Investigation of the Autonomous and Self-learning Management of Customer Requirement Changes in Logistics

• Applicant: Professor Dr.-Ing. Yilmaz Uygun
• Subject Area: Production Systems, Operations Management, Quality Management and Factory Planning
• Term: since 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 525282741

Logistics customer requirements are product-related specifications with regard to transport, handling and storage that are issued by customers and communicated to their suppliers. Customers submit these hardly standardized documents to their suppliers and request the quick implementation of the specifications listed there. These customer requirements and their updates are becoming more and more complex and unstructured. One approach is to improve this process with the help of machine learning. Although machine learning algorithms are viewed as tools for automating complex decision-making and problem-solving tasks, there are still no examples of machine learning applications in requirements management in manufacturing. This topic has yet not been fully explored. In particular, the possibility and the extent of the support of machine learning for these processes must be scientifically evaluated. It must be shown how this process can be mastered with the help of currently available machine learning methods with the claim to general validity. The overarching research goal is the realization of the autonomous evaluation and response to customer change requests to products and services with a constantly increasing accuracy with regard to logistics-related requirements using the example of two main industries (automotive & mechanical engineering). The aim is to gain scientific knowledge regarding the possibilities, the extent and the limits of (1) the usability of the database available today and further data requirements, (2) the properties and special features of both industries in order processing and their influence on changes in customer requirements, (3) standardization of the processes of the management of adjustments of customer requirements with regard to their automatability, (4) the automated recognition of desired adjustments by means of investigating the applicability of methods of machine learning to the management of customer requirement changes, (5) the standardization of customer requirement change requests in clearly defined requirement categories, (6) the anticipation of reaction to change requests as well as (7) the self-learning process for assigning and responding to change requests.

• DFG Programme: Research Grants