If there are two phrases we are hearing much too often today, they are “digital transformation” and “automation.”
Of course, this drastic change was overdue, but the pandemic catapulted it, eliminating any and all excuses for procrastination. If a business has to survive this uncertainty, it has to make digital transformation its key focus, the core of which is automation.
Here’s where the problem comes – all businesses know what to do. But how should they start?
Say hello to process-mining.
And why is Forrester driving this change?
Process-mining is a set of approaches that discovers real processes through event logs, monitors them, and works towards improving them. It is an analytical approach through which knowledge is extracted from the organization’s systems.
Through this process, organizations can detect errors and bottlenecks based on the facts instead of relying on conjectures. Overall, it covers the following:
The process mining software market had a 2022 valuation of $1.13 billion, and it is anticipated to expand from $1.66 billion in 2023 to a substantial $27.72 billion by 2030.
Data mining has been a fad phrase for quite a while now. So, it is easy to confuse process-mining and data mining. In fact, many organizations fail to understand the difference. This is mainly because they are both a part of business intelligence and have quite a few similarities.
The differences, however, are significant.
Process mining is definitely a technique in business intelligence. It is closely related to business process management (BPM) in that it combines data mining with BPM. So, existing data is taken, visualized, and improved.
Process mining combines analysis, control, and improvement of processes.
Once we hear that process mining and BPM are related, this question is bound to arise. You may be confused by the answer, but please allow us to explain.
In short, process mining is not a component of BPM software. But why? Well, that’s because the applications of each are quite different. BPM has existed since the 1970s, while process mining is relatively new. BPM discusses designing and managing operational procedures, while the latter works towards optimizing and redesigning operations through existing data.
There are 3 types of process mining, and we shall explore them in detail now:
In this type, event logs come to the forefront. There’s no prior information, however. A process model is established based on the event logs and algorithms.
Here, real processes and predefined processes are compared to identify similarities and differences. This helps find out deviations and fix them accordingly.
The data of real processes are considered to improve process models. Enhancement process-mining aims to improve existing processes by identifying bottlenecks, inefficiencies, or areas for optimization. It analyzes event logs to uncover opportunities for process improvement.
Extension process mining involves enriching existing process models with additional information, such as performance metrics, resource data, or cost information. It helps gain a more comprehensive understanding of the process and its associated factors.
There are 2 major steps –
These two steps are further broken down into multiple stages.
Santiago Aguirre did a wonderful job of explaining the methodology of process-mining in detail.
When we already have BPM, which is helping organizations transform digitally, do we really need it? How does it benefit organizations? The answer will both surprise and please you.
Some of the most significant benefits of process mining are as follows:
Let’s explore a few use cases to understand the importance of process-mining:
How do you apply process-mining to industries? We have a few examples to help you understand:
One of the most crucial aspects of manufacturing is timely delivery. When a business has multiple factories, delivery cannot rely on one aspect. Many differences come into the picture. Process-mining will help understand the process in every single region, right down to costs, KPIs of the people involved, and the duration. This fact-based analysis will automatically help optimize the process.
Service companies offer higher efficiency and lower costs to their clients through operational excellence. Process mining finds process inefficiencies and helps optimize operations.
Retail business operations are very extensive. They involve logistics, order management, forecasting, supply chain, customer service, etc. Process-mining provides visibility to the entire life cycle and the processes involved in each step. It helps unearth bottlenecks through facts so that they are taken care of immediately.
Rules and regulations are the driving force in the financial sector. Event data helps with process visualization in each step. If there is non-conformance to any rule, process-mining will help find that out along with deviations to the said rule.
Process mining may be relatively new but it is here to stay and optimize business processes. The insights this approach provides through analytics are crucial for growth and efficiency. Begin your journey towards streamlined operations and tailored apps – all with the simplicity of our platform. Get started today to harness the potential of automation.
Process mining is a technique that analyzes data from event logs to understand and improve business processes. It’s important because it helps organizations uncover inefficiencies, bottlenecks, and areas for improvement in their processes, leading to better operational efficiency and cost reduction.
Process mining offers several benefits for businesses. It provides insights into how processes are actually executed, helps identify bottlenecks or compliance issues, enables data-driven decision-making, improves process performance, and enhances overall operational efficiency.
Process mining focuses on uncovering and visualizing real processes, while traditional data analysis often examines summarized data without a process-oriented view. Process mining provides insights into the actual sequence of activities, deviations, and bottlenecks in processes.
Yes, process mining is versatile and applicable across various industries, including healthcare, manufacturing, finance, and logistics. It’s beneficial wherever there are structured processes to analyze and optimize.
A typical process mining project involves data extraction, preprocessing, process discovery, conformance checking, and process improvement. These steps help organizations gain insights, identify inefficiencies, and enhance processes.
Popular process mining tools include Celonis, UiPath Process Mining, and Disco. These tools offer functionalities for data extraction, analysis, and visualization to support process improvement initiatives. Additionally, you can explore Quixy’s Business Process Automation ROI Calculator here for evaluating the potential return on investment when implementing process automation alongside process mining.
Yes, Process-Mining can be synergistically combined with Artificial Intelligence (AI) and Robotic Process Automation (RPA) to optimize and automate business processes effectively. It enables data-driven insights and intelligent decision-making, streamlining operations and enhancing efficiency.
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