Chapter 15 Demand Management and Forecasting McGraw-Hill/Irwin ©2011 The McGraw-Hill Companies, All Rights Reserved Learning Objectives Understand the role of forecasting as a basis for supply chain planning. Compare the differences between independent and dependent demand. Identify the basic components of independent demand: average, trend, seasonal, and random variation. Describe the common qualitative forecasting techniques such as the Delphi method and Collaborative Forecasting. Show how to make a time series forecast using regression, moving averages, and exponential smoothing. Use decomposition to forecast when trend and seasonality is present. 15-2 Characteristics of Forecasts Guessing at the future: educated guessing game Seldom correct No perfect forecast Objective is to minimize forecast errors It is only a tool used to set: Production plan and budgets Work schedules Forecasts are more accurate in aggregation Long-term forecasts are less accurate than short-term forecasts Forecasts are means to an end 15-3 Demand Management Strategic forecasts: forecasts used to help set the strategy of how demand will be met Tactical forecasts: forecasted needed for how a firm operates processes on a day-today basis The purpose of demand management is to coordinate and control all sources of demand Two basic sources of demand LO 2 Dependent demand: the demand for a product or service caused by the demand for other products or services Independent demand: the demand for a product or service that cannot be derived directly from that of other products 15-4 Demand Management Continued Not much a firm can do about dependent demand It is demand that must be met There is a lot a firm can do about independent demand Take an active role to influence demand Offer incentive to customers Wage campaigns to sell products Take a passive role and respond to demand Especially if at full capacity High cost of advertisement LO 1 15-5 Types of Forecasts Basic types of forecasts Quantitative—use historical data Time series analysis Causal relationships Simulation Qualitative—based on subjective estimates/opinion Time series analysis is based on the idea that data relating to past demand can be used to predict future demand Primary focus of this chapter LO 1 15-6 Components of Demand Average demand for a period of time Trend Seasonal element Cyclical elements Random variation Autocorrelation LO 3 15-7 Common Types of Trends LO 3 15-8 Time Series Analysis Short term: forecast under three months Tactical decisions Medium term: three months to two years Capturing seasonal effects Long term: forecast longer than two years Detecting general trends Identifying major turning points LO 5 15-9 A Guide to Selecting an Appropriate Forecasting Method LO 5 15-10 Pick Forecasting Model Based On Time horizon to forecast Data availability Accuracy required Size of forecasting budget Availability of qualified personnel LO 5 15-11 Linear Regression Analysis TC FC VC TC 80000 75 X Regression: functional relationship between two or more correlated variables It is used to predict one variable given the other Y = a + bX LO 5 Y is the value of the dependent variable a is the Y intercept b is the slope X is the independent variable Assumes data falls in a straight line 15-12 Example 15.1: The Data and Least Squares Regression Line LO 5 15-13 Example 15.1: Equations and Calculating Totals LO 5 15-14 Example 15.1: Calculating the Forecast Y a bx Y13 441.6 359.614 5,116.4 Y14 441.6 359.615 5,476.0 Y15 441.6 359.616 5,835.6 Y16 441.6 359.617 6,195.2 LO 5 15-15 Calculating the Forecast 180 Week Sales Forecast 175 170 Y = 143.5 + 6.3x Sales 165 What is forecast for x=100? 