Logistics and sport management are part of the globalized economy and interdisciplinary fields of science. Sporting events necessitate enormous logistical efforts by aiming to deliver certain tasks and materials at an exact time and a specific place in suitable quantity and quality. However, sport logistics not only is involved in the precise execution of events but deals with a wide range of topics. Sporting goods are transported along the supply chain to customers with economic objectives, stadium evacuation simulations are carried out using algorithms that originate in transportation logistics, and sport clubs are confronted with logistical issues both on their game days and during the rest of the year. A systematic literature review was undertaken to provide clarity regarding previous scientific endeavors and to offer orientation for interested practitioners. The authors propose a theoretical framework—the sport logistics triangle—that draws on the academic disciplines of logistics management and sport management and allows to classify all sport logistics activities. An in-depth categorization scheme and ten topic clusters illustrate how sport logistics has been studied thus far. Implications and research opportunities are elaborated for practitioners and scholars. The survey clearly underlines both the importance of sport logistics and its neglection in academics.

Background: This paper is the first-ever discussion of sport logistics as an intersection of the academic disciplines of logistics and sport management. Methods: It reviews past literature and states that logistics in sport is widely overlooked in academic literature. It illustrates the importance and pervasive nature of logistics in the sport industry and questions how sport logistics can be positioned at the interface of logistics and sport management science. Both disciplines are contrasted under the common lens of Porter’s value chain. Results: Operations, organization and marketing are identified along with economics and strategy as areas of sport management (highly) involved with logistics. Sport logistics is defined as a research area dealing with storage and transportation issues in sport organizations. Conclusions: Based on a case example from practice, athlete equipment logistics, the authors elaborate on the unique characteristics of managing logistics in sport, e.g., low competition, personal relationships and high demand predictability. They call for a broader discourse on sport logistics in academia.

This contribution presents the SuLo (Sustainable Logistics) framework developed through a systematic literature review of the drivers and barriers to implementing sustainable sustainability in logistics, with a focus on warehousing. As global trade expands, logistics operations face increasing pressure to balance economic efficiency with environmental and social responsibility. Warehousing plays a crucial role in supply chains, contributing significantly to energy consumption and greenhouse gas emissions. Despite growing interest in sustainable logistics, research on sustainable warehousing remains fragmented. This paper synthesizes the existing literature to identify key drivers, barriers, and best practices for sustainable warehousing. The findings reveal that regulatory policies, cost savings, and stakeholder expectations are primary drivers, while high implementation costs, lack of awareness, and operational complexity hinder progress. A structured framework is proposed to guide sustainability integration, incorporating renewable energy, waste reduction, automation, and employee well-being initiatives. The study highlights the necessity of a holistic approach that aligns environmental, economic, and social dimensions. While challenges remain, sustainable warehousing offers long-term benefits, including reduced environmental impact, improved operational efficiency, and enhanced corporate reputation. In conclusion, future research should explore empirical case studies and technological innovations, particularly artificial intelligence, to further advance sustainability in warehousing.

The performance of Large Language Models, such as ChatGPT, generally increases with every new model release. In this study, we investigated to what degree different GPT models were able to solve the exams of three different undergraduate courses on warehousing. We contribute to the discussion of ChatGPT’s existing logistics knowledge, particularly in the field of warehousing. Both the free version (GPT-4o mini) and the premium version (GPT-4o) completed three different warehousing exams using three different prompting techniques (with and without role assignments as logistics experts or students). The o1-preview model was also used (without a role assignment) for six runs. The tests were repeated three times. A total of 60 tests were conducted and compared with the in-class results of logistics students. The results show that the GPT models passed a total of 46 tests. The best run solved 93% of the exam correctly. Compared with the students from the respective semester, ChatGPT outperformed the students in one exam. In the other two exams, the students performed better on average than ChatGPT.

The performance of Large Language Models, such as ChatGPT, generally increases with each further model release. In this paper, we investigate whether and how well different ChatGPT models solve the exams of three different logistics undergraduate courses. We contribute to the discussion of ChatGPT’s existing logistics knowledge, particularly in the field of warehousing. Both, the free version (GPT-4o mini) and the chargeable version (GPT-4o) completed three different logistics exams using three different prompting techniques (with and without role assignment as logistics expert or student). The o1-preview model was also used (without role assignment) for six attempts. The tests were repeated three times. A total of 60 tests were completed and compared to in-class results of logistics students. The results show that a total of 46 tests were passed. The best attempt solved 93% of an exam correctly. Compared to the students from the respective semester, ChatGPT outperforms students in one exam. On the other two exams the students perform better on average.