Automated Root Cause Analysis in ‘23: A Game Changer for Process Improvement
Have you ever felt frustrated by process issues and delays that hurt productivity, but struggled to pinpoint the root cause? Process inefficiencies that seem impossible to fix? Help has arrived!
Automated root cause analysis using process mining is a game-changing technology that can finally unravel process problems at their source. Companies who harness automated root cause analysis in 2024 will gain huge competitive advantage through optimized processes.
In this article, I‘ll explain what automated root cause analysis is, how you can use it, and four compelling benefits it offers your business. I‘ll also provide tips to get started with process mining and root cause analysis based on real-world examples. Let‘s dive in!
What is Root Cause Analysis?
Root cause analysis means getting to the bottom of process issues by determining the core factor ultimately responsible. Instead of just treating the symptoms, root cause analysis identifies the disease so it can be cured.
Traditionally, teams tried to do this through brainstorming and discussions. But that approach relies on subjective hunches.
Automated root cause analysis uses the power of machine learning algorithms to objectively analyze huge volumes of process data. This reveals the true underlying drivers of process problems based on cold, hard facts.
According to leading research firm Gartner, "Process mining augmenting organizational knowledge with ML to determine root cause analysis is a new capability that delivers faster time to value."
How Automated Root Cause Analysis Works
Modern process mining tools create digital twin models of business processes by mapping real examples from event log data. Each process instance is logged, capturing activities, timestamps, and other attributes.
The system feeds this data to machine learning algorithms which examine all process cases to detect patterns and correlations. The algorithms cluster together cases exhibiting similar issues like delays or compliance breaches.
By comparing the attributes of these problematic clusters versus high-performing clusters, the algorithms pinpoint which factors have the strongest statistical correlation with causing the issues.
These factors are the likely root causes of the process problems. And because the ML keeps improving as more data comes in, the root cause analysis gets smarter and more precise over time.
Comparing Root Cause Analysis Capabilities
Leading process mining platforms like Celonis PAF, UiPath Process Mining, and Minit offer root cause analysis features. But capabilities vary:
- Celonis enables automated root cause analysis across interconnected processes with its Process AI engine. It offers smart clustering and guided process discovery for faster insights.
- UiPath emphasizes ease-of-use with its web-based Process Mining suite. The UiPath Process Mining Algorithms provide automated cause and effect analysis on individual processes.
- Minit focuses on making process mining affordable for mid-market companies. It offers more basic automated root cause analysis and guidance on fixing issues.
I recommend considering both the power of a tool‘s algorithms and how easily it lets you apply those insights. Balancing automation with user guidance maximizes impact.
The 4 Key Benefits of Automated Root Cause Analysis
Let‘s explore 4 compelling ways automated root cause analysis supercharges process excellence:
1. Detect Delays and Bottlenecks
Long process delays destroy productivity. But when processes have multiple interconnected steps, it‘s tricky to isolate the chokepoints.
Automated root cause analysis quickly detects parts of the process with longer durations. It looks at time attributes to pinpoint activities and handovers that regularly exceed targets.
For example, an insurance firm used this capability to find that 20% of dental care claims had long lag times between advisor meetings and next steps. By looking at all steps, they isolated sluggish review and approvals as the culprit. Fixing this sped up the process by 40%!
2. Standardize Operations
Too much process variability makes execution chaotic. But it‘s hard to know which variants cause the most problems.
Automated root cause analysis clusters similar cases, then compares high-performing clusters versus underperforming clusters. This reveals the key differences driving issues.
One company used this analysis to cut down 5,000 process variants into just 3 standardized paths. The increased consistency reduced cost and time by 90%!
3. Enhance Compliance
Monitoring business process compliance is tough. Automated process mining helps detect violations. But root cause analysis takes it further by uncovering why breaches happen.
For example, a procurement team found that certain invoice approval steps opened the door for non-compliant spending. Tightening controls on those specific steps boosted compliance.
According to research firm Everest Group, "Combining process mining with root cause analysis improves audit productivity by 15-25%."
4. Discover Costly Processes
Some processes exhibit much higher costs than others. But without root cause analysis, it‘s hard to know why.
By detecting patterns around high-cost cases, you can isolate the factors needlessly inflating spending. An energy company used this to uncover time management issues that increased termination fees.
Based on my experience, the top process costs that automated root cause analysis can optimize include:
- Compliance fines
- Contract/SLA penalties
- Exception handling
- Rework and scrap
- Unnecessary process loops
Getting Started with Automated Root Cause Analysis
To leverage automated root cause analysis in your organization, here are some tips:
Start small: Pilot process mining on 1-2 key processes before scaling up. Marketing approvals, IT ticket resolution, and customer onboarding are good candidates.
Connect data sources: Comb through your IT systems to identify and connect event log data sources. Pull together end-to-end data across tools.
Involve stakeholders: Get process owners, compliance leaders, and front-line staff involved early to enrich analysis and drive change adoption.
Focus on pain points: Prioritize the top known pain points or cost centers to prove value quickly. Let data-driven insights generate support.
Keep improving: Continuously feed new data to the algorithms to fuel smarter analysis and emerging visibility as processes change.
The Future of Automated Root Cause Analysis
The ML powering automated root cause analysis will only get smarter. By 2023, expect algorithms that can analyze root causes across interconnected processes, not just individual steps.
According to leading process mining expert Dr. R.S. Mans of Warwick University, "While still early days, the next generation of automated root cause analysis will provide even more valuable perspectives as algorithms grow more intelligent."
I‘m excited to see automated root cause analysis empower businesses to optimize processes like never before. This technology can transform how your company identifies and resolves process problems at their foundation.
Reach out if you have any other questions! I‘m happy to chat more about how to tap into the potential of process mining and root cause analysis. Let‘s connect!
