Process optimization in logistics & production with process mining

State-of-the-art process optimization in logistics and production with process mining

Efficient and perfectly coordinated core processes are one of the most important success factors for companies. Only if the processes work can it be ensured that the available resources are used optimally, customer expectations are met and costs are minimized. Process optimization is therefore the key issue when it comes to ensuring the long-term success of a company. You have probably already invested a lot of time in process optimization. And you probably also know how the most important processes in your company should run – but do you also know how your processes really run?

Why process mining for process optimization?

In today’s increasingly complex world, it is difficult to gain a holistic overview of a process. In larger companies in particular, where even internal departmental processes are handled by several sub-managers, there is often a lack of an accurate picture of the quality of process compliance and, above all, a lack of transparency regarding inefficiencies. The more complex and varied a process becomes, the less transparent it usually is.

Companies have long been trying to optimize their processes using various techniques – usually by laboriously recording the current processes via interviews and observations and generating the actual state based on a few case studies. This procedure is not only time-consuming and expensive, but also only creates the appearance of transparency. And this is precisely where process mining comes in. Nowadays, practically all processes are supported by different systems such as ERP, MES or WMS. Every process run leaves unchangeable traces in the form of data in the system. In the following article, we explain how process mining makes use of this data to create complete process transparency and reveal unexpected optimization opportunities – without spending a lot of time.

Data can be used to analyze and improve a process objectively, honestly and holistically.
Dr. Clemens Wolf, Manager Digital Operations

What is process mining?

Process mining is a modern, data-driven technology for the analysis, monitoring, automation and optimization of operational business processes. The technology is based on the traces that each process run leaves behind in the form of data entries in modern operational systems (ERP, MES, WMS, PPS, etc.). The process mining systems connect to these source systems, extract the required data and create a digital image of the process in the form of a data model. If necessary, information from different systems is also combined in a data model so that the process mining system becomes a unique platform for cross-system analyses.

Based on the digital image of the process, comprehensive analyses can be generated quickly and easily on the process mining platform. This allows the process to be formally examined and provides comprehensive transparency about inefficiencies and process deviations. For many standard use cases, common process mining software providers such as Celonis already supply standard apps that can be adapted to any company with little effort. This means that the desired process transparency can often be achieved after a short time.

Visualization of how process mining works

Process mining as a powerful tool for process optimization and automation

However, process mining is more than just another method for generating process transparency. The comprehensive data integration and connection to the source systems also make the technology a powerful tool for process automation and optimization. Using the intuitive interfaces, the results of the analyses can be used directly without extensive programming knowledge, for example to automate work steps. The ability to link data from different source systems as well as to take multi-layered dependencies into account makes it possible to map even complex relationships using automation, which cannot be implemented with the common standard solutions of source system providers (e.g. ERP or WMS) using robotic process automation (RPA).

The process mining platforms also offer the possibility of making either rule-based or AI-supported decisions or predictions. For example, during a current process run in production, the expected end of production can be determined in real time, taking into account the product characteristics, in order to influence the process via (partially) automated prioritization. By means of the data pipelines that have been set up, the analysis is continuously continued and thus enables sustainable and long-term benefits. With this process monitoring option, the software closes the loop for continuous process improvement.

The range of topics for which process mining can be used is constantly growing.
Dr. Clemens Wolf, Manager Digital Operations

White Paper

You can download the full version of this article here. You can also find out more about software and process selection, data requirements and the use of connectors and apps as a turbo for process optimization.

Use cases of process mining in operations

In the early days, process mining was mainly used in the area of indirect processes, particularly in the financial sector. As these processes are similar in almost all companies, standard transformations and applications could be developed quickly. This increased the speed of the projects and anchored the success of the technology.

The case is different for direct processes in operations: these core processes of companies, where the actual value creation takes place, are very different because the production processes are different. This is why the technology had a hard time establishing itself here for a long time.

Thanks to the further development of the software into a platform solution in which individual data models can be created quickly, process mining has also arrived in operations. Consultancy firms such as Rothbaum have the necessary understanding of operations processes and at the same time specialize in examining complex processes with process mining. The range of topics in which process mining is already being used successfully and has shown significant added value is already large and continues to grow. We present a small selection of the possibilities below:

Examples of use cases for process mining in operations

Process mining for process optimization in production

As diverse as the production processes are, so are the possible applications, for example through:

Improve delivery reliability by automatically prioritizing time-critical orders with dynamic queue optimization.
Reduction of buffer sizes and waiting times by reflecting back to production planning what the current plant utilization is and showing exactly what effects planning changes have.
Improving quality by analyzing and monitoring the effects of process parameters and processing results in interlinked, automated systems on product quality in order to identify and counteract problems at an early stage.

