生产运营课件3.pptVIP

FORECAST: A statement about the future value of a variable of interest such as demand. Forecasts affect decisions and activities throughout an organization Accounting, finance Human resources Marketing MIS Operations Product / service design Uses of Forecasts Assumes causal systempast == future Forecasts rarely perfect because of randomness Forecasts more accurate forgroups vs. individuals Forecast accuracy decreases as time horizon increases Elements of a Good Forecast Steps in the Forecasting Process Types of Forecasts Judgmental - uses subjective inputs Time series - uses historical data assuming the future will be like the past Associative models - uses explanatory variables to predict the future Judgmental Forecasts Executive opinions Sales force opinions Consumer surveys Outside opinion Delphi method Opinions of managers and staff Achieves a consensus forecast Time Series Forecasts Trend - long-term movement in data Seasonality - short-term regular variations in data Cycle – wavelike variations of more than one year’s duration Irregular variations - caused by unusual circumstances Random variations - caused by chance Forecast Variations Naive Forecasts Na?ve Forecasts Simple to use Virtually no cost Quick and easy to prepare Data analysis is nonexistent Easily understandable Cannot provide high accuracy Can be a standard for accuracy Uses for Na?ve Forecasts Stable time series data F(t) = A(t-1) Seasonal variations F(t) = A(t-n) Data with trends F(t) = A(t-1) + (A(t-1) – A(t-2)) Techniques for Averaging Moving average Weighted moving average Exponential smoothing Moving Averages Moving average – A technique that averages a number of recent actual values, updated as new values become available. Weighted moving average – More recent values in a series are given more weight in computing the forecast. Simple Moving Average Exponential Smoothing Premise--The most recent observations might have the highest predictive value. Therefore, we should give more we