As we have identified the business motivation and the business activities we need to analyze, it’s now time to determine which processes are involved and which ones we want to analyze. Sometimes it can be several business processes, but in this case we’ll manage the business processes one by one.
Given their criticality and other business criterias (purpose, engagement, outcome, etc.)we may consider choosing the right process to investigate.
These are – given as example – some of the most famous business processes per industry/activity, we use to analyze:
Insurance
Claims Processing: Process mining can be used to analyze the claims processing process and identify bottlenecks and inefficiencies. By analyzing the data, insurers can optimize the claims processing process and reduce cycle times.
Policy Issuance: Process mining can be used to analyze the policy issuance process and identify areas of improvement. By analyzing the data, insurers can optimize the policy issuance process and reduce the time to issue policies.
Underwriting: Process mining can be used to analyze the underwriting process and identify bottlenecks and inefficiencies. By analyzing the data, insurers can optimize the underwriting process and reduce cycle times.
Fraud Detection: Process mining can be used to analyze claims data and identify potential fraud. By analyzing the data, insurers can improve fraud detection and reduce losses due to fraud.
Customer Service: Process mining can be used to analyze the customer service process and identify areas of improvement. By analyzing the data, insurers can optimize customer service and improve customer satisfaction.
Renewal Process: Process mining can be used to analyze the policy renewal process and identify bottlenecks and inefficiencies. By analyzing the data, insurers can optimize the renewal process and reduce cycle times.
Sales Process: Process mining can be used to analyze the sales process and identify areas of improvement. By analyzing the data, insurers can optimize the sales process and improve sales performance.
Claims Handling: Process mining can be used to analyze claims handling processes and identify areas of non-compliance or inefficiencies. By analyzing the data, insurers can optimize claims handling and reduce costs.
Risk Management: Process mining can be used to analyze risk management processes and identify areas of non-compliance or inefficiencies. By analyzing the data, insurers can optimize risk management and reduce the risk of losses.
Policy Administration: Process mining can be used to analyze policy administration processes and identify areas of improvement.
Finance
Accounts Payable Process: Process mining can be used to analyze the accounts payable process and identify bottlenecks and inefficiencies. By analyzing the data, finance can optimize the accounts payable process and reduce cycle times.
Accounts Receivable Process: Process mining can be used to analyze the accounts receivable process and identify areas of improvement. By analyzing the data, finance can optimize the accounts receivable process and reduce overdue payments.
Financial Close Process: Process mining can be used to analyze the financial close process and identify areas of improvement. By analyzing the data, finance can optimize the financial close process and reduce the time to close the books.
Budgeting Process: Process mining can be used to analyze the budgeting process and identify bottlenecks and inefficiencies. By analyzing the data, finance can optimize the budgeting process and reduce the time to complete the budget.
Cash Management Process: Process mining can be used to analyze the cash management process and identify areas of improvement. By analyzing the data, finance can optimize the cash management process and reduce the time to access cash.
Financial Reporting Process: Process mining can be used to analyze the financial reporting process and identify areas of improvement. By analyzing the data, finance can optimize the financial reporting process and reduce the time to produce financial reports.
Internal Controls: Process mining can be used to analyze internal control processes and identify areas of non-compliance or inefficiencies. By analyzing the data, finance can optimize internal controls and reduce the risk of fraud or errors.
Tax Compliance: Process mining can help finance ensure compliance with tax regulations by identifying areas of non-compliance and helping them take corrective actions.
Audit Trail Analysis: Process mining can be used to analyze audit trails and identify areas of risk or inefficiencies. By analyzing the data, finance can optimize audit trails and improve risk management.
Expense Management: Process mining can be used to analyze the expense management process and identify bottlenecks and inefficiencies. By analyzing the data, finance can optimize the expense management process and reduce expense processing time.
Procurement
Processes in Procurement involves selecting vendors, establishing payments terms, strategic vetting, and negotiation as well as the actual purchasing of supplies. There are many process templates in Procurement we may need to consider to streamline other components of your procure-to-pay cycle:
Purchase Order Process: Process mining can be used to analyze the purchase order process and identify bottlenecks and inefficiencies. By analyzing the data, procurement can optimize the purchase order process and reduce cycle times.
Supplier Management: Process mining can be used to analyze supplier management processes and identify areas of improvement. By analyzing the data, procurement can optimize supplier management and improve supplier performance.
Contract Management: Process mining can be used to analyze contract management processes and identify areas of improvement. By analyzing the data, procurement can optimize contract management and reduce contract cycle times.
Spend Analysis: Process mining can be used to analyze spend data and identify patterns and trends. By analyzing the data, procurement can optimize spend and identify cost savings opportunities.
Payment Processing: Process mining can be used to analyze payment processing and identify bottlenecks and inefficiencies. By analyzing the data, procurement can optimize payment processing and reduce payment cycle times.
Purchase Requisition Process: Process mining can be used to analyze the purchase requisition process and identify bottlenecks and inefficiencies. By analyzing the data, procurement can optimize the purchase requisition process and reduce cycle times.
