In this chapter we will discuss: A forecasting framework, qualitative forecasting methods, time-series forecasting, moving average, exponential smoothing, forecasting errors, advanced time-series forecasting, causal forecasting methods, selecting a forecasting method.
Operations Management Contemporary Concepts and Cases Chapter Eleven Forecasting McGrawHill/Irwin Copyright © 2011 by The McGrawHill Companies, Inc. All rights reserved Chapter Outline A Forecasting Framework Qualitative Forecasting Methods TimeSeries Forecasting Moving Average Exponential Smoothing Forecasting Errors Advanced TimeSeries Forecasting Causal Forecasting Methods Selecting a Forecasting Method Collaborative Planning, Forecasting, and Replenishment 112 A Forecasting Framework Focus of chapter is on forecasting demand for output from the operations function – Demand may differ from sales Difference between forecasting and planning – Forecasting: what we think will happen – Planning: what we think should happen Forecasting application in various decision areas of operations (capacity planning, inventory management, others) Forecasting uses and methods (See Table 11.1) 113 Use of Forecasting: Operations Decisions Time Horizon Accuracy Required Number of Forecasts Long Medium Single or few Top Qualitative or causal Long Medium Single or few Top Qualitative and causal Medium High Few Middle Causal and time series Short Highest Many Lower Time series Inventory management Short Highest Many Lower Time series Process design Capacity planning, facilities Aggregate planning Scheduling Management Forecasting Level Method 114 Use of Forecasting: Marketing, Finance & HR Time Horizon Accuracy Required Number of Forecasts Long Medium Single or few Top Qualitative Short High Many Middle Time series New product introduction Medium Medium Single Top Qualitative and causal Cost estimating Short High Many Lower Time series Capital budgeting Medium Highest Few Top Causal and time series Long-range marketing programs Pricing decisions Management Forecasting Level Method 115 ‘Qualitative’ Forecasting Methods Based on managerial judgment when there is a lack of data. No specific model Major methods: – – – – Delphi Technique Market Surveys Lifecycles Analogy Informed Judgment (naïve models) 116 Time-Series Forecasting Components of timeseries data: – – – – – Average level Trend—general direction (up or down) Seasonality—short term recurring cycles Cycle—long term business cycle Error (random or irregular component) “Decomposition” of timeseries – Data are decomposed into four components Moving averages Exponential smoothing 117 Moving Average Assumes no trend, seasonal or cyclical components Simple Moving Average: At Ft Dt Dt N Dt N At Weighted Moving Average: Ft At W1 Dt W2 Dt WN Dt N 118 Moving Average Compute three period moving average (number of periods is the decision of the forecaster) Period Actual Demand 1 10 2 18 3 29 4 Forecast 19 (10+18+29)/3 = 19 Period 5 forecast will be (18+29+actual for period 4)/3 119 Figure 11.2: Time-Series Data Note: The more periods, the smoother the forecast 1110 Exponential Smoothingcalculation Facts: – – – – September forecast for sales was 15 September actual sales were 13 Alpha (α) is 0.2 What is the forecast for October? Calculation: – October forecast = September forecast + α(September actualSeptember forecast) =15+0.2(1315)=15+0.2(2)=150.4=14.6 1113 Forecast Errors In addition to the forecast, one should compute an estimate of forecast error. Its uses include: To monitor erratic demand observations or “outliers” To determine when the forecasting method is no longer tracking actual demand To determine the parameter values that provide the forecast with the least error To set safety stocks or safety capacity 1114 Forecast Errors Cumulative Sum of Forecast Error (CFE) Mean Square Error (MSE) Mean Absolute Deviation (MAD)—measure of deviation in units Mean Absolute Percentage Error (MAPE) Tracking Signal (TS)—relative measure of bias Mean Error 1115 Forecast Errors: Formulas Cumulative sum of Forecast Errors n CFE = i=1 n Mean Square Error Mean Absolute Deviation et MSE = t e i=1 n n MAD = i=1 Mean Absolute Percentage Error n MAPE = i=1 n Tracking Signal | TS = et i=1 MAD n | et | n Mean Error et | 100 Dt n et ME = i=1 n 1116 Tracking Signal Analogous to control charts in quality control, viz. if there is no bias, its values should fluctuate around zero. Is a relative measure, i.e., the numbers mean the same for any forecast 1117 Advanced Time-Series Forecasting Adaptive exponential smoothing – Smoothing coefficient ( ) is varied BoxJenkins method – Requires about 60 periods of past data 1118 Time Series vs Causal Models Time series compares data being forecast over time, i.e., time is the independent variable or xaxis or xvariable Causal models compare data being forecast against some other data set which the forecaster may think is a cause of the forecasted data, e.g., population size causes newspaper sales 1119 Causal Forecasting Models The general regression model: yˆ a bx Other forms of causal model: – – – Econometric Inputoutput Simulation models 1120 Example of Time Series Model Yt = a + b(t) t Intercept (a) Slope (b) Dt 120 124 119 124 125 130 Ft 119.52 121.18 122.84 124.5 126.15 127.81 129.47 Dt = actual sales Ft = forecasted sales t = time period (e.g. year) 117.8667 1.657143 F7 = 117.87 + 1.66 (7) = 129.47 = sales forecast for next year 1121 Example of Causal Model Yt = a + b(t) It 34.6 35.7 36.3 35.2 35.7 36.4 37.6 Intercept (a) Slope (b) Dt 120 124 119 124 125 130 Ft 121.15 123.79 125.22 122.59 123.79 125.46 128.34 Dt = actual sales in year t Ft = forecasted sales It = median family income (000’s) 38.23094 2.396514 F7 = 38.23 + 2.397 (7) = 128.34 = sales forecast for next year (year 7) 1122 Selecting a Forecasting Method User and system sophistication – People reluctant to use what they don’t understand Time and resources available – When is forecast needed? – What is value of forecast? Use or decision characteristics, e.g., horizon Data availability and quality Data pattern Don’t force the data to fit the model! 1123 Forecast Horizons and Forecast Accuracy The longer the forecast horizon, the less accurate the forecast Long lead times require long forecast horizons Lean, responsive companies have the goal of decreasing lead times so they are shorter than the forecast horizon 1124 Collaborative Planning, Forecasting and Replenishment (CPFR) Aim is to achieve more accurate forecasts Share information in the supply chain with customers and suppliers Compare forecasts – If discrepancy, look for reason – Agree on consensus forecast Works best in B2B with few customers 1125 Summary A Forecasting Framework Qualitative Forecasting Methods TimeSeries Forecasting Moving Average Exponential Smoothing Forecasting Errors Advanced TimeSeries Forecasting Causal Forecasting Methods Selecting a Forecasting Method Collaborative Planning, Forecasting, and Replenishment 1126 End of Chapter Eleven 1127 .. .Chapter Outline A? ?Forecasting? ?Framework Qualitative? ?Forecasting? ?Methods TimeSeries? ?Forecasting Moving Average Exponential Smoothing Forecasting? ?Errors Advanced TimeSeries? ?Forecasting. .. Advanced TimeSeries? ?Forecasting Causal? ?Forecasting? ?Methods Selecting a? ?Forecasting? ?Method Collaborative Planning,? ?Forecasting, and Replenishment 112 A Forecasting Framework Focus of? ?chapter? ?is on? ?forecasting? ?demand for output ... Forecasting? ?application in various decision areas of operations? ?(capacity planning, inventory management, others) Forecasting? ?uses and methods (See Table 11.1) 113 Use of Forecasting: Operations Decisions Time Horizon