I can think of a few examples that might show you the possible directions
This site has an introduction to model building with KNIME and uses an example that could be similar to your question:
Concerning the predictions you want you will have to think about how to set up the analysis and prediction. Would you make a prediction for every single product and dealer and what role does the time play in your analysis. Sales within a month or a week.
Then the quality of a model will very much depend on the data you have and if there are informations in them that lead to a pattern to predict sales (like holidays, demographics around the stores). And typically sales can be influenced by several factors like advertisement or (planned) sales campaigns, discounts etc. You might want to think how to include these factors in you model, and if you could reproduce them for future predictions.
And you would want to establish a measure that gives you and idea about the quality of your models. A typical statistic would be RMSE
The KNIME Numeric Scorer has you covered there:
Also a correlation coefficient might be good (Pearson or Spearman).
And the KNIME Learning Hub has a lot of useful informations about data mining and model building:
https://www.knime.com/learning-hub
(under Applications / Data Mining)