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Process mining is a field that involves the analysis of business processes based on event logs. The goal is to extract insights and knowledge from these event logs to improve business processes, optimize workflows, and enhance decision-making. Here's a general overview of how process mining works:

Data Collection:

The first step in process mining is to gather data about the business processes. This data typically comes from event logs generated by information systems, such as Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, or other software that records events related to business processes.

Event Logs:

Event logs contain a chronological record of events that have occurred in a system. Each event is associated with a specific activity, timestamp, and possibly other relevant information. These logs serve as the raw material for process mining analysis.

Data Preprocessing:

Raw event logs may be large and complex, so preprocessing is necessary. This step involves cleaning the data, handling missing values, and organizing it into a format suitable for analysis.

Process Discovery:

Process discovery is a key aspect of process mining. This step involves creating a visual representation of the actual business process based on the event logs. Various algorithms and techniques are used to discover the sequence of activities, the relationships between them, and the overall flow of the process.

Conformance Checking:

Once the discovered process model is obtained, it is compared to the intended or ideal process model. Conformance checking identifies any deviations or variations between the actual process and the expected process. This helps in understanding where processes may not be followed as intended.

Enhancement and Optimization:

Based on the insights gained from process discovery and conformance checking, organizations can identify areas for improvement in their processes. This may involve streamlining workflows, removing bottlenecks, or optimizing resource utilization.

Analysis and Visualization:

Process mining tools often provide visualization capabilities to represent the discovered processes and deviations in an understandable manner. This can include flowcharts, graphs, and other visualizations that make it easier for stakeholders to comprehend and analyze the information.

Continuous Monitoring:

Process mining is not a one-time activity. Continuous monitoring involves regularly analyzing new event data to ensure that the business processes remain efficient and effective. It allows organizations to adapt to changes and continuously improve their processes over time.

Integration with other Systems:

Process mining can be integrated with other business intelligence and analytics systems to provide a comprehensive view of organizational performance. This integration allows for a more holistic understanding of how business processes impact overall business outcomes. Overall, process mining provides a data-driven approach to understanding, analyzing, and improving business processes, making it a valuable tool for organizations seeking operational excellence and efficiency.

Implementing process mining in your organization can offer a variety of benefits, contributing to improved efficiency, better decision-making, and enhanced overall performance. Here are some key advantages of using process mining:

Visibility and Transparency:

Process mining provides a transparent view of your organization's processes by visualizing them based on real data. This transparency allows stakeholders to understand how processes actually work, identify bottlenecks, and recognize areas for improvement.

Process Discovery:

Discovering and visualizing actual processes helps organizations understand how work is really done. This insight is valuable for identifying inefficiencies, variations, and potential areas for optimization.

Performance Analysis:

Process mining allows for a detailed analysis of process performance, including cycle times, throughput, and resource utilization. This information can help organizations identify areas where processes are performing well and where improvements are needed.

Root Cause Analysis:

When deviations from the intended processes are identified, process mining can be used to perform root cause analysis. Understanding the reasons behind deviations enables organizations to address underlying issues and improve overall process adherence.

Compliance Monitoring:

For industries with regulatory requirements, process mining can aid in compliance monitoring. It helps ensure that processes align with regulatory standards and provides an audit trail for demonstrating compliance.

Resource Optimization:

By analyzing the utilization of resources in different processes, organizations can identify opportunities to optimize resource allocation. This includes human resources, machinery, and other assets.

Continuous Improvement:

Process mining is not a one-time activity; it facilitates continuous improvement. Regularly monitoring and analyzing processes allow organizations to adapt to changes, implement optimizations, and continuously enhance efficiency.

Cost Reduction:

Through the identification of inefficiencies and bottlenecks, organizations can make informed decisions to reduce costs. This might involve streamlining processes, reallocating resources, or eliminating unnecessary steps.

Data-Driven Decision Making:

Process mining provides a data-driven approach to decision-making. Instead of relying on assumptions or perceptions, decisions can be based on actual data and insights gained from the analysis of real process data.

Enhanced Collaboration:

Process mining fosters collaboration among different departments within an organization. It provides a common understanding of processes and encourages cross-functional teams to work together to improve overall performance.

