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Improve Your Incident Management

Boost Incident Management efficiency with our 6-step guide.

Challenges (8) Goals (8) 6 Steps Preview Outcomes Data FAQs (5)

Process: Incident Management

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Streamline Incident Management for Faster Resolution

ProcessMind helps you uncover hidden bottlenecks and rework loops within your incident management workflow. Easily identify where delays occur, resources are misallocated, and where processes diverge from the ideal path. By analyzing your data, we provide insights that lead to faster resolutions and improved service quality. Get a clear picture of how your system truly operates.

Download our pre-configured data template and address common challenges to reach your efficiency goals. Follow our six-step improvement plan and consult the Data Template Guide to transform your operations.

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Process mining for Incident Management offers an unparalleled, comprehensive view of your entire resolution workflow, from the initial report to final closure. It transcends the limitations of traditional reporting, diving deep into the actual execution paths of your incidents, regardless of your ERP or source system. By meticulously analyzing event logs, ProcessMind illuminates hidden delays, identifies unexpected rework loops, and uncovers compliance deviations that might otherwise go unnoticed. You will gain clarity on the true sequence of events, pinpointing exact bottlenecks that slow down resolution and revealing variations in how different incident types or teams are handled. This deep insight empowers you to understand the actual behavior of your incident management process, providing a factual basis for optimization instead of relying on assumptions. You will discover exactly where inefficiencies lie and how they impact your service delivery, enabling targeted improvements that truly streamline operations.

Many organizations grapple with common challenges in their Incident Management processes, such as prolonged mean time to resolution, frequent recurring incidents, inconsistent handling procedures across teams, and a general lack of end-to-end visibility. These issues directly impact customer satisfaction, escalate operational costs, and can even expose organizations to compliance risks. ProcessMind tackles these challenges head-on by providing an objective, data-driven map of your process as it truly unfolds. It visualizes every step, every hand-off, and every decision point, revealing the root causes behind slow resolutions and identifying patterns in incidents that often lead to customer frustration. Whether your incidents originate from a ticketing system, a CRM, or any other source system, ProcessMind extracts the critical information to show you where your process deviates from the ideal path, helping you transform reactive responses into proactive problem-solving strategies.

Analyzing your Incident Management process with ProcessMind delivers tangible, transformative benefits. You can achieve significantly faster incident resolution times, leading directly to enhanced customer satisfaction and loyalty. By eliminating rework and optimizing resource allocation, your operational costs will naturally decrease. ProcessMind also strengthens your compliance posture by highlighting deviations from standard operating procedures and regulatory requirements, allowing you to implement corrective actions promptly. Beyond efficiency gains, you will foster a culture of continuous improvement, making data-driven decisions based on factual evidence rather than anecdotal observations. This enables your teams to work smarter, not harder, improving service quality and organizational agility, regardless of the specific tools or platforms your teams currently use. The insights gained are universally applicable, helping you elevate your incident management to a strategic advantage.

Embarking on your journey to optimized Incident Management with ProcessMind is straightforward. We understand that data resides in various systems, which is why our platform is designed for seamless data ingestion from any source system. To help you get started quickly and effectively, we provide a comprehensive data template outlining the key event log fields required, such as Incident ID, Activity, Timestamp, and Resource. This template guides you through preparing your data, ensuring that you capture the essential information needed to generate a precise and insightful process model. Our goal is to make the power of process mining accessible, allowing you to quickly unlock the potential within your existing Incident Management data and begin transforming your operations without extensive integration efforts or system overhauls.

Incident Management Process Optimization IT Service Management Resolution Time Bottleneck Analysis ITSM Operational Efficiency

Common Problems & Challenges

Identify which challenges are impacting you

Incidents consistently exceed their Service Level Agreement targets, particularly those with high severity. This leads to frustrated users, reputational damage, and potential financial penalties for non-compliance, undermining confidence in the service desk.

Process mining analyzes event logs from your source system, pinpointing the exact stages and activities where incidents dwell too long, directly contributing to SLA failures. It helps identify specific bottlenecks, like prolonged investigation phases or delayed approvals, allowing for targeted process improvements.

