Author: Ronald Mueller

Process Mining is changing the face of digital transformation. Companies using Process Mining can fully and transparently see all their process resources end-to-end, can see how well or poorly different parts of their processes are being utilized in reality, and can then go about optimizing them in a prioritized ROI-based approach starting with the lowest-hanging fruit. More and more organizations are turning to Process Mining, with the goal of streamlining and clarifying their business processes. But why, and why now? Why in the last several years is Process Mining becoming increasingly crucial for organizations, when other forms of process management have existed for a long time (including earlier versions of process mining itself)? Why is it so different and important that now makes it highly desirable for companies? Businesses have long been using “Business Process Management” when it comes to process understanding and automation and now, “Process Mining” has been thrown into that pool. In this blog we break down Process Mining to its core functions and show why we think it is now necessary and crucial for Business Process Management as a whole to incorporate Process Mining into its thinking.  What Actually Defines Process Mining? Process Mining uses event logs from all enterprise systems that control and define a specific business process. With the information so collected, a company can thoroughly analyze a given process and break it down to the core in terms of all the activities that are actually going on in that process, and the various paths that things are actually taking to get thru the process. It presents to users the actual way that processes work, not the idealized way that process owners may expect and hope for. It has proven to be incredibly useful in finding bottlenecks, finding all the abnormal paths things take thru a process, and other hidden problems with the process, ones that the company typically wouldn’t notice themselves. Or even if they notice some of them, they would not have the quantitative data to know how much of a problem they are, and to fix them well and avoid creating other problems in the process. It is these abnormal process paths that slow down and make inefficient a company’s process, and can cause process costs to skyrocket in many cases. So knowing about these abnormal paths in detail and over time are key to engineers improving their process. Process Mining also provides a visual representation of all the ways things go thru the process (in reality) so the company can see what events are actually occurring and which ones are causing the problems. The company can then use the process information generated by this tool and compare it to their desired process flows. What is Business Process Management (BPM)? Business Process Management is clearly the critical approach in creating the most desirable process for a given company. It focuses on designing, modeling, executing, monitoring, and optimizing a process in order to keep it steady and controlled. With BPM, companies are able to ensure that their processes are designed to be efficient and effective, resulting in a cost-efficient organization. How do Process Mining and BPM differ? The main difference in these two approaches (tools) is in the end-result: BPM provides a map of the ideal process for a company whereas Process Mining gives the actual, real-time process of the company at any stage as they seek to automate and optimize. What started the use of Process Mining in the first place was that processes analyzed in the BPM tool were often being produced via manual steps in the process that were unknown and unexpected in the BPM process design. This not only caused inefficiency within businesses but also resulted in many inaccuracies in the true process’s depiction. How Process Mining helped is that it completely automated process discovery and quickly found the company’s real process at its core. The question is no longer “are these tools the same?” or “which one is better?” but rather “how do both go hand-in-hand?” Process Mining and BPM are both vital in achieving complete process automation. Process Mining can be considered a step of BPM, taking place in between the designing and the modeling of a company’s ideal process. It makes the whole BPM lifecycle so much more efficient. In the chart below, you can see how Process Mining fits right into the steps of BPM and makes it a more powerful automation tool. By breaking down both tools within the entire process automation cycle, we can see that BPM begins by modeling the ideal process and helps the company decide where they want to be whereas Process Mining shows a company exactly where they are at any given point in time by analyzing the process that is actually there. Using both tools together maximizes the best outcome for a company’s process automation initiatives.   Conclusion Although Process Mining is not an automatic component of BPM, it can be highly beneficial in maximizing the efficiency of the end result. The reason Process Mining is considered to be at the peak of interest in process automation technology is because more and more companies are discovering its benefits. It allows for organizations to know the pre-existing conditions of their processes in order to achieve the highest possible end result. This can be likened to how doctors fully examine patients prior to providing any surgery or medication. By doing this, they ensure that they are aware of a patient’s current state and, therefore, make it easier for themselves to come up with the best solution or diagnosis.

