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Automation

Process Mining for Augmented Workflows

Workflows are the heart of any modern organization. Whether workflows are automated or manual, deployed by small businesses or large enterprises, the systems a company relies on to bring a product or service to market are integral to how well they meet mission-critical goals.

Process mining is the process of identifying, evaluating, and improving workflows within a business organization. They use quantitative and qualitative analyses to determine where inefficiencies are and how workflows can be standardized, streamlined, or automated.

The Goal of Process Mining

Process mining is about data. While used mostly in conversations about modernizing businesses with automated workflows, process mining is a technique of validating operational processes based on metrics of efficiency, output, and speed. Process mining’s empirical perspective has always been legitimate but has taken on new value within a world of digital–and now quantum–transformation. The goal of process mining is to supplant the assumptions managers make about their businesses and replace them with practical understanding. Process mining is often the first step in optimizing a workflow that eliminates bottlenecks, is less prone to mistakes, and is scalable. But unless you know what’s really going on, you cannot tell a persona, or a computer, what to do differently and why.

The underlying system that guides how people, computers, and tasks interact to accomplish a goal is called a workflow. Analyzing a workflow to determine what steps it contains and how those steps can be optimized is called process mining. Any workflow can be systematically analyzed, monitored, and improved to achieve better outcomes.

Process Mining, Automation and Worker-Focused Workflows

Process mining is used frequently in IT but is increasingly applied to human processes. While much has been made about process mining’s relationship to automation and it is therefore generally mistrusted within many companies, the process mining effort and its outcomes–including automation–remain human-centric.

After all, the metrics you choose to tabulate while process mining reflect human productivity, whether it is mediated by automated processes or not. Even “hyperautomation,” the process of configuring multiple automated workflows to work together, still requires human input, monitoring, and adjustment. Whether you are a cutting-edge enterprise business hyperautomating entire business divisions, or you’re an engineering team wanting to systemize and optimize innovation by automating a single workflow, you’ll need to process mine to understand how that automation can improve business outcomes.

Modern business processes leave digital footprints in the form of metrics representing human and computer input and output. This presents an opportunity to identify inefficient, mistake-prone, and mundane tasks so that they can be improved with new processes and enhanced, end-to-end human or automated workflows.

  1. Planning: What does the ideal process look like? What metrics will you use to represent your process? How will you define success? These are some of the basic questions you’ll ask when preparing a process mining program. This is the stage where you will set your goals. It is also the time to develop a preliminary process map to act as a rubric. Maybe you want to allocate resources more efficiently by freeing up manpower or reduce waste with automation.
  2. Extraction: Data extraction involves data collection from event logs. It requires knowing when and where in a process you would collect data. Do your current data collection and integration methods capture the right data? Are there additional data points within a workflow you would like integrated into a process? Consider what metadata you will need.
  3. Data processing: This step involves condensing data into useful metrics using traditional and company-specific data processing methods. You’ll want to consider whether your distilled metrics act as a guide for workflow monitoring and evaluation or whether or not they will be integrated into an automated system.
  4. Mining and analysis: The process of actually collecting data. You’ll want to be sure the correct systems are in place and that employees are acting how they normally do and not attempting to alter results. If you plan on automating a process, you may want to engage your IT department to consider what additional data points can be collected on the back end. You’re likely to conduct further root cause analysis to understand what works and what doesn’t.
  5. Evaluation: Perform root-cause analyses to identify mistakes, repetition, and bottlenecks. Generate a process map for the newly dissected process. Consider how changes could include both human and technological elements of a process. Consider how automating a process could boost productivity and limit errors.
  6. Process improvement: Generate a flowchart of the current process to visualize how to make modifications. Discuss potential changes with employees. Consider whether process inefficiencies are avoidable with instruction and modification or are likely to continue. If you’re keeping the workflow manual, determine how standardization can improve a process. Automate workflows to remove inconsistencies and move faster. Investigate whether multiple automations can be added to create a hyperautomation program that begins with implementing single automated technologies.
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