A call-centre forecasts staffing using last month’s average call volume. Which forecasting model is this?
Simple moving average.
The simple moving average forecasting model utilizes the average of past data points—such as last month's call volume—to predict future staffing needs. This approach smooths out fluctuations and provides a consistent estimation based on historical performance.
The Delphi technique involves gathering expert opinions through a series of questionnaires and is not based on past data averages. It seeks to achieve a consensus among experts rather than relying on historical data trends, making it unsuitable for forecasting staffing based on previous call volumes.
The survey method collects data directly from individuals, typically regarding preferences or behaviors, rather than analyzing historical averages. While surveys can provide insights into expected call volumes, they do not use past data to generate forecasts, which is essential for the scenario described.
Exponential smoothing is a forecasting technique that applies decreasing weights to past observations, giving more importance to recent data. Although it is also based on historical call volumes, it differs from the simple moving average method, which treats all historical data equally without weighting.
The simple moving average calculates the average of a fixed number of past data points, such as last month's call volume, to forecast future needs. This method is straightforward and effective for identifying trends based solely on historical averages, making it the correct choice for the question.
In forecasting staffing needs for a call center, the simple moving average method leverages last month's average call volume to provide a reliable estimate. Other options, like the Delphi technique and survey method, focus on opinion and preferences rather than historical averages, while exponential smoothing introduces weighted calculations not applicable in this context. Thus, the simple moving average stands out as the most relevant and effective model for this scenario.
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