15 Benefits of Process Mining the Incident Management Process

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Process mining is still a relatively new concept in the Incident management arena, yet it has already been proven to deliver real value, by helping drive continuous improvements across the incident management process.

Listed below are a consolidated sample of customer views, when using QPR ProcessAnalyzer, against their respective ITSM systems, such as ServiceNow, Remedy, HP Service Manager etc.

1. Provides visibility and transparency of the true as-is incident management process.
2. Identifies the main process steps in the incident management process as well as the exceptional and unwanted process steps.
3. Predicts the outcome of any incident and provides an early warning of any likely missed SLAs
4. Calculates incident management KPIs, such as SLA (%), Time to Resolution (minutes / hours / days) & First Time Right (%).
5. Identifies the root causes of incident process problems.
6. Provides analysis of the effect of the opening interface (email, phone, web form etc.).
7. Analyses the different working habits of various teams and individuals.
8. Enhances productivity, through understanding the effect of incident categorisation.
9. Compares the Incident management data with the as-drawn incident management process.
10. Identifies the products & services that cause the biggest amount of extra work.
11. Identifies the reasons behind re-opening of incident tickets.
12. Provides the basis for continuous incident management process improvement.
13. Calculates the cost for the incident management process.
14. Verifies how the escalation rules are working and how the escalation is carried out.
15. Provides support for Robotic Process Automation in the incident management process.

 

1. Provides visibility and transparency of the true as-is incident management process.
Process Mining allows you to take the incident management data directly from your ITSM and reproduce that data in the form of a process map visualisation (of each incident), along with a detailed analysis of how that particular incident moves through the process.

2. Identifies the main process steps in the incident management process as well as the exceptional and unwanted process steps.
When analysing any organisation’s ITSM data, we usually find that the majority of incidents are executed in an efficient manner and provide a clean and speedy route to providing a resolution.

However, again in every organisation, we also find a significant number of incidents that are not executed efficiently and the path taken by those respective incidents, through the execution of the specific process steps, can be unnecessary and should be avoided. Examples might be: re-assigning of incident tickets multiple times, excessive waiting times, unnecessary questions to customers, the ‘ping-pong’ effect of bouncing between 1st line helpdesk and 2nd / 3rd line specialists etc.

However, in order to resolve the most complex incidents, many activities may be needed and therefore it is not always so easy to determine which steps are good and which are bad. Indeed, it is the complexity of the incident that dictates the final process path. By using a process mining tool, users can easily see and understand the complexity of the incident and those influencing factors that determine the incidents outcome.

3. Predicts the outcome of any incident and provides an early warning of any likely missed SLAs.
QPR ProcessAnalyzer is able to predict the outcome of any open incident, based on actual incident management process data from earlier incidents. It is possible to show a list of incidents likely to fail their respective SLAs and therefore to provide an early warning to users of possible issues, thus potentially providing the time to deploy additional resources etc. to address the imminent failure.

4. Calculates incident management KPIs, such as SLA (%), Time to Resolution (minutes / hours / days) & First Time Right (%).
Advanced process mining and analysis tools such as QPR ProcessAnalyzer can automatically calculate and generate the process KPIs for each specific incident. These KPIs can be shown directly in the process flow view, through graphs and charts, or dials in pre-built interactive dashboards.

5. Identifies the root causes of incident process problems.
QPR ProcessAnalyzer has a unique Influence Analysis capability which is able to identify those specific factors that influencing the outcome of any particular incident. In short, this can steer the user towards those factors that are having either a positive or negative impact on the outcome of that incident or series of incidents.

6. Provides analysis of the effect of the opening interface (email, phone, web form etc.).
The opening interface may have a significant effect on meeting the incident’s SLA. Many clients have identified that certain service channels (opening interface) work better or worse for a particular category of incident. This information can be used to guide end-users in selecting the most appropriate incident logging interfaces for the respective incident type.

7. Analyses the different working habits of various teams and individuals.
It is very typical to find that similar ‘support’ teams in the same organisation that perform quite differently to their peers. Process mining enables the sharing of best practice by comparing the results of different teams and highlighting the different working practices, habits and behaviours. Having the ability to easily share these visualised best practise operations, can deliver real value, especially for those teams that are spread over multiple locations.

8. Enhances productivity, through understanding the effect of incident categorisation.
All Incidents are not the same. Categorisation can help users to understand, plan the required actions and allocate resources optimally. Using advanced AI methods such as clustering based on unsupervised learning, QPR ProcessAnalyzer is able to suggest categorisations that would lead to a potential increase in the productivity of the incident management support teams.

9. Compare the Incident management data with the as-drawn incident management process.
Every organisation will have some form of incident management process documented and potentially mapped out. However, it is very typical that the actual incident management process, as executed within the ITSM, can be considerably different to the assumed ‘mapped’ process. QPR ProcessAnalyzer provides a process comparison capability. By importing the existing ‘mapped’ data from the user’s process mapping tool (using BPMN output), ProcessAnalyzer can produce a conformance report to show how often the true as-is process (taken from the ITSM data), conforms with the assumed as-drawn process map. This will show for example, how many incidents conform to the standard variations and how much time is wasted in non-conforming incident journeys.

10. Identifies the products & services that cause the biggest amount of extra work.
As part of the drive towards continuous improvement of the incident management process, it is important to understand which products & services are creating the biggest drain on the support team’s time and resources. This information can be shared with the product & service owners, thus allowing for planning to either a) improve the product / service (or replace it with an alternative) or b) improve the competencies and capabilities of the incident management team for solving related incidents more effectively.

11. Identifies the reasons behind re-opening of incident tickets.
From the ITSM process perspective, it is typically undesirable to re-open an already closed incident ticket. However, by analysing any re-open exceptions, users may discover additional knowledge related to the incident management process. With QPR ProcessAnalyzer’s influence analysis capability, it is possible to find activities and process steps that take place early within the process, that have an effect on the probability to re-open the ticket for a second time. This would typically be a sign that something was not done “first time right” in that particular step. Typically re-thinking the instructions and details of those particular tasks may result in a much lower re-open rate and ultimately, improved customer satisfaction.

12. Provides the basis for continuous incident management process improvement.
Making systematic improvements in the incident management process, is a key requirement for long term efficiency and process excellence. Process mining provides true insight and analysis of all new incidents and provides the fact-based information that supports ongoing operational improvement.

13. Calculates the cost for the incident management process.
If we add costs into the analysis, it makes it possible for users to understand the cost structure of the whole incident management operation and based on that, optimally allocate resources to maximise the performance of the respective KPIs.

14. Verifies how the escalation rules are working and how the escalation is carried out.
In broad terms, if the Service Level Agreement (SLA) can be met without escalation, then the process is typically considered to be in good or reasonable order. If however the SLA is not met then we can either a) look to improve the process, or b) look to improve the escalation mechanism. With Process Mining, you are able to select only those incidents that have been escalated and view the escalation process flow in isolation, to show how that particular part of the process is executed and thus identify any particular areas that require further improvement

15. Provides support for Robotic Process Automation in the incident management process.
For organisations that want to automate their operations using RPA, it is possible to automate individual non-value adding manual process steps. Process mining has a key role to play in analysing the current as-is process behaviours, before the RPA project is deployed. If however, there are too many exceptions within the process, then it is important to re-think the process to consider which process steps and incident types should be handled automatically and which should still be treated manually.

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