Since the MOVE kick off in October 2020, Fraunhofer IML together with a great team of research and industry partners is developing methods and tools, using AI techniques, to empower companies with a sustainable and ongoing optimization of their supply chain configurations. Emerging task models for using AI as well as elaborated procedure models and generic solution patterns are tested directly within enterprises.
The global supply chain network of producing enterprises are becoming more and more complex because of intern and extern factors. While numbers and dependencies of several partners are growing, so are influences and insecurities from the outside of the supply chain. At the same time, requirements are boosting due to increasing cost pressure, rising expectations on on-time delivery, latest trends and highly individualization with simultaneously decreasing product life cycles. Therefore, a reliable forecasting of customer demands as well as the monitoring of demand fluctuation effects in supply chains are essentially in order to meet the challenges. Moreover, the digital transformation of process oriented supply chains induces an increasing amount of data, that can’t be handled with conventional methods.
Using AI methods has the potential to address all those problems with automated analysis, assessment and optimization. In the research project MOVE, practical methods and tools are developed and tested over the time span of three years in a close cooperation with industry partners. The first step is to harness the enormous amount of data so that procedures and methods can be deployed effectively and correctly. Subsequently, companies are to be empowered in implementing AI-methods in their own supply chain networks. The main focus of this research project is in generating and integrating expert knowledge through simulative data -driven and hybrid, practical tools.
In MOVE three fields of action are considered for optimizing supply chain networks with the help of artificial intelligence.
The plan is to structure the experiences out of four pilot projects into a MOVE-task-model and to prepare them in form of solution patterns and process models for transfer. Cooperation with application partners from industry will ensure the practical suitability of the developed methods and tools.
The goal of the MOVE research project is the specification of dynamic effects in supply chain networks and the development of AI-based assistance modules for the optimal orchestration of entities. This includes the description of supply chain networks as well as their underlying computational representation in terms of a digital twin. The specification is the basis for the analysis of typical effects in different levels of detail as well as the automated analysis and optimization with AI methods.
With its Supply Chain Engineering department, Fraunhofer IML has extensive knowledge in application-oriented and practical research on supply chain management - especially in the area of demand forecasting. On the basis of technological instruments such as OTD-NET, which was developed internally by the department, we regularly use service-oriented IT solutions in the context of contract research. These solutions deliver promising results in the context of strategic and tactical planning and operational control, for example in availability management or in demand and capacity management. With the help of these tools, which have already been continuously developed, Fraunhofer IML is providing technical expertise and a broad horizon of expertise for the MOVE research project. By their advising function, our experts have direct influence on the developed methods.