INDUSTRIAL STATISTICS AND OPERATIONAL MANAGEMENT
6 : FORECASTING TECHNIQUES
Dr. Ravi Mahendra Gor
Associate Dean ICFAI Business School ICFAI HOuse, Nr. GNFC INFO Tower S. G. Road Bodakdev Ahmedabad-380054 Ph.: 079-26858632 (O); 079-26464029 (R); 09825323243 (M) E-mail: firstname.lastname@example.org Contents Introduction Some applications of forecasting Defining forecasting General steps in the forecasting process Qualitative techniques in forecasting Time series methods The Naive Methods Simple Moving Average Method Weighted Moving Average Exponential Smoothing Evaluating the forecast accuracy Trend Projections Linear Regression Analysis Least Squares Method for Linear Regression Decomposition of the time series Selecting A Suitable Forecasting Method More on Forecast Errors Review Exercise
CHAPTER 6 FORECASTING TECHNIQUES 6.1 Introduction:
Every manager would like to know exact nature of future events to accordingly take action or plan his action when sufficient time is in hand to implement the plan. The effectiveness of his plan depends upon the level of accuracy with which future events are known to him. But every manager plans for future irrespective of the fact whether future events are exactly known or not. That implies, he does try to forecast future to the best of his Ability, Judgment and Experience. Virtually all management decisions depend on forecasts. Managers study sales forecasts, for example, to take decisions on working capital needs, the size of the work force, inventory levels, the scheduling of production runs, the location of facilities, the amount of advertising and sales promotion, the need to change prices, and many other problems. For our purpose forecasting can be defined as attempting to predict the future by using qualitative or quantitative methods. In an informal way, forecasting is an integral part of all human activity, but from the business point of view increasing attention is being given to formal forecasting systems which are continually being refined. Some forecasting systems involve very advanced statistical techniques beyond the scope of this book, so are not included. All forecasting methodologies can be divided into three broad headings i.e. forecasts based on:
What people have done What people Examples: say examples:
Time Series Analysis Regression Analysis Surveys Questionnaires
What people do
examples: Testing Marketing Reaction tests
The data from past activities are cheapest to collect but may be outdated and past behavior is not necessarily indicative of future behavior.
Data derived from surveys are more expensive to obtain and needs critical appraisal - intentions as expressed in surveys and questionnaires are not always translated into action. Finally, the data derived from recording what people actually do are the most reliable but also the most expensive and occasionally it is not feasible for the data to be obtained. Forecasting is a process of estimating a future event by casting forward past data. The past data are systematically combined in a predetermined way to obtain the estimate of the future. Prediction is a process of estimating a future event based on subjective considerations other than just past data; these subjective considerations need not be combined in a predetermined way. Thus forecast is an estimate of future values of certain specified indicators relating to a decisional/planning situation, In some situations forecast regarding single indicator is sufficient, where as, in some other situations
forecast regarding several indicators is necessary. The number of indicators and the degree of detail required in the forecast depends on the intended use of the forecast. There are two basic reasons for the need for forecast in any field. 1. Purpose – Any action devised in the PRESENT to take care of some contingency accruing out of a situation or set of conditions set in future. These future conditions offer a purpose / target to be...
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