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Pallets ensure efficient processes in logistics and are exchanged between the different actors, while passing through various supply chains several times. In common practice, the exchange is often not directly carried out on site, e.g. due to a lack of time, so that additional trips and new pallet purchases become necessary. To reduce these negative effects, a digital cross-actor platform is designed, and its potential is investigated.

The authors developed an agent-based simulation model with mathematical optimization. Using experience from practitioners, as well as real-world datasets which were analyzed, the authors ensure a realistic model of the pallet exchange system in Germany.

The authors demonstrated that, with the help of this platform concept, transport routes can be shortened, debts and receivables can partly be equaled out through balancing, and the quantity of pallets in the overall system can be reduced.

The results are not directly transferable to pallet exchange systems in other countries without considering their general settings.

Digital networking increases the efficiency of the existing pallet exchange system. Even small collaborations prove to be reasonable.

The authors developed new mechanisms for a digital pallet exchange platform, which takes on the role of a central planning instance, in addition to recording pallet receivables and debts. It enables the planning of the commodity flow of empty pallets, which are transported by the forwarders on regular routes, and distributed between the platform participants.

Order picking is one of the most costly logistics processes in warehouses. As a result, the optimization of order picking processes has received an increased attention in recent years. One potential source for improving order picking is the reduction of picker blocking. The purpose of this paper is to investigate picker blocking under different storage assignment and order picker-route combinations and evaluate its effects on the performance of manual order picking processes.

This study develops an agent-based simulation model (ABS) for order picking in a rectangular warehouse. By employing an ABS, we are able to study the behaviour of individual order pickers and their interactions with the environment.

The simulation model determines shortest mean throughput times when the same routing policy is assigned to all order pickers. In addition, it evaluates the efficiency of alternative routing policies–storage assignment combinations.

The paper implies that ABS is well-suited for further investigations in the field of picker blocking, for example, with respect to the individual behaviour of agents.

Based on the results of this paper, warehouse managers can choose an appropriate routing policy that best matches their storage assignment policy and the number of order pickers employed.

This paper is the first to comprehensively study the effects of different combinations of order picker routing and storage assignment policies on the occurrence of picker blocking.

This study identifies and characterizes configurations of generic business models for logistics service providers (LSPs) in the context of industrial additive manufacturing (AM). A literature-based framework of the AM service supply chain (SC) is developed to embed the generic configurations in their SC context.

Following an exploratory research design, 17 interviews were conducted with LSPs, LSPs' potential partners and customers for industrial AM services.

Six generic configurations are identified, the LSP as a Manufacturer, Landlord, Logistician, Connector, Agent and Consultant. The authors outline how these configurations differ in the required locations, partners and targeted customer segments.

The current discussion of reshoring and shorter, decentralized AM SCs confronts LSPs with novel challenges. This study offers guidance for managers of LSPs for designing business models for industrial AM and raises awareness for LSPs' resource and SC implications.

This study contributes to the scarce literature on AM business models for LSPs with in-depth empirical insights. Based on the six identified configurations, this study sets the ground for theorizing about the business models, in particular, the value creation, value proposition and mechanisms for value capture of the business models. In addition, this study suggests how the generic configurations fit the features of specific types of LSPs.

This study extends and refines the current knowledge on emerging supply chain designs (SCDs) for industrial additive manufacturing (AM) and manufacturing firms' rationales in selecting them.

Following an exploratory research design, a multiple-case study is conducted in the context of industrial AM. It focuses on two key dimensions of SCD, the geographic dispersion and governance structure. Four cohesive AM SCD configurations are characterized and form the basis for exploring the rationales for the SCD decision of manufacturing firms.

The findings indicate that manufacturing firms' SCD for industrial AM depends on the trade-off between economies of scale in a centralized setting and the market potential from customer proximity realized by decentral AM. Furthermore, the control of suppliers and the reevaluation of manufacturing firms' core competencies guide the governance choice. Many of the identified rationales currently drive manufacturing firms toward in-house AM at a centralized location or distributed AM in a secure, firm-owned network.

The arguments for the AM SCD choices are illustrated. They provide guidance for managers of manufacturing firms when implementing industrial AM.

The study reveals and enhances the understanding of why the extant academic expectation of decentralized and outsourced AM is not sufficiently reflected in current industry practice. Thereby, the study provides a basis for elaborative decision-support research on AM SCDs.