Digging Deeper: How Process Mining Exposes Hidden Workflow Friction

Every organization has processes that seem efficient on paper but feel sluggish in practice. Manual workarounds, redundant approvals, and silent rework loops often go unnoticed until they cause a major delay. Process mining offers a way to see the actual flow of work by analyzing event logs from your systems. This guide explains how to use process mining to uncover hidden friction, compare different mining techniques, and turn insights into automation priorities.

The Cost of Unseen Friction: Why Processes Diverge from Plans

Process models—whether drawn in workshops or embedded in ERP systems—tend to reflect an idealized version of work. In reality, employees deviate from these models for many reasons: urgency, missing data, system limitations, or simply habit. These deviations create friction that accumulates over time, eroding efficiency and increasing cycle times.

Common Sources of Hidden Friction

Friction often appears in forms that are hard to detect without data. Rework loops, where a task is sent back for corrections multiple times, are a typical example. Another is the “swivel-chair” effect, where staff must manually copy data between systems because integrations are incomplete. Approval chains that involve unnecessary steps also add delays. In a typical order-to-cash process, for instance, a single missing field can trigger a cascade of manual interventions that double the processing time.

The financial impact of such friction is significant. Industry surveys suggest that poor process execution can add 20–30% to operational costs, though exact figures vary by sector. More importantly, friction reduces employee satisfaction and customer experience. When teams spend time firefighting rather than improving, innovation stalls.

Process mining addresses this by reconstructing the actual sequence of activities from digital footprints. Every transaction, timestamp, and user action in your systems tells a story. By analyzing these event logs, you can see where the process deviates from the intended path and measure the frequency and cost of each deviation.

For example, one team in a logistics company discovered that 40% of their invoice exceptions were caused by a single data-entry field that had been removed from a form but still required by a downstream system. The fix was simple—a validation rule—but the problem had persisted for months because no one had visibility into the end-to-end flow. Process mining made the bottleneck visible in minutes.

Understanding this gap between the designed and actual process is the first step toward targeted automation. Instead of automating a broken process, you can first identify and eliminate the root causes of friction. This approach ensures that automation investments deliver real improvements rather than just speeding up flawed workflows.

Core Concepts: How Process Mining Works

Process mining sits at the intersection of data mining and business process management. It uses event logs—timestamped records of activities performed in a system—to discover, monitor, and improve real processes. The three main types of process mining are discovery, conformance checking, and enhancement.

Event Logs: The Raw Material

Every event log contains at least three elements: a case ID (e.g., an order number), an activity name (e.g., “Approve Invoice”), and a timestamp. Additional attributes like resource, cost, or outcome can enrich the analysis. The quality of the event log directly determines the reliability of the insights. Common issues include missing timestamps, duplicate records, and inconsistent naming conventions. Before mining, you must clean and standardize the log—a step that often takes more effort than the analysis itself.

Discovery Algorithms

Discovery algorithms automatically construct a process model from event logs. The most common approaches include:

• Alpha algorithm: A simple, deterministic method that captures causal dependencies. It works well for structured processes but struggles with noise and infrequent paths.
• Heuristic mining: Uses frequency thresholds to filter out rare behavior, making it more robust for real-world logs. It produces a net-like model that highlights the most common paths.
• Fuzzy mining: Designed for highly unstructured processes, it clusters similar activities and abstracts less important details. Useful for exploratory analysis but can oversimplify complex flows.

Each algorithm has trade-offs between precision (how well the model matches the log) and generalization (how well it captures all possible behavior). For a friction-analysis project, heuristic mining often strikes the best balance because it reveals both the dominant path and the deviations that cause delays.

Conformance Checking

Conformance checking compares the discovered model against a predefined “ideal” model. It quantifies deviations using metrics like fitness (how much of the log behavior is allowed by the model), precision (how much of the model behavior is actually observed), and generalization. A low fitness score indicates that the real process frequently violates the intended design—a clear sign of friction. Conformance checking is especially valuable for compliance audits and for measuring the impact of process changes over time.

