How to Identify Process Bottlenecks: 3 Key Steps

When you have a problem with speed in a business process, but you don’t know exactly where the slowdown is happening, frustration builds for both teams and leaders. And it’s a widespread issue for enterprises trying to accelerate their pace and increase their capacity for work in the pursuit of digital transformation goals like improved customer onboarding or faster time to market.

Today’s organizations have a large web of simple and complex processes comprising multiple workflow stages. Take, for example, managing a multi-tier supply chain to support manufacturing operations. Due to issues like data silos and legacy systems, parts of those processes can be hard to uncover. 

To fix different types of bottlenecks, you must first have a clear, comprehensive view of all the activities taking place within your current processes. Only then can you judge where changes and optimizations using automation will have the most impact. 

This is where modern process mining tools excel, helping leaders identify process bottlenecks, gain insights, and take corrective action. Process mining is an important part of a broad process automation or hyperautomation strategy, improving processes by making them more agile, resilient, and compliant. 

Let’s examine how to identify process bottlenecks, step by step, using process mining tools and related automation technologies for process improvement.

[ Want an in-depth explanation of process mining strategy and tools? Get the Process Mining Guide eBook. ]

First, a quick terminology note: what is a workflow vs. a process? A workflow is a set of activities that lead to an individual business outcome. Multiple workflows become a business process. In other words, the process is the overall pattern, and a workflow is one piece of that pattern. 

Within processes, organizations grapple with both short-term bottlenecks that cause temporary setbacks and long-term bottlenecks that repeatedly slow work down. Here, we’ll focus on identifying long-term bottlenecks. 

Process bottleneck analysis will help you root out the causes of these long-term bottlenecks and  process inefficiencies. You can think of process mining as an analytics toolset that helps do the detective work. Process mining tools will bring transparency, delivering a visual representation of an entire process and areas that need optimization, providing insights and a path to corrective action and continuous improvement. 

And a process mining tool gets to those answers by analyzing event logs from the software your teams use. These logs include information about  activities like scanning a package’s barcode or receiving a customer service call, a timestamp, and an ID number that’s unique to a specific process. 

This data turns into a visualization resembling a flow chart, showing any skipped or added process steps or paths. The visualization brings potential problem areas to the surface—from long processing times to actions that could be causing compliance issues. 

Let’s examine the steps to take when using process mining to spot bottlenecks:

First, make sure you have process data available, including timestamps. Support from management and sufficient resources for the project will be critical. Connect with relevant stakeholders like process and data owners, system experts, and decision makers. 

Now you can specify the goals of your analysis. At this point, consider creating a reference or target process model, which will be an optimized version of the process that represents your ideal state. You’ll compare this to the actual process once mapped.

The process mining tool extracts process data from event logs, categorizing the data according to a unique identifier like an order number, customer ID, or incident number. Each activity taking place within a specific process will have the same identifier. Activities will also have descriptions and timestamps. Once the data has been categorized and formatted, the process mining tool can draw a visualization that maps the actual process from start to finish.

Now it’s time to use the visualization to inform the next best actions, like:

• Conformance checking: If you created a reference model in step one, you can compare it to the actual process, to pinpoint the areas most in need of optimization. For example, you may find that people are skipping or duplicating activities,  executing unplanned activities, or doing a lot of rework due to human error. 
• Model enhancement: Here you can use information from the tool about the actual process to extend or enhance the target or reference model. This may uncover bottlenecks or unplanned process sequences that can be eliminated from the model to make it a better representation of the ideal process. 
• Root cause analysis: Studying process deviations in detail can help you identify problematic areas or patterns within deviations.

With the bottlenecks identified, you can construct a targeted plan for optimization then use the process mining tool’s dashboards and reporting features to monitor ongoing performance against KPI measurements and drive continuous improvement.

As you consider potential optimizations, it’s important to remember that you’ll need more than just a tool like robotic process automation (RPA) to streamline a business process end to end. (For more detail on why, read Process automation types, explained.) A modern platform for process automation can enable you to both do process mining and use an array of technologies—like robotic process automation (RPA), intelligent document processing (IDP), workflow orchestration, and artificial intelligence (AI)—for complete process optimization. 

Learn how to successfully implement process automation strategy and tools. Get the Process Automation Guide eBook.