Process Mining

An overview of the basics and benefits

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Process Mining explained in simple terms – what it is

Process Mining is about making business processes visible and optimising them on the basis of precise analyses. To do this, event logs are extracted from a company's IT systems and evaluated using special algorithms. The result is a detailed image of the actual process flows – an ‘X-ray’ of the company's processes, so to speak.

Many companies organise elaborate process workshops to gain an overview of the efficiency of internal processes and to optimise them. However, these are characterised by subjective assessments – Process Mining, on the other hand, provides companies with an objective, data-based insight into their processes. With the help of Process Mining, companies can optimise their processes on the basis of facts. In this way, inefficiencies and bottlenecks are uncovered and eliminated.

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What is Process Mining? An overview of the basics

Process Mining transforms unstructured process data into structured data sets that provide valuable information on individual process steps.
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But what is a process in the context of Process Mining? In Process Mining, a process is understood to be a sequence of activities that are carried out in a specific order in order to achieve a defined goal. Each process has a definable beginning and a definable end. Typical processes that Process Mining analyses in companies are, for example, the processing of an order or the processing of an invoice.
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If a process step is executed in an IT system, a corresponding entry is generated in the event log, which defines the process flow in more detail and must contain at least the following data: Case number (Case ID), Activity, Timestamp, If applicable, information on persons, costs, etc.

By linking the individual events via the case number, the entire process flow can be reconstructed. An event log thus acts as a digital footprint of a process.

Event log data can come from a wide range of sources, such as ERP, CRM, shop or service desk systems. The integration of Process Mining in Power BI makes it possible to collect and prepare this data and make it usable for analysis. In this way, the process knowledge hidden in the data can be made visible and used to optimise the respective processes.

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Why is Process Mining important? The explanation

The origins of Process Mining date back to the late 1990s. At that time, Dutch computer scientist Wil van der Aalst was researching a way to analyse event data and extract process models from event logs. Even then, van der Aalst recognised the potential that lies in digital traces. It is not surprising that in the years that followed, the process developed into a separate field of research at the interface of data science and process management.

Process Mining is becoming increasingly important in times of digital transformation. According to a study by IDC, the global data volume will increase to 175 zettabytes by 2025 – ten times the volume of 2016. This data harbours enormous potential for process improvements. However, many companies find it difficult to exploit this potential – yet efficient and agile processes are the key to success in an increasingly networked, data-driven economy.

Process Mining uncovers process flows by analysing event data and makes inefficiencies, bottlenecks and compliance risks visible that often remain undetected with conventional methods. This gives companies an objective, data-based decision-making basis for process optimisations.

In view of the growing competitive pressure and increasing customer expectations, it is more important than ever for companies to continuously optimise their processes. Process Mining offers a powerful, data-driven approach to this. The advantages are obvious: the process leads to greater transparency, lower costs, better performance and ultimately more satisfied customers.

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Our Process Mining tools display for every step in realtime, where outliers currently occur, to correct them before they establish. Improve lead times, costs and quality. Reduce commercial risks through process optimization.

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What are the different levels of Process Mining?

In Process Mining, a distinction is made between three basic stages: Process Discovery, Conformance Checking and Enhancement. Each of these types has its own specific characteristics and areas of application, but at the same time the processes build on each other and complement each other. In combination, they form a powerful approach to data-driven process management.

Process Discovery

Process Discovery is the starting point. The aim is to create a process model from the existing event data (event logs) without the need for a predefined model. The aim is to make the actual process flow visible. To do this, the sequence of activities in the event logs is analysed and a process model is derived. Process Discovery is particularly suitable if no explicit model exists for a process. The process enables an objective, fact-based insight into the reality of the processes.

Conformance Checking

In conformance checking, an existing process model is compared with the actual event data. The aim is to identify deviations between the target process (the model) and the actual process (reality). Conformance checking is used, for example, to check compliance with rules and regulations, to evaluate the quality of process discovery algorithms or to enrich existing process models with additional information. While process discovery initially creates a model, conformance checking requires an existing model for comparison. This can also be created from the variants found in the discovery phase.

Enhancement

Enhancement goes one step further. Here, the focus is on process optimisation. The aim is to expand or improve processes on the basis of event data. Enhancement builds on an existing model and uses insights from event logs to refine and optimise this model step by step.

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In which areas is Process Mining useful?

Process Mining is used in a wide range of business areas. Here are some of the most important use cases with their respective optimisation potential.

