Process Mining

Process Mining can unlock the full potential of finance transformation

Many CFOs are currently involved in transforming the finance function. This movement is based on focusing the finance department on value-adding tasks (i.e. tasks that are important to steer the business) and at the same time using technological opportunities to digitize transactional and lower-value tasks and processes, thus increasing process efficiency and performance. For some CFOs the starting point can be an inefficient status-quo, high process complexity, and oftentimes thousands of process variants for seemingly “standardized” processes. One methodology that can help to tackle that problem – and therefore enable the finance functions’ transformation – is Process Mining.

With Process Mining business processes can be visualized based on existing data, analyzed and comparisons between the as-is-processes and the target processes can be made. Thus, deviations and optimization potentials can be identified and converted into appropriate measures to optimize, digitize, and automate finance processes. In this article, we want to shed light on where and how Process Mining can enable the finance transformation.

Process Mining can be used to identify potential for almost any finance process

The strength of Process Mining lies in its broad versatility. It needs a solid data basis to work properly, which is given in most finance processes using an ERP-system. Hence, Process Mining can be used in almost every finance process. Standardized, high volume processes can be checked for process efficiency and more individual, lower volume processes can be checked for compliance and best-practices. Following we show some examples, where Process Mining has enabled new insights for the finance transformation.

Process Mining can be used for efficiency improvements within the Purchase-to-Pay process. In addition to viewing the execution of singular purchasing processes and related invoices, Process Mining can make the entire invoice processing within a company’s accounts payable division transparent. For example, it can be analyzed how many purchases are made without a purchase order as well as where and why this problem is caused. Based on this information, appropriate steps can be initiated, like integrating new order systems or reviewing the steps necessary to place a purchase order. Not following P2P process definitions can lead to higher costs because the purchasing division normally pools orders to negotiate better prices and payment terms. In addition to that, by using Process Mining the supplier conditions can be reviewed and, therefore, due dates for payments can be leveraged efficiently to improve the Days Payables Outstanding and the Working Capital. Process Mining can also show the status quo in terms of the digitization degree within the P2P process, e.g. by analyzing which percentage of incoming invoices are identified automatically, posted correctly, and approved for payment by a workflow system.

Simultaneously, Process Mining can be applied within the Order-to-Cash process. It supports a detailed analysis of the subprocesses executed by the sales and accounts receivable division. At receipt or sales order level, the value flow through the entire system landscape can be visualized: from registering an order, to creating sales orders, to fulfilling the order in operational processes through to invoicing and accounts receivables management. Based on this, several conclusions for finance processes can be drawn. Oftentimes (and especially in large companies), the amount of process variants is surprising. This can lead to varying process times which are relevant both for the customer experience and the tied up Working Capital. Also, process deficiencies in later stages of the O2C process (e.g. discrepancies in the dunning process, effortful accounts receivables management or incorrect invoices) are oftentimes rooted in early process stages (e.g. placing order in ERP-systems, opening sales orders, creating workplans, etc.) These early-stage deficiencies can be made visible by Process Mining, making a facts-based root-cause-analysis possible.

Process Mining can be used in the monthly closing process. The tool makes it possible to identify bottlenecks by making idle times, unnecessary loops, or capacity exceedances visible. Also, the way how the process flows through the organization can be evaluated and organizational conclusions can be drawn. Patterns of receipt posting can be analyzed with regards to quantity, booking quality (e.g. account transfers), and key dates. Thus, further potential to shorten the closing process or increasing its data quality can be identified.

Finally, Process Mining is a very useful tool in further digitizing the finance department, especially because it facilitates the usage of Robotic Process Automation (RPA; for further information on RPA also see our article at https://www.draxingerlentz.de/blog/202027-rpa.html). Process Mining can evaluate the automation potential of finance processes and check if they fulfill the criteria for an RPA implementation (i.e. how rules-based they are, how standardized they are, and how many process variants there are) and, at the same time, create templates for the RPA configuration. Based on this input, processes can be automated through RPA and the beneficial impact can be evaluated again retrospectively with Process Mining.

Advantages of Process Mining

In contrast to manual process analysis, which is usually based on interviews and other qualitative analysis, Process Mining stands out due to its consistent focus on the data-based analysis of specific events and activities within the execution of processes. Therefore, all insights gained from Process Mining are objective, data-based, and traceable. Oftentimes, this makes the initiation of measures easier and more accepted by the organization. Process Mining techniques can be used wherever individual steps of business-relevant processes are recorded in IT-protocols. Also, Process Mining is distinguished by the high degree of automation compared to classical techniques for the creation of process models.

By extracting information from the operative business, Process Mining methods realistically visualize process flows. Compared with manual techniques, Process Mining has an advantage in speed and accuracy. In addition, process mining is a scalable technology that enables more accurate results and analyses the more data is available.

It improves the process transparency and shows deviations to the target process workflow. Process Mining enables the process users to identify the most common path as well as all potentially unwanted exceptions and loops that cause unnecessary complexity. Therefore, bottlenecks in the process can be identified that slow down the process or even bring it to a complete standstill. Based on this, appropriate and targeted measures can be initiated which should ultimately lead to lower overall process costs and less tied up capacity.

Many Process Mining tools use access to real-time data from several IT-systems to monitor processes continuously. Users can easily create intuitive analysis views to answer specific questions about processes. This enables continuous operational feedback and real-time monitoring. Therefore, it is possible not only to initiate longer-term initiatives based on Process Mining results but also to optimize the day-to-day governance of high-volume processes.

Implementing Process Mining can be a fast and low-effort task

To push the finance transformation forward, CFOs need to gain a detailed insight into the processes in their organization. Process Mining can be a valuable tool to create this understanding and helpful in defining the right measures based on these insights as well as in communicating them to the organization. Based on the results of Process Mining, CFOs can optimize, standardize, automate, and slim down finance processes to create financial and capacitive space to fill out a stronger role in their companies. Process Mining can be an enabler in this initiative if the right strategic and operational conclusion are drawn from it. However, Process Mining only delivers those insights – without the appropriate actions based on these insights, little to no value is created. Nevertheless, easy and fast usability, low cost (if the right provider is chosen), and an already existing database (the ERP-system) make Process Mining an attractive tool to accelerate the finance transformation. We at Draxinger & Lentz propose to start the implementation of Process Mining within a singular pilot process to get quick results and gain experience with the technology. Get all relevant stakeholders together in an initial workshop to define the expectations, the targets and align on a way forward. Then, you can select a provider based on predefined criteria and start implementing in the chosen process. Thus, it is possible to get from the start to first results within a few weeks. Afterwards, you can continuously roll out Process Mining to more and more finance processes to accelerate the finance transformation in your company.

For further information on the use of Process Mining please contact Marco Lotz (marco.lotz@draxingerlentz.de) or Fabian Winckler (fabian.winckler@draxingerlentz.de).