logistics

Logistics is entering a new era. Climate change and geopolitical uncertainties are shifting the focus to resilience and sustainability. The concept of smart logistics is gaining importance. But what exactly makes logistics smart, and how can it help us organize our societies and the economy? Approaches such as predictive analytics, demand analysis, and machine learning show why smart logistics is more than just a technological trend.

Machine learning takes automation to a new level. But what does this imply for the role of humans, who seem to remain essential for the effective control of AI systems. The development of energy-efficient and fair algorithms and the optimization of data quality are crucial for the future viability of machine learning and artificial intelligence. The articles in this issue examine the technology's key potential and areas of application.

The complex dynamics of block warehouses pose major challenges to the manual stocktaking process. Frequent relocation of pallets, crates or pallet cages without fixed storage locations leads to a time-consuming and error-prone inventory process, wherein goods often have to be searched for and damages due to improper storage can occur. The use of (semi-)autonomous drones offers a promising solution to enable automated stocktaking, especially if these are appropriately equipped for optical goods detection.

In the course of digitalization, more and more digital technologies are being used in various areas of logistics. The occupational field is changing and problem-solving and abstracting skills as well as competencies in dealing with IT systems and data are increasingly required. Based on a literature review, this article provides an overview of the current situation in the logistics working world. Effects of digitalization as well as requirements and future competencies in the working world 4.0 are highlighted. Finally, a guideline for the management level is derived in order to promote the acquisition of competencies. (Only in German)

RPA refers to bots that automate repetitive, rulebased tasks in a business process. This paper describes general areas of application for RPA in logistics as well as two practical logistics examples. In addition, a procedure model for the implementation of RPA in logistics is presented. The paper answers the following questions: What are suitable use cases for RPA in logistics? What criteria support the selection of suitable processes? And how should an implementation guide be designed to systematically support an implementation project taking into account critical success factors?

Due to the high requirements of customers, logistics service providers are getting under pressure to improve their processes more efficiently and economically. In this context, the advancing automation and digitalization in all industrial sectors are producing a multitude of innovative solutions which will help solving the problems mentioned before [1]. The resulting sociotechnical systems created this way are often not in the focus due to the new technology. In this study, a project procedure will be presented as a recommendation which will help to introduce an innovation − in this case especially for an automatic guided vehicle− in consideration of the aspects of socio-technical systems.

With industry 4.0 technologies the new demands on production companies can be made manageable. The most common issue is the lack of transparency and efficient structure in intralogistics processes. In various projects of the Mittelstand 4.0 Kompetenzzentrum Chemnitz the use of Auto-ID and assistance systems has shown how this deficiency can be remedied. In one of these a local company improves its transparency and reduced the intralogistic search effort by 20 %.