Artificial intelligence for supply chain management

Global partners, increased cost pressure, greater demands on adherence to deadlines, shorter product life cycles and the trend towards individualisation: the collaboration between companies and their business partners, suppliers and customers as well as the company processes themselves are undergoing change as a result of digitalisation. At the same time, the digital transformation is fuelling the creation of large volumes of data in companies' value networks.

Data analysis using artificial intelligence offers enormous potential for transparent and optimised supply chain management (SCM). The aim of the MOVE research project is to enable companies to utilise new data-driven SCM approaches.

A strong consortium is trialling AI processes, developing methods and tools and validating them as transferable process models and solution patterns for use in companies. A cross-domain specification technique will enable the standardised description of use cases. This allows both the interdependencies in value networks and the underlying IT systems and data sources to be mapped. The project team is also further developing AI processes for the automated analysis and optimisation of value networks. These include, for example, forecasting models and optimisation processes. The focus of Fraunhofer IEM is primarily on tools for rapid implementation in the company: Solution patterns, methods for data inventory and data understanding as well as methods for integrating expert knowledge.

Um nah an den Anforderungen der Industrie zu sein, werden in den Partnerunternehmen Diebold Nixdorf, Phoenix Contact, Hettich und portavice Pilotprojekte umgesetzt, z. B. zur Prognose von Kundenbedarfen oder zur Beobachtung von Auswirkungen von Bedarfsschwankungen entlang der Lieferkette.