widgets that will affect the widget manufacturer will also affect the organizations providing raw materials or component parts, storing parts or products, delivering products, and selling them to the customer. For these and other reasons, it is important for successful business operations that forecasts be made and that these forecasts be as accurate as possible. With good forecasts, an organization is able to make decisions, develop strategy, and plan for the future.
Deterministic Variables that Affect Operations
There are a number of deterministic variables for which there are specific causes or determiners that can affect the operations and profitability of a business. A trend is the persistent, underlying direction in which something is moving in either the short, intermediate, or long term. Identification of a trend allows managers to better plan to meet future needs. For example, a market trend for an increasing reliance on electronic gadgets may mean that a business needs to rethink its strategy of increasing its emphasis on manual tools. Business cycles are continually recurring variations in total economic activity. These expansions or contractions of economic activity tend to occur across most sectors of the economy at the same time. For example, several years of a boom economy with expansion of economic activity (e.g., more jobs, higher sales) are frequently followed by slower growth or even contraction of economic activity. Seasonal fluctuations are changes in economic activity that occur in a fairly regular annual pattern. Seasonal fluctuations may be related to seasons of the year, the calendar, or holidays. In most situations, for example, it would be unwise for a retail store to hire holiday workers on a permanent basis rather than only for the holiday shopping period.
Determining the Technique for Forecasting
There are many statistical techniques that can be used in forecasting. However, each is not equally applicable in every situation. The first decision a manager needs to make in choosing a forecasting method is to determine whether or not there are sufficient data available for quantitative analysis. If there are not, qualitative methods must be used. On the other hand, as shown in Figure 1, if there are sufficient data available, there are a number of techniques from which to choose. In order to choose the best technique for the data available, several questions must be asked. First, it must be determined whether or not there is useful knowledge available concerning the relationships and associations between the various factors of interest for the forecast. If there are not, then the type of data available -- cross-section or time series -- is a determining factor in which analysis techniques are most appropriate. For cross-section data, it must be considered whether or not the forecast needs to assess policy options or otherwise choose between alternative courses of action. If not, quantitative analogies are the most appropriate tool. In this approach, managers or other experts identify analogous situations and these inputs are used to derive the forecast (e.g., to determine how many seats are needed in a movie theatre in a new development, one could look at average data from movie theatres in similar developments). If, on the other hand, the forecast will be used to make decisions between alternatives, a better approach would be to employ an expert system. These are decision support systems that utilize artificial intelligence technology to evaluate a situation and suggest an appropriate course of action. Expert systems develop rules for forecasting following the reasoning processes used by decision making experts.
Forecasting Techniques Using Time Series Data
If time series rather than cross-section data are available, other techniques are more appropriate. If there is a good knowledge about the subject domain of the forecast, rule-based forecasting should be used. These approaches use an expert system that utilizes both expert domain knowledge and statistical techniques. If there is little or no knowledge about the domain, however, other options are available. Extrapolation techniques analyze times series data in an attempt to forecast future events (see below). Neural nets are an approach to artificial intelligence in which computer processors are connected in a way similar to the connections between neurons. These systems are able to learn through trial and error. Data mining can also be used for this type of situation. In data mining, large collections of data are analyzed to establish patterns and determine previously unknown relationships.
If there is a good knowledge of the relationship between variables and the future which is being forecast is unlikely to differ...
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