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The main goals of this chapter are to: Understand the role of forecasting as a basis for supply chain planning; identify the basic components of demand: average, trend, seasonal, and random variation; show how to make a time series forecast using moving averages, exponential smoothing, and regression; . | Forecasting Chapter 03 McGraw Hill Irwin Copyright 2013 by The McGraw Hill Companies Inc. All rights reserved. Learning Objectives 1. Understand the role of forecasting as a basis for supply chain planning 2. Identify the basic components of demand average trend seasonal and random variation 3. Show how to make a time series forecast using moving averages exponential smoothing and regression 4. Use decomposition to forecast when trend and seasonality is present 5. Show how to measure forecast error 6. Describe the common qualitative forecasting techniques such as the Delphi method and collaborative forecasting 3 2 The Role of Forecasting Forecasting is a vital function and impacts every significant management decision Finance and accounting use forecasts as the basis for budgeting and cost control Marketing relies on forecasts to make key decisions such as new product planning and personnel compensation Production uses forecasts to select suppliers determine capacity requirements and to drive decisions about purchasing staffing and inventory Different roles require different forecasting approaches Decisions about overall directions require strategic forecasts Tactical forecasts are used to guide day-to-day 3 3 decisions Components of Demand Excel Components 3 4 Time Series Analysis Using the past to predict the future 3 5 Forecasting Method Selection Guide Fo re c as ting Me tho d Amo unt o f His to ric al Data Patte rn Fo re c as t Data Ho rizo n Simple moving 6 to 12 months weekly Stationary i.e. no Short average data are often used trend or seasonality Weighted moving 5 to 10 observations Stationary Short average and simple needed to start exponential smoothing Exponential smoothing 5 to 10 observations Stationary and Short with trend needed to start trend Linear regression 10 to 20 observations Stationary trend Short to and seasonality Medium 3 6 Forecast Error Measurements Ideally MAD will be zero MAPE scales the forecast error no forecasting error to the .
