How to effectively manage your Service Level Agreement (SLA) with process intelligence?

Whether you are an operational center of excellence, a customer service department, or a Shared Services Center, you are probably familiar with the concept of Service Level Agreement. This type of contract or procedure is used to define the expected level of service when a client contracts with a provider of goods or services. The SLA defines the resolution time in case of incidents, potential penalties for non-compliance, service availability level, response time, computing capacity, and so on…

It is important to establish performance goals for each of them. These goals must be realistic and measurable, so they can be used to evaluate the effectiveness of your SLA. Here’s how you can proceed: 

  1. Define relevant KPIs based on your business objectives, such as:  
  • First interaction incident resolution rate  
  • Average incident resolution time  
  • Customer response time  
  • Service availability rate  
  • Number of services involved in incident resolution  
  • Incident typology 
  1. For each of these KPIs, define functional rules, such as:  
  • First interaction incident resolution rate: the percentage of incident tickets closed for which the status “processing” is unique.  
  • Average incident resolution time: time elapsed between opening an incident ticket and closing it.  
  • Service availability rate: time elapsed between the moment a user receives an “unavailable service” error message when trying to log in and the moment a user log in normally. The availability rate is the percentage of total downtime compared to the total planned service availability time. 
  1. Finally, define thresholds to judge whether the SLA is being met or not, such as:  
  • Customer response time: less than 4 hours on average and the median response time should not exceed 3 hours.  
  • Service availability rate: 95% from Monday to Friday between 7am and 8pm.  
  • Incident typology: 55% of tickets closed after the first intervention. 

Process Intelligence for SLA Management

Process mining or process intelligence enables the end-to-end modeling of your business processes, including those supported by multiple tools (see how process intelligence works) 

This comprehensive view of processes allows for precise measurement of your SLA’s KPIs. KPIs are calculated from your Information Systems’ data, with calculation rules created directly in the Process Intelligence solution to limit the specific development of your tools. These solutions enable precise measurement of time calculations (business days, holidays, service opening hours, etc.) as defined in contracts/procedures. This great flexibility in parameterization allows for simple initialization and evolution of these KPIs. 

Your teams can now have a dashboard that includes all your KPIs and their real-time progress. You are now able to provide value by sharing these dashboards with other teams (marketing, sales, etc.) to improve customer satisfaction, strengthen sales arguments, or enhance service perception, among other benefits. 

SLA Dashboard
Real-time control of average response time, system unavailability resolution rate, services contributing to incident resolution… With process mining, the possibilities are numerous.

Alerts and Deviation Predictions

Some Process Intelligence tools also allow you to create alerts when KPIs reach a defined threshold. Initially, it is necessary to define the rules triggering an alert (e.g., if an access request to a tool is not handled within 48 hours of the request, notify one or more users in charge of approvals). The mailing lists and frequency of sending alerts can be adjusted in the tool to finely manage this type of process. 

How the alerts work

Based on historical data, some solutions like ABBYY Timeline use Machine Learning which allows predicting the outcome of a process (depending on the fields filled in a form, the probability that the request is legitimate can be calculated). Your teams can now focus on the most compliant requests to respond favorably or, conversely, quickly close illegitimate requests. 

A Modular Architecture

Using a process mining service allows for a modular architecture, and the technical installation is transparent to your technical architecture because process mining retrieves SI logs via connectors or SFTP protocols. This architecture frees you from specific developments for your KPIs’ dashboards and calculation rules. The teams responsible for monitoring these KPIs can configure and evolve management rules and dashboards autonomously: 

  • Each user can create and share dashboards 
  • It is possible to track several types of SLAs depending on your client typology by creating different dashboards 
  • Sensitive data security is ensured, only the information necessary for calculating KPIs is retrieved (timestamp, ticket status, generic attributes such as departments involved or the nature of incidents…) 

Your teams now monitor digital twins of your business processes without the risk of data leakage. 

An Adapted Tool

Depending on the intended goals, the use of a Process Intelligence solution can be a powerful tool for effectively managing your SLA. Real-time monitoring of your KPIs and alerts will help you minimize the risks of penalties or improve customer satisfaction. Implementation can be fast (on average 6 weeks for our clients), it does not impact ongoing IT roadmaps and quickly empowers your users to be autonomous. 

