PowerPoint presentation to accompany Chopra and Meindl Supply Chain Management, 5e 1-1 Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. 1-1 Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. 1-1 Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. 7-1 Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. 7 Demand Forecasting in a Supply Chain

7-2Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. Summary Role of Forecasting in a Supply Chain Characteristics of Forecasts Adaptive Forecasting –Moving Average –Simple Exponential Smoothing Steps in Adaptive Forecasting

7-3Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. Simple Exponential Smoothing Revised forecast using smoothing constant 0 <  < 1 Given data for Periods 1 to n Current forecast Thus

7-4Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. Trend-Corrected Exponential Smoothing (Holt’s Model) Appropriate when the demand is assumed to have a level and trend in the systematic component of demand but no seasonality Systematic component of demand = level + trend

7-5Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. Trend-Corrected Exponential Smoothing (Holt’s Model) Obtain initial estimate of level and trend by running a linear regression D t = at + b T 0 = a, L 0 = b In Period t, the forecast for future periods is F t +1 = L t + T t and F t + n = L t + nT t Revised estimates for Period t L t +1 =  D t +1 + (1 –  )( L t + T t ) T t +1 =  ( L t +1 – L t ) + (1 –  ) T t

7-6Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. Trend-Corrected Exponential Smoothing (Holt’s Model) MP3 player demand D 1 = 8,415, D 2 = 8,732, D 3 = 9,014, D 4 = 9,808, D 5 = 10,413, D 6 = 11,961  = 0.1,  = 0.2 Using regression analysis L 0 = 7,367 and T 0 = 673 Forecast for Period 1 F 1 = L 0 + T 0 = 7, = 8,040

7-7Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall.

7-8Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall.

7-9Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. Trend- and Seasonality-Corrected Exponential Smoothing Appropriate when the systematic component of demand is assumed to have a level, trend, and seasonal factor Systematic component = (level + trend) x seasonal factor F t +1 = ( L t + T t ) S t +1 and F t +l = ( L t + lT t ) S t +l

7-10Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. Trend- and Seasonality-Corrected Exponential Smoothing After observing demand for period t + 1, revise estimates for level, trend, and seasonal factors L t +1 =  ( D t +1 / S t +1 ) + (1 –  )( L t + T t ) T t +1 =  ( L t +1 – L t ) + (1 –  ) T t S t + p +1 =  ( D t +1 / L t +1 ) + (1 –  ) S t +1  = smoothing constant for level  = smoothing constant for trend  = smoothing constant for seasonal factor

7-11Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. Winter’s Model L 0 = 18,439 T 0 = 524 S 1 = 0.47, S 2 = 0.68, S 3 = 1.17, S 4 = 1.67 F 1 = ( L 0 + T 0 ) S 1 = (18, )(0.47) = 8,913 The observed demand for Period 1 = D 1 = 8,000 Forecast error for Period 1 = E 1 = F 1 – D 1 = 8,913 – 8,000 = 913

7-12Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. Winter’s Model Assume  = 0.1,  = 0.2,  = 0.1; revise estimates for level and trend for period 1 and for seasonal factor for Period 5 L 1 =  ( D 1 / S 1 ) + (1 –  )( L 0 + T 0 ) = 0.1 x (8,000/0.47) x (18, ) = 18,769 T 1 =  ( L 1 – L 0 ) + (1 –  ) T 0 = 0.2 x (18,769 – 18,439) x 524 = 485 S 5 =  ( D 1 / L 1 ) + (1 –  ) S 1 = 0.1 x (8,000/18,769) x 0.47 = 0.47 F 2 = ( L 1 + T 1 ) S 2 = (18, )0.68 = 13,093

7-13Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. Time Series Models Forecasting MethodApplicability Moving averageNo trend or seasonality Simple exponential smoothing No trend or seasonality Holt’s modelTrend but no seasonality Winter’s modelTrend and seasonality

7-14Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall. Measures of Forecast Error Declining alpha