NaReRoKI

RPA and AI applications can identify suitable processes and production or service elements, examine feasibility studies and weigh opportunities and risks.
They can also consider critical factors beyond technology, especially employee acceptance and other stakeholder interests.
Raising awareness and transferring knowledge of and for specialists and managers regarding the opportunities and challenges in the area of RPA, AI and resource efficiency with a focus on environmental sustainability as well as corporate resilience (resistance to crises) form the bracket of the project.

The use of RPA and AI contributes significantly to increasing the productivity of businesses and reducing the environmental footprint, even for small businesses.
This approach also enhances companies' resilience to crises, safeguards jobs and enables them to navigate challenging conditions with greater stability.
Throuhg network meetings and the resulting knowledge exchange, companies will be empowered to identify and implement best practices effectively.
A key component of this project is the implementation of benchmarking to identify potential savings in material resources and energy consumption, allowing companies to learn from one another without the risk of competitive disadvantages.
By leveraging knowledge transfer and benchmarking, individual companies can achieve significant reductions in the resource and energy expenditures.

RPA is of particularly valuable to companies as it enables the automatation of even simple business and administrative processes, thereby enhancing productivity and process efficiency.
For SMEs, the application of artificial intelligence to improve resource efficiency is especially significant. For example, sustainability and operational effectiveness can be signigicantly advanced through the implementation of smart monitoring technologies.
Smart monitoring involves intelligent AI-driven applications for monitoring, analyzing and detecting malfunctions or defects in machines and industrial infrastructures at an early stage.
This approach reduces energy and raw material consumption during productions while extending the lifespan of equiptment, machines, or robots through adaptive maintenance strategies.