运营管理第14版蔡斯ipptchap018

FORECASTING Chapter Eighteen Copyright 2014 by The McGraw-Hill Companies, Inc. All rights reserved.McGraw-Hill/Irwin Learning Objectives LO181: Understand how forecasting is essential to supply chain planning. LO182: Evaluate demand using quantitative forecasting models. LO183: Apply qualitative techniques to forecast demand. LO184: Apply collaborative techniques to forecast demand. 18-2 The Role of Forecasting Forecasting is a vital function and affects 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 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 decisions. 18-3 Forecasting and Decoupling Point 18-4 Decoupling point: Point at which inventory is stored, which allows SC to operate independently The choice of the decoupling point in a SC is strategic. Forecasting helps determine the level of inventory needed at the decoupling points. The decision will be affected by the error produced in the forecast and the type of product (easily inventoried or easily perishable). Types of Forecasting There are four basic types of forecasts. 1.Qualitative 2.Time series analysis (primary focus of this chapter) 3.Causal relationships 4.Simulation Time series analysis is based on the idea that data relating to past demand can be used to predict future demand. 18-5 Components of Demand Average demand for a period of time Trend Seasonal element Cyclical elements Random variation Autocorrelation Excel: Components of Demand 18-6 Trends Identification of trend lines is a common starting point when developing a forecast. Common trend types include linear, S-curve, asymptotic, and exponential. 18-7 Time Series Analysis Using the past to predict the future Used mainly for tactical decisions Short term forecasting less than 3 months Used to develop a strategy that will be implemented over the next 6 to 18 months (e.g., meeting demand) Medium term forecasting 3 months to 2 years Useful for detecting general trends and identifying major turning points Long term forecasting greater than 2 years 18-8 Model Selection Choosing an appropriate forecasting model depends upon 1.Time horizon to be forecast 2.Data availability 3.Accuracy required 4.Size of forecasting budget 5.Availability of qualified personnel 18-9 Forecasting Method Selection Guide Forecasting MethodAmount of Historical DataData PatternForecast Horizon Simple moving average 6 to 12 months; weekly data are often used Stationary (i.e., no trend or seasonality) Short Weighted moving average and simple exponential smoothing 5 to 10 observations needed to start StationaryShort Exponential smoothing with trend 5 to 10 observations needed to start Stationary and trend Short Linear regression10 to 20 observationsStationary, trend, and seasonality Short to medium 18-10 Simple Moving Average Forecast is the average of a fixed number of past periods. Useful when demand is not growing or declining rapidly and no seasonality is present. Removes some of the random fluctuation from the data. Selecting the period length is important. Longer periods provide more smoothing. Shorter periods react to trends more quickly. 18-11 Simple Moving Average Formula 18-12 Simple Moving Average Example 18-13 Weighted Moving Average The simple moving average formula implies equal weighting for all periods. A weighted moving average allows unequal weighting of prior time periods. The sum of the weights must be equal to one. Often, more recent periods are given higher weights than periods farther in the past. 18-14 Selecting Weights Experience and/or trial-and-error are the simplest approaches. The recent past is often the best indicator of the future, so weights are generally higher for more recent data. If the data are seasonal, weights should reflect this appropriately. 18-15 Exponential Smoothing A weighted average method that includes all past data in the forecasting calculation More recent results weighted more heavily The most used of all forecasting techniques An integral part of computerized forecasting Well accepted for six reasons 1.Exponential models are surprisingly accurate. 2.Formulating an exponential model is relatively easy. 3.The user can understand how the model works. 4.Little computation is required to use the model. 5.Computer storage requirements are small. 6.Tests for accuracy are easy to compute. 