Northcutt Bikes Case Answers 1 Q 1 Demand

Northcutt Bikes Case Answers 1

Q 1: Demand Data Plot 2000 Demand - Northcutt Bikes 1800 Demand 1600 1400 1200 1000 800 600 400 200 0 0 5 10 15 20 25 30 Month 35 40 45 50 55 2

Q 1: Plot Shows n There is seasonality n There is a trend n Forecast should take into account both 3

Construction of base indices Year: January February March April May June July August September October November December Mean Demand: 2008 0. 53 0. 74 0. 88 1. 09 1. 10 1. 60 1. 29 1. 19 1. 00 1. 09 0. 73 0. 74 2009 0. 72 0. 74 0. 84 1. 00 1. 16 1. 57 0. 94 1. 30 1. 13 0. 74 0. 99 0. 88 818. 42 990. 50 2010 0. 59 0. 95 0. 79 1. 18 1. 15 1. 39 1. 35 1. 43 0. 91 0. 96 0. 78 0. 51 2011 0. 59 1. 09 0. 98 0. 92 1. 27 1. 51 1. 56 0. 71 1. 08 0. 77 0. 84 0. 67 1032. 08 1181. 25 Mean Base 0. 61 0. 88 0. 87 1. 05 1. 17 1. 52 1. 28 1. 16 1. 03 0. 89 0. 84 0. 70 4

Multiple Regression Results: X is Period and Base Regression Statistics Multiple R 0. 982917071 R Square 0. 966125969 Adjusted R Square 0. 964620456 Standard Error 59. 82147676 Observations 48 ANOVA Regression Residual Total Intercept Period Base df 2 45 47 SS MS F 4592970. 404 2296485. 202 641. 7256395 161037. 4087 3578. 609082 4754007. 813 Coefficients Standard Error t Stat P-value -219. 4209094 35. 31667659 -6. 212954633 1. 50687 E-07 8. 730540524 0. 623285303 14. 00729407 5. 12015 E-18 1011. 295853 30. 74315604 32. 89499139 4. 07081 E-33 5

Q 2: Forecasting Methods n n n Multiple regression or MR (Y is forecast, X’s are period and base) MAD ≈ 45. 096 Simple regression or SR (deseasonalize demand, seasonalize forecast, X is period) MAD ≈ 32. 403 Exponential Smoothing or ES (adjusted for trend and seasonality) MAD ≈ 13. 258 6

Q 2: Forecast for January – April 2012 Month Mean Base Period MR SR ES January 0. 61 49 825. 27 745. 12 720. 56 February 0. 88 50 1107. 05 1082. 68 1039. 50 March 0. 87 51 1105. 66 1078. 04 1027. 69 April 1. 05 52 1296. 43 1310. 32 1240. 31 7

Q 3: Best Forecast: n n Exponential smoothing forecast has lowest MAD Disadvantages: the exponential smoothing forecast should be updated frequently (say once a month). 8

Q 4: Additional Information Jan’s knowledge of market could be used to: - Add additional independent variable to multiple regression - Be used to adjust other forecasts (caution should be used, however) Monthly increments best as forecast can react to latest information, provided this is not costly 9

Q 5: Ways to Improve Operations Quicker response: reduce manufacturing lead times; possibly implement online ordering n Suppliers: reduce lead times; set contracts n Improve information systems n Work force: increase flexibility; temps n 10

Q 6: Recommendations n Operation is likely not too large - Jan control operation effectively if she: n n n delegates improves information system reduces lead times implements lean (to be discussed) uses different modes of operation for different style bikes Information needed on costs of above 11

Questions ? ? ? ? 12