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Innovating Process Mining
Achieve greater agility and optimise performance
Investing in new effective process mining solutions, organisations can optimise processes, stay competitive, and drive sustainable success in a data-based competitive arena
Four Innovation Areas
Process mining solutions are advancing beyond traditional capabilities by integrating new technologies and functionalities. Experts at Reply have identified four key areas of innovation: Artificial intelligence (AI) integration in process mining, object-centric process mining, end-to-end process platforms, and S/4HANA migrations.
These developments are crucial for companies seeking competitive advantage and operational success, as they enable more efficient process analysis and optimisation. Understanding their impact and selecting the right tools can help process owners and ICT managers adapt to evolving demands in process excellence.
A significant shift in Analysing and Optimising Processes
Traditional process mining tools rely on historical event data and often require substantial manual effort to structure and interpret information, limiting their ability to provide real-time insights. AI automates key aspects of this process, including data cleansing, integration, and anomaly detection, ensuring greater accuracy and efficiency. AI-driven tools can identify inefficiencies, predict potential disruptions, and recommend optimal process improvements dynamically, shifting organisations from reactive problem-solving to proactive, strategic process management. By leveraging machine learning and predictive analytics, they can gain a more comprehensive view of their operations and continuously refine workflows to maximise efficiency and compliance.
AI-powered process mining introduces a broad range of applications across industries, addressing challenges in process optimisation, customer experience, and risk management. Compliance monitoring is enhanced in highly regulated industries like finance and healthcare, where AI can detect workflow deviations and ensure adherence to internal policies and legal frameworks such as GDPR or Sarbanes-Oxley. AI also plays a crucial role in fraud detection, analysing transactional data for suspicious patterns and minimising financial risks. AI-driven process mining could also identify bottlenecks in service delivery, such as delays in order fulfilment or inefficiencies in banking processes, and suggest automation or workflow adjustments to improve customer satisfaction and retention.
Businesses operating in data-intensive environments or those with fragmented systems can benefit significantly from AI’s ability to streamline data preparation and enhance process accuracy. Organisations prioritising continuous improvement, automation, and predictive decision-making can leverage AI’s advanced analytics to gain a competitive edge. The integration of AI into process mining could move companies from static process analysis to a dynamic, data-driven approach that fosters agility, optimising resource allocation and ensuring long-term operational success.
Moving Focus from Event Logs to Objects
Unlike classical process mining, which views processes as a sequence of events tied to a single case ID, object-centric process mining allows for a more realistic and data-rich representation of how different objects, such as orders, invoices, or products, interact within a business process. This shift addresses the disconnect between how processes are traditionally modelled and how they exist in IT systems, providing a more comprehensive and dynamic analysis. By capturing multiple perspectives within a process, object-centric process mining enables organisations to gain deeper insights into dependencies, inefficiencies, and opportunities for optimisation, particularly in complex environments where multiple objects interact simultaneously.
The primary impact of this approach is its ability to enhance existing use cases while enabling new ones that were previously unattainable due to the constraints of classical process mining. By leveraging multiple object types within an analysis, businesses can assess processes from various viewpoints, such as evaluating an order-to-cash process from both the order and billing perspectives. Industries such as manufacturing can now achieve a more granular representation of production lines, identifying bottlenecks and inefficiencies with greater accuracy. This enhanced analytical capability extends further to the concept of a “digital process twin,” which seeks to map an organisation’s entire process landscape. By integrating all process-relevant data, companies can simulate the effects of changes across departments, predict operational outcomes, and make data-driven decisions with a holistic understanding of interconnected workflows.
However, object-centric process mining introduces new challenges that must be carefully managed. The increased complexity in data collection and preparation requires significant effort to extract, structure, and integrate relevant information from multiple sources. This can result in longer setup times compared to traditional process mining. Additionally, the vast amount of data incorporated into analyses makes interpretation more complex, demanding a higher level of expertise to derive meaningful insights. For organisations already experienced in classical process mining, transitioning to this approach can provide substantial benefits by addressing gaps in existing analyses. For newcomers, the additional complexity may necessitate a gradual adoption, focusing first on use cases that cannot be effectively handled with traditional process mining before expanding to broader applications.