Predictive Analysis:

Some advanced process mining tools incorporate predictive analysis capabilities, allowing organizations to anticipate future process behavior based on historical data. This can be valuable for proactive decision-making and risk management.

Quick ROI (Return on Investment):

Depending on the size and complexity of the organization, process mining can often deliver quick returns on investment by identifying and addressing inefficiencies and improving overall operational effectiveness. By leveraging process mining, organizations can gain a deeper understanding of their business processes, leading to more informed decision-making, increased efficiency, and a competitive edge in today's dynamic business environment.

Registration is via the auriga website. Currently auriga in in a controlled roll-out to out existing clients. Please register your interest to be kept up to date with the latest news.

Any modern browser can be used to access auriga. A fast network connection is recommended, especailly for large data uploads.

auriga is planned to be free to most users. Corporate clients with larger datasets and multiple users will pay a small subscription fee. More details will be released soon.

The recommended approach is to work with auriga experts to clearly define your objectives and to get your initital project delivering results as soon as possible. Typical projects are weeks, not months nor years. Please contact auriga: sales@auriga.ltd

Process mining supports various data sources that capture information about the activities and events within an organization's processes. The key is to have event data that reflects the sequence of actions taken during different processes.

Event Logs:

The most common and essential data source for process mining is event logs. These logs contain records of events or activities, typically time-stamped, that occur during the execution of a process. Each entry in the log corresponds to an action or task and may include information such as the activity name, timestamp, case identifier, and resource involved.

Enterprise Resource Planning (ERP) Systems:

ERP systems, which integrate various business processes into a single system, often generate event logs that can be used for process mining. These logs can capture data related to procurement, order fulfillment, inventory management, and other business activities.

Customer Relationship Management (CRM) Systems:

CRM systems track customer interactions, sales processes, and service-related activities. Event logs from CRM systems can be analyzed to understand and improve customer-facing processes, such as lead management, order processing, and customer support.

Workflow Management Systems:

Workflow management systems automate and streamline business processes. These systems generate event logs that record the flow of tasks and activities, making them suitable for process mining analysis.

Healthcare Information Systems:

In healthcare, process mining can be applied to event data from information systems that track patient care processes. This includes data from electronic health records (EHR), appointment scheduling systems, and other healthcare-specific applications.

Supply Chain Management Systems:

Supply chain processes involve various activities, from order placement to delivery. Event logs from supply chain management systems provide insights into the movement of goods, inventory levels, and order fulfillment processes.

IT Service Management Systems:

IT service management systems, such as those based on ITIL (Information Technology Infrastructure Library), generate event logs that can be analyzed to improve IT service delivery and support processes.

Human Resources Systems:

HR systems capture data related to employee onboarding, performance reviews, and other HR processes. Event logs from these systems can be used to analyze and optimize HR-related workflows.

Log Files and Audit Trails:

Systems generate log files and audit trails for security and auditing purposes. These logs can be leveraged for process mining to understand how users interact with systems and to detect any irregularities or security issues.

Mobile Applications and Web Logs:

With the increasing use of mobile applications and online platforms, event data from user interactions with mobile apps or websites can be valuable for understanding customer journeys and optimizing digital processes.

Sensor Data:

In manufacturing or IoT (Internet of Things) contexts, sensor data can be used as a data source for process mining. This may include data from sensors monitoring equipment, production lines, or other physical processes. It's important to note that the quality of the data and the completeness of the event logs are critical factors in the success of process mining. The data should accurately reflect the real execution of processes to ensure meaningful insights and improvements.

Connecting your data to auriga involves several steps.

Identify Data Sources:

Determine the data sources you want to analyze with auriga. This could include event logs from systems such as ERP, CRM, workflow management, or other relevant applications.

Extract Event Data:

Extract the event data from your chosen data sources. This may involve exporting data in a format that the process mining tool supports, such as CSV (Comma-Separated Values) files, XES (eXtensible Event Stream) files, or direct database connections.

Data Preprocessing:

Preprocess the data to ensure it is clean, accurate, and in a format suitable for analysis. This may include handling missing values, dealing with outliers, and organizing the data into a structured format. Some process mining tools have built-in capabilities for data preprocessing.

