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How Process Mining Can Help Improve Your Order-To-Cash Process

Process mining is a data driven approach to understanding and optimizing the processes within your organization. By automatically extracting, transforming and loading (ETL) system logs, process mining provides an easy way to audit your processes, and uncover process deviations and non-compliance with Standard Operating Procedures (SOPs) for immediate remediation.


For the Finance order-to-cash (O2C) process, delivery time is a key metric that tends to be closely monitored. After all, late deliveries can adversely impact the O2C process in several ways, including:

  • Lower customer satisfaction and loyalty

  • Greater strains on cash flows as invoices are often issued only upon delivery confirmation

  • Delayed revenue recognition affecting financial reporting and forecasting

  • Increased costs arising from expedited shipping, customer compensation, etc

  • Disruption to inventory planning and supply chain management


To illustrate this, have a look at the summary dashboard presented below. A cursory glance is sufficient to inform that late deliveries is indeed a problem with the late delivery rate at an unsatisfactory 36.6% and enduring a recent spike.


To analyze the problem, we can perform a root cause analysis (RCA). RCA refers to the process of discovering the underlying causes of problems in order to formulate appropriate solutions. In process mining, we can choose to perform RCA across multiple dimensions. For example, when we analyze based on distribution channels, we can see that the “sold for resale” channel contributes significantly to the late deliveries issue (note: a large positive percentage indicates a strong contribution).


Of course, we should not simply stop here. We can drill further down into the data, analyzing the impact of other factors like material group and material on late deliveries as well.


For us to better understand the problem, we can also perform a side-by-side comparison of the non-conformant distribution channel (i.e. sold for resale) with a conformant distribution channel (e.g. internet sales). In the visual below, the former is represented by scenario A in orange, while the latter is represented by scenario B in purple. In the middle, you have the process graph which is a visual representation of the order in which events took place based on the data.


It is clear that the non-conformant channel is performing poorly, with 36 process variants as compared to only 7 for the conformant channel. In addition, the average throughput time for “sold for resale” is 7.6 days while that for “internet sales” is a mere 1.8hr. Looking at the process graph, it appears that the additional processing steps for the “sold for resale” channel (e.g. remove SO credit block, change SO item exchange rate, etc) are bottlenecks that could be causing the late deliveries.


Armed with this insight, one has the information needed to engage meaningfully with the relevant stakeholders to reduce the incidence of late deliveries. Some of the actions that can be taken include:

  1. Simplifying the process to eliminate redundant process steps

  2. Redesigning the process to improve the experience of the various stakeholders, as well as the key process metrics

  3. Automating the manual tasks so as to improve throughput time


Hopefully, this provides you with a sneak peak into how process mining can help to your finance teams to improve their O2C process. If you are interested to learn more about process mining and how it can be applied to your own processes, schedule a complimentary consultation with us now!



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