The digitalisation toolkit combines technological and methodological competencies tailored for small and medium-sized enterprises. In a series of workshops, participants learn the fundamentals of process optimisation and digitalisation through a learning game and prototypical digitalisation of own use cases. Proving the potential for improvement of optimisation measures with concrete measured values is particularly difficult in flexible production environments without it support. This paper presents a technical solution that provides the necessary data for a performance analysis with process mining methods without technical preconditions.
The learning game depicts a traditional drilling machine factory. At different workstations drilling machines are manufactured in different variations. Figure 1 illustrates the manufacturing process of drilling machines the game covers, the process inputs and outputs as well as the work stations the processes are fulfilled at. The process begins with prefabrication of parts till the provisioning of the drilling machines to the customer. The participants take on the role of a worker and are distributed among the works stations.
Starting with a chaotic and complex production, the drill factory is transferred to a lean production system together with the participants. In the course of the training, they learn methods and tools to independently optimise the factory. The game already starts with a running factory. An error-prone production control and a function-oriented layout quickly lead to a chaotic state. Key figures such as delivery reliability or errors illustrate the effects on the profitability of the company. In a second round of the game, the participants themselves propose and implement optimisation measures. Usually, however, these measures do not lead to the desired improvements, sometimes the result even deteriorates. Using an integrated business process model, the trainer finally illustrates the complex control and order flow and the problematic dependencies in the existing layout. Now an optimised model is developed step by step together with the participants and its implementation is realised.
In order to make the success or failure of the optimization measures clear to the participants, it is
essential to underpin the individual game levels with key figures. Thus, the learning game differs from
typical optimization and digitalisation projects in the industry only in its scope. Here, too, it is necessary
to back up measures with facts to enable an evaluation. With the goal to gather process
orientedknowledge, a lot of research in the last decade focused on the application of process mining techniques
in real business process analysis. By analysing event logs of information systems, such as
manufacturing execution systems (MES), enterprise resource planning (ERP) tools or workflow
management systems (WFMS), process mining can deliver useful insight into the actual behaviour of
processes [
The process in Figure 1 is modelled using the Integrated Enterprise Modelling [
This new API for the PA will be used, to track orders along the manufacturing process. It expects
two values, a ReaderID, to identify the AutoID device the API is triggered by, as well as the OrderID,
which identifies the product/product part to manufacture. The PA creates logs for every event triggered
by the API and stores them in a MongoDB [
Digital transformation can only be motivated by concrete benefits. Performance analyses with process mining methods can prove concrete benefits by before/after comparisons. The necessary data is often not available, especially in flexible manufacturing environments such as those found in SMEs. Proven in an educational game, the presented solution offers an easy way to collect the necessary data without technical preconditions.
This research and development project is funded by the european regional development fund. The author is responsible for the contents of this publication.