Operations often suffer from poor coordination and disconnected departments, leading to inefficiencies and unmet customer demands. To thrive in a competitive landscape, organizations need a deep understanding of their actual practices. Process Mining is such technology that unifies departments, systems, and processes, offering a comprehensive view of current operations.
This insight identifies bottlenecks and optimization opportunities, enhancing efficiency and customer satisfaction. For example, in document collaboration, integrating document AI technologies into RPA can optimize processes, improving work and collaboration efficiency. Keep looking for fully understanding what Process Mining is and find the proper tools for your business.
What Is Process Mining?
Process mining is a data science and process management technique used to analyze event data to understand and improve operational processes. It is typically based on logs that contain case IDs, activities, timestamps, and other information (such as resources, costs, etc.). This data helps organizations better understand their business processes by extracting meaningful connections. Process mining techniques can be categorized into three types: process discovery, conformance checking, and process enhancement.
Process Discovery:
To conduct process mining, the first step is process discovery. The result of process discovery is typically a process model that represents the event logs. Event logs can come from any system that records data, generally including case IDs of activities, textual descriptions of performed activities, and their timestamps. Common algorithms used to construct process discovery models include the alpha algorithm, heuristics miner, and inductive miner.
Conformance Checking:
This technique helps compare event logs with existing process models to detect deviations (compliance checking), evaluate discovery algorithms, or enrich existing process models. Available techniques include:
- Token-based replay: This method checks the conformance of the process with its model by replaying each trace on the model. Using four counters (produced tokens, consumed tokens, missing tokens, and remaining tokens), it records situations that enforced transitions and the remaining tokens after replaying. From these counters, we can calculate the fitness value between the trace and the model.
- Streaming conformance checking: This technique reports deviations immediately as they occur. Unlike traditional methods, streaming conformance checking does not use event logs but instead takes event streams and process models as input, comparing each received event from the stream with the model.
Process Enhancement:
This technique aims to improve the performance of existing models based on specific process performance metrics (such as processing time, cycle time, waiting time, costs, etc.).
What Problem Does Process Mining Solve (Advantages)?
It provides companies with the essential insights needed to intuitively understand how their processes actually operate within systems, departments, and procedures. This allows them to identify opportunities, determine which opportunities may have the greatest impact, decide on the actions to take, and measure the value they generate.
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Prevent unclear/unreliable reports and gain a comprehensive understanding of your processes:
- Reveal hidden differences among variables such as departments and suppliers. Gain deep insights into issues and their root causes.
- Obtain a 100% objective, real-time view of processes based on data from IT systems.
- Quantify the impact of process improvements to demonstrate value before and after implementation.
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Address issues of excessive input:
- Optimize departmental collaboration processes and reduce unnecessary resource allocation.
- Develop data-driven recommendations that align with anticipated ROI, fostering stakeholder alignment and consensus.
- Prioritize and allocate resources based on the quantified value that can be achieved.
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Solve data chaos issues:
- Analyze growing system data and efficiently complete automated data cleaning and analysis.
- Break down silos of different data sources and merge them into a single data set for easy monitoring.
The Limitations of Process Mining
Data Quality and Availability: Process mining relies on high-quality and complete data. Inaccurate data can distort models and lead to erroneous insights. Expert involvement in the initial stages can ensure data integrity.
Inability to Capture Tasks: Process mining may miss manual tasks not recorded in event logs, limiting its application range. Combining it with task mining can address this gap, enhancing workflow analysis and task optimization.
Integration Barriers: Some IT systems are challenging to integrate with process mining due to a lack of connectors or data format issues. Pre-packaged solutions can simplify integration, ensuring seamless processes.
Concept Drift: As processes evolve, keeping models up-to-date is challenging, with outdated models posing higher risks. Advanced process mining solutions can analyze nearly in real-time, ensuring models remain current and relevant.
Complexity in Large Organizations: The number and complexity of processes in large organizations increase the difficulty of mining and impact insights. Object-centric or multi-level techniques can better manage and analyze complex processes.
Potential Resistance to Change: Changes introduced by process mining may encounter resistance from employees accustomed to existing workflows. Effective change management strategies, such as training and employee engagement, are crucial for smooth implementation and adoption.
Process Mining Use Cases & Example
Business Process Mining Scenarios
Process mining can be applied across various sectors to enhance corporate efficiency. Below are some example areas where businesses can make use of process mining:
- System Migration and Tech Updates: By examining existing workflows, process mining identifies risks and bottlenecks during system migrations, ensuring smooth updates and minimizing interruptions.
- Compliance Programs: Process mining ensures business processes comply with regulations, reducing violation risks and enhancing overall compliance.
- Shared Services Transformation: Optimizing shared service processes reduces redundancies, turning them into efficient, flexible "value magnets."
- Financial and Operational Improvement: Identifying inefficiencies in financial and operational processes, process mining improves cash flow management, cost control, and financial reporting.
- Supply Chain Enhancement: Process mining improves supply chain transparency and resilience by identifying weak links and redundancies, helping businesses adapt to market changes.
Examples: Document Processing & Process Mining
A large law firm needs to process a significant number of legal contracts daily. These contracts are typically stored in PDF format, and each contract review process is highly cumbersome and time-consuming. To enhance efficiency, the firm decided to employ process mining to automate and optimize the contract review process. The main steps involve identifying key review steps, discovering bottlenecks, optimizing processes, and implementing changes.
Data Collection:
First, collect logs and data related to contract reviews. Data sources include:
- PDF contract files.
- Email records (communications related to contracts).
- Activity logs from the review system.
This data can capture every movement of the contract during the review process, from receipt to final approval.
Process Discovery:
Use process mining tools (e.g., ProM, Celonis) to analyze the collected data. Main steps include:
- Event Log Generation: Extract event logs from system logs and PDF contract records. Each event log includes timestamps, event names, related documents, and involved review personnel.
- Model Generation: Generate a model of the contract review process from the event logs, detailing each step such as contract receipt, initial review, detail modifications, legal opinion solicitation, and final approval.
Bottleneck Analysis:
By analyzing the generated process model, the following can be identified:
- Steps that consume the most time.
- Steps most prone to errors.
- Differences in time taken by different personnel to complete identical tasks.
Process Optimization:
Based on the analysis results, execute the following optimization measures:
- Automated Initial Review: Use Natural Language Processing (NLP) technology to automatically read and analyze the content of PDF contracts, identify preliminary issues, and generate an initial review report, thereby reducing the time required for manual initial review.
- Intelligent Task Allocation: Assign tasks based on the efficiency of different reviewers. An automated allocation system can identify and assign complex contracts to experienced reviewers.
- Anomaly Detection: Develop an automated alert system that notifies relevant personnel when a review step exceeds the expected time, enabling timely intervention.
Implementation Results:
- Efficiency Improvement: Automating the initial review and intelligent task allocation reduced the total review time by 30%.
- Quality Enhancement: Automation in the initial review stage reduced human errors, significantly increasing the accuracy of contract reviews.
- Personnel Management Optimization: The intelligent task allocation system improved the rationality of resource usage, ensuring that each contract was reviewed by the most suitable personnel.
Wrapping Up
From here, you should have known how process mining empower your businesses. Similar to the document process mining example provided above, Process Mining technology can offer a unified, data-driven view of actual processes across various industries. This approach helps identify gaps in operations, uncover bottlenecks and inefficiencies, and discover potential optimization opportunities, thereby enabling innovation and gaining a competitive edge in a demanding market environment.