Process mining and process discovery are the main elements of business process management. In the ever-changing business world, businesses aim to reach their goals, amplify efficiency and keep themselves competitive through different tools and strategies. Despite the fact that process mining and process discovery are always mentioned together, they have distinct purposes and operate differently. Organizations that want to realize their untapped potential should be aware of the difference between process mining software and process discovery.
This article discusses the essentials of process mining and process discovery, their specific uses, and how they can drive operational excellence. So you will have enough information to decide which method is the best and how the two can interact during the discussion.
What Is Process Mining?
Process mining is a data-driven technology that analyzes event logs from enterprise IT systems, allowing it to visualize and understand the business process that has to be optimized and further transformed into digital reality so that a fact-based view of how a process actually gets executed in an organization can be obtained. Advanced algorithms and AI are employed to identify inefficiencies and bottlenecks in deviation from intended workflows in processes.
Key features of process mining software include:
- Process visualization: Mapping end-to-end processes as they occur in real life.
- Conformance checking: Ensuring processes align with predefined models and compliance standards.
- Performance analysis: Performance Analysis is done on the process efficiency metrics basis such as throughput time, idle time, rework rates, etc.
- Root cause identification: Identify the underlying issues that are causing inefficiencies.
The strength of the process mining tool lies in its ability to offer real actionable insights from massive data amounts. Organizations can utilize that to drive continuous improvement, optimize allocation of resources, and work on customer satisfaction.
What Is Process Discovery?
Process discovery refers to a mode that creates new process models out of nothing. Process discovery is not like process mining which normally uses pre-existing event logs but rather, it starts from scratch without any data. Instead, it relies on user input, interviews, and manual observations to map workflows.
Process discovery is typically used in scenarios where:
- No event logs exist: Processes are not yet digitized or recorded in IT systems.
- Processes are undocumented: Organizations lack formal documentation of workflows.
- Understanding is subjective: There is a need to align diverse stakeholder perspectives.
The goal of process discovery is to create an accurate representation of current workflows. This representation serves as a baseline for improvement initiatives, enabling organizations to identify inefficiencies and design optimized processes.
Comparing Process Mining and Process Discovery
Here’s a comparison of the key differences:
1. Data Dependency
- Process Mining: Depends on digital event logs that enterprise systems produce such as ERP, CRM, or BPM tools. Without this data, process mining cannot function.
- Process Discovery: Does not require existing data. It uses human inputs, workshops, and observations to create process models.
2. Accuracy and Objectivity
- Process Mining: Presents an objective, fact-based view of processes by analyzing actual data. It removes bias and inaccuracies associated with human methods.
- Process Discovery: May introduce subjectivity, as it depends on stakeholder input and manual observations. Human error or bias can affect the accuracy of the resulting models.
3. Speed and Scalability
- Process Mining: It quickly analyzes big complex data sets which make it very scalable for company-wide usage.
- Process Discovery: It is time-consuming and labor-intensive, especially for large-scale or complex processes.
4. Scope of Application
- Process Mining: Best suited for organizations with mature IT infrastructure and access to digital event logs.
- Process Discovery: Ideal for undocumented or non-digitized processes, as well as for gaining initial insights in process improvement projects.
5. Use Cases
- Process Mining: Used for various purposes such as performance optimization and compliance checking in finance, manufacturing and logistics industries.
- Process Discovery: Typically used when the digital transformation is at an early stage or the processes are undocumented.
Use Cases: Process Mining vs. Process Discovery
Process Mining in Action
Consider an international manufacturing enterprise plagued with delay in order-to-cash. Implementing process mining software enables the company to analyze its ERP event logs and uncover why things are taking time. Results reveal redundant approval steps and inconsistencies in data entry. From these insights, the organization optimizes workflow and slashes the order processing time by 30%. The organization, consequently, realizes an increase in cash flow.
Process Discovery in Action
Mid-size retailer that plans to digitalize its procurement process. Undocumented procurement workflows, highly dependent on manual work and word-of-mouth. Process discovery workshops are used to create an as-is map of the current process, identify inefficiencies (e.g., a duplicate purchase order), and create an efficient workflow design. Then, this new process will be digitized and implemented in an organizational setting to create a platform for future optimization.
How Process Mining and Process Discovery Complement Each Other
Process mining and process discovery are two different methodologies that may not be mutually exclusive. Rather, they can complement each other in various scenarios:
Kickstarting Digital Transformation: Process discovery can help organizations map undocumented workflows, providing a baseline for digitization. Once processes are digitized, process mining can take over to provide continuous monitoring and improvement.
Gap Filling in Process Data If event logs are not present or are incomplete, the process discovery can fill up these gaps by creating models for the process mining to validate and refine later.
End-to-End Optimization: It leads to the identification of high-level workflows using process discovery, whereas process mining goes granular into data-driven insights. They work in tandem for thorough optimization.
Choosing the Right Approach for Your Organization
The choice between process mining and process discovery is always dependent on the specific needs, maturity level, and available resources of your organization. Consider the following factors:
IT Infrastructure: If your organization has robust IT systems generating event logs, process mining is the logical choice for fact-based analysis.
Process Documentation: If your processes are undocumented or manually executed, process discovery is the starting point for improvement.
Scalability Requirements: For large-scale, enterprise-wide initiatives, process mining’s speed and scalability make it the preferred option.
Budget and Resources: Process discovery may require more time and effort upfront, whereas process mining relies on existing data but may involve higher initial investment in software.
Conclusion
Processes come in their own way; both software and process mining play unique roles in getting to this operational excellence stage of the continuum. Process discovery lays much groundwork for proper digitization and transformation that process mining excels, providing actionable data-driven information geared toward perpetual improvement. These methodologies understand their strengths or differences in specific applications and support business sustainability. For most organizations, the best approach is a combination of these methodologies that would give a seamless blend of insight, optimization, and transformation.
Whether you’re uncovering undocumented workflows or refining digitized processes, both tools are essential allies in your pursuit of efficiency and excellence.