The average time taken to fully resolve and close incidents is unnecessarily long, leading to extended service downtime and reduced user productivity across the organization. This directly impacts operational efficiency and user satisfaction, regardless of the system used.

Process mining provides an end-to-end view of your Incident Management process, visualizing the complete flow from reporting to closure. It identifies hidden delays and inefficient pathways within your operational data, revealing the true drivers behind extended resolution cycles.

Incidents are frequently transferred between different support groups or agents, often bouncing back and forth. This constant re-routing introduces significant delays and wastes valuable time as new teams get up to speed on the issue.

Process mining maps every handover and reassignment within your data, revealing the most common 'ping-pong' scenarios. It helps identify which groups are mis-assigning tickets, where knowledge gaps exist, or if initial categorization is flawed, leading to more efficient routing.

Incidents are often miscategorized or incorrectly prioritized at the initial stage, leading to them being routed to the wrong support groups or not receiving the urgency they require. This initial misstep delays diagnosis and final resolution.

Process mining uncovers patterns where incidents are re-categorized multiple times during their lifecycle. By correlating initial categorization events with subsequent changes, it highlights areas where intake processes need improvement to ensure accurate routing from the start.

Many incidents do not follow the defined, optimal path for resolution, instead taking longer, more complex routes with additional, unauthorized steps. These deviations introduce inefficiencies, increase costs, and complicate compliance and auditing.

Process mining automatically discovers all actual process variants within your incident data. It visually highlights common deviations from the ideal path, such as skipped steps or rework loops, enabling you to identify why these occur and enforce standardized procedures.

Incidents are frequently re-opened shortly after being marked as resolved, indicating the initial fix was incomplete, incorrect, or not properly verified. This creates frustrating rework for agents and damages user trust in the resolution quality.

Process mining tracks the entire lifecycle of an incident, identifying patterns where tickets transition from 'Resolved' or 'Closed' back to an active state. It helps uncover root causes for re-opening, such as inadequate testing or flawed user confirmation processes in your system.

Even after a resolution is applied, incidents remain open for an extended period awaiting user verification or final administrative closure. This artificially inflates resolution times and impacts reporting accuracy, creating a backlog of 'resolved but open' incidents.

Process mining analyzes the time spent in the final stages of the process, specifically between resolution and closure. It pinpoints where this final handoff or user confirmation process is lagging, helping you streamline the path to full closure.

A significant portion of incidents are recurrences of previously resolved issues, indicating a failure to address the underlying root cause. This reactive cycle strains support resources and frustrates users who experience repeated service interruptions.

Process mining links incidents to identify groups of identical or highly similar issues, analyzing their lifecycle and resolution patterns. It uncovers the specific types of incidents that recur most often and helps identify points in the process where permanent fixes are lacking, improving problem management.

Typical Goals

Define what success looks like

Failing to meet Service Level Agreements for critical incidents erodes user trust and can cause significant business disruption. This goal focuses on minimizing the number of high-priority incidents that fail to meet their agreed-upon resolution times, ensuring business continuity and maintaining service quality.

ProcessMind uncovers the root causes of SLA breaches by visualizing the end-to-end incident lifecycle from your system's data. It pinpoints the exact activities, handoffs, or queues that cause delays, allowing you to compare actual process times against SLA targets and implement targeted interventions to improve adherence.

Long resolution times directly impact user productivity, increase operational costs, and can indicate underlying process inefficiencies. This objective aims to significantly decrease the average time it takes to resolve all incidents, from initial report to final closure, enhancing system availability and user satisfaction.

ProcessMind provides a clear, data-driven view of your entire resolution workflow, automatically highlighting the longest process paths and most time-consuming activities. By analyzing activity durations, reworks, and idle times, the tool enables you to identify and eliminate bottlenecks, leading to measurable improvements in efficiency.

When an incident is transferred repeatedly between support groups, it creates significant delays, increases costs, and frustrates users. Frequent reassignments often point to incorrect initial routing, skill gaps, or unclear team responsibilities, creating unnecessary friction in the resolution process.

ProcessMind maps the actual flow of incidents through your organization, clearly visualizing all reassignment loops and their impact on resolution time. By analyzing these pathways, you can identify common patterns of incorrect transfers, improve initial assignment logic, and refine support group responsibilities to streamline the process.

