In intralogistics, manufacturing companies are confronted with increasingly volatile sales and procurement markets: The ability to respond to dynamic changes in general conditions and customer requirements is becoming a competitive advantage. In order to master the challenges associated with this, current developments in the field of 'artificial intelligence' promise novel approaches to solutions. The aim of the project is to use appropriate approaches to achieve significant improvements in the fulfillment of the core services of intralogistics: Processes in warehousing, transport and order processing are to be optimized by at least 10 to 15 percent.To this end, methods and tools are being developed with which companies can analyze and optimize their internal logistics. On this basis, pilot projects are being implemented in companies for the production of household appliances, separators and pumps for buildings.

In the intralogistics of manufacturing companies, the focus has so far been on approaches to reduce complexity. Solution approaches such as Lean Production or Six Sigma are used for this purpose. The megatrend of digitization is accompanied by frequent and rapid changes in general conditions and customer requirements. At the same time, a BITKOM survey also rates the increase in flexibility as the aspect with the highest potential as a result of the digitization of production. Surveys such as the Hermes Barometer show, however, that optimizations in logistics, which serve in particular to increase responsiveness and minimize throughput times, are not always successful due to the high level of complexity. Current developments in the field of artificial intelligence offer new approaches to mastering this complexity. Studies by the McKinsey Institute, IDG and the BMWI demonstrate the high potential of artificial intelligence for production and logistics. The results of research projects such as Cypros show that intelligent solutions can reduce the time and effort required in intralogistics by around 27% in the first step. One of the success factors here is better networking and evaluation of data and information, something that is lacking in established methods such as lean production. However, due to the various framework conditions that need to be taken into account, the systematic development of the potential benefits of AI applications in intralogistics is often unclear. This relates both to the possible applications and to the effects of AI applications on the area of tension between people, organization and technology.

The aim of the project is to develop a set of tools for the design of AI-supported intralogistics in manufacturing companies. On the one hand, methods and tools are to be developed that support the analysis of AI potentials in intralogistics, the definition of goals and the implementation of AI applications. On the other hand, concrete AI applications are to be implemented in the form of pilot projects in the application companies. In this context, the activities for the development of methods and the implementation of AI applications are closely interlinked. The central value proposition of the tools to be developed is significant improvements in the fulfillment of the core services of intralogistics (warehousing, transport, order processing) of at least 10-15%. The objective will be translated into a three-stage solution approach. Initially, methods and tools will be developed under the leadership of the research partners (stage 1). To this end, Fraunhofer IEM and Fraunhofer IOSB-INA will contribute their methodological and AI expertise. On this basis, the pilot projects are carried out, in which the companies GEA, Miele and Wilo contribute their intralogistics expertise (stage 2). For the broad transfer of the results, the associated networks contribute their transfer competence in the form of dedicated event formats (stage 3).

In logistics, there is a high degree of standardization across all industries, with comparable challenges in many manufacturing companies in the region as a result. Due to the close integration of the companies involved in the project with other companies in the region, a broad transfer of the results by the project partners will also be possible beyond the project duration and thus be usable for a large number of other companies. In particular, the integration of the network Industrie Ruhr Ost, the leading-edge cluster it's OWL as well as the competence center Mittelstand-Digital-Zentrum Ruhr-OWL ensures that the project results are made available to interested companies. The participating institutes or chairs will also incorporate the findings into university teaching and further multiply them as part of their scientific work.