Forecasting Methodology

Forecasting Methodology

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. Forecasts are vital to every business organization and for every significant management decision. Forecasting, according to Armstrong (2001), is the basis of corporate long-run planning. Many times, this unique approach is used not only to provide a baseline, but also to offer a prediction into the corporation’s future. In the functional areas of finance and accounting, forecasts provide the basis for budgetary planning and cost control. Marketing relies on sales forecasting to plan new products, compensate sales personnel, and make other key decisions. Production and operations personnel use forecasts to make periodic decisions involving process selection, capacity planning, and facility layout, as well as for continual decisions about production planning, scheduling, and inventory. 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.

Most people view the world as consisting of a large number of alternatives. Futures research evolved as a way of examining the alternative futures and identifying the most probable. Forecasting is designed to help decision making and planning in the present. Forecasts empower people because their use implies that we can modify variables now to alter (or be prepared for) the future. There is no perfect forecast, management should try to find and use the best forecasting method available. According to Chase, Jacobs and Aquilano (2006), Forecasting can be classified into four basic types: qualitative, time series analysis, causal relationships, and simulation.

Qualitative techniques are subjective or judgmental and are based on estimates and opinions. Under Qualitative are Grass roots, Market research, Panel consensus, Historical analogy and Delphi method. Grass roots derive a forecast by compiling input from those at the end of the hierarchy who deal with what is being forecast. Market research sets out to collect data in a variety of ways to test hypotheses about the market. Panel consensus is free open exchange at meetings, discussion by a group will produce better forecast than any one individual. Historical analogy ties what is being forecast to a similar item, plan new product where a forecast may be derived by using the history of a similar product. Delphi method is when a group of experts respond to a questionnaire, there is a learning process for the group as it receives new information and there is no influence of group pressure or dominating individuals.

Time Series Analysis is based on the idea that the history of occurrences over time can be used to predict the future. Under time series are Simple moving average, Weighted moving average, Exponential smoothing, Regression analysis, Box Jenkins technique, Shiskin time series and Trend projections. Simple moving average is a time period containing a number of data points is averaged by dividing the sum of the point values by the numbers of points. Weighted moving average is where specific points may be weighted more or less than others (experience), Exponential smoothing is where recent data points are weighted more with weighting declining exponentially as data became older. Regression analysis fits a straight line to past data generally relating the data value to time. Box Jenkins, most accurate statistical technique, relates a class of statistical models to data and fits the model to the time series by using Bayesian posterior distributions. Shiskins time series is a method to decompose a time series into seasonal, trends and irregular, it needs three years of history. Trend projections, fits a mathematical trend line to the data points and projects it into the future.

Causal tries to understand the system underlying and surrounding the item being forecast. Under Causal type there are Regression analysis, Econometric models and Leading indicators. Starting with Regression analysis, similar to least squares method in time series but may contain multiple variables, basis is that forecast is caused by the occurrence of other events. Econometric models the forecast attempts to describe some sector of the economy by a series of interdependent equations. An input/Output model focuses on sales of each industry to other firms and governments; it indicates the changes in sales that a producer industry might expect because of purchasing changes by another industry. Leading Indicators type are statistics moving in the