Purchasing and procurement (procure-to-pay)

In the area of purchasing and procurement, Process Mining helps to identify inefficiencies and bottlenecks in the procurement process. For example, the analysis of event data can be used to uncover unauthorised orders (maverick buying). Process Mining also optimises the use of discounts. The method shows where discount potential exists and whether it is being utilised. KPIs such as ‘discounts realised’ or ‘discounts missed’ can be easily recorded and monitored.

Order processing and distribution (order-to-cash)

Process Mining can be used to optimise order-to-cash processes. For example, analysing process data makes it possible to identify the reasons for late deliveries or cancellations. The optimisation measures derived from this improve process synchronisation and production planning, which ultimately increases delivery reliability.

Customer service

The process can also reveal optimization potential in customer service. For example, throughput times and first-time resolution rates can be optimized by improving service processes. Thanks to data-driven analysis, companies not only react to customer problems, but proactively implement service improvements – for long-term improved customer loyalty and satisfaction.

Finance and accounting

Process Mining also provides valuable insights in finance and accounting. Inefficiencies in the invoicing and dunning process can be identified, as can compliance violations such as invoices without an order reference. Process optimization can help to minimize payment delays and bad debt losses.

Production and logistics

In the areas of production and logistics, Process Mining can be used to identify and eliminate bottlenecks. Optimized processes can lead to reduced throughput times and transport costs, while at the same time increasing plant productivity and delivery reliability.

IT Service Management

In IT service management, Process Mining helps to analyze faults and service requests and to identify the causes of problems. Optimizing support processes can shorten throughput times and improve first-time resolution rates, which leads to greater IT system stability and user satisfaction.

Human Resources (HR)

Finally, Process Mining can also provide important insights in human resources. Here, Process Mining can contribute to a smooth onboarding. Processes related to employee development can also be optimized using Process Mining.

Process Mining advantages – at a glance

Process Mining Tools are powerful tools – data-based analysis allows companies to uncover potential for improvement and eliminate bottlenecks. Process Mining creates transparency and enables companies to optimise processes in a targeted manner.

Process Mining Implementation in BI-Systems.

Objective, data-based process analysis
Process Mining uses digital traces (event logs) to gain a realistic picture of actual process flows. In contrast to manual methods such as interviews and workshops, which are often characterised by subjective assessments, Process Mining provides objective, fact-based insights. This gives companies a solid basis for decision-making and optimisation.

Visualization of processes with a deviating process.

Identifying inefficiencies and bottlenecks
‍Based on the analysis, inefficiencies and bottlenecks that affect productivity and efficiency can be identified. Process Mining shows where processes are slowing down, where resources are not being optimally utilised and where there is potential for improvement. On this basis, companies can take targeted measures to shorten throughput times, reduce costs and improve process quality.

More customer satisfaction
‍Inefficient processes often lead to delays, errors and poor service quality – with negative consequences for customer satisfaction. Process Mining enables companies to identify and eliminate weaknesses in customer-relevant processes such as order processing or customer service.

Visualization of a process data analysis.

Data-driven decision-making
‍Process Mining provides decision-makers with valuable insights into the performance and efficiency of business processes. By integrating Process Mining with business intelligence, process KPIs can be monitored in real time and deviations can be detected at an early stage. This enables companies to make data-driven decisions and respond quickly to changes.

Visualization of a compliance violation found.

Process improvement
‍Thanks to regular analyses, companies can continuously optimise their processes. Process Mining can thus be used to achieve sustainable increases in efficiency and quality.

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Implementing Process Mining – with process.science

The implementation of Process Mining solutions usually requires several steps. First, the relevant data sources must be identified and connected. These are usually IT systems such as ERP, CRM or SCM, in which the process data is available in the form of event logs. The data is then prepared accordingly and processed by the Process Mining software. The actual process analysis then takes place – process models are created from the event data, visualizing the process flow. The insights gained form the basis for process optimization.

Implementing Process Mining tools is a challenge for many companies. Many solutions require advanced knowledge in the areas of data integration and analysis. process.science's solutions are specifically designed to make getting started with Process Mining as intuitive as possible. Our tools can be seamlessly integrated into business intelligence platforms such as Qlik Sense and Microsoft Power BI. As a company, you can easily use your existing BI infrastructure and integrate Process Mining directly into existing data analysis and reporting processes. Whether you want to integrate Process Mining into Qlik Sense or Power BI, both solutions automatically transfer all relevant event data from the source systems and convert it into interactive process diagrams. Our Process Mining tools can be customized to your requirements and can also process large amounts of data without any problems.

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Process Mining

Process Mining explained in simple terms – what it is

What is Process Mining? An overview of the basics

Why is Process Mining important? The explanation

Start with
process.science now!