A variety of forecasting methods exist, and their applicability is dependent on the time frame of the forecast (i.e., how far in the future we are forecasting), the existence of patterns in the forecast (i.e., seasonal trends, peak periods), and the number of variables to which the forecast is related . We will discuss each of these factors separately.
In general, forecasts can be classified according to three time frames : short range, medium range, and long range. Short-range forecasts typically encompass the immediate future and are concerned with the daily operations of a business firm, such as daily demand or resource requirements. A short-range forecast rarely goes beyond a couple months into the future. A medium-range forecast typically encompasses anywhere from 1 or 2 months to 1 year. A forecast of this length is generally more closely related to a yearly production plan and will reflect such items as peaks and valleys in demand and the necessity to secure additional resources for the upcoming year. A long-range forecast typically encompasses a period longer than 1 or 2 years . Long-range forecasts are related to management's attempt to plan new products for changing markets, build new facilities, or secure long- term financing. In general, the further into the future one seeks to predict, the more difficult forecasting becomes.
These classifications should be viewed as generalizations . The line of demarcation between medium- and long-range forecasts is often quite arbitrary and not always distinct. For some firms a medium-range forecast could be several years, and for other firms a long-range forecast could be in terms of months.
A trend is a gradual, long-term, up-or-down movement of demand .
Forecasts often exhibit patterns, or trends. A trend is a long-term movement of the item being forecast. For example, the demand for personal computers has shown an upward trend during the past decade , without any long downward movement in the market. Trends are the easiest patterns of demand behavior to detect and are often the starting point for developing a forecast. Figure 15.1(a) illustrates a demand trend in which there is a general upward movement or increase. Notice that Figure 15.1(a) also includes several random movements up and down. Random variations are movements that are not predictable and follow no pattern (and thus are virtually unpredictable).
Figure 15.1. Forms of forecast movement: (a) trend, (b) cycle, (c) seasonal pattern, and (d) trend with seasonal pattern
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A cycle is an undulating movement in demand, up and down, that repeats itself over a lengthy time span (i.e., more than 1 year). For example, new housing starts and thus construction-related products tend to follow cycles in the economy. Automobile sales tend to follow cycles in the same fashion. The demand for winter sports equipment increases every 4 years, before and after the Winter Olympics. Figure 15.1(b) shows the general behavior of a demand cycle.
A cycle is an up-and-down repetitive movement in demand .
A seasonal pattern is an up-and-down, repetitive movement within a trend occurring periodically .
A seasonal pattern is an oscillating movement in demand that occurs periodically (in the short run) and is repetitive. Seasonality is often weather related. For example, every winter the demand for snowblowers and skis increases dramatically, and retail sales in general increase during the Christmas season . However, a seasonal pattern can occur on a daily or weekly basis. For example, some restaurants are busier at lunch than at dinner, and shopping mall stores and theaters tend to have higher demand on weekends. Figure 15.1(c) illustrates a seasonal pattern in which the same demand behavior is repeated each period at the same time.
Of course, demand behavior will frequently display several of these characteristics simultaneously . Although housing starts display cyclical behavior, there has been an upward trend in new house construction over the years. As we noted, demand for skis is seasonal; however, there has been a general upward trend in the demand for winter sports equipment during the past 2 decades. Figure 15.1(d) displays the combination of two demand patterns, a trend with a seasonal pattern.
There are instances in which demand behavior exhibits no pattern. These are referred to as irregular movements , or variations. For example, a local flood might cause a momentary increase in carpet demand, or negative publicity resulting from a lawsuit might cause product demand to drop for a period of time. Although this behavior is causal , and thus not totally random, it still does not follow a pattern that can be reflected in a forecast.
The factors discussed previously determine to a certain extent the type of forecasting method that can or should be used. In this chapter we discuss the basic types of forecasting: time series , regression methods , and qualitative methods . Time series is a category of statistical techniques that uses historical data to predict future behavior. Regression (or causal) methods attempt to develop a mathematical relationship (in the form of a regression model) between the item being forecast and factors that cause it to behave the way it does. Most of the remainder of this chapter is about time series and regression forecasting methods. In this section we focus our discussion on qualitative forecasting.
Types of forecasting methods are time series, regression, and qualitative.
Qualitative methods use management judgment, expertise, and opinion to make forecasts. Often called "the jury of executive opinion," they are the most common type of forecasting method for the long-term strategic planning process. There are normally individuals or groups within an organization whose judgments and opinions regarding the future are as valid or more valid than those of outside experts or other structured approaches. Top managers are the key group involved in the development of forecasts for strategic plans. They are generally most familiar with their firms' own capabilities and resources and the markets for their products.
The sales force is a direct point of contact with the consumer. This contact provides an awareness of consumer expectations in the future that others may not possess. Engineering personnel have an innate understanding of the technological aspects of the type of products that might be feasible and likely in the future.
Consumer, or market, research is an organized approach that uses surveys and other research techniques to determine what products and services customers want and will purchase, and to identify new markets and sources of customers. Consumer and market research is normally conducted by the marketing department within an organization, by industry organizations and groups, and by private marketing or consulting firms. Although market research can provide accurate and useful forecasts of product demand, it must be skillfully and correctly conducted , and it can be expensive.
The Delphi method is a procedure for acquiring informed judgments and opinions from knowledgeable individuals, using a series of questionnaires to develop a consensus forecast about what will occur in the future. It was developed at the RAND Corporation shortly after World War II to forecast the impact of a hypothetical nuclear atack on the United States. Although the Delphi method has been used for a variety of applications, forecasting has been one of its primary uses. It has been especially useful for forecasting technological change and advances.
Technological forecasting has become increasingly crucial for successful competition in today's global business environment. New enhanced computer technology, new production methods, and advanced machinery and equipment are constantly being made available to companies. These advances enable them to introduce more new products into the marketplace faster than ever before. The companies that succeed do so by getting a technological jump on their competitors through accurate prediction of future technology and its capabilities. What new products and services will be technologically feasible, when they can be introduced, and what their demand will be are questions about the future for which answers cannot be predicted from historical data. Instead, the informed opinion and judgment of experts are necessary to make these types of single, long-term forecasts.