160 Y = 143.5 + 6.3(100) = 774 155 150 145 1 2 3 4 5 Week 15-16 Decomposition of a Time Series Time series: chronologically ordered data that may contain one or more components of demand Decomposition: identifying and separating the time series data into these components Seasonal variation Additive: the seasonal amount is constant Multiplicative: the seasonal variation is a percentage of demand LO 6 15-17 Additive and Multiplicative Seasonal Variation Superimposed on Changing Trend LO 6 15-18 Example 15.3: The Data and Hand Fitting Y a bx 170 55x LO 6 15-19 Example 15.3: Computing Seasonal Factors and Computing Forecast LO 5 15-20 Decomposition Using Least Squares Regression Determine the seasonal factor Deseasonalize the original data Develop a least squares regression line for the deseasonalized data Project the regression line through the period of the forecast Create the final forecast by adjusting the regression line by the seasonal factor LO 6 15-21 Steps 1-3 Deseasonalized Demand LO 6 15-22 Steps 4 – 5 LO 6 15-23 Simple Moving Average Useful when demand is neither growing nor declining rapidly and does not have seasonal characteristics Moving averages can be centered or used to predict the following period Important to select the best period Longer gives more smoothing/less sensitive Shorter reacts quicker to trends LO 5 15-24 Simple Moving Average Formula At 1 At 2 At 3 At n Ft n Ft Forecastfor thecomingperiod n Number of periods to be averaged At 1 Actualoccurrencein t hepast period At 2 , At 3 and At n Actualoccurrences two periodsago, three periodsago, and so on up to n periodsago LO 5 15-25 Forecast Demand Based on a Three- and a Nine-Week Simple Moving Average LO 5 15-26 Forecast Demand Based on a Three- and a Nine-Week Simple Moving Average 15-27 Weighted Moving Average The moving average formula implies an equal weight being placed on each value that is being averaged The weighted moving average permits an unequal weighting on prior time periods All the weights must sum to one if fractions Otherwise, weights can be real numbers. If so divide by sum of weights: Ft = wi Dt 1 / wi Ft = w 1 A t-1 + w 2 A t- 2 + w 3 A t-3 +...+w n A t- n LO 5 15-28 WMA Example Question: Given the weekly demand information and weights of 0.6, 0.1, and 0.3, what is the weighted moving average forecast for the 5th period or week? Week Demand 1 820 2 775 3 680 4 655 F5 = (0.6)(655)+(0.1)(680)+(0.3)(755)= 688 15-29 Choosing Weights Experience and trial-and-error are the simplest ways Generally, the most recent past is the best indicator When data are seasonal, weights should be established accordingly LO 5 15-30 Exponential Smoothing Most used of all forecasting techniques Integral part of all computerized forecasting programs Widely used in retail and service Widely accepted because… Exponential models are surprisingly accurate Formulating an exponential model is relatively easy The user can understand how the model works Little computation is required to use the model Computer storage requirements are small Tests for accuracy are easy to compute LO 5 15-31 Exponential Smoothing Model Ft = Ft-1 +(At-1 - Ft-1) Where: Ft = Forecast value for the coming t time period Ft 1 = Forecast value in 1 past time period At 1 = Actual occurrence in the past 1 time period = Alpha smoothing constant Premise: The most recent observations might have the highest predictive value Therefore, we should give more weight to the more recent time periods when forecasting LO 5 15-32 Exponential Smoothing Example (=0.20) LO 5 Week 1 2 3 4 5 6 7 8 9 10 Demand 820 775 680 655 750 802 798 689 775 0.2 820.00 820.00 811.00 784.80 758.84 757.07 766.06 772.45 755.76 759.61 820 0.2820 820 820 0.20 820 820 0.2775 820 820 0.2 45 820 9.0 811 811 .2680 811 811 .2 131 811 26.2 784.8 15-33 ES Example (=0.10, 0.60) Week 1 2 3 4 5 6 7 8 9 10 Demand 820 775 680 655 750 802 798 689 775 0.1 0.6 820.00 820.00 815.50 801.95 787.26 783.53 785.38 786.64 776.88 776.69 820.00 820.00 793.00 725.20 683.08 723.23 770.49 787.00 728.20 756.28 15-34 ES Example (=0.10, 0.60) 850 Note how the smaller alpha results in a smoother line in this example 800 Demand 750 700 Demand 650 Alpha=0.1 Alpha=0.6 600 1 2 3 4 5 6 7 8 9 10 Week 15-35 Trend Effects in Exponential Smoothing An trend in data causes the exponential forecast to always lag the actual data Can be corrected somewhat by adding in a trend adjustment To correct the trend, we need two smoothing constants Smoothing constant alpha () Trend smoothing constant delta (δ) LO 5 15-36 Exponential Forecasts versus Actual Demand over Time Showing the