Accurate initial categorization is crucial for efficient routing, correct prioritization, and effective problem management. Miscategorized incidents are often sent to the wrong teams, leading to reassignments, significant delays, and wasted effort before the resolution process can even begin.

ProcessMind helps identify instances where incidents are frequently re-categorized or misrouted, indicating issues with initial classification. By correlating categorization events with subsequent process paths and resolution outcomes, the tool can pinpoint areas where training, documentation, or automated classification rules need improvement.

Deviations from standard operating procedures can lead to inconsistent service quality, increased risk, and unpredictable outcomes. This goal focuses on ensuring that all incident handling activities adhere to defined best practices, improving process control and providing a more reliable service experience.

ProcessMind automatically discovers and visualizes the actual process flow from your source system, comparing it against your intended model. This comparison instantly reveals all process variations, skipped steps, and non-compliant activities, allowing teams to identify and address deviations to improve operational integrity.

A high rate of reopened incidents indicates that resolutions are not effective, leading to rework, wasted effort, and diminished user confidence. This goal focuses on improving the quality of the initial fix to ensure that once an incident is marked as resolved, it stays resolved.

ProcessMind maps the complete lifecycle of reopened incidents, identifying common triggers and patterns associated with their return to an active state. By analyzing the activities preceding the re-opening, teams can identify the root causes of ineffective resolutions and improve the quality of incident closure.

A high volume of recurring incidents overloads support teams and signals that underlying systemic problems are not being addressed. This leads to continuous firefighting rather than proactive improvement. Reducing recurrences frees up resources and improves overall service stability.

ProcessMind analyzes incident characteristics and resolution paths to detect patterns of recurring issues, even if they are logged differently each time. It helps identify incidents that are frequently reopened or are similar to previously closed ones, enabling you to focus on proactive problem management and permanent fixes.

Delays during the final verification and closure stages can keep incidents open long after a solution has been implemented, skewing performance metrics and tying up resources. An efficient closure process ensures that resolved incidents are closed out promptly and accurately.

ProcessMind analyzes the durations and activities within the verification and closure phases of the incident lifecycle. It highlights bottlenecks, such as long waits for user confirmation or unnecessary administrative steps, enabling process owners to simplify procedures and accelerate the transition from 'resolved' to 'closed'.

The 6-Step Improvement Path for Incident Management

Connect & Discover

Map Your Process

Identify Bottlenecks

Analyze Root Causes

Design Improvements

Monitor & Iterate

Connect & Discover

What to do

Gather incident data from your system, ensuring all relevant event logs and case identifiers are included. Prepare the data for analysis by standardizing formats and identifying key activities.

View Data Template Guide Download Template

Why it matters

This foundational step ensures you have a complete and accurate view of your incident management process, allowing for reliable analysis. Without quality data, insights are flawed.

Expected outcome

A clean, comprehensive dataset ready for process mining, accurately reflecting all incident activities and their sequence.

WHAT YOU WILL GET

Discover the Real Story of Your Incident Management

ProcessMind uncovers the actual paths your incidents take, revealing hidden inefficiencies and compliance gaps. Gain clarity on every step from detection to resolution.

Visualize your actual process flow
Identify bottlenecks and delays
Optimize resource allocation
Improve overall process efficiency

Discover your actual process flow

Identify bottlenecks and delays

Analyze process variants

Design your optimized process

TYPICAL OUTCOMES

Achieve Smarter Incident Resolution

These outcomes represent the measurable improvements organizations typically achieve by applying process mining to their incident management workflows. By identifying bottlenecks, deviations, and rework, process mining enables targeted optimizations that enhance efficiency and service delivery.

0 % faster

Faster Incident Resolution

Average time reduction

By pinpointing and eliminating process bottlenecks, organizations significantly decrease the time required to resolve incidents, leading to quicker service restoration and improved user satisfaction.

0 % fewer

Reduced SLA Breaches

Decrease in non-compliant incidents

Identifying the root causes of service level agreement breaches allows for targeted interventions, ensuring adherence to commitments and minimizing negative business impact.

0 % reduction

Minimized Rework & Handoffs

Reduction in inefficient transfers

Process analysis reveals unnecessary reassignments and rework loops, enabling organizations to streamline workflows, reduce operational costs, and accelerate incident resolution.

