Revolutionizing Business Efficiency: The Synergy of AI and Process Mining

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In an era marked by rapid digital transformation and escalating competition across various industries, businesses are continuously searching for innovative solutions to enhance efficiency and operational excellence. Enter the dynamic duo of artificial intelligence (AI) and process mining, technologies that when synergized, promise to revolutionize business operations. Process mining, a technique for analyzing event logs to understand business processes, combines with AI's predictive analytics to offer unprecedented insights into process optimization, compliance monitoring, and customer experience improvement. This convergence not only accelerates digital transformation but also propels businesses toward achieving industry 4.0 standards, ensuring they remain competitive in the information technology, manufacturing, healthcare, and retail sectors among others.

This article delves into the realm of AI and process mining, illuminating their individual capabilities and the transformative potential of their synergy. Discussions will span across key benefits such as enhanced data analysis for root cause analysis, fraud detection, and smart manufacturing. Additionally, it encompasses strategic implementation methodologies, real-world applications across diverse sectors including operational technology and workflow management, and the essential technological requirements for successful integration. Furthermore, it addresses measuring success and ROI, forecasting future trends in AI process mining, and concludes by reinforcing the pivotal role of these technologies in advancing business process management, operational excellence, and process automation.

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Understanding AI and Process Mining

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Process mining emerges as a rapidly evolving field, promising substantial benefits to organizations across various industries by focusing on analyzing and improving business processes. This technique has seen significant evolution over the past few decades and is anticipated to continue its trajectory of development. Initially, process mining relied on manual techniques such as flowcharting and process mapping to comprehend and enhance business processes, lacking the capabilities for root cause analysis or predictions. However, the advent of computers and the proliferation of data have transformed process mining into a more automated and data-driven practice.

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Definitions and Core Concepts

Process mining uses data, machine learning, and cutting-edge decision-making business intelligence technology to monitor, troubleshoot, and improve business process IT systems. It represents an easy and affordable way to apply data science and process analysis methodology to enhance, monitor, and measure business process performance. This approach enables better-informed decisions, real-time adjustments, and minimal human intervention in monitoring results. Although the concept of using automation and artificial intelligence for business process management is relatively new, the foundation of process mining itself is not. Historically, companies expended thousands of personnel hours manually scanning documents to identify problems, guess at optimization solutions, understand root causes, implement changes, and monitor results by hand. Process mining techniques and algorithms digitally transform these steps, offering a more efficient and accurate method for process improvement.

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Historical Context and Development

The journey of process mining began with the need for optimizing workflows, which dates back to the 19th century with time and motion studies aimed at standardizing and optimizing manufacturing processes. This pursuit of process excellence evolved over time, giving rise to disciplines like Lean manufacturing, Six Sigma, and Agile manufacturing, all focused on eliminating inefficient processes. The invention of the hard disk in the 1950s by IBM marked a significant milestone, leading to the development of the first database management systems, data warehousing, and data mining. This technological advancement helped companies realize the potential of their IT systems in improving operations. The formal recognition of business process management (BPM) as a crucial approach to process enhancement came in 2003, with the Association of Business Process Management Professionals' inaugural meeting. However, the groundbreaking work of Wil Van der Aalst, a Dutch computer scientist, in originating process mining has been instrumental in its adoption and evolution. The IEEE's publication of the Process Mining Manifesto in 2011 aimed to promote the research, development, education, implementation, and understanding of process mining, marking a significant milestone in its journey.

Process mining has now become an indispensable tool for companies aiming to thrive in the digital transformation era, impacting consumer and business landscapes globally. It is utilized in three main areas: discovering how processes actually work using event logs, conformance, which assesses the accuracy of event log records against real-world activities, and enhancement, focusing on improving and optimizing business processes to eliminate inefficiencies. The integration of process mining with technologies like robotic process automation (RPA) and BPM has significantly increased, marking a new era in process analysis and optimization. The use of machine learning and artificial intelligence (AI) to enhance the accuracy and effectiveness of process mining represents a key trend, enabling organizations to analyze and interpret vast amounts of process data more quickly and accurately. This technological synergy allows for the identification of patterns and anomalies that traditional methods would find challenging or impossible to detect, paving the way for more accurate predictions and effective process optimizations.

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The Synergy Between AI and Process Mining

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The integration of Artificial Intelligence (AI) into the realm of Information Technology (IT) is a transformative development at the intersection of two rapidly evolving disciplines. AI, a branch within computer science dedicated to creating machines that perform tasks requiring human intelligence, has seen significant advancements recently. These advancements are particularly notable in IT, a sector crucial to the operations and strategic planning of modern organizations. AI revolutionizes IT by enhancing data analysis, automating repetitive tasks, and generating predictive insights. This innovation serves as a cornerstone for the synergy between AI and process mining, leading to unprecedented efficiencies and improvements in business operations.