Machine Learning (ML) and Artificial Intelligence (AI) are like two sides of the same coin, with artificial intelligence being the underlying technology driving machine learning. Machine learning delivers a huge amount of data, which can be processed using big data analytics and appropriate predictions can be derived upon using AI technology. This is a next step towards transforming the future as human intervention is no longer a necessity. It is possible to setup an error-proof system with this sort of technology, ensuring greater efficiency, productivity and high levels of accuracy.  Traditional legacy IT systems aren’t flexible enough to quickly scale with the ever changing digital economy. Need for greater efficiency and faster decision-making are driving enterprises to bank on modern technologies leveraging a combination of big data analytics, artificial intelligence and mechanical automation. Machine learning and AI form an inevitable force driving innovation across information technology, effectively serving three specific segments: Customer Service and Retention Management – Bots also known as web robots have gone a long way in bringing about greater customer satisfaction. These automated applications perform highly repetitive tasks having the ability to process natural language like a chatterbot program simulating human talk. Bots are currently being used in automatic railway enquiry, banking services, hotel reservations, shopping guides or even scheduling appointments. Such services heighten customer experience resulting in loyalty and most of all retaining the customer base. Business Operations and Process Management – Automation alone can greatly improve the efficiency of business processes and workflows. Redundant processes that involves unnecessary human intervention are taken care off during automation reducing operational overheads. This when combined with AI enables organizations improve operational snags with real-time analytics and extended intelligence being retrofitted onto the system. AI makes sense of the vast volume of redundant data by forecasting useful statics that helps plug process inefficiencies. Larger organizations are focusing their research in AI to reduce maintenance costs by anticipating breakdowns and operational failures. Cyber Security and Risk Management – Ever increasing cyber risks have to a great extent propelled AI research. AI’s ability to quickly track patterns and deviations in vast amounts of data gives it a sort of sixth sense in tracking potential threats. With machine learning around the corner, threats can be continuously monitored in real-time averting any security breaches. This system is already being used in banks to prevent unauthorized access of user credentials and in the IT sector safeguarding business critical data from networking threats and hacking. One obvious question that arises is – are we losing control with AI and machine learning. As always, any new technology comes with pros and cons, as long as they are deployed within the business framework. Same holds well in case of AI and machine learning, with AI suggesting on how to improve operations and mechanical systems learning to generate their own algorithms. In spite of all this the human intervention will play a judicious role in deciding whether to implement such recommendations or forgo it. Take for example, IBM’s supercomputer Watson that’s predicting patient’s condition with a reasonably good accuracy by way of continuous learning. Similarly, IT barons like Amazon, Google, Flickr and Netflix are changing their digital business models by incorporating advanced automation and AI techniques.  We are yet to witness a mechanical revolution powered by AI taking over the modern era. Advanced AI technologies coupled with robotics will find better ways of delivering businesses across the globe with least human intervention. Business organizations can pin their hope on future machine learning models to deliver a shared architecture across businesses. As of now we have only explored the tip of the ice berg with more to emerge. Let’s hope AI will pave the path for a high-tech automated future.

Analytics & RPA is the Key to Major Success RPA is currently one of the most widely successful digital technologies. RPA enables companies to reduce human errors, operate at much higher efficiencies, and increase employee satisfaction by shifting focus from repetitive menial tasks to value-added roles, and in the process further enhancing customer satisfaction and innovation. Companies are now going one step further – towards Intelligent RPA. In many automation journeys, cognitive technologies such as AI, ML, NLP when blended with RPA, bring smart capabilities to existing RPA initiatives and help magnify and enhance the positive outcomes vis-à-vis standard RPA. Analytics is essential to providing RPA users with the measuring tools and KPI visibility necessary to ensure they maximize the value of their automation and understand the quantitative impact of these automation efforts on positive business outcomes. In this blog, we discuss one analytics framework that opens the door to a full understanding of your RPA program – UiPath Insights. If you would like to learn more about UiPath Insights, or about analytics in general in the context of RPA, please contact us, we will be happy to discuss further. UiPath Insights is THE Analytics platform for RPA UiPath Insights is an end-to-end RPA analytics solution that enables users to track, measure and forecast the performance of their entire automation program — so users know the impact it’s having and how to scale to the next level. The basic steps needed to get UiPath Insights up and running include: The chart below shows the basic steps in UiPath Insights implementation, and also shows what you gain for your company once it is in place. With UiPath Insights you can measure the true impacts of RPA on your Processes and your Business Among the most important benefits of UiPath Insights are the following four. The rest of this section briefly describes each benefit and its impact on your RPA automation initiatives. 1.  Start measuring so you can start improving Measure the performance of your RPA operations, including situational awareness of events happening in your operations. You can measure and analyze everything from robot and process performance to individual transactions and exceptions. Start with the robot and process dashboards to get immediate visibility into your RPA operations. See the example below showing a robot performance dashboard. 2.  Calculate RPA’s Impact on Bottom Line Each process in your operations requires a unique set of KPI’s to define success. With UiPath Insights you can customize the way you calculate and align your automation goals to the distinct needs of your business. Start by tracking the time and money saved by each of the automation you have implemented. You can generally do this dashboard right out of the box, with little customization required. See the example of business ROI shown in the chart below. 3.  Forecasting and Anomaly Detection Utilize historical data to forecast future operational states, and ML-based anomaly detection and alerting to get notified about critical events and major milestones. Easily gain new analytical perspectives of your operations with Insights-recommended breakdowns of your existing data. Start by forecasting robot utilization to ensure you are maximizing the benefits of your RPA robots in all your processes. See the example time chart below. 4.  Shareable Reports The value of performance reporting should continue past a set dashboard – it should drive strategic discussions and action across the entire enterprise. With UiPath Insights, you can easily share reports with key stakeholders and process owners. Start by creating a drag-and-drop report based on one of the existing templates provided in the system, and then set it to be shared with the entire team on a regular schedule. Conclusion: Analytics can significantly boost your RPA advantage Analytics is key to measuring and assuring that your RPA automation initiatives yield the best possible results for your company. Contact us to discuss further how we can assist you in implementing an analytics framework, like UiPath Insights, in conjunction with your RPA program.