Last week, we introduced you to the advantages of process mining in manufacturing, the principles of data extraction and the results that can be achieved for your IT landscape even before the implementation. This week, we will focus on the actual implementation and process analysis.
Process Mining – data basis & implementation
Depending on the complexity of the process and the degree of heterogeneity in the corresponding IT landscape, the implementation effort for a process mining solution takes 15 days on average. However, initial results can be expected after just a few days. Afterwards, the established data link is permanently used for continuous analysis and process improvement.
As already described, after the initial process linkage, the next step should be an analysis of the data. This often reveals first findings and potential for improvement. For example, the user can identify missing or outdated master data and the effects of existing system breaks or gaps in the data storage. With a focus on data-based process optimization, this can for example lead to decisions on new IT system solutions.
Automated process analysis with LANA Process Mining & MES data
Data from MES systems are ideally suited for process mining and can often be put to use in just a few steps. MES systems cannot yet perform process analyses on their own, hence process mining offers an ideal extension and integration option. Open interfaces ensure a seamless data exchange. For example, LANA Process Mining is integrated by LANA mAPP (manufacturing app) on the MIP (manufacturing integration platform) of MPDV, an MES provider.
Following the link, the process mining software visualizes the process in all its variants and measures different metrics, such as quantities, durations and deviations. LANA’s advanced functionalities allow for the following use cases:
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Process visualization – full transparency in the actual process
Within a split second, LANA creates a precise picture of the actual processes by visualizing the entire production process at the push of a button. This allows you to directly identify unplanned process sequences such as process loops, rework and bottlenecks.
Automated conformance checking – detect deviations from target
The actual process can directly be compared to a target process. The user uploads a previously defined process model into the tool, or alternatively creates it in the tool using a Camunda integration. The automated conformance checking shows all deviations between actual and target process.
Automated root cause analysis – Improvement approaches made intelligently
As part of LANA’s automated root cause analysis, a machine learning algorithm identifies patterns in the data and thus reveals weaknesses in the processes in the form of specific attributes. These are, for example, locations, product characteristics or responsible departments. These provide meaningful and data-based clues for optimization measures.
In addition to standard performance analyses, explorative analyses can also be easily implemented using a wide range of filter settings. If there are already assumptions about certain weaknesses in the process, Process Mining makes it easy to back up your gut feeling with facts – or refute it. Where there is no assumption, automated the root cause analysis quickly leads to the goal.
Use Case – IT system migration or new introduction
In addition, Process Mining offers the possibility to support and accompany IT system migrations. Before the project start or during the early phase of the migration, process mining can be used to analyze process flows in the system (process discovery). Conformance checking in the old system already uncovers potential bottlenecks and discrepancies. During a migration project, the processes of the test system are displayed and analyzed, thus checking the configuration and standardization of the processes. Thus, it can be seen to what extent the system should be further adjusted to bring the processes closer to the target state. In addition, the achieved key figures can be compared with each other, thus highlighting the added value of the new system.
Efficient realization of continuous improvement with Process Mining
Once the data has been transformed and the IT system connection established, and a continuous data flow exists, Process Mining continuously analyzes the process implementation. In order to check the success of the improvement measures, after a certain period of time the software analyzes the current data and process flows. Hence, it establishes a direct comparison with the previous results.
For this reason, Process Mining is the optimal monitoring system for continuous process improvement and for triggering it again and again – the effects of improvement measures become visible and above all measurable, newly emerging problems in the process are detected directly. Therefore, Process Mining ensures complete process transparency, continuous monitoring and support of strategic decisions.
Process Mining thus offers a simple, effective method to master the challenges of digital transformation and to sustainably advance digital business development.