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How AI Enhances Process Mining

1. Automated Data Analysis: AI excels in automating the labor-intensive and time-consuming tasks of data cleaning, preprocessing, and pattern recognition. This automation is achieved with remarkable speed and accuracy, enabling businesses to quickly derive actionable insights from their data.
2. Pattern Recognition: One of AI's most significant contributions to process mining is its ability to uncover complex patterns within datasets that might escape traditional analytical methods. Machine learning algorithms, in particular, are adept at identifying non-linear relationships and subtle correlations, offering valuable insights for strategic decision-making.
3. Predictive Analytics: Beyond retrospective analysis, AI enables businesses to embrace predictive analytics. Machine learning models can forecast future trends based on historical data, allowing organizations to anticipate market shifts, customer preferences, and potential risks effectively.
4. Scalability: As datasets grow in size and complexity, the ability to scale becomes a critical factor. AI technologies, including distributed computing frameworks and cloud-based solutions, provide the necessary scalability to efficiently process massive volumes of data.
5. Natural Language Processing (NLP): AI-powered NLP facilitates the extraction of meaningful information from unstructured data, enriching the analysis process and offering a more comprehensive view of the business landscape.
6. Continuous Learning: AI systems are not static; they continuously learn and adapt. Through techniques like reinforcement learning, AI algorithms refine their performance over time, optimizing analysis processes and improving their understanding of data patterns.

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The Role of AI in Process Optimization

1. Predictive Maintenance: AI enables the prediction of machinery and equipment failures through real-time data analysis. This capability allows for proactive maintenance, minimizing unplanned downtime and maximizing asset availability.
2. Supply Chain Optimization: AI analyzes historical data, demand forecasts, and other relevant factors to optimize production planning, inventory management, and distribution. This optimization helps reduce logistics costs, shorten delivery times, and enhance customer satisfaction.
3. Automated Quality Control: Through real-time analysis of visual and sensory data, AI can efficiently detect defects and anomalies in production, leading to improved product quality and a significant reduction in production line errors.
4. Process Optimization: AI analyzes large volumes of data to identify inefficiencies and bottlenecks in industrial processes. By providing precise recommendations for optimization, AI reduces cycle times and maximizes overall efficiency.
5. Enhanced Occupational Safety: By analyzing real-time data and detecting risk patterns, AI contributes to safer working environments, preventing accidents and facilitating informed decision-making to minimize operational risks.

The synergy between AI and process mining heralds a new era in business operations, offering a systematic approach that continually improves through machine learning. This synergy not only optimizes workplace processes but also empowers organizations to achieve operational excellence, reduce costs, and enhance customer satisfaction.

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Key Benefits of AI and Process Mining in Operations

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Operational Efficiency

One of the primary advantages of integrating artificial intelligence (AI) with process mining in operations is the substantial enhancement in operational efficiency. This synergy allows organizations to automate the analysis of vast datasets generated by business processes. Traditional process mining techniques, which often relied on predefined rules and algorithms, are now supercharged with AI's capability to learn from data, identify patterns, and make intelligent decisions. This evolution enables organizations to uncover hidden insights, detect anomalies, and optimize processes with an unprecedented level of accuracy and efficiency.

Moreover, the application of machine learning and AI extends beyond descriptive analytics into predictive and prescriptive analytics. Organizations can leverage these technologies to analyze historical process data, forecast future process behavior, anticipate bottlenecks, and proactively optimize processes. This predictive capability ensures that businesses are not merely reacting to issues as they arise but are instead anticipating and mitigating potential challenges before they impact operations.

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Cost Reduction and Resource Optimization

Another key benefit of combining AI with process mining is the significant potential for cost reduction and resource optimization. AI-driven process mining tools extract transaction-level insights from systems such as ERP, providing teams with an end-to-end view of processes. This comprehensive visibility allows for the identification of inefficiencies and deviations, enabling targeted interventions that lead to substantial performance improvements.

For instance, a high-tech manufacturer applied process mining to its order-to-cash process and identified initiatives that reduced end-to-end activity time by 20 to 50 percent, improved customer satisfaction, and increased efficiency. Similarly, a company in the aerospace and defense sector used task mining to reveal that employees spent a significant portion of their time on repetitive analyses. By automating these common tasks and improving process governance, the company was able to reduce process times by more than 40 percent.