Import Data into auriga:

Import the preprocessed data into auriga.

Map Data Attributes:

Map the attributes in your data to the corresponding elements in auriga. For example, map activity names, timestamps, case identifiers, and other relevant information to the appropriate fields in the auriga.

Configure Parameters:

Configure any additional parameters or settings in auriga to tailor the analysis to your specific needs. This may include specifying the type of process discovery algorithm, conformance checking options, and visualization preferences.

Run Process Mining Analysis:

Initiate the process mining analysis using the configured settings. The tool will use the imported data to generate process models, identify patterns, and provide insights into your business processes.

Interpret and Analyze Results:

Once the analysis is complete, interpret the results provided by the process mining tool. Explore visualizations, review key performance indicators, and identify areas for improvement or optimization within your processes.

Iterate and Refine:

Process mining is often an iterative process. Based on the insights gained, refine your analysis, make adjustments to the data, and run additional analyses to continuously improve your understanding of the processes.

Your maximum data size for the free subsription is 1 GB of data. For larger datasets a premium subscription is available. For more details contact sales@auriga.com

auriga allows for various types of analyses to gain insights into business processes.

Process Discovery:

Identifying and visualizing the actual flow of processes based on event data.

Conformance Checking:

Comparing actual process executions with the designed or expected processes to identify deviations.

Performance Analysis:

Evaluating the efficiency and effectiveness of processes, including bottlenecks and time delays.

Root Cause Analysis:

Investigating the underlying causes of process issues or inefficiencies.

Variant Analysis:

Examining different paths or variations within a process to understand its flexibility and complexity.

Compliance Analysis:

Ensuring processes adhere to regulatory or organizational compliance standards.

Predictive Analysis:

Using historical data to predict future process behavior and optimize resource allocation.

Resource Utilization:

Analyzing how resources (human or automated) are utilized throughout the process.

Social Network Analysis:

Understanding interactions and communication patterns among individuals or systems within a process.

There are two views within auriga:

Data analysis view:

Data is displayed in a grid that can be enriched with data from multiple source systems. The data can easily be sliced and diced into multiple views that can be saved and shared. There are graphical views of the data including Group Data, Hierarch Charts, Tree Map and Text Analysis.

Process analysis view:

Processes are mapped out and can be filtered, pivoted, saved, shared, subsets created and compared. Least used connections can be filtered out. Measured KPI's are calculated and displayed. Comparison to best practice process flows using BPMN's (Business Process Modeling Notation) shows deviation from the ideal process paths.

auriga enables multple views of data and analysts define views of data that can be shared. Data can be joined, enriched, subsets created, mapped against best practice process flows, measured against KPI's. Joining data from multiple source systems enables an enterprise wide view of business processes. The data visualizations are flexible and easy to use. If there is a requirement for additional tools or data visualizations our development team can assist.

Data is encrypted through the network using SSL. Access to the data center is restricted and all servers sit behind firewalls and reverse web proxy servers. If data security is of paramount importance auriga can also be installed on your internal servers (or laptops). Please contact sales@auriga.ltd for more details.

Each customer's dataset is stored in separate database schema which is only accessible by that client. In addition, auriga can be installed on client's premises. For more details, contact sales@auriga.ltd.

Yes, multiple users can collaborate on the same project. User access can be defined for different role types to ensure effective team management.

All users collaborating on a project can share saved views and reports. Furthermore, views, reports and other outputs can be exported for sharing with other colleagues.

Yes, results can be exported in a variety of formats.

Yes, please visit our help section for support on using auriga.

Our pricing plans are outlined below:

Licence type	Bronze	Silver	Gold
Monthly fee	£0	£100	£500
Monthly data	1GB	5GB	100GB
User allowance	1	5	Unlimited
Max. projects	3	10	Unlimited
Storage time	1 month	3 months	12 months
Business process templates	No	Yes	Yes
Advanced functions	No	Yes	Yes

Yes, our Bronze subscription level is free and offers an introductory set of features.

Subscriptions are billed monthly in advance. Consultancy time is billed monthly in arrears. Invoices carry 30 days' credit.