Abstract
This master’s thesis focuses on optimizing the storage and loading processes at Laakirchen Papier AG following its shift to Containerboard production. The objective is to adapt the logistics system to efficiently handle an annual production volume of 1 million tons, while also laying the foundation for the introduction of an automated storage system.The current state of storage and loading processes is first analyzed, revealing bottlenecks, particularly at loading stations and during truck handling, which hinder process efficiency. Key challenges include weather-related delays, uneven truck arrival times, and long waiting times at loading stations. Based on this analysis, simulations were conducted to assess the impact of various optimization measures.
A significant part of the thesis is dedicated to simulating the loading processes. The simulations indicate that the following measures can lead to substantial efficiency improvements:
– Automation of truck call-up and browser-based registration: These measures eliminate waiting times at registration terminals, ensuring a seamless truck call-up and smooth processing.
– Covering loading stations: This removes weather-related delays and increases predictability.
– Creation of preparation zones: By introducing preparation areas for load securing and hygiene checks, loading times per truck are reduced, allowing loading stations to be used exclusively for loading operations.
– Installation of a second scale: Simulations show that the addition of a second scale significantly reduces waiting times, especially during peak hours.
The simulation results demonstrate that implementing the proposed measures can halve loading times, effectively doubling processing capacity. This is particularly relevant given the planned increase in production volumes.
In the long term, the introduction of an automated crane system and self-driving forklifts is envisioned to further improve efficiency. These technologies would enable full automation of the storage process, facilitating optimal handling of Containerboard and managing the growing production volumes.
Future optimization efforts should focus on integrating Artificial Intelligence (AI) and machine learning to further enhance logistics processes. This could include data-driven predictive models for optimizing storage and loading procedures, as well as real-time monitoring of the entire supply chain.
Overall, this thesis demonstrates that through targeted optimization measures and the use of modern technologies, Laakirchen Papier AG can not only improve the efficiency of its storage and loading processes but also lay the groundwork for future automation.
Date of Award | Oct 2024 |
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Original language | German (Austria) |
Supervisor | Holger Gröning (Supervisor) |