To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-1 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ PERTEMUAN 14

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-2 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Forecasting

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-3 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Learning Objectives Students will be able to: 1.Understand and know when to use various families of forecasting models 2.Compare moving averages, exponential smoothing, and trend time-series models 3.Seasonally adjust data. 4.Understand Delphi and other qualitative decision-making approaches

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-4 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Learning Objectives - continued Students will be able to: 5.Identify independent and dependent variables and use them in a linear regression model. 6.Compute a variety of error measures.

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-5 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Chapter Outline 5.1 Introduction 5.2 Types of Forecasts 5.3 Scatter Diagrams 5.4 Measures of Forecast Accuracy 5.5 Time-Series Forecasting Models 5.6 Causal Forecasting Models 5.7 Monitoring and Controlling Forecasts 5.8 Using the Computer to Forecast

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-6 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Introduction Eight steps to forecasting: 1.Determine the use of the forecast 2.Select the items or quantities to be forecasted 3.Determine the time horizon of the forecast 4.Select the forecasting model or models 5.Gather the data needed to make the forecast 6.Validate the forecasting model 7.Make the forecast 8.Implement the results

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-7 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Forecasting Models - Fig. 5.1 Moving Average Exponential Smoothing Trend Projections Time Series Methods Forecasting Techniques Delphi Methods Jury of Executive Opinion Sales Force Composite Consumer Market Survey Qualitative Models Causal Methods Regression Analysis Multiple Regression Decomposition

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-8 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Scatter Diagram for Sales Fig. 5.2 Radios Televisions Compact Discs

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-9 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Decomposition of Time Series Time series can be decomposed into: Trend (T): gradual up or down movement over time Seasonality (S): pattern of fluctuations above or below trend line that occurs every year Cycles(C): patterns in data that occur every several years Random variations (R): “blips”in the data caused by chance and unusual situations

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-10 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Decomposition of Time Series Two Models Multiplicative model: demand = T * S * C * R Additive model: demand = T + S + C + R

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-11 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Product Demand Showing Components Trend Actual Data Cyclic Random

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-12 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Moving Averages n Moving average:  demand in previous n periods

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-13 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Calculation of Three- Month Moving Average MonthActual Shed Sales Three-Month Moving Average January10 February12 March13 April16 May19 June23 July26 ( )/3 = 11 2 / 3 ( )/3 = 13 2 / 3 ( )/3 = 16 ( )/3 = 19 1 / 3

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-14 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Table 5.2

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-15 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Weighted Moving Averages Weighted moving average =

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-16 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Calculating Weighted Moving Averages Weights Applied Period 3 Last month 2 Two months ago 1 Three months ago 3*Sales last month + 2*Sales two months ago + 1*Sales three months ago 6 Sum of weights

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-17 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Calculation of Three- Month Moving Average MonthActual Shed Sales Three-Month Moving Average January February March April May June July26 [3*13+2*12+1*10]/6 = 12 1 / 6 [3*16+2*13+1*12]/6 =14 1 / 3 [3*19+2*16+1*13]/6 = 17 [3*23+2*19+1*16]/6 = 20 1 / 2

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-18 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Table 5.3

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-19 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Exponential Smoothing New forecast = previous forecast+  (previous actual - previous) or: where F t = F t-1 +  (A t-1 - F t-1 ) F t-1 = previous forecast  = smoothing constant F t = new forecast A t-1 = previous period actual

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-20 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Selecting the Smoothing Constant (  ) Select  to minimize: Mean Absolute Deviation = MAD Mean Square Error = MSE Mean Absolute Percent Error = MAPE Bias =  forecast errors

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-21 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Table 5.4

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-22 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Table 5.5

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-23 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Exponential Smoothing with Trend Adjustment Forecast including trend (FIT t+1 ) = new forecast (F t ) + trend correction(T t ) where T t = (1 -  )T t-1 +  (F t – F t-1 ) T i = smoothed trend for period 1 T i-1 = smoothed trend for the preceding period  = trend smoothing constant F t = simple exponential smoothed forecast for period t F t-1 = forecast for period t-1

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-24 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Exponential Smoothing with Trend Adjustment Simple exponential smoothing - first-order smoothing Trend adjusted smoothing - second-order smoothing Low  gives less weight to more recent trends, while high  gives higher weight to more recent trends

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-25 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Trend Projection General regression equation:

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-26 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Midwestern Manufacturing’s Demand Forecast points Trend Line Actual demand line

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-27 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Seasonal Variations MonthSales Demand Average Two-Year Demand Average Monthly Demand Seasonal Index Year 1 Year Jan Feb Mar Apr May … …………… Total Average Demand 1,128 Seasonal Index: = Average 2 -year demand/Average monthly demand

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-28 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Using Regression Analysis to Forecast Y Triple A' Sales ($100,000's) X Local Payroll ($100,000,000)

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-29 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Using Regression Analysis to Forecast - continued Sales, YPayroll, XX2X2 XY  Y = 15  X 2 = 80  X = 18  XY = 51.5

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-30 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Using Regression Analysis to Forecast - continued Calculating the required parameters:

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-31 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Standard Error of the Estimate

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-32 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Standard Error of the Estimate - continued  points data of number equation regression the from computed variabledependent the of value point data each of value        n Y YY where n YY S c c X,Y          n XYbYaY S X,Y or:

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-33 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Triple A’s Calculations

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-34 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Triple A’s Calculations - continued        n XYbYaY S X,Y   .. ).)(.()..(. S X,Y    

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-35 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Correlation Coefficient

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-36 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Triple A’s Calculations - continued

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-37 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Correlation Coefficient - Four Values - Fig. 5.7

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-38 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Monitoring/Controlling Forecasts The Tracking Signal

To accompany Quantitative Analysis for Management, 8e by Render/Stair/Hanna 5-39 © 2003 by Prentice Hall, Inc. Upper Saddle River, NJ Monitoring/Controlling Forecasts The Tracking Signal