ITSM is complex. The practice requires all the processes, culture and technologies conforming to well-defined management principles and framework best practices to deliver services with highly reliable and available business operations. When an IT incident occurs, business operations can potentially come to a standstill until the issue is resolved.
The IT Service Desk uses a range of metrics to understand the true impact of an IT incident, each presenting a different cost perspective IT incidents. In this post, we will discuss an important IT Service Desk metric, Mean Time to Resolve (MTTR) and its importance in understanding the true business impact of unresolved IT support requests.
Mean Time to Resolve refers to the average time it takes for a system to recover from a downtime incident. The metric offers a procedural approach to predict reliability of system components over their lifecycle. The value is expressed as a measure of time. In an IT operational system, MTTR would reflect the time required to replace a faulty component. The time taken to identify and act upon the resolution procedure can further impact MTTR, though a precise measurement of MTTR would only account for the specific duration between identification or reporting of an incident until the incident is resolved.
The MTTR metric is mathematically defined as follows:
MTTR = Total Hours of Maintenance/Total Number of Repairs
MTTR = Total Hours of Downtime/Total Number of Incidents
In the IT Service Desk domain, the incident is represented by the support ticket lifecycle process, where the maintenance or downtime imply the time duration of an active support tickets, and the number of incidents or repairs is simply represented by the number of active support tickets.
Most organizations have an elaborate mechanism to manage expenses. And as the management guru Peter Drucker once said, “[only] what gets measured, gets managed”. It often takes a compelling business case to introduce drastic changes into the existing operational framework and organizational culture; metrics and KPIs are one way to identify opportunities of improvements in the ITSM domain.
However, the converse is also true: it’s often hard and sometimes impossible to manage what doesn’t get measured. And time, mostly goes unmanaged for a simple reason: a lacking understanding of the optimum productivity levels of employees at various hierarchical levels of the organization and how the lost productivity maps to the business bottom line. Consider the following case example at a typical large [UK] organization:
|Level||Annual Salary||Productivity Rate * X||Productive Value||Staff||Total Productivity|
|Admin||£ 60,000||5||£ 300,000.00||10000||£ 3,000,000,000|
|Rev Gen||£ 120,000||10||£ 1,200,000.00||1000||£ 1,200,000,000|
|C Suite||£ 240,000||20||£ 4,800,000.00||50||£ 240,000,000|
The table above also provides another insight into the challenges associated with measuring and managing time. Not all ticket costs and resolution delays impact the business bottom line in the same way. MTTR can mean different opportunity costs at the different hierarchical levels of the organization. On the other hand, the Service Desk may not be programmed to optimize resolution for the least overall productivity loss to the business.
For instance, it may not be possible to understand the true cost of operational downtime facing a CEO during the 30-minute resolution window for a simple PC fix instead of simply replacing it with a working machine. The same time could be spent closing long-term business deals and engaging partners but the Service Desk ticketing system continues to follow broken and under-optimized workflows, disregarding the true cost of downtime.
Swish.ai massively reduces MTTR, the key metric for Business Productivity. When the IT organisation reduces the time to resolve issues, user productivity is enhanced dramatically. Swish.ai takes this concept one step further and quantifies the true cost of IT Service Desk operations and operational efficiency, ultimately measuring the positive impact on your company revenue.
Swish.ai customers see an average reduction in MTTR of 50% and even up to 75% returning $millions in user productivity and lost opportunity.