The first step in process improvement is obtaining a true understanding of the current, or "as-is" state of them.
To endeavor to extricate information about day-to-day processes from the enterprise information system is to perform a complex set of manual tasks in order to gather data and synthesize it.
Process mining solves both of these problems by giving companies a hands-off way to gather data about their business processes.
While processes happen, process mining software creates event logs of every step of the process. For example, when an item is sold, when a payment is received, and when a shipment is made, event logs are created for each of these happenings. The detailed capture of every event allows companies to identify when variation occurs — like which specific conditions tend to cause a certain process to get delayed, for example. Using this data, the software creates key performance indicators for any given process, thus allowing us to know exactly where things need to be improved.
RoboPro, with the power of Celonis Systems, can help you learn exactly where your business processes can be improved.
Process mining is the perfect complement to RPA, and that’s why we have chosen to partner with the industry leader in process mining, Celonis. Together with Celonis, we are bringing innovation to what has been a long-standing weakness in most businesses.
We don't suggest forming a traditional-style tiger team or other such classic conventions which often become bogged down in brainstorming sessions or subjective employee interviews. Rather, the initial mandate of this team is simple: select the process mining tool best suited for the business, and start running it. Not sure which tool to choose? This is where Roboco can help! For starters, see our descriptive list of process mining tools (here).
Assessment of potential processes is arguably the most challenging portion of process mining. Thankfully, modern process mining tools and the experts at Roboco can greatly assist in this effort. How does it work? The selected process mining tool will monitor network transactions throughout the organization and generate detailed logs of each transaction. From these logs, suggested visual representations of process flows are generated which can be adjusted by your team until they perfectly match reality.
Next, the process mining software will use visualization components which clarify the real network traffic and interactions. It will allow you to drill down and identify undesired process patterns, bottlenecks, and compliance issues within the organization. The software will make it simple for your team to find inconsistencies in your processes and detect their root causes. It will generally provide a clear representation of the actual process flow compared to the ideal process flow.
Robotic process automation is not the answer for every problem. Your team (or the process mining software) may find that many processes are not mature enough to allow RPA bots to function effectively. You may find that the right solution for a convoluted process is training, rewriting the process, standard automation, or migration to an alternative system. All of these options may correct the process issues without the overhead of integrating RPA. Therefore, your team should consider them prior to settling on RPA as the ideal solution. In cases where bots are ideal, from here you will proceed with selecting, testing and implementing them.
The final step of process mining takes place after implementing the RPA systems. Process mining is a disruptive process, and will incite many changes in current processes within the company. After the new processes are finalized, and even before, it is important to continuously inspect them and compare them to the previous methods. Key process indicators should be established to allow your team to see the benefits (or potential issues) resulting from the implementation.
Down the road, further process and RPA adjustments may be required. It is important to establish the correct monitoring interval for the organization to avoid making changes too quickly and cause a "chasing your tail" situation. Avoid choosing a data timeline that is shorter than either the target processes, or the fixes that you implement. If your data is too limited you may be viewing a symptom of the issue at hand, and missing the true root-cause.
According to the Harvard Business Review, process management and improvement has been one of the few fundamental challenges in business management, and has persisted for the last forty years. Properly managing and improving business processes is often seen as a low-priority issue for executives.
When executives do get around to analyzing and improving their processes, it is often conducted in a highly subjective and time-consuming manner, such as through interviewing employees. Subjective analysis provides little benefit in any context, and these initiatives often fail or have little real impact. This is where process mining can shine.
Process mining involves using an advanced software solution to interface with the various existing systems of a client's business to obtain data-driven and detailed information about the processes used. Process mining is primarily concerned with the as-is or current state of processes—an area often overlooked by managers excited about improved processes and future plans.
It can be thought of as the foundation on which we build the architecture of RPA systems. RPA bots excel at performing highly repetitive and standardized tasks.
However, many business processes that are built around humans are riddled with manual intricacies and variations, and are often poorly documented and poorly understood. Process mining first assists in identifying those processes which are highly standardized already.
These are the low-hanging fruit for RPA, and will provide the quickest return on investment. Next, it helps identify opportunities for improving standardization among the remaining processes, allowing execs to drill down and clearly identify variations, paving the way for standardization and eventual RPA integration.