How LANA’s Explorative Analysis Optimizes Production
Which bottlenecks disrupt my logistics? Which machines often lead to production errors? How much savings potential does my purchasing show? How compliant are my business processes with the general Service Level Agreement? These and countless other questions drive the success stories in Process Mining. Because a precise question is the first step towards sustainable improvement. This aspect is so central to Process Mining projects that we have included it as one of the fundamental parts of an analysis in our own Process Mining Guide.
But what if you haven’t even been able to develop such precise questions? Isn’t the purpose of Process Mining to shed light on the confusion of data and processes in the first place?
Correct! In cases where no concrete questions exist at the beginning of the analysis – for example due to a lack of process transparency – we use an alternative method: explorative analysis.
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Altronic and Transparency in Production
Altronic LLC, HOERBIGER Engine Division, is a US manufacturer of innovative ignition technology. Altronic manufactures performance-determining components and control technology for industrial engines. From the very beginning, the company was fully aware that optimized processes represent an immense competitive advantage, especially in the manufacturing industry. However, it initially remained unclear which aspects of the production process should best be analyzed.
With LANA Process Mining, Altronic has been able to start an explorative analysis. In concrete terms, this means that the software has processed hundreds of thousands of data records from production in order to first develop a detailed, holistic mapping of the current status. This foundation enabled Altronic for the first time to gain in-depth insight into every detail of their process flows. In the next step, the company developed more focused fields of analysis.
Focus Area: Faster Process Times
The method is called explorative analysis because it allows users to develop questions and hypotheses even during the analysis. The comprehensively visualized virtual process model in LANA provides the perfect basis for this.
The duration of individual process cycles has proven to be a particularly relevant risk area for Altronic. A comparison between the actual state of production and the target process shows that a number of process variants have above-average throughput times. Altronic therefore used LANA’s Machine-Learning-supported Root Cause Analysis to automatically find the causes of the observed delays within seconds.
And indeed, by optimizing their Material Resource Planning (MRP) system and avoiding unnecessary rework and repetitive steps, Altronic has seen a significant improvement in production process performance. Production is faster and has less variance, which conserves resources and makes processing orders more efficient. This is reflected not only in customer satisfaction, but also directly as a competitive advantage for Altronic.