Strategies for continuously assessing and improving model performance

Question

I am building a supervised machine learning model to generate forecast.

So I would have historic data like this:

SKU, Month, .... other features, Actual Volume

That I can use a model to generate forecast, using the actual volume as the label.

Of course, there would be a variance between the forecast volume and the actual volume

What are the proper ways to leverage such data, without generating any data leakage, to incorporate such info to train the model to minimize the variance?

Should the data be fed back to the data with moving average, etc. and retrain, or is there other better strategy to properly assess the performance of the model and learn from it?

The data will be time series data with various features such as exchange rate, salesperson, etc.