Supplier Performance Management: Process mining can be used to analyze supplier performance and identify areas of improvement. By analyzing the data, procurement can optimize supplier performance and improve supplier relationships.
Procure-to-Pay Process: Process mining can be used to analyze the procure-to-pay process and identify bottlenecks and inefficiencies. By analyzing the data, procurement can optimize the procure-to-pay process and reduce cycle times.
Purchase Order Approval Process: Process mining can be used to analyze the purchase order approval process and identify bottlenecks and inefficiencies. By analyzing the data, procurement can optimize the purchase order approval process and reduce cycle times.
Compliance: Process mining can help procurement ensure compliance with legal and regulatory requirements by identifying areas of non-compliance and helping them take corrective actions.
Human Resource Management
Recruitment: Process mining can help HR managers optimize the recruitment process. By analyzing data on the hiring process, it can identify bottlenecks and inefficiencies, and help improve the quality and speed of the hiring process.
Onboarding: Process mining can be used to analyze the onboarding process and identify areas for improvement. By analyzing data on the onboarding process, it can help HR managers optimize the process and ensure new employees are integrated effectively.
Performance Management: Process mining can help HR managers analyze the performance management process and identify areas for improvement. By analyzing data on performance evaluations, it can help HR managers identify patterns and trends, and help to improve the process.
Training and Development: Process mining can be used to analyze the training and development process and identify areas for improvement. By analyzing data on training sessions, it can help HR managers optimize the process and improve the quality of training.
Employee Engagement: Process mining can be used to analyze employee engagement processes and identify areas for improvement. By analyzing data on employee surveys and feedback, it can help HR managers identify areas of concern and take corrective action.
Absence Management: Process mining can help HR managers analyze absence management processes and identify areas for improvement. By analyzing data on absenteeism, it can help HR managers identify patterns and trends, and help to improve the process.
Benefits Administration: Process mining can be used to analyze benefits administration processes and identify areas for improvement. By analyzing data on benefit enrollments and claims, it can help HR managers optimize the process and improve employee satisfaction.
Compliance: Process mining can help HR managers ensure compliance with regulatory requirements by identifying areas of non-compliance and helping them take corrective actions.
Succession Planning: Process mining can be used to analyze succession planning processes and identify areas for improvement. By analyzing data on employee performance and potential, it can help HR managers identify potential successors and improve the succession planning process.
Employee Exit: Process mining can help HR managers analyze the employee exit process and identify areas for improvement. By analyzing data on employee turnover
Manufacturing Uses Cases
Supply chain analysis: Process mining can be used to analyze the entire supply chain process, from procurement to delivery. This analysis can help identify bottlenecks, delays, and inefficiencies, allowing manufacturers to optimize their supply chain process.
Production line analysis: Process mining can be used to analyze the production line process, identifying areas where production is slowing down or stopping, and helping manufacturers to optimize production efficiency.
Maintenance analysis: Process mining can be used to analyze the maintenance process, identifying areas where maintenance is needed most frequently, and helping manufacturers to optimize their maintenance schedule to minimize downtime.
Quality Control: Process mining can be used to identify the root cause of defects in manufacturing processes. By analyzing the process data, it can identify patterns that are leading to quality issues, and help manufacturers take corrective actions to improve their products.
Production Optimization: Process mining can be used to identify bottlenecks in production processes and optimize the flow of materials and resources to increase output and efficiency.
Inventory Management: Process mining can be used to analyze inventory levels and help manufacturers optimize inventory levels to meet demand while reducing excess inventory.
Maintenance and Repair: Process mining can help manufacturers optimize their maintenance and repair processes. By analyzing data from machines and equipment, it can identify patterns that can lead to breakdowns and downtime, and help manufacturers take corrective actions to prevent future issues.
Production Planning: Process mining can be used to analyze production planning processes and identify inefficiencies. By analyzing the data, it can help manufacturers optimize production schedules, reduce setup times, and improve productivity.
Energy Efficiency: Process mining can be used to analyze energy usage in manufacturing processes and identify opportunities to reduce energy consumption and costs.
Health and Safety: Process mining can be used to identify safety risks in manufacturing processes and help manufacturers take corrective actions to prevent accidents and injuries.
Compliance: Process mining can help manufacturers ensure compliance with regulatory requirements by identifying areas of non-compliance and helping them take corrective actions.
Customer Service: Process mining can be used to analyze customer service processes and identify inefficiencies that are leading to customer dissatisfaction. By analyzing the data, manufacturers can improve customer service and increase customer satisfaction.
Other Uses Cases
- Content promotion & publication
- Accounting management
As a best practice, it’s also important to look at the potential complexity of the business process and balance it with the potential outcome and effort need for its analysis. Indeed, analyzing a very big process ( a process which has more that 100-200 steps for example) at the first stage can really lead to a long and costly project. We highly recommend two to break down the “big process” into several more little processes. Analyzing separately each part of the process will facilitate quick wins rapidly.