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Process Mining: The Future of Process Optimization

What is process mining?
Example for sustainable process optimization
Advantages
Process mining step by step
Prerequisites
A glimpse into the future
Links

Process Mining is essential for the analysis of business processes: According to a study by IDG Research Services, 62 percent of all companies want to improve existing processes using the digital analysis method.

Especially in the course of the digitization strategies of companies, Process Mining plays a crucial role. Why? Quite simple: Process Mining goes beyond classical Process Management, the so-called BPM (Business Process Management). BPM provides a holistic overview of business processes. In contrast, Process Mining deepens into individual processes and details. Only when companies have gained a detailed insight into existing individual processes can they implement solutions for optimization and, for example, create an error-free basis for automated technologies. The created transparency facilitates digitization, also in the form of Robotic Process Automation (RPA).


In this article we discuss the advantages of Process Mining technology, explain how it is used in practice and explain what the requirements are for successful use of the technology.

What is Process Mining?

Process Mining is a Process Management technique that allows companies to optimize individual processes sustainably by means of fully automated preparation.


Compared to conventional methods of Process Management, Process Mining is able to analyse previously unexplored regions of the process landscape. The strong point of this technology lies in the identification and elimination of extreme cases, outliers, loops and inefficiencies in individual process flows, which have so far been lost or cannot be explained in statistically averaged KPIs.


Process Mining is to be understood as a kind of X-ray image of real process data, in which a large number of process variations are recorded. Classical analysis methods cannot identify these variations. Process Mining technology enables companies, regardless of their industry, to present their own operating processes in a completely transparent way. This transparency makes it possible to identify deviations from the intended processes and to develop suitable measures for optimization. In the further course of the process, companies can then continuously check and improve these optimization measures with regard to their effect.

ArvatoSystems_Medien_BPM_Process-Mining_Screen_apromore

An example of Process Mining based on a use case of BIC Process Mining by apromore


Among the classic users of the technology are primarily large and medium-sized companies from a wide range of industries. Especially in the context of companies' digitization strategies, Process Mining is a key technology for their success.

The Process Mining Technology Can

Map fully automated and visualise complex process paths
Find deviations and bottlenecks in business processes
Identify weak points and inefficient processes
Detect possible errors or fraud in business processes
Continuously check the effect of optimisation measures
Create automated reports via e.g. Excel and PowerPoint

Process Mining: An Example for Sustainable Process Optimization

The basic idea of Process Mining can best be illustrated with a practical example:


Due to frequent complaints on the phone, a renowned supplier of custom-made bicycles found out that the promised delivery times had repeatedly not been kept for several months. An exact analysis had to be carried out as the responsible persons could not explain why this was the case.

The first end-to-end review based on the processes modelled in BPM - from ordering to delivery - did not show any abnormalities. The processes of the respective departments involved had always been completed within the specified time frame.


The processes had to be examined more thoroughly, which led to the use of Process Mining software. The order-to-cash process from the SAP system served as the data basis. With the Process Mining tool it was possible to separate the data of the slow process instances from the normal ones and compare them in a process simulation. This revealed a significant deviation in the lead time between completion of the frame and installation of the suspension fork. When further focussing within the Process Mining Tool, it turned out that the delay, regardless of the type of bicycle frame, only occurred with purchased suspension forks from a recently added manufacturer. The analysis of the affected order process revealed that, contrary to expectations, the automatic order confirmation did not arrive immediately, but always on Mondays. This clearly indicated a problem in the IT interface.


 With this information, the IT department was able to quickly identify the actual problem - an incorrectly configured batch run. Instead of directly triggering the order, the orders were first collected and sent on Monday. This delayed the delivery of the suspension fork and therefore also the assembly, which ultimately delayed the delivery to the customer.


Our conclusion: By analysing the causal chain with Process Mining, the problem could be narrowed down, found and solved quickly. The delivery dates were met again. As a further measure from the lessons learned, the IT interfaces were assigned to the business processes, so that deviations from the target can be seen more quickly in the future.