0 % improvement

Enhanced Process Adherence

Higher compliance to standard steps

By visualizing actual process flows against predefined standards, organizations can identify and rectify deviations, ensuring all incidents are handled consistently and according to established guidelines.

0 % improvement

Higher First-Time Resolution

Decrease in reopened incidents

Understanding why incidents are reopened helps to improve initial diagnoses and fixes, significantly reducing the need for rework and improving overall user satisfaction.

0 % fewer

Reduced Recurring Issues

Decrease in repeat incidents

Identifying patterns and root causes of repeat incidents allows for permanent fixes, reducing the overall volume of recurring problems and freeing up resources.

Results vary based on the specific incident management process complexity, data quality, and organizational context. These figures illustrate the typical improvements observed across implementations.

Recommended Data

Start with the most important attributes and activities, then expand as needed.

New to event logs? Learn how to create a process mining event log.

Attributes

Key data points to capture for analysis

The unique identifier assigned to each incident. This ID serves as the primary key for tracking an incident throughout its entire lifecycle.

Why it matters

It is essential for linking all related activities and events together to reconstruct the end-to-end incident lifecycle for process mining.

The name of a specific business activity, event, or status change that occurred during the incident's lifecycle.

Why it matters

This attribute defines the steps in the process, enabling the visualization and analysis of the incident lifecycle flow.

The precise date and time when a specific activity or event occurred for an incident.

Why it matters

It provides the chronological order of events, which is essential for calculating durations, identifying bottlenecks, and analyzing process performance over time.

The current or historical state of the incident within its lifecycle, such as 'New', 'In Progress', or 'Closed'.

Why it matters

It is key to understanding the incident's progress and is often used to generate activities for the process map. Analyzing time spent in each status helps locate delays.

The assigned priority level of the incident, which determines the urgency and order of resolution.

Why it matters

It enables the analysis of process performance for different urgency levels, helping to verify if critical incidents are handled faster than non-critical ones.

The measure of the incident's business impact, indicating how significantly it affects users or services.

Why it matters

It helps in segmenting incidents to understand if high-impact issues are resolved differently or more efficiently than low-impact ones.

The support team, queue, or group currently responsible for working on the incident.

Why it matters

It is critical for analyzing handoffs between teams, measuring queue times, and understanding team-specific performance and workload distribution.

The individual support agent or user assigned to handle the incident.

Why it matters

It enables detailed analysis of individual workload, performance, and reassignment patterns within or between teams.

The classification of the incident, often organized in a hierarchical structure (e.g., Hardware > Laptop > Battery).

Why it matters

It is essential for root cause analysis, identifying trends in recurring incidents, and understanding how different types of issues are handled.

A code, category, or description indicating how the incident was ultimately resolved.

Why it matters

It provides insight into how problems are being solved, which is key for identifying opportunities for automation, knowledge base improvement, and training.

The method or channel through which the incident was reported, such as Email, Phone, or Self-Service Portal.

Why it matters

It helps analyze the efficiency and resolution paths of incidents based on their source, which can inform channel strategy and resource allocation.

Activities

Process steps to track and optimize

This activity marks the formal creation of an incident record in the system. It is the definitive start of the incident lifecycle, capturing the initial report from a user or monitoring tool.

Why it matters

This is the primary start event for the process. Analyzing the time from creation to other milestones is fundamental for measuring overall resolution time and identifying front-end delays.

Signifies the initial assignment of the incident to a specific support group or team for investigation. This represents the first official handoff and the start of the resolution workflow.

Why it matters

This is a key routing step. Delays in assignment or incorrect routing can significantly increase resolution times and cause unnecessary handoffs between teams.

Indicates that an assigned agent has started actively working on the incident. This is often represented by a status change from an 'Assigned' or 'New' state to an 'In Progress' state.

Why it matters

This milestone marks the end of the initial queue time and the beginning of active work. Measuring the time to this activity helps understand agent capacity and response delays.

A calculated event that occurs when the time taken to respond to or resolve an incident exceeds the targets defined in its Service Level Agreement (SLA). This is not a manual user action but a result of elapsed time.

