BSOP 330 Week 1 Problems 4.11a_b

BSOP 330 Week 1 Problems 4.11a_b - given in Problem 4.10 To...

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Chapter 4: Problem 4.11a Alpha 0.3 Data Forecasts and Error Analysis Year Registrations Forecast Error Absolute Squared Abs Pct Err 1 4,000 5,000.00 -1,000.00 1,000.00 1,000,000.00 25.00% 2 6,000 4,700.00 1,300.00 1,300.00 1,690,000.00 21.67% 3 4,000 5,090.00 -1,090.00 1,090.00 1,188,100.00 27.25% 4 5,000 4,763.00 237.00 237.00 56,169.00 04.74% 5 10,000 4,834.10 5,165.90 5,165.90 26,686,522.81 51.66% 6 8,000 6,383.87 1,616.13 1,616.13 2,611,876.18 20.20% 7 7,000 6,868.71 131.29 131.29 17,237.33 01.88% 8 9,000 6,908.10 2,091.90 2,091.90 4,376,061.09 23.24% 9 12,000 7,535.67 4,464.33 4,464.33 19,930,265.47 37.20% 10 14,000 8,874.97 5,125.03 5,125.03 26,265,961.33 36.61% 11 15,000 10,412.48 4,587.52 4,587.52 21,045,366.99 0.3058348646 Total 22,629.11 26,809.11 104,867,560.20 280.03% Average 2,057.19 2,437.19 9,533,414.56 25.46% Bias MAD MSE MAPE SE 3,413.50 Next period 11,788.73 Use exponential smoothing with a smoothing constant of 0.3 to forecast the registrations at the seminar
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Unformatted text preview: given in Problem 4.10. To begin the procedure, assume that the forecast for year 1 was 5,000 people signing up. 1 2 3 4 5 6 7 8 9 10 11 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 Forecasted Registrations Registrations Forecast Year Registrations Chapter 4: Problem 4.11b What is MAD? Answer: MAD= n Source: Render, Jay Heizer and Barry. Operations Management, 10th Edition. Pearson p.113 Mean Absolute Devation (MAD) is a measure of the overall forecast error for a model. This value is computed by taking the sum of the absolute values of the individual forecast errors (deviations) and dividing by the number of periods of data (n): Σ|Actual − Forecast|...
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