Enhancement, the third type, uses the discovered model and log to extend or repair the existing process model. For example, you might add a new activity that was missing from the original model or adjust timings based on actual durations. This step turns mining from a diagnostic tool into a design aid.

Understanding these core concepts helps you choose the right technique for your specific friction. If you want to see what actually happens, start with discovery. If you need to measure compliance, use conformance checking. If you plan to redesign the process, enhancement provides the data to support your changes.

Step-by-Step: Running a Friction-Analysis Project

A structured approach ensures that process mining leads to actionable improvements. The following steps outline a typical friction-analysis project, from data collection to implementation.

Step 1: Define the Scope and Objective

Start by selecting a specific process that has visible pain points—long cycle times, high error rates, or frequent escalations. Avoid trying to analyze the entire enterprise at once. Focus on one end-to-end flow, such as order fulfillment, invoice processing, or customer onboarding. Clearly state what success looks like: for example, “Reduce the average time from order to shipment by 30%.”

Step 2: Extract and Prepare Event Logs

Identify the systems that record the activities in the chosen process. Common sources include ERP, CRM, workflow engines, and spreadsheets. Export the relevant tables or logs, ensuring each record includes case ID, activity, and timestamp. Clean the data by removing duplicates, standardizing activity names, and handling missing values. This step is iterative—you may need to go back to the source system to fill gaps.

Step 3: Apply Discovery and Conformance Checking

Use a process mining tool (such as Celonis, Disco, or open-source options like PM4Py) to run discovery algorithms. Start with heuristic mining to see the most frequent paths, then apply conformance checking against your documented process. Note the deviations: Are there loops that shouldn't exist? Steps that are skipped? Activities that take much longer than expected? Quantify the frequency and cost of each deviation.

Step 4: Identify Friction Points

Look for patterns that indicate friction. Common signals include:

• High rework rates (same activity repeated multiple times for the same case)
• Long waiting times between consecutive activities
• Many cases following a rare or non-standard path
• Activities performed by multiple resources that could be consolidated

Create a list of friction points, ranked by impact on cycle time or cost. For each, hypothesize the root cause—is it a system limitation, a policy issue, or a training gap?

Step 5: Validate with Stakeholders

Share the findings with process owners and frontline staff. They can confirm whether the deviations are intentional workarounds or genuine problems. This step also builds buy-in for changes. Often, staff already know about the friction but lack the data to prove its impact. Process mining provides that evidence.

Step 6: Design and Implement Improvements

Based on the validated friction points, design targeted improvements. These might include automation of a manual step, removal of an unnecessary approval, or a system integration to eliminate swivel-chair work. Prioritize changes that offer the highest return with the least disruption. Implement changes incrementally, using process mining to measure the before-and-after effect.

Choosing the Right Approach: Comparing Three Mining Techniques

Different mining techniques suit different types of friction. The table below compares three common approaches—algorithmic discovery, fuzzy mining, and conformance checking—across key dimensions.

For a friction-analysis project, we recommend starting with heuristic mining to get a clear picture of the most common flow and its deviations. If the process is highly chaotic (e.g., knowledge work), fuzzy mining may reveal patterns that heuristic mining misses. Use conformance checking to quantify the gap between actual and intended behavior, especially when compliance is a concern.

No single technique is universally best. The choice depends on the process structure, data quality, and the specific friction you aim to uncover. A pragmatic approach is to use two techniques in parallel: heuristic mining for discovery and conformance checking for measurement. This combination provides both a visual map and objective metrics.

Real-World Examples: Uncovering Friction in Practice

Abstract concepts become clearer with concrete scenarios. Below are two anonymized examples based on common patterns observed in process mining projects.

Example 1: Order-to-Cash Rework Loop

A mid-sized manufacturer noticed that order processing times were highly variable, with some orders taking five times longer than others. Process mining of their ERP event logs revealed that 30% of orders entered a rework loop where the “Credit Check” activity was repeated three or more times. Further analysis showed that the loop was triggered by a missing customer credit limit—a field that sales representatives often left blank. The fix was to make the credit limit mandatory before order submission, reducing rework by 80% and cutting average cycle time from 4 days to 2.5 days.

