OM 300 1nd Edition Lecture 23 Outline of Last Lecture I What is forecasting II Forecasting Time Horizons III Influence of Product Life Cycle IV Types of Forecasts V 7 Steps in Forecasting and Realities VI Quality Methods Outline of Current Lecture I Overview of Quantitative Approaches a Native approach b Moving averages c Exponential smoothing d Trend projection e Linear regression Current Lecture Time Series Forecasting Set of evenly spaced numerical data o Obtained by observing response variable at regular time periods Forecast based only on past values no other variables important o assumes that factors influencing pas and present will continue influence in future These notes represent a detailed interpretation of the professor s lecture GradeBuddy is best used as a supplement to your own notes not as a substitute Trend Component Persistent overall upward or downward pattern Changes due to population technology age culture etc Typically several years duration Seasonal Component Regular pattern of up and down fluctuations Due to weather customs etc Occurs within a single unit Cyclical Component Repeating up and down movement Affected by business cycle political and economic factors multiple years duration Often causal or associative relationships Random Component Erratic unsystematic residual fluctuations Due to random variation or unforeseen events Short duration and nonrepeating Na ve Approach Assumes demand in next period is the same as demand in most recent period o Ex If January sales were 68 then February sales will be 68 Sometimes cost effective and efficient Can be good starting point Moving Average Method Used if little or no trend Used often for smoothing Provides overall impression of data over time Weighted Moving Average Used when some trend might be present o Older data usually less important Weights based on experience and intuition Potential Problems with Moving Average Increasing n smooths the forecast but makes it less sensitive to changes Does not forecast trends well Requires extensive historical data Exponential Smoothing Form of weighted moving average o Weights decline exponentially o Most recent data weighted most Requires smoothing constant o Ranges from 0 to 1 o Subjectively chosen Involves little record keeping of past data Choosing a The objective is to obtain the most accurate forecast no matter the technique Forecast error Actual demand Forecast value Common Measures of Error
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