Q1: Plot Shows • There is seasonality • There is a trend • Forecast should take into account both
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
Q3: Best Forecast: • Exponential smoothing forecast has lowest MAD • Disadvantages: the exponential smoothing forecast should be updated frequently (say once a month).
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
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
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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