Process mining for process optimization in logistics, supply chain and procurement

Delivery reliability and transparency can also be improved, complexity reduced and costs and working capital saved in the indirect areas surrounding production by, for example

causes of late deliveries in complex production networks can be identified and rectified.
The number of redirections and incorrect shipments can be reduced, which also improves the CO2 balance.
Supplier performance and master data can be intelligently monitored and automatically adjusted.
Complex ordering processes are automated, freeing up capacity for your specialists.
Transparency in the cost structure: Use Process Mining to determine the actual production costs incurred on an order basis and improve your order costing.

Process mining in order processing

Fast and reliable order processing increases customer satisfaction and reduces costs. Process mining can be used here, for example:

Early and more comprehensive information is provided, e.g. on changes to delivery dates, and the customer or the department involved is informed in good time in the event of changes to planning data.
The entire end-to-end process can be monitored and, for example, outstanding billing blocks can be pointed out or automatically removed directly. An optimized sequence of process steps during order release can also reduce additional work and speed up the process.

Process mining in system migration (e.g. ERP system):

The system landscape of companies is subject to constant change. A precise understanding of processes is key to always being able to react to changes and quickly modify systems. With process mining, for example:

The scope and costs can be controlled by determining the need for workshops, surveys and time studies through automatic process documentation based on the system data.
The risk is minimized by avoiding common errors thanks to an objective view of all processes. Tailor-made solutions help to evaluate performance, measure suitability and prioritize tests.
Value creation is ensured by monitoring user adaptation and issuing early warnings in the event of undesirable developments.

The use cases described here only cover a small part of the possibilities that are possible with the technology. If you are facing a challenge in the operational area, process mining can certainly help. Seek advice to understand how process mining can optimize your process and solve your challenge.

Standard procedure for process mining projects

Six phases to project completion

Our proven process mining project approach

Understanding the process: In the first step, it is crucial to define the process and the specific requirements together with the process experts. This can be done using process diagrams, holding workshops or carrying out on-site visits.
Data understanding: During the development of process understanding, a bridge is always built to the world of data in order to determine the data requirements and develop the necessary data understanding.
Data preparation: Different data formats, poor data quality or system complexity make it necessary to prepare the data. The result of this phase should be a suitable data model for process modeling and process analysis.
Modeling: Based on the data model, the analysis can begin, whereby this phase is very closely linked to data preparation. Based on the evolving process model, analyses are developed and prepared for discussion with the process experts.
Evaluation: Process modeling is used to verify assumed correlations as well as to discover new, purely data-driven correlations. It is important to work closely with the process experts, who validate the model and the results with their technical know-how and expert knowledge. This also makes it possible to identify practical and effective options for automation and optimization.
Integration: As the motivation behind a process mining project is not to create new dashboards but to actively improve the process, the knowledge gained must ultimately be converted into added value. Therefore, the optimizations – whether platform-supported or classic – as well as the automations are ultimately implemented. Active monitoring can ultimately ensure the sustainable benefits of the technology.

White Paper

Find out more about what needs to be considered when selecting processes for process mining, what requirements the data should meet and how connectors and apps can be used effectively.

Summary

Process mining is currently becoming the standard procedure when it comes to optimizing indirect and direct business processes. Fast implementation using apps and standard connectors, 360° transparency thanks to the consideration of all process flows from standard to exotic and the possibility of active improvement through optimization and automation via the software’s EMS functionalities make the technology a powerful tool. Process mining does not replace traditional process optimization, it complements and completes the approach. Therefore, process understanding also plays a decisive role in a process mining project – only with the right partners and process experts will you be able to make the best possible use of the technology.

Digital Operations

Our approach to process mining projects

Find out more about our expertise and our approach to a process mining project: from readiness assessment, proof of value and implementation through to automation and machine learning.

Learn more

Dr. Clemens Wolf

Manager Digital Operations, Frankfurt

He is responsible for the Digital Operations business area at Rothbaum. His goal is to drive digitalization forward with our customers from the manufacturing industry and to support operations with modern technologies and system solutions.

Philipp Kappus

Senior Manager, Frankfurt

Philipp Kappus has gained in-depth practical experience in factory, production and machine planning at a large number of companies. The industrial engineer has headed the Rothbaum office in Frankfurt since 2019.