Forecasting In Business

Forecasting in Week Four

Forecasting is an integral part in planning the financial future of any business and allows the company to consider probabilities of current and future trends using existing data and facts. Many times, this unique approach is used not only to provide a baseline, but also to offer a prediction into the corporationÐŽ¦s future. Planning problems, whether dealing with services or merchandise, can cause any manager headaches easily solved by forecasting. It is important that any manager realizes that the past is a key to the future. Although no long-term plan is perfect, using the correct forecasting tool, along with continual evaluation, allows the manager to review and update corporate financial plans.

Types of forecasting

Forecasting can be classified into four basic types: qualitative, time series analysis, causal relationships, and simulation (Chase, Jacobs, & Aqualino, 2006, p. 513). The purpose of forecasting is to translate experience from the past into an educated guess for the future. Each of the four types has its own merit and can be useful to the business manager. The type chosen will be determined according to the result of the forecasting prediction that is desired.

A qualitative forecast involves observation, is expressed in terms of quality, and most often is subjective. It is non-numerical and involves the opinions or views of the appropriately educated individuals making the forecast. Many managers prefer using their own judgments of those of other professionals rather than using information produced by a computer. Examples of qualitative forecasting methods are the Delphi method and the market research method.

Causal and time series forecasting are both examples of quantitative forecasting and involve the use of numerical data and make specific the hypothesis and procedures to produce forecasts. Quantitative forecasting can only be used when the information being forecast has past numerical data available. A quantitative forecast will assign a margin of error to the forecast, thus assigning an uncertainty to the prediction.

Qualitative forecasting

Qualitative forecasting methods seek opinions on which to base decisions such as consumer panels or focus groups. The Delphi method of forecasting is a group decision procedure based on the predictions of a panel of experts who agree on the likelihood that certain events will occur. The professionals anonymously answer a series of questions. The answers are then tabulated by an independent moderator and the responses are then given back to the original panel that may make changes to their own answers. This method is very time consuming and expensive, however, the end result is reliable and creative. Delphi is used to assist with the formation of a group judgment.

The key elements of the Delphi system are

ÐŽ§structuring of information flow, feedback to the participants, and anonymity

for the participants. Clearly, these characteristics may offer distinct advantages over the conventional face-to-face conference as a communication toolЎЁ (Illinois Institute of Technology, 2006).

The moderator will filter out any unnecessary interactions occurring in the group keeping

the group task oriented.

Market research is a forecasting tool that formulates a hypothesis and then ÐŽ§organizes sample surveys, polls, focus groups, and other techniques to study market characteristics (e.g., ages and incomes of consumers; consumer attitudes) and improve the efficiency of sales and distributionЎЁ (The Columbia Electronic Encyclopedia, 2003). The instrument is used by corporations to expand new manufactured goods, launching of new markets, measurement of advertising success and value, and data related to corporate opponents.

Quantitative forecasting

Quantitative forecasting uses statistical data such as identifying trends, moving averages, and extrapolation to help in making an informed decision. Causal forecasting is based on a known or perceived relationship between the factor to be forecast and other external or internal factors (Idea Velocity, 2005) and is used when a corporation can identify a relationship that is well established, stable over a period of time, have independent variables that are legitimate causal factors and independent of each other, and the variables being considered are easy to predict. Extrinsic variables shape the forecast. A causal forecast asks the question, ÐŽ§Will history repeat itselfЎЁ? Identifying causal factors can be a difficult chore for a business to accomplish and require an astute manager familiar with various business patterns and influences.

Time series forecasting tracks a specific variable over time. A business using time series forecasting assumes that variables are stable over a period of time and by looking at past time series patterns, it is possible to predict future patterns. Time series patterns have predictability and patterns that reoccur. Time series forecasting methods are based on analysis of historical data (time series: a set of observations measured at successive times or over successive periods) and make an assumption that past patterns in data can be used to forecast future data points (Idea Velocity, 2005). Time series models can make short range predictions, lasting a day to a quarter, medium range predictions, lasting a quarter to a year and long range predictions, lasting a year to five years.

Similarities and differences

All forecasting methods, regardless of whether quantitative or qualitative, seek to have the same end results; maximized accuracy and minimized bias. The company strives to make good decisions to accurately predict the future using appropriate forecasting types. Regardless of the forecasting method used, the manager must use eight basic steps:

„« Determine the use of the forecastÐŽXwhat are the objectives?

„« Select the items to be forecast

„« Determine the time horizon of the forecast

„« Select the forecast (s) model

„« Gather the data

„« Validate the forecasting model

„« Make the forecast

„« Implement the rules