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What is MTTR (Mean Time to Repair)? MTTR Defined

Mean time to repair measures how well your systems and services are running and how well your IT teams are responding and repairing them.

Mean time to repair, or MTTR, is exactly what it sounds like—the average time to repair a service or system and get you back to business as normal. That’s important because any downtime can negatively impact your business, your people, your customers, and your brand.

What To Know About MTTR

While mean time to repair is the most common usage of MTTR, it’s also an abbreviation for other mean time measurements.

  • Mean time to repair
  • Mean time to recovery

    Mean time to recovery refers to the average time that a device (such as a cloud system) will take to recover from any failure. This is a measurement that’s typically part of a service level agreement (SLA) or maintenance contract to hold vendors accountable.

  • Mean time to resolve

    Mean time to resolve is the time needed for a system to regain normal operation performance after a failure occurs. DevOps professionals discuss this MTTR to understand the potential impact of delivering a risky build iteration in production environment. Business executives and financial stakeholders question downtime in the context of financial losses incurred due to an IT incident.

  • Mean time to respond

    Mean time to respond refers to the longest amount of time before your maintenance organization will send someone to look at your issue, i.e., the maximum on the arrival time window for a service repair technician—not the completion of the repair.

How To Calculate MTTR (Mean Time to Repair)?

A simple way to calculate mean time to repair is to divide the total time (minutes/hours/days) spent on unplanned maintenance by the number of failures.

MTTR Calculation Example

Here is an example of how to calculate MTTR.

Total time spent on unplanned maintenance = 72 hours (3 days)

Total number of failures = 10

72/10 = 7.2. The Mean Time to Repair is 7.2 hours.

5 Key Benefits Of Reducing Mean Time to Repair

When outages occur and services and systems are down, the negative impacts cascade across the business and out to customers and stakeholders. There are quantifiable benefits to reducing mean time to repair.

Performance benchmarking

MTTR can help organizations meet performance benchmarking reporting, which is now often part of budget and contract line items. Performance benchmarking measures an organization’s performance, or lack thereof, in terms of service disruptions and outages—as gauged by MTTR and other key performance indicators (KPIs)—against competitors and industry bests.

These measurements help you identify and determine how to close performance gaps. When MTTR is documented, and then reduced, that’s a measurable performance improvement, which is then reflected in metrics such as time to market, cost per unit, Net Promoter Score (NPS), and customer retention rates.

Improved system reliability

Reliability is the probability that a system performs correctly during a specific time duration. During correct operation, no repair is required or performed, and the system adequately follows defined performance specifications. Reliability measurement is driven by the frequency and impact of failures. When the mean time to repair is reduced, i.e., failures are less frequent and presumably less impactful, then the reliability of systems, services, and processes is improved. That improved system reliability then cascades to better service delivery and customer and employee experiences.

Minimizing business disruption

By improving and increasing system and service availability, organizations can reduce the downtime and outages that disrupt the business; negatively impact customers, stakeholders, and the brand; and potentially incur penalties or fines for missed service level agreements (SLAs). Faster or less frequent repairs help the business resume and maintain normal activities sooner. And the business-critical tasks and personnel that depend on those services and systems can get back to work, keeping customers and stakeholders happy and maintaining the brand in good standing.

Increased productivity

When IT teams must spend considerable time and effort firefighting issues and outages, their everyday tasks take second priority. If the system or service outage impacts them directly, then they’re idled until it’s repaired. Greater periods of service and system stability and availability mean that IT teams can get back to work and focus on the projects they want to be working on, which both better reflect their specialized training and add value to the business. Happy employees are also productive employees, and by increasing employee satisfaction, organizations also boost their retention.

AIOps processes that not only help reduce MTTR but also drive productivity are increasingly important. According to a recent IDC survey, half (50.5 percent) of respondents measured the success of their AIOps solution by how it improved the productivity of their IT teams, with 34.9 percent measuring success by the productivity and satisfaction of their end users. In a separate survey, IDC predicted that skills development powered by automation and generative AI (which factor into AIOps) will help organizations drive $1 trillion in productivity gains worldwide by 2026.

Cost savings due to reduced downtime and less system repairs

The average cost of a critical outage can be as much as $300,000 an hour. When outages occur and repairs take too long, that can trigger a loss of productivity, revenue, and customers. Efficiency gains in well-maintained services and solutions are also reflected in a better return on investment (ROI) because organizations are getting more out of them. When repair times are reduced, customers and stakeholders experience greater periods of availability, and IT teams can dedicate their efforts to activities that meet customer and stakeholder demands and tasks that add value—and drive revenue for the business.

How AIOps Can Reduce Your Mean Time to Repair

Artificial intelligence for IT operations (AIOps) solutions that leverage AI and machine learning (ML) and automation can help reduce mean time to repair in several ways.

