These analyses can help companies to identify, evaluate and eliminate process weak points. Traditional process analysis methods, however, do not always yield optimal results. More often than not, process documentation is not up to date, process workshops only provide limited insights into the process performance, and the use of benchmarks only makes sense if reference values exist, which is rarely the case.
Benefits:
- Fact-based: Traditional process analyses are based on the subjective opinion of a few experts and can thus result in a distorted perception of processes.
- Thanks to being fully based on data, process mining is 100% relevant and visualizes your processes “as is.”
- Efficient: Traditional process analyses entail multiple rounds of elaborate in-depth interviews with subject matter experts.
- Process mining enables a fast and complete process analysis based on your system data.
- Repeatable: Traditional process analyses result in a one-time, static snapshot of your process.
- Process mining is based on data transformation paths (ETL), which can be used at any time for follow-up analyses or to validate process adaptations.
How can you use process mining in concrete terms?
Customer Journey Analysis & Optimization
- Visualize the process through the eyes of the customer.
- Identify customer challenges.
- Optimize the customer journey.
Process Performance
- Understand how long certain operations take.
- Identify areas where idle times occur.
- Learn what causes long cycle and idle times.
Make Sound Automation Decisions
- Make system data analyzable.
- Identify automatable processes.
- Make sound and objective automation decisions.
SLAs and Performance Analysis
- Map defined SLAs in a data-driven manner.
- Detect deviations at an early stage.
- Initiate countermeasures in good time.
Analyze and optimize compliance risks & controls
- Map defined SLAs in a data-driven manner.
- Detect deviations at an early stage.
- Initiate countermeasures in good time.
Benefit from Our Strengths
Process mining project experience – We have been helping our customers successfully implement process mining projects for more than six years.
Holistic optimization – In addition to performing the actual process mining analysis, we identify concrete ways of how you can optimize your processes. We also assist you in setting up a continuous improvement process (CIP).
The perfect team – Our agile process mining team delivers all the skills that are essential for your process mining project: process optimization, data science and industry knowledge.
Technology expertise – We are familiar with various process mining tools, such as Celonis and Fluxicon Disco, and know the ins and outs of big data technologies and data science tools including R and Python.
Project Insights
Our success is our customers’ success.
Process mining uncovers potential for improvement in the quality assurance process of a pharmaceutical company.
Focus on process transparency, cycle times and process time drivers
More information
We initially performed a quantitative analysis of our customer’s product release process to identify concrete factors that had an adverse effect on process cycle times. Using a customized process mining approach and individualized advanced analytics and programming tools, our team was able to quickly identify underlying problems in the release process. By drawing on process visualizations and conducting further analyses of the status quo, we also uncovered inconsistencies between the data pools of individual business units. Based on this comprehensive root cause analysis, the customer was able to derive targeted measures for improvement to bring more efficiency to the product release process.
Focus on incorrect classifications and rework
More information
On behalf of an insurance company, we performed a data-driven process analysis and quickly identified root causes for process inefficiencies and related issues. Thanks to our experience in process mining and the technical analysis of data, we were able to create fact-based process transparency for our customer. This made it possible to identify and analyze error rates, process regressions as well as actual cycle and idle times in service accounting processes.
By means of structured hypothesis generation, we were able to find answers to all items relevant to the customer and also identified potentials for optimization. Taken the findings, we derived suitable improvement measures to augment the efficiency of our customer’s service accounting process.
Data-driven process analysis brings transparency to an insurer’s service accounting workflow
Optimization of sales and inventory processes at a private health insurance company based on process mining
Focus on increasing the degree of automation and accelerating process cycle times
More information
We performed a data-driven process analysis for our client to identify inefficiencies in customer-focused multi-channel processes for insurance products. The findings were used to optimize manual workflows and cycle times. We successfully applied automatic process and data visualization techniques, providing our customer with critical insight into their process flows. The resulting transparency into processes made it possible to perform an even deeper analysis in order to collect all relevant data and evaluate cycle times, reasons for manual workflows and how these impact the bottom line. The results of our process analysis led to a quick identification of optimization potentials and the formulation of effective improvement measures.
Would you like more information?
Your contact
Dr. Gregor Scheithauer
metafinanz Informationssysteme GmbH
Leopoldstrasse 146 | 80804 München, Germany
Tel. +49 89 360531-5533
