Variability models are used to build configurators. Configurators are programs that guide users through the configuration process to reach a desired configuration that fulfils user requirements. The same variability model can be used to design different configurators employing different techniques. One of the elements that can change in a configurator is the configuration workflow, i.e., the order and sequence in which the different configuration elements are presented to the configuration stakeholders. When developing a configurator, a challenge is to decide the configuration workflow that better suites stakeholders according to previous configurations. For example, when configuring a Linux distribution, the configuration process start by choosing the network or the graphic card, and then other packages with respect to a given sequence. In this paper, we present COLOSSI, an automated technique that given a set of logs of previous configurations and a variability model can automatically assist to determine the configuration workflow that better fits the configuration logs generated by user activities. The technique is based on process discovery, commonly used in the process mining area, with an adaptation to configuration contexts. Our proposal is validated using existing data from an ERP configuration environment showing its feasibility. Furthermore, we open the door to new applications of process mining techniques in different areas of software product line engineering.