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identifying the KPIs, the question ‘‘how to improve the process in terms of its KPIs?’’ still needs to be answered, i.e., how to improve the process KPIs in order for these to meet the envisaged targets. One promising direction to better link BPM to the concrete improvement of process KPIs is to exploit event data present in the organization. For example, Six Sigma(Pyzdek 2003) has applied statistical analysis tools to organizational data for a long time, in order to measure and reduce the degree of business process variability. The idea is to identify and remove the causes for such variability, e.g., in terms of errors, defects or SLA violations in the output of business processes, and to control that such processes effectively perform within the desired performance targets (e.g., ensuring that there are no more than 10 SLAs per month). Six Sigma is focused on improving business processes by statistically quantifying process performance changes, the data used for such analyses is typically collected manually, e.g., through surveys or observation. This makes the employment of such techniques, when carried out properly, very costly and time consuming. Six Sigma rarely looks inside end-to-end processes. The focus is on a specific step in the process or on aggregate measures. This problem can be obviated through the use of techniques that automatically extract process knowledge from event data logged by common info systems, e.g., ERP or ticketing systems. In this context, the process mining research area (van der Aalst 2011) has emerged, proposing a range of methods and tools for exploiting such data to automatically discover a process model, or check its compliance with existing reference models or norms, or to determine the causes for process deviations or variants. The advantage of relying on logged data as opposed to data that has been collected manually is that any insight extracted from this data is based on evidence, rather than on human confidence, and thus is a more accurate representation of reality. the artifacts extracted through process mining, e.g., process models, can be enhanced with (live) process performance info such as statistics on activity duration and resource utilization. This allows orgto look inside end-to-end processes. For these reasons, process mining methods are now being used across all phases of the BPM lifecycle, from discovery through to monitoring. , while a wide range of techniques have been developed in this field, the research community has mostly devoted its attention to the quality of the artifacts produced (e.g., the accuracy of the process models extracted from the logs), rather than to improving the actual processes for which such logs are available. Therefore, a possible research direction is to bridge the current gap between process mining and Six Sigma. For instance, process mining techniques could be used to extract detailed and accurate process performance measurements (e.g., in the form of process models enhanced with performance statistics) on top of which Six Sigma techniquescould be applied to pinpoint causes for variability, and to identify and evaluate the impact of different process changes on the process KPIs. Another avenue to obtain better processes consists in applying techniques from Operations Research to the realm of business processes.