Before the usage of Celonis for the onboarding of Business Clients & Ecosystems (BC&E) the available management information (MI) was inadequate in pin-pointing root-causes for the cross-departmental process. ​

There are many initiatives being theorized that do not make the priority lists as there is no reliable process data to be used to create convincing business cases.​ Also, due to the process complexity, there are multiple big departments involved and within those many key stakeholders with own understanding, goals and priorities. This makes it very hard to oversee the process and leads to siloed process improvement initiatives. ​

Next to this, the process has a high throughput time that is associated with negative impact on the customer experience and conversion. ​Lastly, the process has a high percentage of cases going through the most labour-intensive variants. This makes the process costly and prone for inefficiencies. ​

A solution plan to decrease the onboarding time of business clients and ecosystems. The plan includes five business cases, each discussing one concrete process improvement initiative describing the insights it is based on, the proposed solution, and the expected reduction in throughput time. The plan is validated with, and supported by, all departments in the end-to-end chain and is already being implemented. ​

The first deliverable along the way was a new process understanding. The project team supported the bank in defining the cross-departmental end-to-end onboarding process. The new definition was the first to cover all process variations, including low-level process information about activities, responsibilities and systems, and be approved by all BC&E departments and Process Owners.​

Next, process mining was used to discover the as-is process, and create dashboards and insights into the actual numbers, flows, and throughput time. These insights are used by the whole chain as the end-to-end single source of truth and for fact-based steering. ​

The process mining baseline showed a big mismatch between business ambition and actuals, alongside gaps in the steering metrics. The data was also used to show a correlation between throughput time and conversion and underlined the need to reduce the throughput time of the end-to-end process. ​

Process mining was used to discover the process inefficiencies, pinpoint the biggest throughput time leaks in terms of single activities, process phase, department, and other case characteristics and find their root causes. Solution workshops were used to investigate new and existing improvement ideas and choose the most beneficial and low-effort ones to include in the solution plan. ​

Mining the onboarding process delivered many expected and unexpected benefits. All findings that may not directly lead to throughput time reduction but will improve the process steering and efficiency incl. suggestions for solving them are delivered as an extra to the solution plan. ​