Example 2: IT Service Management Swivel-Chair

An IT service desk used two separate systems: one for ticket logging and one for asset management. Technicians had to manually copy asset details from the asset system to the ticket system for each incident. Process mining of the ticket logs showed that this manual step added an average of 15 minutes per ticket, and that 20% of tickets had errors due to typos. By integrating the two systems via a simple API, the team eliminated the manual step, reduced error rates to near zero, and saved 40 hours of technician time per week.

These examples illustrate two key lessons. First, friction often hides in plain sight—the rework loop was known to some staff but its frequency was not visible. Second, the most impactful fixes are often simple: a mandatory field or a system integration. Process mining helps you find these low-effort, high-impact opportunities.

Risks and Pitfalls: What to Watch Out For

Process mining is powerful, but it is not a silver bullet. Several common pitfalls can undermine its effectiveness.

Data Quality Issues

Event logs are only as good as the data they contain. Missing timestamps, incorrect case IDs, and inconsistent activity names can lead to misleading models. For example, if a system records only the start time of an activity but not the end time, you cannot measure duration accurately. Invest time in data cleaning and validation before mining. If the log covers only part of the process, the model will be incomplete.

Over-Reliance on Tools

Process mining tools generate beautiful diagrams, but a pretty map is not the same as actionable insight. Teams sometimes spend weeks tweaking algorithms without connecting findings to business outcomes. Always tie each deviation to a measurable impact—cost, time, or quality. If a deviation has no impact, it may not be worth fixing.

Ignoring Context

The event log shows what happened, but not why. A deviation might be a legitimate workaround for a broken system, not a problem in itself. Always validate findings with process participants before making changes. They can explain the rationale behind deviations, which may reveal deeper issues that the log alone cannot capture.

Scope Creep

It is tempting to analyze every process at once, but that often leads to analysis paralysis. Start with one high-value process, prove the approach works, then expand. A successful pilot builds credibility and momentum.

Failure to Act

The biggest risk is not mining at all—it is mining without following through. Insights that sit in a report do not reduce friction. Assign ownership for each improvement, set a timeline, and track the impact using the same event logs. Process mining should become a continuous improvement cycle, not a one-time audit.

Decision Checklist: Is Process Mining Right for Your Friction?

Before launching a process mining initiative, use this checklist to assess readiness and choose the right approach.

• Do you have digital event logs? If your process is entirely manual (paper-based), process mining is not feasible until you digitize the steps.
• Is the process well-defined? For conformance checking, you need a baseline model. If no model exists, start with discovery.
• What is the primary goal? If you want to understand the actual flow, use discovery. If you need to measure compliance, use conformance checking. If you want to redesign, use enhancement.
• How much noise is in the data? For noisy logs, fuzzy mining is more robust. For clean logs, heuristic mining gives finer detail.
• Do you have stakeholder buy-in? Without support from process owners, findings may be ignored. Involve them early.
• Can you measure impact? Define success metrics before mining. Common metrics include cycle time, cost per case, error rate, and throughput.
• Is the scope focused? Limit the first project to one end-to-end process. Broader scope can wait.

If you answered “no” to the first question, consider digitizing the process first. If you answered “yes” to most others, process mining is likely to uncover valuable friction points. Start small, validate, and scale.

From Insight to Action: Next Steps for Your Team

Process mining transforms vague frustration into precise, data-driven improvement opportunities. By revealing the gap between designed and actual workflows, it helps teams focus automation efforts where they matter most—eliminating rework, reducing handoffs, and streamlining approvals.

To get started, pick one process that causes visible pain. Extract the event logs, clean them, and run a heuristic discovery. Share the resulting map with the team and discuss the deviations. Identify the top three friction points by impact, design simple fixes, and implement them. Then measure the change using the same mining technique. This cycle of discovery, action, and measurement turns process mining from a one-time analysis into a continuous improvement engine.

Remember that process mining is a means, not an end. The goal is not to produce perfect models but to reduce friction and improve outcomes. Start with a small win, build organizational capability, and gradually expand your scope. Over time, you will develop a culture of evidence-based process improvement that drives real automation value.