Forecasting_2008

Forecasting_2008 - Demand Forecasting Four Fundamental...

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Unformatted text preview: Demand Forecasting Four Fundamental Approaches Time Series General Concepts Evaluating Forecasts How good is it? Forecasting Methods (Stationary) Cumulative Mean Nave Forecast Moving Average Exponential Smoothing Forecasting Methods (Trends & Seasonality) OLS Regression Holts Method Exponential Method for Seasonal Data Winters Model Other Models Demand Forecasting Forecasting is difficult especially for the future Forecasts are always wrong The less aggregated, the lower the accuracy The longer the time horizon, the lower the accuracy The past is usually a pretty good place to start Everything exhibits seasonality of some sort A good forecast is not just a number it should include a range, description of distribution, etc. Any analytical method should be supplemented by external information A forecast for one function in a company might not be useful to another function (Sales to Mkt to Mfg to Trans) Four Fundamental Approaches Subjective Judgmental Sales force surveys Delphi techniques Jury of experts Experimental Customer surveys Focus group sessions Test Marketing Objective Causal / Relational Econometric Models Leading Indicators Input-Output Models Time Series Black Box Approach Uses past to predict the future Time Series Concepts 1. Time Series Regular & recurring basis to forecast 2. Stationarity Values hover around a mean 3. Trend- Persistent movement in one direction 4. Seasonality Movement periodic to calendar 5. Cycle Periodic movement not tied to calendar 6. Pattern + Noise Predictable and random components of a Time Series forecast 7. Generating Process Equation that creates TS 8. Accuracy and Bias Closeness to actual vs Persistent tendency to over or under predict 9. Fit versus Forecast Tradeoff between accuracy to past forecast to usefulness of predictability 10. Forecast Optimality Error is equal to the random noise Evaluating Forecasts Visual Review Errors Errors Measure MPE and MAPE Tracking Signal Demand Forecasting Generate the large number of short-term, SKU level, locally dis-aggregated demand forecasts required for production, logistics, and sales to operate successfully. Focus on: Forecasting product demand Mature products (not new product releases) Short time horizon (weeks, months, quarters, year) Use of models to assist in the forecast Cases where demand of items is independent Historical Data 50 100 150 200 250 300 350 400 10 20 30 40 50 Forecasting Terminology Initialization ExPost Forecast Forecast Historical Data We are now looking at a future from here, and the future we were looking at in February now includes some of our past, and we can incorporate the past into our forecast. 1993, the first half, which is now...
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This note was uploaded on 12/11/2011 for the course EIN 5346 taught by Professor Lee during the Fall '11 term at FIU.

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Forecasting_2008 - Demand Forecasting Four Fundamental...

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