Wei Zhe Low

Perturbing event logs to identify cost reduction opportunities: A genetic algorithm-based approach

Organisations are constantly seeking new ways to improve operational efficiencies. This research study investigates a novel way to identify potential efficiency gains in business operations by observing how they are carried out in the past and then exploring better ways of executing them by taking into account trade-offs between time, cost and resource utilisation. This paper demonstrates how they can be incorporated in the assessment of alternative process execution scenarios by making use of a cost environment. A genetic algorithm-based approach is proposed to explore and assess alternative process execution scenarios, where the objective function is represented by a comprehensive cost structure that captures different process dimensions. Experiments conducted with different variants of the genetic algorithm evaluate the approachs feasibility. The findings demonstrate that a genetic algorithm-based approach is able to make use of cost reduction as a way to identify improved execution scenarios in terms of reduced case durations and increased resource utilisation. The ultimate aim is to utilise cost-related insights gained from such improved scenarios to put forward recommendations for reducing process-related cost within organisations.

Change your history: Learning from event logs to improve processes

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 lions 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 teams 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.

Change visualisation

With organisations facing significant challenges to remain competitive, Business Process Improvement (BPI) initiatives are often conducted to improve the efficiency and effectiveness of their business processes, focussing on time, cost, and quality improvements. Event logs which contain a detailed record of business operations over a certain time period, recorded by an organisations information systems, are the first step towards initiating evidence-based BPI activities. Given an (original) event log as a starting point, an approach to explore better ways to execute a business process was developed, resulting in an improved (perturbed) event log. Identifying the differences between the original event log and the perturbed event log can provide valuable insights, helping organisations to improve their processes. However, there is a lack of automated techniques and appropriate visualisations to detect the differences between two event logs. Therefore, this research aims to develop visualisation techniques to provide targeted analysis of resource reallocation and activity rescheduling. The differences between two event logs are first identified. The changes between the two event logs are conceptualised and realised with a number of visualisations. With the proposed visualisations, analysts are able to identify resource- and time-related changes that resulted in a cost reduction, and subsequently investigate and translate them into actionable items for BPI in practice. Ultimately, analysts can make use of this comparative information to initiate evidence-based BPI activities. HighlightsProposes a new approach to compare and visualise the differences between two logs.Pinpoints areas of potential resourcing and timing inefficiencies.Implemented as plug-ins within an open source process mining environment.