Facts instead of gut feeling: Using process mining to understand and optimise processes in any industry based on data

Process Mining adds value in every implementation

In an age when efficiency and rapid adaptability are crucial, Process Mining offers significant and, above all, quickly accessible added value: it enables companies to automatically visualise, analyse and optimise their business processes in detail using digital fingerprints. It transforms data into transparency and uncovers inefficiencies that would otherwise remain hidden.

In this blog post, we explain in more detail how process mining can help – in practically every implementation and regardless of industry or process.

Where traditional BI tools stop, process mining is just getting started.

How business processes were actually modelled in a ‘perfect world scenario’ (target process) and how they actually work in daily reality (actual process) can be two very different things. In fact, deviations are the rule rather than the exception. This is usually reflected in so-called ‘spaghetti diagrams’, which are created when Process Mining reveals the numerous process variants that exist alongside the target process.

At the same time, organisations must strive for operational excellence, whether due to increasing pressure on the cost-income ratio, growing shortages of resources and skilled workers, or ever more extensive compliance requirements. This raises many questions: How often and to what extent do the processes in my organisation deviate from the ideal? Does this affect the effectiveness or quality of the processes? Are there good reasons for the deviations? Are the deviations relevant to compliance or do they pose a business risk? And this is just the tip of the iceberg.

The young discipline of process mining comes into play when answering these questions. Due to its transformative character, it has already become an integral part of the digitalisation of many companies. It makes knowledge from event logs usable and offers deeper levels of analysis than conventional BI technologies. Process mining not only uncovers inefficiencies, but also their causes, and highlights potential improvements for reducing time and costs. It forms the interface between process management and data science and adds data-driven approaches to process optimisation to the toolbox of methods.

The secret: it uses existing data in a targeted way

One key prerequisite for the success of process mining is that, as a general rule, all companies use a variety of IT systems to control their operational processes. In addition to technical information, documents and planning data, most applications also contain detailed process data that documents the use of the software – in other words, data that provides information about when which activity was carried out in an application. An example would be marking an order as paid in an ERP system. This activity and process data, which is often available in the form of so-called ‘event logs’, contains thousands, and in larger companies quickly millions, of data points that can be used for fact-based decisions. Capturing this data is the essential prerequisite for process mining.

As structured collections of activity data, they contain the essential attributes for reconstructing process models. A well-known example is the procurement to pay (P2P) process, which manufacturing companies in particular, but also many other companies in different industries, go through every day:

• Event cases (the individual purchases)
• Activity (individual steps per purchase such as ordering or payment)
• Timestamp for each activity

These attributes enable the three main types of process mining, which can be summarised as follows in terms of input and output:

• a) Discovery: Discovery algorithms can be used to uncover the process models hidden in process and activity data, which are automatically reconstructed from the contained data. This helps to identify the so-called ‘happy path’ - the optimal process - and the variants that exist alongside it.
• b) Conformance checking: Conformance checking makes it possible to compare a previously defined or modelled target process with the actual processes identified from the event logs, to analyse deviations and to determine whether processes are running in accordance with the rules or not.
• c) Enhancement: Finally, the insights gained can be used to adapt, expand and optimise the process models.

Processes can be optimised in a variety of ways. This includes optimising individual process steps, for example, unwanted adjustments or changes, repeated or skipped steps, and incorrect sequences. Other important aspects are process throughput times, their degree of automation and the identification of possible correlations between process variants on the one hand and other recorded attributes such as materials, machines and the like used on the other.

From theory to practice: hands-on project examples of process mining at adesso

The process perspective that process mining enables is particularly helpful in achieving the following goals:

• Transparency of processes, no matter how complex or ramified they are.
• Increased efficiency by identifying bottlenecks and inefficient processes.
• Cost reduction by identifying optimisation potential.
• Data-based decision-making based on actual data, from interventions in ongoing cases to comprehensive process transformations
• Automation, now often even directly with the process mining software
• As well as for competitive advantages that arise from optimised business processes, reduced compliance risks and higher customer satisfaction.

How these theoretical added values have also been realised in practice in different industries is shown by some of our practical projects in different industries and with different processes.

Reducing backlogs at a medical technology company

The group had no transparent overview of customer and supplier orders, which led to many backlogs. Process Mining enabled a holistic process visualisation that includes status overviews, root cause analyses and KPI calculations. The following illustration shows how the Process Mining perspective now supports our customer in analysing and reducing their backlog. This has helped to identify bottlenecks and improve the prioritisation and planning of orders. Within a week, the backlog of orders in arrears was reduced by 65 per cent.

Optimised processing of health claims in the insurance industry

With our self-developed process mining product for insurance providers, customers who use the adesso IT system ‘in|sure Health Claims’ can optimise their processing of health claims (service invoices). This not only helps us to create transparency across all processes, but also to understand why processes take longer and to identify where process optimisations have the greatest leverage (if this has sparked your interest, feel free to visit our application website).

Transforming and automating processes at a bank

The bank's processes were often inefficient, costly and associated with high compliance risks. Bottlenecks were evident but could not be identified and localised due to a lack of transparency. This meant that rapid interaction with customers was not possible. By connecting all key source systems, from the core banking system to CRM and ERP, we were able to use process mining to create complete process transparency, identify bottlenecks and compliance risks, and establish continuous process monitoring. Process quality and profitability increased significantly.

These are just a few examples of the countless possibilities that process mining offers – anyone with processes that leave a digital footprint can benefit:

• Energy industry: analysis of maintenance processes to maximise plant availability and reduce maintenance costs
• Manufacturing companies: analysis of production processes to optimise productivity, reduce downtime and costs, improve material flow and quality, and ensure delivery times.
• Retail: analysis of orders, deliveries and returns to reduce overstocks, wrong deliveries and return rates and improve the shopping experience.
• Telecommunications: Analysis of customer service processes to shorten and accelerate them, thus increasing customer satisfaction.
• Logistics & transport: Analysis and optimisation of warehouse utilisation, improvement of picking and packing processes, and increase of transport efficiency to avoid delivery delays, high transport costs and dissatisfied customers.
• Public administration: analysis of approval procedures to reduce processing times and increase citizen-friendliness.

Whether you are a newcomer or a professional – adesso supports end-to-end

To successfully advance process mining, it is crucial to clearly define the targeted added value. For companies at the beginning of their process mining journey, we recommend an explorative process mining value assessment. This helps to identify, pre-sort and specifically qualify possible use cases. Through a subsequent quantification, we can then determine exactly which added values can be quickly achieved in order to prioritise the implementation of the use cases with the highest, fastest or most strategically relevant added value in relation to the required effort.

Our comprehensive E2E Process Mining service portfolio also offers support in tool selection, use case implementation and employee enablement. We are also happy to advise on setting up your own ‘Process Mining Center of Excellence’ for successful scaling.

Would you like to learn more about exciting topics from the adesso world? Then take a look at our previously published blog posts.