Building an Ecosystem
End-to-end (E2E) process platforms integrate the entire process lifecycle, combining process mining with modelling, management, automation, and simulation within a single ecosystem. Unlike traditional process mining tools, which operate independently from automation and business process management (BPM) solutions, E2E platforms bridge these gaps, allowing organisations to seamlessly transition from process discovery to optimisation and execution. This integration eliminates inefficiencies caused by fragmented tools and data silos, enabling businesses to continuously improve processes while ensuring strategic alignment. By leveraging real-time insights, these platforms facilitate data-driven decision-making, empowering organisations to streamline operations, enhance compliance, and drive digital transformation more effectively.
A key advantage of E2E platforms is their ability to accelerate process improvement by eliminating the delays typically associated with separate tools. Traditionally, organisations had to extract insights from process mining, manually model optimised workflows, and then implement changes using external automation solutions. E2E platforms remove this complexity by providing a unified environment where businesses can identify inefficiencies, simulate potential improvements, and automate workflows instantly. This integration enhances collaboration between business, IT, and operations teams, ensuring that process changes are both technically feasible and aligned with organisational objectives.
E2E platforms support diverse use cases across industries, from optimising manufacturing workflows and enforcing compliance in finance to improving customer experiences in retail and banking. By integrating process mining with automation and simulation, businesses can proactively address operational bottlenecks, enforce regulatory standards, and enhance service delivery. In logistics and supply chain management, for example, these platforms enable organisations to simulate disruptions and optimise contingency plans, ensuring resilience in dynamic environments. E2E solutions are particularly valuable for enterprises undergoing digital transformation, as they provide a structured approach to modernising legacy systems and optimising end-to-end processes. While these platforms offer significant benefits, their adoption requires careful consideration of factors such as implementation complexity, data integration requirements, and the organisation’s overall process maturity.
Supporting Digital Transformation
Process mining could be a critical enabler of SAP S/4HANA migrations, providing organisations with deep insights into their existing business processes to ensure a smooth and efficient transition. Unlike traditional migrations, which often rely on assumptions or incomplete documentation, process mining analyses real-time and historical process data to uncover inefficiencies, bottlenecks, and deviations in workflows. By identifying these issues before migrating, businesses can streamline operations and align them with the best practices supported by SAP S/4HANA. This approach reduces complexity, minimises disruptions, and ensures that organisations do not carry over outdated or inefficient processes into their new system. Process mining helps map out the as-is state of operations, allowing businesses to compare it with the desired future state and make data-driven decisions on process reengineering and automation.
One of the most significant advantages of process mining in SAP S/4HANA migrations is its ability to improve data readiness, a crucial factor in ensuring a successful transition. Migrating to S/4HANA requires high-quality, structured, and deduplicated data, as modern ERP systems rely on accurate real-time information for operational efficiency. Process mining helps identify data inconsistencies, such as duplicate vendor records, missing fields, or outdated entries, allowing organisations to clean and standardise data before migration. Additionally, compliance monitoring is another essential aspect, as regulatory frameworks and industry standards must be adhered to throughout the transition. Process mining offers visibility into how well existing workflows align with compliance requirements, highlighting risks such as unauthorised approvals or deviations from procurement policies. By proactively addressing these challenges, organisations can ensure their new SAP system meets both operational and regulatory standards from day one.
Beyond the migration itself, process mining supports post-implementation performance monitoring, ensuring that new workflows function as intended within S/4HANA. Organisations can continuously track process efficiency, detect deviations, and make adjustments based on real-time data. This iterative optimisation is particularly valuable for large-scale enterprises that need to ensure a seamless transition across multiple departments or global operations. Furthermore, process mining facilitates user adoption by identifying friction points in existing systems and guiding targeted training strategies for employees transitioning to S/4HANA. Businesses can simulate different migration scenarios, assess potential disruptions, and proactively mitigate risks, reducing downtime and accelerating the realisation of value from the new system. Whether in a Brownfield approach focused on system continuity or a Greenfield transformation that reimagines processes from the ground up, process mining ensures that SAP S/4HANA migrations are strategic, efficient, and aligned with long-term business objectives.
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