Together they are stronger: process engineering, RPA, process mining. The CFO should build an integrated Digital Process Management to exploit the potential. Whether in accounting, payments, or purchasing, process disruptions such as incomplete invoices, delayed processes, and manual procedures are commonplace and a thorn in the side of every CFO.
Companies are now using digital means to get to grips with these cost drivers. They are using approaches such as process engineering, process mining and robotic process automation (RPA). But rarely are the forces combined.
Process engineering is the discipline in which companies define and document their business processes. They are visualized with business process management (BPM) tools and improved with business process optimization (BPO) methods.
Various tools help with visualization. However, empirical performance such as the duration of processes and the variance of processes - i.e., the extent to which employees deviate from the target process - is not completely revealed by this discipline. Classic process workshops always show only part of the truth.
That's what process mining is for. This discipline makes IT-supported actual processes visible in their full breadth on the basis of real data - with all inefficiencies, loops, automation rates, and variants that do not conform to the target process.
KPIs and analyses lead to a kind of "business intelligence tilted into the process perspective". With the broadest possible master data in the model, the benefit grows because patterns can be better identified. The deficit: Manual steps outside the system and the content of individual work instructions are not visible.
Companies achieve process automation by means of elaborate optimizations in IT systems, upstream workflow management systems and RPA modules, and additionally with the help of AI engines.
The extent to which these methods automate the processes in the result reliably and without errors is not always immediately visible. There is a lack of transparency over the entire process, i.e., the added value that process mining delivers. Professional as well as technical interfaces should therefore be brought together much more strongly.
The individual disciplines therefore have their own gaps. In order to close these gaps, a combined approach with all three instruments is recommended. Companies develop a target process. This is stored as a reference in a process mining tool via the BPM interface.
There, deviations in work steps and throughput times can be identified and optimized by means of BPO. The effectiveness of improvement measures and the level of automation potential only become really tangible with empirical support. Established process models such as "Plan - Do - Check - Act" are given a whole new dynamic with these tools.
Companies avoid certain error patterns by using delegation rules in a workflow management system or by having a software robot take over the process. RPA can also reduce throughput times. Process mining tools, in turn, reveal which tasks RPA can lead to the greatest savings.
Process mining tools also reveal whether automation rules work or possibly lead to additional effort and errors in later process steps. Companies can also use process monitoring to check classic optimization measures for effectiveness and interactions.
Embedded in a continuous improvement process, a digital process management is created that delivers significantly better results than separate optimization projects. The weak points and blind spots of the individual disciplines are eliminated, giving the CFO a more powerful tool in the fight against inefficiencies.