1. Time-Series Forecast Models EXAMPLE Monthly Sales ( in units ) Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Data Point or (observation) MGMT E-5070 Data Mining and Forecast Management Wallace Garden Supply

2. Storage shed sales over the past year were as follows: PeriodDemandPeriodDemand January10July26 February12August30 March13September28 April16October18 May19November16 June23December14 MGMT E-5070

3. Wallace Garden Supply REQUIREMENTS: Predict sales for next January via : naïve 1.The naïve model. moving average 2.The 3 - month moving average model. weighted moving average 3.The 3 - month weighted moving average model with weights of 3, 2, 1 respectively. exponential smoothing 4. The exponential smoothing model with ά =.7 trend projection 5. The trend projection model. 6. Which model is the most accurate?

4. Applied Management Science for Decision Making, 2e © 2014 Pearson Learning Solutions

5. Forecast Model Options

35. Performance Summary Forecast ModelMADBiasMSE Standard ErrorMAPE January Forecast 3.27.36164.42.1714 Moving Average ( 3 - mo ) 6.48.7047.647.83.3416 Weighted Moving Av ( 3 - mo ) 5.44.4835.936.80.2815.33 Exponent Smoothing ( a =.7 ) 4.12.6622.915.29.2215.05 Trend Analysis 5.07033.26.31.2823.55 Naïve

36. Performance Summary Forecast ModelMADBiasMSE Standard ErrorMAPE January Forecast 3.27.36164.42.1714 Moving Average ( 3 - mo ) 6.48.7047.647.83.3416 Weighted Moving Av ( 3 - mo ) 5.44.4835.936.80.2815.33 Exponent Smoothing ( a =.7 ) 4.12.6622.915.29.2215.05 Trend Analysis 5.07033.26.31.2823.55 Naïve BEST ( lowest )

37. Forecasting with Excel

47. Forecasting with Excel QM 3

56. Time-Series Forecast Models EXAMPLE Monthly Sales ( in units ) Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec Data Point or (observation) MGMT E-5070 Data Mining and Forecast Management Wallace Garden Supply