Sustainability

Hydrogen fuel cells promise to be a sustainable alternative to fossil fuel or battery-electric material handling equipment (MHE) in various production or warehouse contexts. Short refuelling times, an absence of carbon emissions, and constant power input put fuel cell-powered MHE at an advantage in high-intensity work environments. However, various barriers to the adoption of fuel cells remain, including considerations surrounding cost and efficiency.

This study examines the contribution of natural building materials, in particular loam and wood, to the sustainable development of logistics infrastructure, assessing ecological, economic, and technical dimensions across the entire life cycle. Potentials, restrictions, and supportive framework conditions are identified based on literature analyses and expert interviews. Wood proves to be technically mature and ecologically advantageous, especially in high rack construction, while loam offers high potential for energy- and resource-efficient construction. The study concludes with recommendations for research, policy, and practice to establish circular construction methods in logistics.

This article outlines the results of ten expert interviews on the use of machine learning to promote corporate sustainability and then compares them with relevant literature. The study shows that economic factors drive the use of machine learning, the introduction of which is initiated by both top management and specialist departments. However, grounded strategies for implementing machine learning are rarely available and use cases are often based on supervised learning. The environmental impact (the reduction of emissions, for example) outweighs the social impact, though quantification is difficult. Additionally, a lack of trust, expertise, and communication hinders the adoption of machine learning, while some technical challenges regarding data requirements also pose problems.

Although SMEs are not directly affected by the statutory reporting obligations for carbon accounting, as suppliers they are obliged to meet the requirements of sustainability reporting. In addition to a holistic life cycle analysis, this requires a high-quality database within production in order to determine the specific CO₂ footprint. A central element is the implementation of a Machine Carbon Footprint (MCF). This article aims to develop and implement an MCF focusing on its applicability for SMEs. For this purpose, data is recorded and visualized in real time on a machine tool. The measurement data is then processed, stored and visualized using open-source low-code platforms. Real-time data flows enable the precise determination of the production-specific carbon footprint and, in conjunction with order data, the Product Carbon Footprint.

Digital twins connect physical and digital systems, furthering efficiency, enabling predictive maintenance, and allowing the production of more customized products. Despite these advantages, challenges such as high costs, data synchronization, and security risks hinder widespread adoption. This article explores the potential of digital twins and examines key barriers to integration and implementation, also considering some industrial applications including additive manufacturing as a relevant use case.

As environmental challenges such as climate change and resource scarcity intensify, with Earth Overshoot Day highlighting overconsumption, the circular economy emerges as a crucial solution. Legislation at the national and EU level obliges companies to become more sustainable. Simultaneously, the circular economy strengthens economic resilience, promotes innovation and creates competitive advantages. However, the impact on the labor market is controversial, as fewer primary resources and new products are needed. Sustainable corporate management requires a balanced consideration of the triple bottom line: Planet, People, and Profit, treating each as equally important. In contrast to the linear economy, the circular economy follows the ‘cradle to cradle’ principle and integrates the ten R-Strategies of sustainability. These strategies range from refuse (avoidance) and reduce (reduction) to recycling and repurpose (reuse). Companies should identify which strategies can be swiftly ...