Process Mining Offers These Advantages for Companies

The overriding goal of the data-based evaluation of digital traces is to sustainably improve processes in the operational area of the company. The method is particularly useful when conventional techniques do not allow a formal description of the process because a suitable data basis is missing.


The advantages of Process Mining at a glance:

  • Reduce business costs in the long term by identifying and eliminating weak points and bottlenecks in process steps
  • Understand and optimize key parameters of business processes
  • Eliminate subjective opinions and assumptions and replace them with fact-based findings
  • Creating a well-founded, data-based basis for decision-making processes

Application in Practice: How Process Mining Works

The concrete deployment of Process Mining methods in practice always depends on the technical status of the company. In the following we explain how you can deploy Process Mining in practice step by step:

  • Step 1: Which processes should be analysed?

    The first step is to define specific business processes for further analysis. It is recommended to prioritize processes with obvious weaknesses and potentials. An example: The KPIs of a medium-sized company indicate that invoices are paid late and negotiated discounts are not taken advantage of as a result. In this case, it is of course useful to look at the order-to-cash process from SAP and analyze the release and posting of invoices in a targeted manner.

  • Step 2: The data basis

    The basis for every Process Mining tool is the existing database. Many companies do not even know to what extent they collect data digitally in their business processes. For example, invoices in the purchasing department are often received by post and processed manually - but in the end the data is usually stored in a tool such as SAP and is thus available for digital evaluation. The second step is therefore to collect the data basis for the business process to be analyzed, with support from Arvato Systems. Especially in the complex SAP environment we provide so-called extractors and connectors to the Process Mining tool through our SAP expertise, so that a data basis can be easily extracted for later analysis.

  • Step 3: Read in data and create processes

    In this step the visualization of the business processes takes place. The software imports the existing database, recognizes the coherences and visualizes the process data in a model. A representation of this scope, which the software achieves in a few seconds, can hardly be realized with conventional methods.


    Tip: If your company is not yet experienced in Process Mining, individual or several steps can be outsourced to an external service provider (outsourcing).

  • Step 4: Analysis of causes

    In the fourth step - the analysis of causes - you will also be supported by the software. The program, for example, displays loops, bottlenecks and outliers in a targeted manner. Different process views, with focus on the duration or number of cases, allow you to quickly find the right perspective. With the numerous filter and analysis options, you can now limit the displayed process to specific time periods, customers and industries or even resources. The filtered data can easily be stored as a separate process and compared with each other, for example in a process simulation. An optimized target model can also be created or uploaded directly, which can be compared with the actual model collected in step 3.

  • Step 5: Derive optimisation measures

    In the fifth and final step, the process knowledge gained is used to derive and implement suitable measures for optimization. At this point it is important to continuously check the effectiveness of the measures subsequently and to optimize the measures themselves if necessary.

The Prerequisites for Using Process Mining Successfully

Every Process Mining software works according to the same principle in its basic function. It brings process activities into a logical sequence by means of a time stamp and assigns them to the process instances. The processes can also be enriched with further attributes, such as customers, industries, resources, etc.


This means that the IT systems and databases used must record these log data. Typical source systems for Process Mining include systems such as Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES) or Supply Chain Management (SCM).

A Glimpse Into the Future: Big Data and Digitization

Unstoppably forward: The digitalization of business processes will continue to advance in the future and fundamentally change the way we work. We are convinced that data-based analytics-methods, Process Mining and Process Automation will be key technologies for companies' digitization strategy.


The trend towards digital processes and Big Data technologies represents an important success factor for companies in all industries to continuously improve their own business model. Process Mining is ideally suited to present Big Data in clearly visualized processes and thus allows the precise reconstruction of business processes in a dimension never seen before.


We at Arvato Systems would like to accompany you on this path and support your company efficiently in the optimal transformation of processes and IT.

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Your Contacts for Business Process Optimization

MA_Daniel_Heer_Media
Daniel Heer
Expert for Business Process Management
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Witali Glazyrin
Presales and Partner Management