Operation management 9e stevenson mcgrwhill chap003

3 Forecasting McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc All Learning Objectives     List the elements of a good forecast Outline the steps in the forecasting process Describe at least three qualitative forecasting techniques and the advantages and disadvantages of each Compare and contrast qualitative and quantitative approaches to forecasting 3-2 Learning Objectives     Briefly describe averaging techniques, trend and seasonal techniques, and regression analysis, and solve typical problems Describe two measures of forecast accuracy Describe two ways of evaluating and controlling forecasts Identify the major factors to consider when choosing a forecasting technique 3-3 FORECAST:  A statement about the future value of a variable of interest such as demand  Forecasting is used to make informed decisions  Long-range  Short-range 3-4 Forecasts  Forecasts affect decisions and activities throughout an organization  Accounting, finance  Human resources  Marketing  MIS  Operations  Product / service design 3-5 Uses of Forecasts Accounting Cost/profit estimates Finance Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy MIS IT/IS systems, services Operations Schedules, MRP, workloads Product/service design New products and services 3-6 Features of Forecasts  Assumes causal system past ==> future  Forecasts rarely perfect because of randomness  Forecasts more accurate for groups vs individuals  Forecast accuracy decreases as time horizon increases I see that you will get an A this semester 3-7 Elements of a Good Forecast Timely Reliable f g n i n a e M ul Accurate Written y s Ea to e s u 3-8 Steps in the Forecasting Process “The forecast” Step Monitor the forecast Step Make the forecast Step Obtain, clean and analyze data Step Select a forecasting technique Step Establish a time horizon Step Determine purpose of forecast 3-9 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 3-10 Linear Model Seems Reasonable X 14 15 16 12 14 20 15 Y 15 10 13 15 25 27 24 20 27 44 34 17 Computed relationship 50 40 30 20 10 0 10 15 20 25 A straight line is fitted to a set of sample points 3-31 Linear Regression Assumptions  Variations around the line are random  Deviations around the line normally distributed  Predictions are being made only within the range of observed values  For best results:  Always plot the data to verify linearity  Check for data being time-dependent  Small correlation may imply that other variables are important 3-32 Forecast Accuracy  Error - difference between actual value and predicted value  Mean Absolute Deviation (MAD)  Average absolute error  Mean Squared Error (MSE)  Average of squared error  Mean Absolute Percent Error (MAPE)  Average absolute percent error 3-33 MAD, MSE, and MAPE MAD = ∑ Actual − forecast n MSE = ∑ ( Actual − forecast) n -1 MAPE = ∑( Actual − forecast / Actual*100) n 3-34 MAD, MSE and MAPE  MAD  Easy to compute  Weights errors linearly  MSE  Squares error  More weight to large errors  MAPE  Puts errors in perspective 3-35 Example 10 Period MAD= MSE= MAPE= Actual 217 213 216 210 213 219 216 212 Forecast 215 216 215 214 211 214 217 216 (A-F) -3 -4 -1 -4 -2 |A-F| 22 (A-F)^2 16 25 16 76 (|A-F|/Actual)*100 0.92 1.41 0.46 1.90 0.94 2.28 0.46 1.89 10.26 2.75 10.86 1.28 3-36 Controlling the Forecast  Control chart  A visual tool for monitoring forecast errors  Used to detect non-randomness in errors  Forecasting errors are in control if  All errors are within the control limits  No patterns, such as trends or cycles, are present 3-37 Sources of Forecast errors  Model may be inadequate  Irregular variations  Incorrect use of forecasting technique 3-38 Tracking Signal •Tracking signal –Ratio of cumulative error to MAD (Actual-forecast) ∑ Tracking signal = MAD Bias – Persistent tendency for forecasts to be Greater or less than actual values 3-39 Choosing a Forecasting Technique  No single technique works in every situation  Two most important factors  Cost  Accuracy  Other factors include the availability of:  Historical data  Computers  Time needed to gather and analyze the data  Forecast horizon 3-40 Operations Strategy  Forecasts are the basis for many decisions  Work to improve short-term forecasts  Accurate short-term forecasts improve  Profits  Lower inventory levels  Reduce inventory shortages  Improve customer service levels  Enhance forecasting credibility 3-41 Supply Chain Forecasts  Sharing forecasts with supply can  Improve forecast quality in the supply chain  Lower costs  Shorter lead times  Gazing at the Crystal Ball (reading in text) 3-42 Exponential Smoothing 3-43 Linear Trend Equation 3-44 Simple Linear Regression 3-45 ... activities throughout an organization  Accounting, finance  Human resources  Marketing  MIS  Operations  Product / service design 3-5 Uses of Forecasts Accounting Cost/profit estimates Finance... Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy MIS IT/IS systems, services Operations Schedules, MRP, workloads Product/service design New products and services 3-6 Features