Die Optimierung der Lagerprozesse durch die Integration von Ki-Technologien

Abstract

Due to increasing customer expectations, continuous technological advancements, and labor shortages in many regions, manufacturing companies are facing growing demands, particularly in intralogistics. Optimizing and enhancing this area through the integration of artificial intelligence (AI) in combination with other technologies offers significant potential to meet these demands. Given the numerous technologies available, the approach to their evaluation is a critical success factor. The aim of this thesis is to develop a structured approach for the selection of AI-Technologies and to provide generic recommendations for manufacturing companies. The first chapter provides a fundamental understanding of intralogistics. This includes storage aids, storage techniques, storage processes, and IT systems. The integration of Enterprise Resource Planning, Warehouse Management Systems, and Warehouse Control Systems is essential for the efficient control of material flows and logistics processes. The second chapter addresses the use of artificial intelligence in intralogistics, exploring the various applications of AI solutions. Highly promising technologies such as Automated Guided Vehicles, Natural Language Processing, Computer Vision, and Predictive Maintenance are described in detail. Their role in the automation and digitization of intralogistics, as well as their resulting potentials, are highlighted. The final chapter focuses on the procedure for selecting suitable AI technologies. It begins with an analysis of warehouse processes, followed by the evaluation of appropriate technologies and providers. Concurrently, a process analysis of previously defined workflows in LISEC’s warehouse was conducted. For the final recommendation of the AI solution, a scaling system was developed. This system, designed with consideration of LISEC's intralogistics processes, allows for objective evaluation of the solutions. The insights gained from the LISEC project have been incorporated into practical recommendations, which can assist other companies in evaluating AI solutions. Through an analysis of warehouse processes, improvement potentials were identified and a desired state was developed. An integrative approach was chosen for defining the to-be state, involving relevant departments such as logistics, IT, and customs, whose inputs were considered in the tendering process. The specified requirements were communicated to the respective providers and discussed in detail during several direct meetings. In the evaluation phase, the manufacturers' offers were compared with the previously defined to-be state and other specific parameters, and then assessed accordingly. The developed recommendations are generalizable, allowing other companies to use the scaling system for the evaluation and selection of AI technologies. This enables the identification of the most suitable technologies, thereby enhancing the efficiency and cost-effectiveness of warehouse processes.

Date of Award	2024
Original language	German (Austria)
Supervisor	Denise Beil (Supervisor)