BUS 660 WEEK 2 DQ 1

BUS 660 WEEK 2 DQ 1 Discuss the strategic importance of forecasting at your organization (or in one with which you are familiar). Provide two examples of ways that forecasting could improve organizational processes or strategic decisions. Support your rationale with evidence from the readings or external research. Error measurements play a significant role in developing moving averages, weighted moving averages, and exponential smoothing algorithms. certain error measurements offer helpful information on the exactness and effectiveness of certain forecasting methods. To minimize highs and lows in time series data, moving averages are frequently used. Moving averages can assist in spotting trends and patterns by averaging a set number of before observations. But for the moving average to produce reliable forecasts, the right number of periods must be chosen. The most accurate moving average model can be identified by comparing various moving average models using error measurements like mean absolute error (MAE) or root mean square error (RMSE). Each observation is given an equal weight by weighted moving averages depending on how significant or relevant it is. Different techniques, such as exponential smoothing or linear regression, can be used to calculate these weights. We can compare the effectiveness of several weighting plans and choose the one that minimizes forecast mistakes using error metrics. On the other hand, exponential smoothing techniques provide historical observations slowly losing weights. This method slowly reduces the impact of earlier observations while emphasizing more recent data points. By comparing several smoothing constants (alpha values) and choosing the one that results in the fewest forecast mistakes, error measurements aid in the optimization of exponential smoothing. The use of error measurements in moving average, weighted moving average, and exponential smoothing method optimization is important. They give us the ability to evaluate the accuracy of various forecasting methods and decide on models and parameter settings with knowledge. Organizations can increase their overall forecasting capabilities and decision-making processes by reducing forecast mistakes through error measure analysis.
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