Furthermore, the integration of AI and process mining aids in optimizing resource allocation, enhancing productivity, and freeing up human resources to focus on higher-value activities. This optimization not only reduces operating costs but also contributes to a more agile and responsive operational framework.

By leveraging the combined power of AI and process mining, organizations can achieve a dual objective: enhancing operational efficiency and reducing costs. This dual benefit is crucial for maintaining competitiveness in today's fast-paced business environment, where efficiency and cost-effectiveness are key drivers of success.

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Implementation Strategies

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Steps to Integrate AI and Process Mining

1. Define the Problem and Solution: Start by clearly defining the business process issues and determining how AI can address these problems. This involves understanding the specific process inefficiencies or areas where automation and advanced analytics could bring improvements.
2. Data Extraction and Preparation: Collect and prepare data from various sources that feed into the process mining tools. This step is crucial as the quality and comprehensiveness of data directly influence the effectiveness of subsequent analysis and AI modeling.
3. Feature Generation and Labeling: Use process mining tools to analyze the extracted data, generating features and labels that will be used in AI models. This step transforms raw data into a format suitable for machine learning and other AI techniques.
4. Model Development: Apply AI methodologies such as machine learning, natural language processing, or computer vision to create models that can learn from the processed data. This involves selecting the appropriate algorithms and configuring them to address the defined business problems.
5. Validation and Testing: Rigorously test the AI models to ensure they perform accurately and reliably. This includes validating the models against unseen data sets to check their effectiveness in real-world scenarios.
6. Deployment: Integrate the AI models with process mining tools to enhance the capabilities of process analysis and improvement. Ensure that the deployment is seamless and that the tools work in sync to provide continuous insights and optimizations.

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Key Considerations and Challenges

• Data Quality and Integration: Ensuring high-quality data is fundamental for successful implementation. Data from different sources must be cleaned, standardized, and integrated to provide a comprehensive view of processes.
• Choosing the Right Tools: Selecting appropriate process mining and AI tools that align with the organization's IT infrastructure and business goals is critical. The tools should be scalable, secure, and compatible with existing systems.
• Managing Change: Implementing AI and process mining can lead to significant changes in how processes are managed. Preparing the workforce for these changes through training and communication is essential to overcome resistance and improve adoption.
• Continuous Monitoring and Optimization: Post-deployment, it is vital to continuously monitor the performance of the integrated system and optimize it based on feedback and evolving business needs. This ensures the system remains effective and relevant.
• Privacy and Compliance: Adhering to regulatory requirements and ensuring data privacy must be prioritized. The system should be designed to handle sensitive information securely and comply with all applicable laws and standards.

By addressing these steps and considerations, organizations can effectively implement AI and process mining to enhance their business processes, leading to improved efficiency, reduced costs, and better decision-making capabilities.

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Real-World Applications

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Industry Use Cases

Process mining has revolutionized operations across various sectors by providing deep insights into process flows and identifying inefficiencies. In manufacturing, companies like Company X have utilized process mining to streamline their supply chain operations. They achieved this by gaining visibility into the entire supply chain, from procurement to delivery, allowing them to identify bottlenecks and inefficiencies and implement targeted improvements. This strategic application not only reduced lead times but also enhanced supplier collaboration and inventory management, leading to significant cost savings and improved service levels.

In the telecommunications sector, Company Y transformed their customer service operations through process mining. By analyzing customer service processes, they identified significant delays in their complaint resolution process. Implementing automated case management systems and real-time monitoring enabled them to reduce customer churn rates and enhance overall customer satisfaction.

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Success Stories from Various Sectors

1. Healthcare: In the healthcare industry, process mining has been pivotal in enhancing patient care and administrative efficiency. For instance, large language models have been employed to sift through health records and medical data to support clinical decisions and recommend personalized treatments, significantly improving patient outcomes.
2. Retail: Retail giants have leveraged process mining to optimize customer service and logistics operations. By analyzing customer feedback and browsing trends, they have been able to tailor recommendations and improve customer satisfaction, thereby driving business growth and customer loyalty.
3. Financial Services: In this sector, process mining has improved customer experiences by identifying inefficiencies in customer interactions and aiding in regulatory compliance. This has helped firms enhance service delivery and maintain trust while minimizing risks.
4. Manufacturing: Companies in the manufacturing sector have used process mining to optimize production lines by analyzing operational data to anticipate equipment failures and recommend enhancements, ensuring smooth and efficient production processes.

