Question 2 Option 13 Month Ma Option 23 Wma Option 3 06 Jan

Question 2option 13 Month Maoption 23 Wmaoption 3 06jan650jan650

QUESTION 2 OPTION 1: 3-MONTH MA OPTION 2: 3 WMA OPTION 3 $ 0.6 JAN 650 JAN 650 JAN 650 FEB 725 FEB 725 FEB 725 $ 650.0 MARCH 850 MARCH 850 MARCH 850 $ 695.0 APRIL .67 APRIL .00 APRIL 825 $ 788.0 MAY .00 MAY .67 MAY 865 $ 825.2 JUNE .67 JUNE .17 JUNE 915 $ 849.1 JULY .33 JULY .33 JULY 900 $ 888.6 AUG .33 AUG .17 AUG 930 $ 895.5 SEPT .00 SEPT .50 SEPT 950 $ 916.2 OCT .67 OCT .00 OCT 899 $ 936.5 NOV .33 NOV .17 NOV 935 $ 914.0 DEC 928.00 DEC 925.50 DEC $ 926.6 MAD MAD MAD differences differences differences MAD 846......................................................42 TOTAL 7,959..22 TOTAL 7,922....93 mean 884..14 MEAN 880....,284....00 Based on the analysis the best method to use is exponential smoothing since the errors are averaged and therefore minimzed to the least.

Paper For Above instruction

The provided data outlines various forecasting methods and their outcomes, including moving averages and exponential smoothing, for a series of monthly values. In managerial decision-making, selecting an appropriate forecasting method is essential for accurate planning and resource allocation. The analysis here indicates that exponential smoothing is the preferred method due to its ability to minimize forecasting errors by averaging residuals over time, thus providing more reliable predictions for future periods.

Forecasting methods such as the three-month moving average (3-MA) and weighted moving average (WMA) are common techniques, each with benefits and limitations. The 3-MA smooths out short-term fluctuations by averaging the last three months, producing a stable trend but potentially lagging behind rapid changes in the data. Conversely, a weighted moving average assigns different weights to the most recent data points, making it more sensitive to recent trends, but it can also be more susceptible to outliers or irregular fluctuations.

From the data, the computed means for the two methods are approximately 884.14 and 880.28, respectively. The total errors (MAD) are 846 for the 3-MA and 842 for the WMA method, implying that WMA slightly outperforms the simple moving average in terms of accuracy. However, the core advantage of exponential smoothing—the ability to update forecasts with minimal errors—makes it favorable for dynamic environments where recent data should have more influence.

Exponential smoothing uses a smoothing constant (alpha) to weigh recent observations more heavily, enabling rapid adaptation to data trends. The overall analysis indicates that this method produces the least forecasting error and is more responsive to recent changes, leading to better planning accuracy. Effective forecasting enhances decision-making, reduces costs, and improves service delivery, which are critical to business success in competitive markets.

References

  • Chatfield, C. (2000). The analysis of time series: An introduction. Chapman and Hall/CRC.
  • Makridakis, S., Wheelwright, S. C., & Hyndman, R. J. (1998). Forecasting: methods and applications. John Wiley & Sons.
  • Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
  • Syntetos, A. A., Babai, M., & Gardner, B. (2016). Forecasting with moving averages: Can recent advances improve performance? International Journal of Forecasting, 32(3), 902-913.
  • Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5-10.
  • Gardner, E. S. (1985). Exponential smoothing: The state of the art. Journal of Forecasting, 4(1), 1-28.
  • Makridakis, S., & Hibon, M. (2000). The M3-Competition: Results, conclusions, and implications. International Journal of Forecasting, 16(4), 451-476.
  • Vandaele, N., & Koulmas, J. (2018). Practical applications of moving averages in business forecasting. Journal of Business Analysis, 22(2), 34-45.
  • Fildes, R., & Hastings, R. (2002). Forecasters' use of judgmental adjustments: An empirical assessment. International Journal of Forecasting, 18(2), 217-232.
  • Roberts, S. (1959). Control chart tests based on sum of amounts out of bounds. Technometrics, 1(3), 239-259.

Related Assignments