Forecast Lag LO 5 15-37 Trend Effects Equations FITt Ft Tt Ft FITt 1 At 1 FITt 1 Tt Tt 1 Ft FITt 1 Ft T heexponentially smoothedforecastfor period t Tt T heexponentially smoothedtrendfor period t FITt T heforecastincluding trendfor period t FITt -1 T heforecastincluding trendmade for theprior period A t -1 T heactualdemandfor theprior period LO 5 Smoothingconstant Smoothingconstant 15-38 Forecast Error Sources of errors Projecting the past into the future Wrong relationships Wrong information (data) Errors outside of our control Goal is to minimize the errors 15-39 Forecast Error Bias errors: when a consistent mistake is made Random errors: errors that cannot be explained by the forecast model being used Measures of error Mean absolute deviation (MAD) Mean absolute percent error (MAPE) Tracking signal LO 5 15-40 The MAD Statistic to Determine Forecasting Error The ideal MAD is zero which would mean there is no forecasting error The larger the MAD, the less the accurate the resulting model n A MAD = t t=1 n - Ft 1 MAD 0.8 standard deviation 1 standard deviation 1.25 MAD LO 5 15-41 Example: Find the MAD Month Sales 1 220 2 250 3 210 4 300 5 325 Forecast Abs Error — 255 205 320 315 Total = n MAD = A t - Ft t=1 n 40 = = 10 4 — 5 5 20 10 40 Note that by itself, the MAD only lets us know the mean error in a set of forecasts 15-42 Tracking Signal The tracking signal (TS) is a measure that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand Depending on the number of MAD’s selected, the TS can be used like a quality control chart indicating when the model is generating too much error in its forecasts RSFE Running sum of forecast errors TS = = MAD Mean absolute deviation LO 5 15-43 Computing the MAD, the RSFE, and the TS from Forecast and Actual Data LO 5 15-44 Example: Tracking Signal Period Forecast Demand Error |E| RSFE - 50 50 - 50 - 75 - 75 75 75 - 125 - 200 Sum |E| MAD TS 50 125 50.0 62.5 -1 -2 66.7 62.5 -3 -4 1 2 250 325 200 250 3 4 400 350 325 300 - 50 50 - 250 200 250 5 375 325 - 50 50 - 300 300 60.0 -5 6 450 400 - 50 50 - 350 350 58.3 -6 + 4 3 2 1 TS 0 - 1 - 2 Out of Control - 3 - 4 - 5 - 6 1 2 3 4 5 6 Period 15-45 Causal Relationship Forecasting Causal relationship forecasting: using independent variables other than time to predict future demand The independent variable must be a leading indicator Must find those occurrences that are really the causes LO 5 15-46 Qualitative Techniques in Forecasting Qualitative forecasting techniques take advantage of the knowledge of experts Most useful when the product is new or there is little experience with selling into a new region The following are samples of qualitative forecasting techniques LO 4 Executive judgment Grass roots Market research Panel consensus Historical analogy Delphi method 15-47 Qualitative Methods Executive Judgment Grass Roots • Used for new products introduction • Decisions are broader and at a higher level • Builds forecast by adding successively from bottom • Those closest to customer know better Market Research Historical analogy • Existing product used as model for another • Example: buying CDs on Internet put you on mailing list for related products Delphi Method • • • • Based on expert opinion Experts asked question anonymously Goes thru several rounds of questioning Results tabulated, iterated until a consensus is reached Qualitative • Consumer surveys and interviews • Used to improve existing products Methods Panel Consensus • Open meetings with free exchange of ideas • Power play possibilities 15-48 Web-Based Forecasting: (CPFR) LO 5 Collaborative planning, forecasting, and replenishment (CPFR): a Web-based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners Used to integrate the multi-tier or n-Tier supply chain Objective is to exchange selected internal information to provide for a reliable, longer term future views of demand CPFR uses a cyclic and iterative approach to derive consensus forecasts 15-49 Web-Based Forecasting: Steps in CPFR Creation of a front-end partnership agreement Joint business planning Development of demand forecasts Sharing forecasts Inventory replenishment LO 5 15-50