Understanding & exploiting the benefits of Process Mining

Advantages of Process Mining: unlocking potential

Smooth, well-coordinated processes are crucial for companies' efficiency and competitiveness. However, establishing these processes is often not easy in practice - historically grown processes, scattered data, and a lack of transparency lead to delays, increased costs, and inefficient resource allocation. Many companies lack an objective view of their actual process flows. This means there is no reliable basis for decision-making, which encourages inefficiency. Process Mining counteracts this: the technology uses existing traces from IT systems to shed light on what is actually happening in the process. The method provides objective, data-driven insights, enabling companies to identify potential for optimization. We show you what's behind it and how Process Mining can benefit your company.

What is Process Mining? Basics and advantages explained

All work steps and process activities leave traces. Every order in the ERP system, every customer inquiry in the CRM, and every booking in the financial software is logged in the respective systems with timestamps, processors, and other attributes. These traces, known as event logs, document in detail when which action was performed and, when combined, represent the actual sequence of operational processes.
Process Mining tools use these event logs to gain structured insights from raw data. They analyze the individual events and automatically compile them into a complete, visually representable process model. A key advantage of Process Mining is that the model maps the actual process, including all variants, loops, detours, and deviations. Process Mining is based on the processing of objective data, which makes the method both robust and scalable.

Process-Mining-Techniques

Depending on the objective, different techniques are used in Process Mining. In practice, these usually overlap: While some analyses are primarily aimed at gaining an overview of actual processes, in other cases the focus is on systematically identifying target /actual deviations or improving existing processes in a targeted manner. A basic distinction is made between three main techniques.

Process Discovery: Forms the starting point of an analysis. Process models are automatically generated from event logs stored in IT systems, which reflect the actual status of business processes. The generated process model makes all variants, branches, and repetition loops transparent and creates an objective basis for further optimization.
Conformance checking: Building on the findings from process discovery, conformance checking compares actual process flows with defined target processes or compliance requirements. This technique identifies deviations from the intended process flow and quantifies their frequency and impact. The results of this analysis are particularly valuable for quality assurance and compliance management, which is why Process Mining is used in healthcare, for example.
Process enhancement: While discovery and conformance primarily aim to achieve transparency and comparability, enhancement focuses on the targeted enrichment of existing models with additional information. This is where the numerous advantages of Process Mining come to the fore: optimizations can be implemented, for example, by eliminating bottlenecks, automating manual activities, or redesigning inefficient process steps.

Advantages of Process Mining at a glance

Process Mining offers companies a wide range of advantages that go far beyond traditional methods of process analysis and optimization. Let's take a closer look at the specific advantages of Process Mining.

Process transparency: There is often little transparency about how processes actually run in day-to-day business. This is especially true when multiple systems, departments, or locations are involved. Process Mining provides clarity here by objectively mapping real process flows based on existing data. It creates a common understanding of processes across different departments and hierarchical levels. This makes Process Mining in the construction industry a valuable tool.
Decision making: Process Mining uses existing event logs to generate an objective and detailed picture of actual process flows. This data-driven approach enables decision makers to make informed decisions based on facts. They can see exactly where bottlenecks occur, which process variants occur most frequently, and how changes affect the entire process flow.
Process monitoring and improvement: Process Mining monitors business processes in real time. This enables companies to respond quickly to changes in their processes. In addition, the effects of process optimizations can be measured immediately and adjusted as needed. Process Mining creates the foundation for an agile and adaptive process landscape that can be flexibly adapted.
Increased efficiency: Inefficient process steps are often invisible to the naked eye. Process Mining identifies such weak points based on data. The analysis shows where delays occur, how often rework is necessary, and which process variants are particularly resource-intensive. Based on this, companies can take targeted measures to streamline processes, shorten throughput times, and make better use of capacities.
Compliance and risk management: Compliance with defined processes is of central importance, especially in regulated industries. Process Mining supports this by comparing all actual processes with applicable requirements and automatically identifying deviations. This means that Process Mining also creates tangible added value for insurance companies and other industries where compliance is important.

Disadvantages of Process Mining – where does the technology reach its limits

Despite its numerous advantages, Process Mining sometimes reaches its limits in practical implementation. A key challenge lies in technical integration. Especially in historically grown IT landscapes, where different ERP, CRM, or MES systems are used in parallel, data connection can prove to be time-consuming. Extracting, transforming, and merging this heterogeneous data can be a considerable undertaking and usually requires close cooperation between different departments.
Another often underestimated aspect is data quality. Process Mining is only as good as the data it collects, and not all systems log the relevant process steps in the required depth. In such cases, an incomplete picture emerges, which can lead to distorted or misleading process models and misinterpretations.

Exploit the advantages of Process Mining – with Process.Science

Despite the disadvantages mentioned above, companies can tap into enormous potential with Process Mining. At Process.Science, we deliberately make access to Process Mining low-threshold: our solution can be seamlessly integrated into existing business intelligence platforms such as Microsoft Power BI or Qlik Sense without additional infrastructure. Thanks to its straightforward implementation, you can achieve initial results within a few days. We also provide support in terms of data quality, helping you to visualize and systematically improve the quality of your data. With us, you can quickly reap the benefits of Process Mining without the typical hurdles.