These real-world applications across diverse sectors illustrate the transformative impact of process mining, driving significant improvements in operations and customer experiences. By tailoring process mining to industry-specific needs, organizations can uncover deep-seated inefficiencies and gain a competitive edge.

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Technological Requirements

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Infrastructure and Tools Needed

The integration of Artificial Intelligence (AI) and process mining into business operations necessitates a robust technological infrastructure. Enterprises must consider several critical components to support these advanced technologies effectively.

1. Data Collection and Management Systems: Essential for gathering and organizing the vast amounts of data required for AI and process mining. This includes systems like ERP, CRM, and IT ticketing systems, which provide valuable transaction and event logs.
2. Advanced Analytical Tools: Machine learning and deep learning frameworks are necessary to process and analyze the data. These tools require substantial computational power and are often supported by platforms like Amazon Comprehend or Azure OpenAI Service.
3. High-Performance Computing (HPC) Resources: AI and machine learning algorithms demand powerful computing resources. This might include hyper-converged infrastructure (HCI) systems, which offer scalability and flexibility, or dedicated AI accelerators like GPUs designed for high-speed data processing.
4. Secure Storage Solutions: Given the sensitive nature of the data involved, secure storage solutions are crucial. These should ensure data integrity and protect against potential breaches.
5. Integration Capabilities: Seamless integration of AI and process mining tools with existing IT infrastructure is vital. This ensures that data flows smoothly across different systems and that insights generated are easily accessible across the organization.

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Data Security and Privacy Concerns

When deploying AI and process mining technologies, organizations must rigorously address data security and privacy concerns to protect sensitive information and comply with regulatory standards.

1. Compliance with Data Protection Regulations: Organizations must adhere to regulations such as GDPR in the European Union and CCPA in the United States. These regulations dictate stringent requirements for data handling, including obtaining consent and anonymizing data to protect individual privacy.
2. Implementation of Privacy-Preserving Techniques: Techniques such as differential privacy, secure multiparty computation, and homomorphic encryption help protect data privacy during the mining process. These methods ensure that individual data points are not exposed during analysis.
3. Robust Security Protocols: Enterprises should implement strong encryption, authentication, and authorization protocols to safeguard data against unauthorized access. Regular audits and updates to security measures are necessary to address emerging threats.
4. Stakeholder Engagement and Transparency: It is essential to maintain transparency with stakeholders concerning data use and privacy policies. Regular communication helps build trust and ensures that privacy concerns are addressed promptly.
5. Continuous Privacy Training: Educating employees about privacy best practices and the importance of data security is crucial. Training programs should be updated regularly to reflect the latest privacy standards and technological advancements.

By addressing these technological requirements and privacy concerns, organizations can leverage the power of AI and process mining securely and effectively, ensuring that they remain compliant and protect sensitive information.

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Measuring Success and ROI

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Key Metrics to Track

To effectively measure the success and return on investment (ROI) of integrating artificial intelligence (AI) with process mining, organizations must focus on several key performance indicators (KPIs). These metrics provide insights into the efficiency, effectiveness, and overall impact of the implemented technologies.

1. Process Efficiency Metrics: Key indicators such as cycle time, resource utilization, and throughput rates are crucial. They help in identifying how effectively resources are being used and how swiftly processes are completed.
2. Process Effectiveness Metrics: These include quality of output, error rates, and customer satisfaction scores. Monitoring these metrics ensures that the outputs meet the expected standards and customer requirements.
3. Financial Metrics: ROI analysis is vital, considering factors like cost reduction, productivity improvements, and efficiency gains. Organizations should compare these financial benefits against the initial investments and ongoing operational costs.
4. Compliance Metrics: These involve tracking adherence to internal standards and external regulations. Metrics like risk mitigation timeframe and mean time to issue resolution are critical for maintaining compliance and avoiding penalties.
5. Innovation and Growth Metrics: It's important to measure how AI and process mining contribute to new business opportunities and enhancement of existing services. Metrics might include new revenue streams generated or increased market share.

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Evaluating the Impact on Business

The impact of AI and process mining on business operations can be profound and multifaceted. To fully appreciate the extent of this impact, organizations should consider both quantitative and qualitative evaluations:

1. Operational Improvements: Analyze improvements in process speed, reduction in errors, and better resource allocation. These changes often lead to higher productivity and operational agility.
2. Customer Experience: Enhanced process accuracy and efficiency typically improve customer satisfaction. Surveys and feedback can provide direct insights into how process changes affect customer perceptions and loyalty.
3. Strategic Decision Making: With more accurate data and insights, businesses can make informed decisions that align closely with their strategic goals. This might include expanding into new markets or adjusting service offerings based on predictive analytics.
4. Employee Satisfaction: Automating mundane and repetitive tasks can lead to higher job satisfaction, as employees are able to focus on more meaningful and strategic work. This change can also lead to better retention rates and attract top talent.
5. Long-term Financial Performance: Beyond immediate ROI, it's important to assess the long-term financial impact, such as sustained cost savings, profitability, and financial health of the organization.

