Can Process Mining Help Find the Most Suitable Workflows For Hyperautomation?

More and more businesses are implementing automation to increase productivity and reduce costs. However, if automation is applied to inefficient processes it may introduce more complexities. A hyperautomation service provider can help organisations to apply process mining, AI, RPA and other digital tools to find more efficient workflows. By applying process mining for hyperautomation, businesses can analyse how operations are running, discover bottlenecks and identify areas to apply automation. This will ensure only areas that will help to bring real value to the business will be invested in.

Understanding Hyperautomation

Hyperautomation is the use of various technologies to fully automate business processes. It can use AI, RPA, machine learning, process mining and application development.

For example, it can use AI to extract data from hundreds of invoices, RPA to input this data into an accounting software and validation to help highlight any differences.

This can help to improve processes to continually improve. However, identifying the best processes to implement is key. Organisations will need to understand how their processes work before choosing to implement automation.

Understanding Process Mining

Process mining is a data-driven approach that analyses event logs from business applications such as ERP, CRM and Supply Chain Management. These logs usually include case id’s, activity names, and timestamps.

Generally, there are three stages to consider:

• Process Discovery: To create visual representations of how workflows are running.
• Conformance: To compare how activities are being performed against set processes.
• Improvement: To improve processes using insights.

Process mining and task mining are different even though they are often confused with each other. Process mining looks at workflows across systems, whereas Task Mining looks at individual tasks. Using process mining alongside task mining will give a full picture of opportunities. 

How Process Mining Can Help Spot Opportunities

By using businesses can identify inefficiencies. This will help to highlight areas within the workflow that take too long, have errors and require manual intervention.

This is usually done by:

• Gathering data: Collect event logs from business applications.
• Visualising processes: Understand what processes are running and how they differ.
• Finding bottlenecks: Look for delays, multiple approvals, rework and unnecessary handover.
• Identify automation opportunities: Identify repetitive and rule-based processes that can be reliably performed by software.
• Prioritise: Compare potential gains, cost, technical feasibility and risk to the business.
• Test: Use pilot projects to test workflows.

For example, you may find that your invoice processing is taking several days due to manual verification of data that is already available within your accounting system. By using automation to validate data and route approvals you can reduce delays and stop repetitive tasks.

A good hyperautomation service provider will be able to help you implement these findings into your automation strategy.

Benefits Of Process Mining For Hyperautomation

There are many benefits to using process mining:

• Better decision making: You can identify opportunities by using real data. This will help managers to prioritise processes with the most potential.
• More efficient processes: You can identify unnecessary tasks and handovers.
• Cost saving: Repetitive tasks can be reduced meaning less manual effort, fewer errors and better resource utilisation.
• Transparency: You can visualise processes to identify any issues, non-compliance and differences between departments.

Ongoing improvements: You can monitor processes to understand if automation is providing the benefits expected.

Overall process mining can help to bring real improvements.

Identifying The Most Suitable Workflows

Not all processes will be suitable to automate. If processes require unpredictable decisions, many exceptions or unclear ownership then they may need to be redesigned.

When identifying the most suitable processes for hyperautomation, consider:

• Repetition: If tasks are frequently repeated then they are often suited to automation by software.
• Rules: Processes with clear rules will be easier to reliably automate.
• High Volume: If workflows are high volume then you can save a lot of time.
• Errors: Processes that have recurring manual errors can benefit from automation.
• Impact: Tasks that impact revenue, compliance and customer satisfaction should be prioritised.
• Technical Feasibility: Systems must be able to be integrated.

Common processes to consider are invoicing, onboarding, order fulfillment, customer support tickets and insurance claims.

For example, you may suffer delays with your order fulfillment. Process mining may show that your employees are manually updating inventory data across multiple systems. By integrating the inventory systems with RPA you can reduce processing times and increase accuracy.

You can also use Data Mining Services to help identify trends from large amounts of data to help make decisions. Whilst data mining will identify patterns, process mining will look at the order and performance of business activities. Both can be used to help with your automation strategy.

Using AI & RPA

Process mining will help to identify areas to improve. AI and RPA can help to implement these improvements.

RPA uses bots to complete repetitive tasks such as moving data, updating records and processing structured forms. AI can help to take this further by analysing documents, categorising requests, identifying patterns and helping with decisions around unstructured data.

Many platforms have process mining and automation features:

• Microsoft Power Automate: Process mining and automation. See Microsoft process mining documentation.
• Celonis: Process Intelligence tools: See Celonis process mining platform.
• UiPath: Process and task discovery with RPA and AI.

The right solution will depend on your current systems, how much integration you will need, security requirements and your business goals. A good hyperautomation service provider will be able to advise on the best compatible tools to use.

Challenges To Consider

There are a few challenges to consider when using process mining:

• Data quality: If timestamps aren’t present, data isn’t consistent and logs aren’t complete this can cause inaccurate process mining. Data validation should be carried out.
• Integration: Often processes will use multiple applications so it can be difficult to gather data and connect systems.
• Privacy & Security: Process logs may contain sensitive business or customer data. Ensure the right access is in place.
• Employees: Employees may be worried about changes to their roles. Be transparent and provide training.
• Uncertain ROI: Not all inefficiencies will be worth automating. Consider the cost of implementation, maintenance and exceptions.
• Data quality: Process mining can help to identify processes that have been recorded however anything that hasn’t left a digital footprint cannot be reliably identified. Ensure you validate findings with process owners and test automation solutions.
• Quality Assurance: QA testing can be used to ensure workflows are functioning correctly, exceptions are handled and processes are performing as expected. If looking for external data capabilities consider Top Web Scraping firms in the USA. However this is a completely different requirement to process mining and should be chosen based on the use case.

Conclusion 

Process mining can help organisations to identify opportunities to automate by showing how processes are running, where inefficiencies are and where the biggest improvements can be made. By using process mining alongside AI, RPA and monitoring you can create a more structured strategy. Firstly it is important to have clear goals, analyse processes and set measurable benchmarks. A good specialist with Data Mining Expertise in the U.S. can help with other data analysis initiatives. Low-Code No-Code Development Services can help businesses create and adapt applications. Ultimately choosing the right processes, ensuring accurate data and continually measuring results will help to successfully implement hyperautomation.

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