Automated incident resolution

Incident management is usually defined in SLA or contracts as the customer-agreed-upon timelines for responding to and resolving incidents, according to priority, as a function of impact and urgency. Automating the sequential detection, logging, classification, and diagnosis of incidents establishes processes so they can be resolved, closed, and reviewed. Automated incident resolution leverages data about previous known issues and incidents to suggest and apply repeatable resolutions, with minimal or no manual intervention required.

Root cause analysis

To get to the source of an outage, you need to determine why, how, and where it started, i.e., its root cause. This can be a time-consuming, painstaking effort if done manually. AIOps speeds that up, leveraging AI/ML-enabled algorithms that analyze changes, events, logs, and topology, as well as past incidents and data clusters, to help teams identify issues faster, without spending additional time decoding output errors.

With an AIOps-enabled topology view, you can eliminate inaccuracies or speculation in finding problem areas by surfacing top causal nodes, such as where the problem is and its associated events, reduce the waiting time to build a large amount of observable data, and correlate that data to identify and determine the problem cause. Understanding the root cause can help teams take proactive steps to prevent it from repeating, and have a plan of action to resolve it quickly if it does.

Predictive analytics

Predictive analytics leverages AI/ML and now, generative AI, to analyze and learn from previous issues and outages to identify patterns and predict issues ahead of time. AIOps-powered advanced anomaly detection can analyze and correlate massive amounts of data quickly, find outliers in the data, and proactively alert the operator that there’s an issue with a service or multiple services based on events coming into the system.

Being proactive instead or reactive allows organizations to get ahead of issues that could impact the business, employees, and customers; take timely actions when they do occur to prevent small problems from becoming big ones; and instead focus on key value drivers. As a result, those analytics helps organization reduce not just their MTTR, but their mean time between failures (MTBF), too.

Continuous monitoring

The purpose of continuous IT monitoring is to determine how well your IT infrastructure and the underlying components perform in real time. Monitoring is the process of instrumenting specific components of infrastructure and applications to collect data like metrics (resource consumption, response times, CPU and memory usage, and error rates), events, logs, and traces and interpreting it against thresholds, known patterns, and error conditions to turn it into meaningful and actionable insights.

Monitoring is focused on the external behavior of a system, and is most effective in relatively stable environments, where key performance data and normal versus abnormal behavior is known. AIOps enables continuous, real-time monitoring of a service health environment, which allows operators and site reliability engineers (SREs) to observe usage trends, make decisions on provisioning, and identify anomalies, issues, and vulnerabilities, analyze their cause, and quickly remediate them to restore the health of the impacted services.

Augmented collaboration among teams

With AIOps, data is ingested in the form of logs, events, and metrics and taken through a set of algorithms that select specific data points, which are then identified, correlated, and analyzed and passed into a collaborative work environment. Because AIOps solutions automate monitoring and management processes, they elevate the role of ITOps teams, allowing them to spend less time troubleshooting and more time collaborating with business units to advance their strategies and put innovation to work.

AIOps gives also IT teams spanning the service desk, change management, infrastructure operations, development, and QA a single dashboard with a unified view of the health of the service environment, as well as real-time monitoring of logs, events, and metrics so they can collaborate and share knowledge, working together to resolve issues much faster than they could working on siloed teams with disparate sets of data.

Companies can leverage MTTR (mean time to repair) in several ways:

Customer experience

Delivering quality customer experiences can make or break a business, and repeat or lengthy outages that impact customer service and delivers a subpar experience can send them to a competitor. In fact, 63 percent of consumers are less likely to forgive a disappointing digital experience than they were before the pandemic. A bad experience can also impact your NPS or create bad word of mouth if customers take their dissatisfaction to social media. Understanding and improving mean time to repair can help companies ensure they’re running normally—or get them there as soon as possible—to keep customers happy, increase customer loyalty, encourage repeat business and positive word of mouth, and bolster brand reputation.

Competitive advantage

Improving MTTR helps businesses identify and address problem areas that can be improved, which can positively impact their financial and operational performance and give them a competitive edge. Resolving failures quickly also helps organizations focus their attention on the business-critical, day-to-day operations that are integral to delivering optimal customer experiences and dedicate resources to the innovations that address evolving customer demands so they can bring new enhancements to market faster.

Data-driven decision-making

Having concrete MTTR data gives organizations the metrics they need to better understand recurring issues and weaknesses; track how efficiently they’re addressed; identify areas for improvement; take action through upgrades, enhancements, and training; and measure the effectiveness of those improvements and actions. By combining big data and ML to automate the IT operations processes that previously required significant time and effort, AIOps creates efficiencies at scale, enables visibility across your infrastructure, and helps teams derive the insights they need to make powerful, data-driven business decisions more easily.

For example, measuring MTTR against customer surveys and self-service feedback about how service issues impacted the customer experience, or which issues had the most direct impact, can help organizations prioritize them. In a recent DataOps report commissioned by BMC, 77 percent of organizations with a more mature DataOps strategy (that leverages AI and automation technology) said their use of data has had a significant impact on customer satisfaction.

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