With the rise of online sales and services dedicated to advertising aimed at the profiles of online users, companies selling general products need to adapt their business rules to improve sales strategies. In this paper, we present a simulation tool to support business decision-making, and improve the efficiency of an e-commerce system. It allows testing different sales strategies within the context of Business-to-Business models which focuses on the purchase and sale of non-strategic products. The idea is to provide the client's company with different alternative products when the desired product is out-of-stock. We propose and implement in our simulation tool, three recommendation algorithms, based on sales parameters such as the price and the popularity of products and the similarity between the recommended product and the one out-of-stock. The proposed algorithms are tested with real datasets, which allows evaluating the performance of the recommendation algorithms and their effectiveness.