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.