Why it matters

SLA breaches are a primary Key Performance Indicator (KPI). Analyzing when and why they occur is critical for improving service delivery and meeting contractual obligations.

This activity indicates that a resolution has been implemented and the service is believed to be restored for the user. It is a critical milestone that typically stops the SLA resolution clock.

Why it matters

This is a key endpoint for measuring resolution time. The period between this and final closure is important for analyzing user confirmation delays or auto-closure policies.

Occurs when a previously resolved incident is returned to an active state. This usually happens when the user reports that the issue has recurred or the provided solution was not effective.

Why it matters

A high reopen rate points to issues with resolution quality, incomplete root cause analysis, or premature closure. This is a critical metric for rework analysis.

The final activity in the lifecycle, where the incident record is formally closed and becomes a read-only historical record. This often occurs automatically after a set period in the 'Resolved' state.

Why it matters

This marks the absolute end of the incident lifecycle. Analyzing the full time from creation to closure provides a complete view of the process duration, including any post-resolution administrative periods.

For customized data recommendations, choose your specific process.

FAQs

Frequently asked questions

How can process mining improve Incident Management?

Process mining visualizes the actual flow of your incident management, revealing bottlenecks like frequent SLA breaches or prolonged resolution times. It identifies deviations from standard procedures, excessive handoffs, and inaccurate categorizations that hinder efficiency. By providing data-driven insights into your real processes, it helps you optimize resolution paths and enhance overall service delivery.

What data is required from our system for process mining Incident Management?

You will primarily need incident activity logs, including a case identifier such as Incident ID, activity type, timestamp of each event, and the user or system responsible. Additional attributes like incident priority, category, and assignment group are valuable for richer analysis. This core data allows process mining tools to reconstruct the complete journey of every incident.

How quickly can we expect to see actionable insights?

Initial discovery and high-level insights can often be generated within a few weeks after data extraction and preparation are complete. Comprehensive analyses, leading to specific optimization opportunities and actionable recommendations, typically emerge within 1 to 3 months. The speed depends on your data readiness and project scope.

What are the technical requirements for implementing process mining?

Beyond the ability to extract historical incident data from your source system, you will need access to a process mining software platform. Expertise in data engineering for extraction and transformation, along with process analysis skills, are essential to effectively interpret the findings and drive improvements. Secure data handling and privacy compliance are also critical.

How does process mining differ from traditional reporting or dashboards?

Traditional reporting provides static, aggregated views of metrics, often based on predefined assumptions about the process. Process mining, conversely, visually reconstructs the complete, actual process flow directly from event logs. It uncovers hidden variations, unexpected paths, and true bottlenecks that reports cannot reveal, showing how and why things happened.

Can process mining help reduce SLA breaches and incident reassignments?

Yes, absolutely. Process mining precisely maps out every reassignment and pinpoints exactly where incidents get stuck or are incorrectly routed, leading to delays and potential SLA breaches. By visualizing these inefficiencies, you can identify root causes like unclear roles, skill gaps, or flawed escalation paths. This enables targeted interventions to streamline the process.

Is process mining intrusive or does it require changes to our live system?

No, process mining is a non-intrusive analytical approach. It operates by analyzing historical data exports from your system, meaning it does not require any modifications to your live production system, configurations, or ongoing incident management processes. This ensures minimal disruption to your operations.

Can process mining identify recurring incidents or root causes of issues?

Yes, process mining can effectively identify patterns of recurring incidents and highlight processes that frequently lead to the same issues. By visualizing the common paths and activities, it assists in pinpointing potential root causes that contribute to these repeated problems. While it provides the evidence, your experts determine the underlying causes.

Is process mining only for large organizations with complex processes?

No, process mining is beneficial for organizations of any size looking to optimize their processes, regardless of complexity. Even smaller teams can gain significant, actionable insights into their incident management performance. It helps identify areas for efficiency gains and process adherence that might otherwise remain unseen.

How does process mining contribute to reducing operational costs?

By identifying and eliminating inefficiencies like rework, excessive handoffs, and prolonged resolution times, process mining directly reduces the manual effort and resources spent on each incident. This optimization translates into lower operational costs, allowing your teams to handle more incidents effectively or reallocate resources. It drives significant economic benefits.

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