Auctions are an important means for purchasing materials. Consequently, a supply chain management system has to be able to decide whether to participate in auctions and how much to bid. To address these issues, we generalize results from auction theory in several ways. First, auction theory often assumes that decision makers want to maximize their expected profits. However, decision makers are often risk-averse when faced with the possibility of losing the auction and then incurring huge penalties for not being able to satisfy some orders. We take their risk attitudes into account by changing the objective from maximizing the expected profit to maximizing the expected utility of the profit according to a risk-averse utility function. Second, auction theory often assumes that decision makers know their valuations for the auctioned item. However, their valuations depend on how they can use the item in the production process. We therefore integrate auction strategies into a production planning system to derive the valuation automatically. Third, auction theory often assumes that the probability distribution over the competitors’ valuations is known. We use simulations of the production planning system with integrated auction strategies to approximate these probability distributions automatically. The combination of these three research contributions results in a prototype of a supply chain management system with integrated auction strategies for the paper industry.