As businesses continue their quest for operational efficiency, artificial intelligence has taken centre stage. For organizations to capitalize on this cutting-edge technology, they must look beyond traditional processes and adopt process mining to gain valuable insights from event logs, paving the way for real-time process monitoring and constant improvement.
Process mining technology, once limited to traditional business systems like ERP, has now expanded its reach to cover various technical, human, and business processes, thanks to improvements in AI and machine learning algorithms. The birth of hyperautomation, which blends traditional automation with innovative AI tools, has further propelled the potential of process mining, enabling more efficient processes across different domains like customer relationship management, supply chain, and finance.
Benefits and Challenges of Hyperautomation
Hyperautomation doesn’t just reduce costs – it also enhances customer experiences by refining their journeys and shortening processing times. Alongside its potential to transform entire industries, hyperautomation optimizes the entire delivery chain, resulting in increased collaboration between stakeholders, such as suppliers, logistics providers, and carriers. Improved Order-to-Cash and Procure-to-Pay processes can revolutionize the finance sector, leading to increased productivity and competitiveness.
However, adopting hyperautomation isn’t without challenges. Identifying processes to automate can be difficult due to cognitive biases, erroneous assumptions, and an incomplete understanding of operations. This is where process mining steps in, offering a precise understanding of process performance, allowing organizations to accurately analyze their current processes, pinpoint bottlenecks, and identify areas that can be automated for maximum efficiency.
Process Mining – The Solution to Common Hyperautomation Challenges
Merging hyperautomation with process mining helps businesses make informed decisions on which processes to automate, resulting in successful implementations and optimized outcomes. This integrated approach ensures hyperautomation is strategically implemented with a clear understanding of existing processes, empowering organizations to reap the rewards of digital transformation.
According to computer scientist Wil van der Aalst, there are three main types of process mining: discovery, conformance, and enhancement. Discovery involves creating process models based solely on event data, whereas conformance checks ensure that the intended process model aligns with real-world execution. Enhancement, also known as extension or performance mining, utilizes extra information to improve existing process models, addressing inefficiencies and bottlenecks.
How is Process Mining Different From Data Mining?
Process mining distinguishes itself from data mining by focusing on analyzing event logs and process-related data, enabling organizations to understand their processes by identifying trends, patterns, and discrepancies, visualizing actual process flows, discovering automation opportunities, and unearthing fresh ways to enhance efficiency.
The versatility of process mining has proven advantageous in identifying potential automation areas and prioritizing them based on ROI. Furthermore, it plays a critical role in hyperautomation, helping businesses integrate various tools and technologies to automate work. Combining analyzed data with AI/ML-driven analytics allows companies to detect new opportunities for optimization and automation.
Effective change management, along with leadership buy-in and aligned team incentives, is vital for successful process automation. Integrating cognitive technologies like ML, NLP, OCR, and AI into RPA through process mining can significantly improve process efficiency and accuracy.
In conclusion, process mining can help organizations derive maximum value from hyperautomation by offering the insights required to optimize processes, streamline operations, and achieve greater efficiency and effectiveness. As the technology evolves, it is likely to be incorporated into other critical use cases – such as compliance, auditing, sustainability, and business architecture – enabling models to access APIs and eliminate repetitive workflows.