Forecasting involves the generation of anumber, set of numbers, or scenario thatcorresponds to a future occurrence. It isabsolutely essential to short-range and long-range planning. By definition, a forecast is basedon past data, as opposed to a prediction, whichis more subjective and based on instinct, gutfeel, or guess.
The past will repeat itselfA forecast for tomorrow will be more accurate than a forecastfor next month; a forecast for next month will be moreaccurate than a forecast for next year; and a forecast for nextyear will be more accurate than a forecast for ten years in thefuture.Forecasts are seldom accurate and almost never totallyaccurate, although some are very close. Therefore, it is wise tooffer a forecast “range.” If one were to forecast a demand of100,000 units for the next month, it is extremely unlikely thatdemand would equal exactly 100,000. However, a forecast of90,000 to 110,000 would provide a much larger target forplanning.
AccurateReliableTimelyEasy to use andunderstandCost-effective
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Quantitative forecasting techniques aregenerally more objective than qualitativeforecasting methods. Quantitative forecastscan be time-series forecasts (i.e., aprojection of the past into the future) orforecasts based on associative models (i.e.,based on one or more explanatoryvariables). Time-series data may haveunderlying behaviors that need to beidentified by the forecaster.
Econometric methods, such as autoregressiveintegrated moving-average model (ARIMA), use complexmathematical equations to show past relationshipsbetween demand and variables that influence thedemand. An equation is derived and then tested andfine-tuned to ensure that it is as reliable arepresentation of the past relationship as possible. Oncethis is done, projected values of the influencingvariables (income, prices, etc.) are inserted into theequation to make a forecast.
Determine the forecasts purposeEstablish a time horizonSelect a forecasting technique