Analysing data from enterprise systems and applications to understand and optimise business processes
Process Mining is a process analysis discipline that seeks to discover, monitor and improve processes by extracting knowledge from event logs.
Its objective is to use and transform a large amount of existing data available in corporate information systems into knowledge in terms of business processes. In this way, bottlenecks, rework, deviations and sources of waste in processes can be identified, and opportunities to optimise performance and maximise business results can be uncovered.
Process Mining has 5 main phases:
Automatically collect transactional data from digital event traces left by processes.
- Capture data from the different applications and systems related to the processes.
- Additionally, data obtained from the manual process discovery study can also be incorporated
Create a visual model of how processes work, showing clearly what is done in reality against a theoretical model expected or intended to occur.
- Identify tasks, with their times, frequencies, sequence variations, who performs them, when, rework, etc.
- Automatically create an end-to-end visual model of the processes. Both in the form of a network and the automated construction of BPMN (workflow) and DMN (decision rules) models.
Advanced analysis capabilities detect weaknesses and areas for improvement based on KPIs.
- Analyse processes in detail to identify deviations, inefficiencies, bottlenecks and other improvement points.
It allows you to simulate different scenarios, identify the most beneficial ones and optimise processes accordingly.
- Determine what to optimise, how to optimise it, and the return on these initiatives.
- Easily create and prioritise automation roadmap and process optimisation initiatives.
Once optimised, it allows you to monitor the performance of your processes so that you can adapt and improve them even based on real-time data.
- Control performance indicators at all times to track and maintain compliance.
- Anticipate possible deviations from planned KPIs.
- Compare the processes, tasks, indicators and traces resulting from automation and optimisation actions with the starting point data.
In the past, mapping business processes required expert teams and days of work. But thanks to Process Mining’s powerful algorithms, the identification, mapping and optimisation of business processes is done faster and more efficiently.
Capabilities and benefits of Process Mining
Process Mining provides techniques and tools that allow you to:
- Identify and analyse processes to understand how business operations are executed.
- Visualise and understand how processes contribute to business value in any functional area.
- Identify bottlenecks, deviations or inefficient processes that need to be rethought or automated, and relate these friction points to KPIs.
- Understand the root causes of deviations and quantify the impact of these deviations on process performance.
- Predict the future performance of a process in different scenarios to make better decisions and prioritise automation and process improvement efforts.
- Continuously monitor processes and size improvements.
- Determine best practice actions and/or corrections on the people involved in the execution of the tasks of the studied processes.
It can answer questions such as:
- Are there very big differences between the defined process and the reality, how much, where and in which variants are there deviations?
- Do I know the actual performance of my processes? What are the pain points?
- Am I complying with current regulations?
- What are the costs, times and resources involved in each of the process variants? Which variant or tasks should be optimised?
- What is the financial performance of the process and why have costs increased?
- What is the customer satisfaction for each of the variations? Why have we lost customers in the last month?
- Do I control the omnichannel exchange and proliferation of data from different digital channels?
- What are the candidate tasks within a process to apply automation?
- What is the ROI of implementing automation developments before they are executed?
- Are transformation, digitisation, automation, etc. projects delivering the expected results?
In the last decade, process mining has become an essential business intelligence and business process management toolkit, being used in almost every industry sector: banking and financial services, telecommunications, energy, healthcare, logistics, manufacturing, etc.
More and more companies are becoming aware of the importance of being able to analyse and understand the actual performance of their operations and business processes, for example, before starting any process redesign and automation initiatives.
In a Hyper-automation approach, Process Mining brings great advantages and provides a complete context for improving processes and ensuring that maximum benefit is obtained from automation initiatives.
78% of companies that have implemented automation say that Process Mining has been key to making their automation initiatives viable.
Process Mining Sector Scan, January 2020
Process Mining bridges the gap between traditional model-based process analysis (e.g. simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining.
This technology has only recently become available but can be applied to any type of business process. It helps to achieve significant goals in terms of efficiency, organisational transparency and customer service in an accelerated manner by interconnecting data from different processes and systems within the organisation. In addition to the use of process and rule mining tools and algorithms; and expertise in analysing, interpreting and prescribing actions.
At decide4AI we have expert teams in the four fundamental blocks to carry out a successful project with the use of Process Mining:
- Integration and architecture team: data capture.
- BPM team: analysis and optimisation of processes.
- Digital Decisioning Area: compliance and compliance.
- Analytical Intelligence Team: development of predictive models and data transformation.
Interested in harnessing the power of your data to achieve operational excellence?
If you are interested in one of our business solutions, contact us.