D. Changing from forecasting at the product family level to the stock keeping unit (SKU) level**
Improving forecast accuracy often involves **forecasting at a more granular level**, such as at the **stock keeping unit (SKU) level**, rather than at the broader product family level. This allows for more precise predictions because it accounts for the specific demand patterns of individual products, which can vary significantly even within the same product family. Granular forecasting reduces aggregation errors and provides better insights into customer demand.
The other options are less directly related to improving forecast accuracy:
While all of the options listed can contribute to improving forecast accuracy, reducing cumulative product lead time is generally considered a key action for improving forecast accuracy.
Explanation:
Reducing cumulative product lead time: This means shortening the time it takes for a product to go from conception to delivery to the customer. A shorter lead time allows for more accurate forecasts because businesses have a better understanding of current demand and can adjust production accordingly. It also allows for quicker responses to changing market conditions. Shorter lead times enable more frequent and accurate forecasts closer to the actual demand. This reduces the risk of overstocking or understocking.
Using a tracking signal:
A tracking signal monitors the difference between actual sales and forecasted sales, alerting businesses to potential forecast errors and the need for adjustments. It helps identify bias in the forecasting model.
The use of a tracking signal allows an organization to monitor the accuracy of its forecasts and make adjustments when a systematic trend of forecast errors is detected. This can be an effective tool for improving forecast accuracy over time.
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