Businesses have historically relied on static visual models, such as UML and BPMN, in their continuous efforts to comprehend, optimize, and oversee their operations. Although these models are indispensable during the system design phase, they often falter when confronted with real-world intricacies. Process mining addresses this limitation, revolutionizing business process analytics by enabling authentic process discovery, visualization, and dynamic representation using logs. On the other hand, the emerging notion of a 'process digital twin' offers real-time process monitoring and simulation, going beyond the process mining capabilities.
Process Mining: A Recap of Limitations
- Lack of Actionable Insights: Process mining effectively spotlights deviations and inefficiencies, but often, it doesn't provide reasons behind these findings. For instance, recognizing a supply chain delay is valuable, but understanding its root cause—whether due to a supplier setback or equipment malfunction—is crucial for effective intervention. This limitation stems from the fact that process mining maps are not causal models.; They don't capture the elements of agency, such as systems and individuals carrying out tasks, nor do they account for capacity at individual and activity levels. With these insights, objective decision-making regarding performance improvement becomes easier.
- Dynamic Processes: The technique struggles when faced with dynamic processes that often change or show variable input behaviors, such as the seasonal increase in airline passengers. This variability can make the insights outdated or distorted.
- Lack of Context: Event logs, the primary data source for process mining, often need more context. Contextual factors can influence symptoms of underperformance. For example, the transportation industry might be affected by weather, real estate by interest rates, and retail by seasonal changes. Such information is often absent in raw logs, making it challenging to consider these factors in process mining.
Process Digital Twin: An Overview and Its Advantages
A process digital twin creates a virtual replica of business processes, enabling real-time monitoring and what-if scenarios. Unlike process mining, which is predominantly retrospective, a process digital twin is both real-time and forward-looking. The key to this capability lies beneath the surface: it utilizes a causal model of the process, encompassing agents, capacity, randomness, and context. This approach can bridge the gap between the model and reality.
- Real-Time Monitoring: Process digital twins offer continuous monitoring. This capability ensures that businesses can respond to deviations or inefficiencies as they occur rather than analyzing them post-facto, as with process mining.
- Simulative and Predictive Analysis: The dynamic nature of a digital twin allows for simulations. Businesses can virtually test process changes in a risk-free environment before actual implementation, offering a predictive view of potential outcomes.
- Incorporation of Context: Process digital twins can integrate various data sources, not just event logs. This holistic approach provides a more contextual view of processes.
Contrasting Process Mining and Process Digital Twin
- Temporal Orientation: Process mining often dwells in retrospection, analyzing past events. The digital twin, in contrast, operates in real-time and ventures into the future with simulations.
- Data Utilization: Process mining primarily relies on event logs. On the other hand, digital twins integrate multiple data streams and broader contexts, offering a more nuanced understanding.
- Scope of Analysis: Process mining focuses on discovering and monitoring existing processes. Process digital twin extends this by allowing for the testing and simulation of potential process changes, offering a sandbox environment for innovation.
The Intersection of Two Worlds
I believe the marriage of process mining and digital twins seems inevitable. Without the grounded reality of process mining, a digital twin risks becoming a mere simulation scaffolded on assumptions.
To transform the process mining map into a digital twin model, we need to add parameters essential for the causal model. Once added, we should calibrate this model using the original process mining map. A well-crafted digital twin model should generate an event log identical to the one used to construct the process mining map. This calibrated digital twin not only reflects reality accurately but also serves as a foundation for predictive analytics and root cause analysis.
Process mining was a game-changer, giving businesses a clearer view of their operations. But it has its limits. The emerging concept of process digital twins offers an even deeper layer of insight. As technology evolves, it seems inevitable that we'll see process mining and digital twinning merge. This combination could provide the best of both worlds. Businesses should remain alert, selecting the tools that best drive their improvement.
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