By continuously monitoring these metrics and evaluating their impact, businesses can not only justify the initial investment in AI and process mining but also refine their approaches to maximize future benefits and ROI.

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Future Trends in AI and Process Mining

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Emerging Technologies

The landscape of AI and process mining is poised for significant transformation driven by emerging technologies. Generative and predictive AI, such as advancements exemplified by large language models, are set to redefine process management. These technologies will enable organizations to not only identify existing inefficiencies but also predict future challenges and opportunities, potentially offering solutions before issues arise. The integration of AI with Object-Centric Process Mining (OCPM) illustrates a move towards more holistic and interconnected process analysis, capturing interactions across various systems and objects. This approach is expected to enhance process intelligence across industries, including sectors like healthcare and telecommunications where it has been underutilized.

Object-centric approaches provide a system-agnostic way to structure information, which could lead to standardized event data, fostering collaborative networks and aligning processes across different entities. This standardization is crucial for leveraging the full potential of federated process mining, where insights are shared across organizational boundaries, enhancing overall process efficiency and compliance.

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Predictions for the Next Decade

1. Customization and Personalization: The next decade will witness a shift towards more customized AI systems tailored to specific business needs. This shift will spur innovation in product development and service offerings, setting new industry benchmarks. As AI becomes more integrated into distinct business scenarios, its impact on strategic differentiation will grow, allowing companies to carve unique competitive advantages in crowded markets.
2. Advancements in Non-Transformer AI Architectures: The emergence of non-transformer AI architectures promises more efficient resource utilization, faster processing times, and reduced energy consumption. These new models are expected to handle complex data types and sequences more effectively, potentially revolutionizing AI integration in technologies where computational power was a limiting factor.
3. Growth of Multimodal AI: AI's ability to process and understand multimodal inputs—combining text, sound, and visual data—will enhance human-machine interactions. This development is anticipated to create new user engagement opportunities, making AI systems more intuitive and adaptable to complex human inputs.
4. Expansion of Open-Source AI Models: The proliferation of open-source AI models will democratize access to AI technologies, accelerating innovation and collaboration across the global tech community. This trend is likely to shift the competitive dynamics within the tech industry, emphasizing cooperation over secrecy and promoting a more inclusive AI development ecosystem.
5. Regulatory and Ethical Considerations: As AI and process mining technologies become more pervasive, regulatory scrutiny is expected to intensify. Organizations will need to navigate new guidelines and standards, particularly concerning data privacy and ethical AI use, ensuring that innovations in AI and process mining contribute positively to society without compromising individual rights or competitive fairness.

These trends underscore a future where AI and process mining are not only intertwined but also central to driving operational excellence and innovation across industries. As organizations prepare to embrace these advancements, the focus will be on creating robust, ethical, and compliant frameworks that harness the power of AI while respecting privacy and promoting sustainable practices.

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Conclusion

Through this comprehensive exploration, we've delved into the powerful combination of AI and process mining, illustrating their pivotal roles in transforming business efficiencies across various sectors. The fusion of these technologies enables organizations to automate and optimize processes, enhance decision-making, and significantly reduce operational costs. As demonstrated, the implementation of AI and process mining is not a mere technological upgrade but a strategic investment towards achieving operational excellence and staying competitive in the digital age. The journey towards fully integrating these technologies involves comprehensive planning, from defining the problem and solution to ensuring rigorous data collection and preparation, ultimately leading to successful deployment and continuous optimization.

As the business world continues to evolve, the synergy between AI and process mining will only grow stronger, offering new opportunities for innovation and efficiency. The real-world applications and success stories across industries underscore the transformative potential of these technologies. Organizations that leverage this synergy not only streamline their processes but also forge a path toward digital transformation that is both sustainable and forward-thinking. For those eager to embark on this journey, the opportunity to harness the full potential of AI and process mining to revolutionize your operational processes is merely a consultation away.

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Feel free to visit our website and reach out for a meeting with our process advisors, who are ready to guide you through the next steps in leveraging these cutting-edge technologies for your business success.

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Please reach out to me or www.perceptif.ai if you would like more information.

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