Forecasting

6.1. Concept of Forecasting

Many new car buyers have a thing or two in common. Once they make the decision to buy a new car, they want it as soon as possible. They usually don’t want to order it and then have to wait six weeks or more for delivery. If the car dealer they visit doesn’t have the car they want, they’ll look elsewhere. Hence, it is important for a dealer to anticipate buyer wants and to have those models, with the necessary options, in stock. The dealer who can correctly forecast buyer wants, and have those cars available, is going to be much more successful than a competitor who guesses instead of forecasting-and guesses wrong, and gets stuck with cars customers don’t want. So how does the dealer know how many cars of each type to stock? The answer is, the dealer doesn’t know for sure, but based on analysis of previous buying patterns, and perhaps making allowances for current conditions, the dealer can come up with a reasonable approximation of what buyers will want.

Planning is an integral part of a manager’s job. If uncertainties cloud the planning horizon, managers will find it difficult to plan effectively. Forecasts help managers by reducing some of the uncertainty, thereby enabling them to develop more meaningful plans.

“A forecast is a statement about the future value of a variable such as demand”- Stevension

“Forecast is a prediction of future events used for planning purpose”-Krajewski.

People make and use forecasts all the time, both in their jobs and in everyday life. In everyday life, they forecasts answers to questions and then make decision based on their forecasts. Typical questions they may ask are: “Will I get the job?”, “When should I leave to make it to class, the station, the bank, the interviews………., on time?” To make these forecasts, they may take into account two kinds of information. One is current factors or conditions. The other is past experience in a similar situation.

Forecasting for business purpose involves similar approaches. In business, however, more formal methods are used to make forecasts and to assess forecast accuracy. Forecasts are the basis for budgeting, planning capacity, sales, production and inventory, personnel, purchasing, and more. Forecasts play an important role in the planning process because they enable managers to anticipate the future so they can plan accordingly.

There are two uses for forecasts. One is to help mangers plan the system and the other is to help them plan the use of the system. Planning the system generally involves long-range plans about the types of products and services to offer, what facilities and equipment to have, where to locate, and so on. Planning the use of the system refers to short-range and intermediate-range planning, which involve tasks such as planning inventory and workforce levels, planning purchasing and production, budgeting, and scheduling.

Business forecasting pertains to more than predicting demand. Forecasts are also used to predict profits, revenues, costs, productivity changes, prices and availability of energy and raw materials, interest rates, movements of key economic indicators (e.g., GDP, inflation, government borrowing), and prices of stocks and bonds.

Forecasts aid in determining what resources are needed, in scheduling existing resources, and in acquiring additional resources. Accurate forecasts allow schedulers to increase customer satisfaction, reduce customer response times, use capacity efficiently, and cut inventories.

6.2. Features Common to all Forecasts

A wide variety of forecasting techniques are in use. Certain features are common to all, and it is important to recognize them.

·         Forecasting techniques generally assume that the same underlying casual system that existed in the past will continue to exist in the future.

·         Forecasts are rarely perfect; actual results usually differ from predicted values.

·         Forecasts for groups of items tend to be more accurate than forecasts for individual items because forecasting errors among items in a group usually have a cancelling effect. Opportunities for grouping may arise if parts or raw materials are used for multiple products or if a product or service is demanded by a number of independent sources.

·         Forecast accuracy decreases as the time period covered by the forecast-the time horizon­-increases. Generally speaking, short-range forecasts must contend with fewer uncertainties than longer-range forecasts, so they tend to be more accurate.

6.3. Elements of A good Forecast

A properly prepared forecast should fulfill certain requirements:

         i.            The forecast should be timely. Usually, a certain amount of time is needed to respond to the information contained in a forecast. For example, capacity cannot be expanded overnight, nor can inventory levels be changed immediately. Hence, the forecasting horizon must cover the time necessary to implement possible changes.

       ii.            The forecast should be accurate and the degree of accuracy should be stated. This will enable users to plan for possible errors and will provide a basis for comparing alternative forecasts.

      iii.            The forecast should be reliable, it should work consistently. A technique that sometimes provides a good forecast and sometimes a poor one will leave users with the uneasy feeling that they may get burned every time a new forecast is issued.

      iv.            The forecast should be expressed in meaningful units. Financial planners need to know how many dollars will be needed, production planners need to know many units will be needed, and schedulers need to know what machines and skills will be required. The choice of units depends on user needs.

       v.            The forecast should be in writing. Although this will not guarantee that all concerned are using the same information, it will at least increase the likelihood of it. In addition, a written forecast will permit an objective basis for evaluating the forecast once actual results are in.

      vi.            The forecasting technique should be simple to understand and use. Users often lack confidence in forecasts based on sophisticated techniques; they do not understand either the circumstances in which the techniques is an obvious consequence. Not surprisingly, fairly simple forecasting techniques enjoy widespread popularly because users are more comfortable working with them.

    vii.            The forecasts should be cost-effective.

6.4. Steps in the Forecasting Process

There are six basic steps in the forecasting process:

         i.            Determine the purpose of the forecast. How will it be used and when will it be needed? This will provide an indication of the level of detail required in the forecast, the amount of resources (personnel, computer time, dollars) that can be justified, and the level of accuracy necessary.

       ii.            Establish a time horizon. The forecast must indicate a time interval, keeping in mind that accuracy decreases as the time horizon increases.

      iii.            Select o forecast techniques.

      iv.            Gather and analyze relevant data. Before a forecast can be made, data must be gathered and analyzed. Identify any assumptions that are made in conjunction with preparing and using the forecast.

       v.            Make the forecast.

      vi.            Monitor the forecast. A forecast has to be monitored to determine whether it is performing in a satisfactory manner. If it is not, reexamine the method, assumptions, validity of data, and so on; modify as needed, and prepare a revised forecast.

6.5. Approaches to Forecasting

Forecasting methods may be based on mathematical models that use available historical data, on qualitative methods that draw on managerial experience and customer judgments, or they may be based on a combination of both. So, we can say that there are basically two general approaches to forecasting: qualitative and quantitative.

·         Qualitative methods consist mainly of subjective inputs, which often defy precise numerical description.

·         Quantitative methods involve either the projection of historical data or the development of associative models that attempt to utilize casual (explanatory) variables to make a forecast.

Qualitative techniques permit inclusion of soft information (e.g., human factors, personal opinions) in the forecasting process. Those factors are often omitted or downplayed when quantitative techniques are used because they are difficult or impossible to quantify. Quantitative techniques consist mainly of analyzing objective, or data. They usually avoid personal biases that sometimes contaminate qualitative methods. In practice, either or both approaches might be used to develop a forecast.

6.6. Techniques of Forecasting

Techniques of forecasting are as follows:

·         Judgmental forecasts rely on analysis of subjective inputs obtained from various sources, such as consumer surveys, the sales staff, managers and executives, and panels of experts. Quite frequently, these sources provide insights that are not otherwise available.

·         Time series forecasts simply attempt to project past experience into the future. These techniques use historical data with the assumption that the future will be like the past. Some models merely attempt to smooth out random variation in historical data; others attempt to identify specific patterns in the data and project or extrapolate those patterns into the future, without trying to identify causes of the patterns.

·         Associative models use equations that consist of one or more explanatory variables that can be used to predict future demand. For example, demand for paint might be related to variables such as the price per gallon and the amount spent on advertising, as well as specific characteristics of the paint. (e.g., drying time, ease of cleanup).