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Northcutt Bikes CaseAnswers

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Q1: Demand Data Plot

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Q1: Plot Shows

There is seasonality There is a trend Forecast should take into

account both

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Construction of base indices

Year: 2008 2009 2010 2011 Mean Base

January 0.53 0.72 0.59 0.59 0.61February 0.74 0.74 0.95 1.09 0.88March 0.88 0.84 0.79 0.98 0.87April 1.09 1.00 1.18 0.92 1.05May 1.10 1.16 1.15 1.27 1.17June 1.60 1.57 1.39 1.51 1.52July 1.29 0.94 1.35 1.56 1.28August 1.19 1.30 1.43 0.71 1.16September 1.00 1.13 0.91 1.08 1.03October 1.09 0.74 0.96 0.77 0.89November 0.73 0.99 0.78 0.84 0.84December 0.74 0.88 0.51 0.67 0.70

Mean Demand: 818.42 990.50 1032.08 1181.25

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Multiple Regression Results:X is Period and BaseRegression Statistics

Multiple R 0.982917071

R Square 0.966125969

Adjusted R Square 0.964620456

Standard Error 59.82147676

Observations 48

ANOVA

  df SS MS F

Regression 2 4592970.404 2296485.202 641.7256395

Residual 45 161037.4087 3578.609082

Total 47 4754007.813   

  Coefficients Standard Error t Stat P-value

Intercept -219.4209094 35.31667659 -6.212954633 1.50687E-07

Period 8.730540524 0.623285303 14.00729407 5.12015E-18

Base 1011.295853 30.74315604 32.89499139 4.07081E-33

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Q2: Forecasting Methods

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

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Q2: 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

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Q3: Best Forecast:

Exponential smoothing forecast has lowest MAD

Disadvantages: the exponential smoothing forecast should be updated frequently (say once a month).

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Q4: 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

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Q5: Ways to Improve Operations

Quicker response: reduce manufacturing lead times; possibly implement online ordering

Suppliers: reduce lead times; set contracts

Improve information systems Work force: increase flexibility; temps

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Q6: Recommendations

Operation is likely not too large - Jan can control operation effectively if she: 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

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Questions ?

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