Understanding Process Dynamics: Sense-Making with Context

By Thomas Grisold, Han van der Aa, Sandro Franzoi, Sophie Hartl, Jan Mendling, and Jan vom Brocke

Recently completed and accepted for presentation at the International Conference for Process Mining (ICPM) by our very own Jan Mendling and a team of specialist researchers, this study offers a fresh perspective on interpreting process mining results. Here’s an exclusive sneak preview into the innovative framework developed by the research team.

In today’s fast-paced business environment, understanding the inner workings of business processes is critical for maintaining efficiency and competitiveness. Process mining has emerged as a powerful tool, providing deep insights into the performance of these processes. However, a significant gap remains: while process mining can tell us what is happening within a process, it often falls short in explaining why these events occur. This is where the latest research in this paper. Their newly developed framework, set to be unveiled at the International Conference for Process Mining (ICPM), aims to bridge this gap by incorporating contextual factors into the analysis.

The Need for Context in Process Mining

Process mining has revolutionized how businesses analyze and optimize their operations. By converting event logs into visual maps of processes, organizations can identify bottlenecks, inefficiencies, and opportunities for improvement. Despite these advancements, one crucial aspect has been overlooked: the context within which these processes operate.

Contextual factors are the various internal and external conditions that influence how a process functions. Without considering these factors, process mining results can be misleading or incomplete. For instance, a sudden spike in process variations might be interpreted as inefficiency, when in fact it could be an adaptive response to a changing market condition.

Introducing the Context Framework

The newly developed framework by Grisold and his colleagues addresses this very issue. It categorizes contextual factors into three levels: process-immediate, organization-internal, and organization-external. Each level encompasses specific dimensions that can impact process dynamics.

1. Process-immediate context: This includes factors directly related to the process itself, such as changes in the sequence of activities, availability of resources, and IT system features.
2. Organization-internal context: These are factors within the organization that can affect processes, such as intra-organizational dynamics, structural and procedural changes, and identity-related aspects like corporate culture and practices.
3. Organization-external context: External influences such as environmental dynamics (e.g., the impact of COVID-19), inter-organizational relationships, and regulatory changes fall into this category.

Practical Application: A Real-World Example

To illustrate the practical application of this framework, the research team analyzed the customer onboarding process of a European financial institution. Over a period of two years, they collected and examined data from 901 cases, identifying significant variations in process complexity.

For example, a sharp increase in process complexity in mid-2020 was initially perplexing. By applying their context framework, the researchers discovered that this spike was due to an IT system change that introduced a new customer questionnaire. Due to inadequate testing, this change led to errors and subsequent workarounds by process participants, ultimately increasing complexity. Once the system issues were resolved, process complexity decreased.

Implications for Business Leaders

The implications of this framework are profound for business leaders and process analysts. By systematically incorporating contextual factors into process mining analysis, organizations can achieve a more nuanced understanding of their processes. This enables more accurate diagnoses of issues and more informed decision-making.

For instance, an unexpected rise in process variations might prompt a closer look at recent organizational changes or external factors, rather than a knee-jerk reaction to impose stricter controls. This context-aware approach can help organizations respond more flexibly and effectively to the dynamic conditions in which they operate.

Future Directions

The research team’s framework opens the door to numerous future research opportunities. One intriguing possibility is the development of computational techniques to automatically detect contextual factors, further enhancing the utility of process mining tools. Additionally, studying how analysts use this framework in practice can provide insights into refining and expanding its applications.

Food for Thought

As you reflect on this new framework, consider the following questions to explore how you can apply these insights to your own organization’s processes:

• How can incorporating contextual insights into your process analysis improve decision-making at both operational and strategic levels? What specific decisions could benefit from a more nuanced understanding of process dynamics?
• When you identify variations or inefficiencies in your processes, do you consider potential contextual factors before implementing changes? How might understanding these factors alter your approach to process improvement?

Conclusion

As businesses continue to navigate an increasingly complex and dynamic environment, the need for a deeper understanding of process dynamics becomes ever more critical. The context framework developed by Grisold and his colleagues represents a significant step forward in this endeavor. By incorporating context into process mining, organizations can move from merely observing what is happening to understanding why it is happening, paving the way for more effective and responsive business strategies.

Here at Noreja, we’ve built our solution around maximizing the amount of context information is available to you when performing process mining using our solution. This is part of our drive to push our field in a novel direction with Causal Process Mining. If you want to know more about this, check out either our technical whitepaper here or our decision-making focused whitepaper here. Otherwise, feel free to reach out to us for a demo to see how our solution supports using context-sensitive sense-making for your process analyses.

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