The start of a successful digital transformation is favored by a high level of lean maturity.
Depending on the initial situation of the machine and plant engineering company, companies do not necessarily have to aim to achieve a fully autonomously running Smart Factory. And not every company should actually aim for a Smart Factory – this was the introductory statement to the ProduktionNRW event “Smart Factory” on February 21, 2022.
“Smart Factory” could also mean that self-optimizing processes and machines as well as A Smart Factory would also mean that self-optimizing processes and machines as well as complete autonomy of the factory would be achieved. Therefore, what is more interesting for most companies at this point in time are the first steps towards a Smart Factory. The speakers Alexander Schnichels, Smart Factory Expert from LMX Business Consulting GmbH, and Dr. Christian Pixberg, Managing DirectorMX/IOT from thyssenkrupp Materials IoT GmbH, pointed out the importance of lean management as the basis for the digital transformation process. As always, the motto is: “A bad process is also a bad digital process”.
Six stages of the digital transformation to the smart factory
On the way to a smart factory, different stages or phases of digitization must be taken into account. One approach presented divides the process of digital transformation into six “evolutionary stages”. At the beginning, for example, the following questions should be clarified: Where do we currently stand? Where do we want to go on the path to digitization? What steps do we need to take and what might be the obstacles to transformation? The first two stages are about dealing with the digitization of data. That is, around the collection of data and the subsequent networking of data sources. It is necessary to consider how this data is to be collected – is this done via sensor technology or does manual input make more sense?
The third stage focuses on making information available. Dashboards, for example, can be helpful for visualizing the actual status and the history of the information. The task is to find out what is currently happening in the specific machine or plant. Once this step has been completed, the fourth step is to take a closer look at the “why?”: Why is there a deviation in temperature or pressure, for example – data analysis is used to find correlations and anomalies as well as patterns and trends to understand cause and effect.
The fifth stage involves predicting what might happen – in other words, predictive analytics aims to identify trends and patterns at an early stage. Here, the focus is on the question “What will happen”. To achieve an actual smart factory, in stage six the processes and machines would have to be able to optimize themselves.
Three transformation approaches to the smart factory
Three possible transformation approaches on the way to a smart factory were also discussed during the event:
- the top-down introduction with a focus on technologies.
- the top-down transformation with a focus on the vision or strategy of top management.
- the preferred variant of “bottom-up piloting”. Here, the value stream serves as a guide and the focus is on the process or people. The emphasis is on a “lighthouse with measurable benefits” that inspires all employees involved.
Organizer
The event is offered by ProduktionNRW. ProduktionNRW is the competence network of mechanical engineering and production technology in North Rhine-Westphalia and is implemented by VDMA NRW. ProduktionNRW sees itself as a platform to connect, inform and market companies, institutions and networks among each other and along the value chain. Significant parts of the services provided by ProduktionNRW are funded by the European Regional Development Fund (ERDF).