Change your history: Learning from event logs to improve processes

Abstract

The abundance of event data enables new forms of analysis that facilitate process improvement. Process mining provides a novel set of tools to discover the real process, to detect deviations from some normative process, and to analyze bottlenecks and waste. The lion's share of process mining focuses on the “as-is” situation rather than the “to-be” situation. Clearly, analysis should aim at actionable insights and concrete suggestions for improvement However, state-of-the-art techniques do not allow for this. Techniques like simulation can be used to do “what-if” analysis but are not driven by event data, and as a result, improvements can be very unrealistic. Techniques for predictive analytics and combinatorial optimization are data-driven but mostly focus on well-structured decision problems. Operational processes within complex organizations cannot be mapped onto a simulation model or simple decision problem. This paper provides a novel approach based on event logs as used by process mining techniques. Instead of trying to create or modify process models, this approach works directly on the event log itself. It aims to “improve history” rather than speculate about a highly uncertain future. By showing concrete improvements in terms of partly modified event logs, the stakeholders can learn from earlier mistakes and inefficiencies. This is similar to analyzing a soccer match to improve a team's performance in the next game. This paper introduces the idea using event logs in conjunction with flexible “compatibility” and “utility” notions. An initial prototype -serving as a proof-of-concept- was realized as a ProM plug-in and tested on real-life event logs.