18-16 18-17 Exponential Smoothing ModelExponential Smoothing Model Exponential Smoothing Example WeekDemandForecast 1820820 2775 820 3680 811 4655 785 5750 759 6802 757 7798 766 8689 772 9775 756 10 760 18-18 Exponential Smoothing Effect of Trends 18-19 Example Exponential Smoothing with Trend Adjustment 18-20 Choosing Alpha and Delta 18-21 Linear Regression Analysis Regression is used to identify the functional relationship between two or more correlated variables, usually from observed data. One variable (the dependent variable) is predicted for given values of the other variable (the independent variable). Linear regression is a special case that assumes the relationship between the variables can be explained with a straight line. Y = a + bt 18-22 Example 18.2 Least Squares Method QuarterSalesQuarterSales 160072,600 21,55082,900 31,50093,800 41,500104,500 52,400114,000 63,100124,900 The least squares method determines the parameters a and b such that the sum of the squared errors is minimized “least squares” 18-23 Example 18.2 Calculations 16006001360,000801.3 21,5503,10042,402,5001,160. 9 31,5004,50092,250,0001,520. 5 41,5006,000162,250,0001,880. 1 52,40012,000255,760,0002,239. 7 63,10018,600369,610,0002,599. 4 72,60018,200496,760,0002,959. 0 82,90023,200648,410,0003,318. 6 93,80034,2008114,440,0003,678. 2 104,50045,00010020,250,0004,037. 8 114,00044,00012116,000,0004,397. 4 124,90058,80014424,010,0004,757. 1 Sum 78 33,350 268,200 650 112,502,500 The forecast is extended to periods 13-16 18-24 Regression with Excel Microsoft Excel includes data analysis tools, which can perform least squares regression on a data set. 18-25 Time Series Decomposition Chronologically ordered data are referred to as a time series. A time series may contain one or many elements. Trend, seasonal, cyclical, autocorrelation, and random Identifying these elements and separating the time series data into these components is known as decomposition. 18-26 Seasonal Variation Seasonal variation may be either additive or multiplicative (shown here with a changing trend). 18-27 Determining Seasonal Factors : Simple Proportions Example 18.3 The seasonal factor (or index) is the ratio of the amount sold during each season divided by the average for all seasons. SeasonPast SalesAverage Sales for Each Season Seasonal Factor Spring200 Summer350 Fall300 Winter150 Total1000 18-28 Example 18.3 Continued 18-29 Expected Demand for Next Year Average Sales for Each Season (1,100y4) Seasonal Factor Next Years Seasonal Forecast Spring275X0.8=220 Summer275X1.4=385 Fall275X1.2=330 Winter275X0.6=165 1100 Decomposition Using Least Squares Regression 1.Decompose the time series into its components. a.Find seasonal component. b.Deseasonalize the demand. c.Find trend component. 2.Forecast future values of each component. a.Project trend component into the future. b.Multiply trend component by seasonal component. 18-30 Decomposition Steps 1 and 2 18-31 Using the data for periods 1-12, apply time series analysis (decomposition, linear regression, trend estimate & seasonal indices) to forecast for periods 13-16 Decomposition Steps 3 and 4 Develop a least squares regression line for the deseasonalized data. Project the regression line through the period of the forecast. Regression Results: Y = 555.0 + 342.2t Forecast for periods 13-16 18-32 Decompostion Step 5 Create the final forecast by adjusting the regression line by the seasonal factor. PeriodQuarterY from RegressionSeasonal FactorForecast (F x Seasonal Factor 13I5,003.50.824,102.87 14II5,345.71.105,880.27 15III5,687.90.975,517.26 16IV6,030.11.126,753.71 18-33 Forecast Errors Forecast error is the difference between the forecast value and what actually occurred. All forecasts contain some level of error. Sources of error Bias when a consistent mistake is made Random errors that are not explained by the model being used Measures of error Mean absolute deviation (MAD) Mean absolute percent error (MAPE) Tracking signal 18-34 Forecast Error Measurements Ideally, MAD will be zero (no forecasting error). Larger values of MAD indicate a less accurate model. MAPE scales the forecast error to the magnitude of demand. Tracking signal indicates whether forecast errors are accumulating over time (either positive or negative errors). 18-35 Computing Forecast Error 18-36 Causal Relationship Forecasting Causal relationship forecasting uses independent variables other than time to predict future demand. This independent variable must be a leading indicator. Many apparently causal relationships are actually just correlated events care must be taken when selecting causal variables. 18-37 Multiple Regression Techniques Often, more than one independent variable may be a valid predictor of future demand. In this case, the forecast analyst may utilize multiple regression. Analogous to linear regression analysis, but with multiple independent variables. Multiple regression supported by statistical software packages. 18-38 Qualitative Forecasting Techniques Generally used to take advantage of expe