Learn more about process mining
by David Sizer and Maximilian Holsman
Today's business landscape is riddled with turbulence and uncertainty caused by volatile external factors that can impact efficiency and the bottom line. By understanding and optimizing processes, businesses can adapt and thrive. However, the traditional approach to process discovery and improvement relies on subjective interviews and workshops, making it time-intensive, invasive, costly, and, most importantly, inaccurate. Even in the best-case scenarios, the traditional approach can only result in one-time improvements as it can’t measure the impact of process improvements on key performance indicators (KPIs), leaving the business’ performance susceptible to changing conditions.
Process mining offers a data-driven, efficient, and accurate approach to process discovery and optimization that reveals business processes according to data, not human perception. It enables businesses to implement improvements that optimize their processes and performance while quantitatively measuring the impact these improvements have on KPIs. By enhancing businesses’ operational resilience through continuous process optimization and enabling impactful synergies, process mining is revolutionizing the future of business process management across all industries. This methodology has the power to transform businesses and change the world–and it is the keystone to Slalom’s process optimization approach.
Processes are the heart of any business: it’s how things get done, how inputs become outputs, and how value is created. Any given business process comprises individual business cases, such as individual customer orders or invoice requests, that move through the stages of the process from creation until completion. At each stage of the process, a case leaves behind a piece of data in the underlying enterprise systems called an event. An instance of event data specifies at least three pieces of information: case identifier, timestamp, and process stage. For some enterprise systems, event data can also include information such as the size of the associated customer order.
As each case moves through the process, the event data is recorded in the system’s event log. Thus, an accurate picture of the entire business process is hidden within the collective event log data. Process mining software takes these system event logs as input and uses them to reveal this picture, producing a model of the business process in all its complexity.
So, why should business leaders care about process mining? What insight can it reveal that isn’t already provided by the people involved in a process?
Process mining thrives at the pain points of traditional process analysis. Because it produces a model that relies solely on the event log data, process mining captures the reality of a process instead of depending on the subjective and biased views of employees or managers. By taking the subjective side out of process discovery, more quantitative and metric-driven learnings are derived from the pain points. Process mining also accounts for every case recorded in the event log, capturing all variances and exceptions within the process and not just the most common or best-case “happy path”. With this in-depth, end-to-end understanding of their processes, businesses can identify inefficiencies that would otherwise remain hidden to optimize their operations.
Partnering with Slalom, a manufacturing client used process mining to uncover inefficiency in the client’s accounts payable process–resulting in $22 million in wasted spending identified.
In addition, because new event log data can be continuously fed into the software, process mining can continuously monitor processes as they change. This means that, unlike the non-iterative process snapshot of the traditional approach, process mining produces a dynamic model that evolves with the business landscape. Through this continuous monitoring, process mining enhances a business’ operational resilience with the tools to quickly identify and react to the changing business environment.
A consumer goods client saw a 20% reduction of credit holds after Slalom implemented process mining to optimize their order-to-cash process.
As most business processes exist within enterprise systems, process mining can broadly apply to any process and industry. From simple process discovery to complex process enhancement, the wide range of use cases for process mining include optimizing the order-to-delivery process, maximizing on-time order management, increasing free cash flow, reducing incorrect payments, and countless more, making it an invaluable tool.
Process mining is a transformative technology, and its power is further amplified by other business technologies.
Task mining captures the parts of a business process that take place outside enterprise applications and does not leave behind event log data. Using optical character recognition (OCR), natural language processing (NPL), and machine learning algorithms, task mining tools capture desktop-level user actions, such as mouse clicks, keystrokes, or application logins. This desktop-level data is analyzed to understand the tasks users are completing across applications and is used to identify key processes. Combined with process mining, task mining can produce a comprehensive, end-to-end view of entire business processes, including the parts that take place locally on desktops, giving businesses even deeper insights.
Robotic process automation (RPA) refers to software tools that automate repetitive, manual tasks, freeing up employees to focus on value-adding tasks. Often, the difficulty of applying RPA is identifying routines and workflows to automate effectively to create long-term sustainable value. Process mining enables businesses to discover routines that can be automated for the greatest impact and monitor performance, making it easy to evaluate RPA initiatives and determine their ROI. According to process mining provider QPR, leveraging process mining during the implementation stage of RPA initiatives leads to a 40% increase in RPA business value while decreasing implementation time and project risk by 50% and 60%, respectively.
Machine learning (ML) refers to statistical models and algorithms that learn from past data. Because ML models thrive on large data sets, it pairs well with process mining to leverage collected event log data to make decisions and predictions about the process. Diagnostic process mining can identify a problem or bottleneck by analyzing the event log data to find correlations, and predictive process mining can use past event log data to make predictions about the future state of a process. These ML methodologies enable businesses to intervene and prevent adverse outcomes. Additionally, once predictive process mining has identified likely future problems, prescriptive process mining can explore a large set of possible process changes to find the best courses of action to optimize costs and process performance.
The data-driven approach of process mining resolves pain points in ways that traditional methodologies fall short. By enabling businesses to understand the reality of their processes and giving them the insight they need to optimize, process mining is fundamentally changing how companies operate and thrive in turbulent conditions.
To find out more about how process mining can help your company dream bigger, move faster, and build better tomorrows for all, fill out this form and we will be in touch!
Sources
AIMultiple, Top 6 Application of Machine Learning in Process Mining
Apromore, Process Mining in 2021 and Beyond
Celonis, What is process mining?, How does process mining work?, What is Task Mining?
Gartner, 2021 Market Guide for Process Mining
IEEE Task Force on Process Mining, Process Mining Manifesto
QPR, QPR Process Analyzer