Process mining in production at the door factory

Supporting production through process transparency and real-time information, and identifying opportunities

Prüm Türenwerke GmbH, a subsidiary of the Swiss company Arbonia AG, manufactures interior doors and door frames, producing up to 25,000 doors per week at its facility in Weinsheim. Due to the complex, multi-stage production process, the goal was to make better use of existing production data and create transparency regarding process flows. To this end, Rothbaum Consulting developed a process mining pilot using Celonis and, in collaboration with production experts, created customized analyses and use cases. This made it possible, among other things, to transparently analyze equipment performance, scrap rates, and dwell and transport times, and to identify specific opportunities for optimization.

Thanks to Rothbaum’s expertise in process mining and operations, we were quickly able to achieve the process transparency that helps us optimize wait times and turnaround times.
Harald Valentin, Head of Production Planning, Prüm Türenwerke GmbH

Uncovering Production Potential Through Process Mining

Challenge

The wide variety of products results in a complex, multi-stage production process in which downtime and transport times significantly exceed the actual processing times. Given the limited analyzability of the available machine data, the potential applications of process mining for creating process transparency should be investigated.

Implementation

Working closely with experts from the production department, we created a Celonis data model and built a digital representation of the production process. Through joint walkthroughs, we identified bottlenecks and developed appropriate use cases. Building on this, we analyzed, among other things, equipment performance, scrap, and idle and transport times.

Results

Using a digital representation of the production process, real-time analyses and production performance dashboards were developed. This made it possible to identify bottlenecks and highlight specific opportunities for optimization, including in areas such as downtime and scrap.

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Digital representation of the entire production process

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Key Areas of Analysis

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Celonis Data Model for the Production Process

Occurrence and timing of loss reports. By monitoring the scrap per system, even the smallest increases can be detected in future and action taken at an early stage to proactively prevent scrap. — By monitoring scrap rates for each piece of equipment, even the smallest increases will be detected in the future, allowing for early action to proactively prevent scrap.

Detailed Results

A valid digital representation of the production process was created based on the existing production data.
Production performance dashboards and master data reconciliation provide transparency into relevant production data and enable quick answers to recurring questions.
We analyzed plant performance, target-actual variance comparisons, and lead times in order to quickly and effectively address recurring issues.
By analyzing dwell and transport times, a use case was also developed for the automatic prioritization of time-sensitive orders.
Scrap monitoring provides transparency regarding scrap reports for each piece of equipment and also enables the automatic detection of increases in scrap.
The insights gained will make it possible in the future to prioritize time-sensitive orders using rules or AI, in order to reduce dwell times and better meet delivery deadlines.

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WHITE PAPER

State-of-the-Art Prozessoptimierung mit Process Mining

Wie funktioniert Process Mining? Welchen Nutzen bringt die Technologie? Und welche konkreten Anwendungsfälle gibt es? Antworten auf diese Fragen erhalten Sie in unserem White Paper zu moderner Prozessoptimierung mit Process Mining.

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