Optimization of a life insurance product subscription path thanks to Process Intelligence

How can Process Intelligence tools be a springboard to your operational efficiency objective?

Historical review

For many years, Robotic Process Automation (RPA) solutions have emerged as the tool of choice to accelerate processes and gain operational efficiency. The principle:

  • make a robot perform repetitive tasks without added value on the one hand,
  • let humans concentrate on tasks with higher added value (analysis, decision making, creativity…) on the other hand.

Everyone wins: the risk of error decreases drastically, the capacity to do things is concentrated in the right place. This market is mature, with many RPA solutions available, and today we talk about Smart Automation. Artificial intelligence is indeed enabling more and more sophisticated robots. Process Intelligence (PI) solutions have appeared more recently on the market.

So what is Process Intelligence?

Process Intelligence allows to collect and analyze process data in order to understand each step of a process (duration of the whole process, average waiting time between each step, teams in charge, …), to visualize the bottlenecks, and to find solutions to the problems encountered. To do this, the data used comes from logs collected on all the systems used to run the process studied (ERP, CRM, business tools, etc.). Mainly, process intelligence is based on several tools such as process mining, process modeling (BPMN type), task mining, or the digital twin. All of these tools are based on artificial intelligence and more specifically on machine learning.

The lessons learned from a Process Intelligence solution allow organizations to base their strategy for improving the operational efficiency of processes on an in-depth analysis of historical data and not only on qualitative interviews. Indeed, Process Intelligence allows an analysis of all the processes as they have taken place, by updating all its variances, frequencies, costs, durations… This essential information, which improves the knowledge of the business towards its own functioning, then allows to define the axes of improvement to be prioritized, whether it is on the way of improvement, simplification, automation of the process, or that of the accompaniment to the change (trainings, communication, etc).

What kind of gains can be expected from process intelligence?

Best practice development : By uncovering how processes are executed “in real life”, and not how they are supposed to be executed, the optimal process can be identified and used as a reference for the development of good practices internally.

Highlighting of process malfunctions : Bottlenecks, duplications / redundancies in the process, which limit productivity, are easily identified, visually.

Cost optimization : Improving the efficiency of the process, both by highlighting the target process and by eliminating bottlenecks and various dysfunctions identified, will have a real impact on the cost of the process.

Time to obtain a clear vision of the business processes : This path can be long and complex due to the number of actors involved in the process, the number of IS used, and the lack of complete knowledge of the process from end to end. Process Intelligence tools can significantly reduce the time needed to select the processes or parts of processes to be automated (RPA).

Process Intelligence thus allows to take a step aside in the process of automation in which organizations are launched. Furthermore, the ability of Process Intelligence to calculate and measure the impact of each process improvement strategy studied (automation, tool redesign…), and therefore to compare the ROI of the different scenarios, allows to minimize the risks and to ensure that the effort is put in the right place. Finally, as Process Intelligence offers the possibility to monitor processes and their performance in real time, it becomes a tool for steering your operational efficiency strategy.

In conclusion, the implementation of this type of solution allows you to draw a more efficient and impactful path towards the continuous improvement of your processes.

[Webinar] Accelerate your Process Automation by 30% with Process Intelligence

Regardless of your industry, a process automation strategy remains complex and ROI can sometimes seem long to achieve. You must, among other things, select the right processes to automate and master them, then proceed to the automation phase and measure the impacts on your organization. 

ABBYY, Blue Prism and Novelis offer a unified solution that combines process intelligence with intelligent automation for process exploration, optimization end-to-end monitoring of the automated processes on AWS. 

This webinar will be an opportunity to present 3 detailed customer cases to discover the relevance and power of ABBYY process intelligence technology combined with Blue Prism digital workers. 

  • Mehdi Nafe from Novelis will detail the exploration and optimization of a subscription process in the Savings sector, with measurable impacts related to the subscription volume or their unit cost. 
  • Benoît Cayla from Blue Prism will give you an end-to-end demo on a real order to cash process (retail/manufacturing) 
  • Pierre Hagot from ABBYY will describe how the implementation of the solution helps mitigate regulatory risk in the Finance sector.