KI-LIAS

  Copyright: © Mario Irrmischer

  

 

Artificial Intelligence for Industrial Assistance Systems That Promote Learning

 

Synopsis

The use of artificial intelligence (AI) has already led to changes in how a lot of employees work. Further far-reaching effects can be expected. While AI is already being successfully used for machine automation, working in direct cooperation with employees still poses major challenges.

Challenges

The potentials of AI can only be realized if employees have confidence in and accept the effectiveness and benefits of the corresponding applications. However, insufficient employee participation in the relevant change processes usually leads to mistrust and a lack of acceptance of AI applications. As a result, employees do not use the systems at all or only to a limited extent. This then leads to problems in the quality of the results due to the lack of feedback from the training and introduction phase which in turn leads to increased rejection of the systems.

Goals

The aim of the KI-LIAS project is to develop a strategy for acceptance-promoting application decisions, development, introduction and use of AI-based assistance systems in manufacturing companies. Both small and medium-sized enterprises (SMEs) and large enterprises in the manufacturing industry are target groups. As internal target groups, employees in planning, control and execution of production processes are involved. Taking into account the respective challenges of the employees in SMEs and large companies, processes for the introduction of AI and collaborative AI demonstrators will be designed to sustainably improve work processes and conditions as well as the production of the employees and thus to secure the competitiveness of the manufacturing industry in the long term.

Methodology

At the beginning, the potential for AI applications in manufacturing companies will be identified. Each of the participating companies will be represented by an application for which an AI demonstrator will be developed. In addition to the potential analysis and the associated application decision, the development, introduction and operational use of these AI applications will be accompanied on an ongoing basis. The experimental space thus created will enable the development of an approach for the acceptance-based introduction of AI-based industrial assistance systems.

Funding

The project is funded by the BMAS – Bundesministerium für Arbeit und Soziales (German Federal Ministry of Labour and Social Affairs) – as part of the INQA – Initiative Neue Qualität der Arbeit (New Quality of Work Initiative). The project is supported by the BAuA – Bundesanstalt für Arbeitsschutz und Arbeitsmedizin (Federal Institute for Occupational Safety and Health). Administrative support is provided by the gsub – Gesellschaft für soziale Unternehmensberatung. The authors are responsible for the content of this publication.

Funding code: EXP.01.00018.20

Project Partners

Research Partners

Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University

FIR e.V.

Development Partners

WBA Tooling Academy

Peers Solutions GmbH

Qualification Partners

d-ialogo

Industry Partners

Beiersdorf AG

Belfor DeHaDe GmbH

Horstmann GmbH

Mauser + Co GmbH

Miele & Cie. KG

Scherzinger Pump Technology GmbH & Co KG

Stacke GmbH