Order picking in manufacturing warehouses is a labor intensive activity with critical implications to the well-being of staff and operational performance of companies. This study addresses the need for applying digital technologies that lead to enhancing a human-centered approach in order picking. It proposes the use of artificial intelligence (AI)-enabled vision systems to facilitate the generation and analysis of information about tasks in manufacturing warehouses. We present the results of a collaborative project between academic and industrial partners from a case in automotive manufacturing. This consists of the development of a pilot study in a laboratory environment and includes two findings. First, we show the steps of implementing an AI-enabled vision system in order picking. This findings is important for automatically generating and analyzing information of tasks in order picking such as setup, travel, search, and picking of parts, which directly affect staff performance. Second, we discuss the implications of this findings for manufacturing companies and its contribution a future in order picking with improved human-centricity.

Blockchain technologies can support traceability, transparency and trust among participants. This has primarily been explored in established supply chains and not in the growing use of business networks or ecosystems, which is a notable limitation since supply chains typically are organised with a dominant actor that ensures common information systems and standards that negate blockchain benefits. Hence, this study explores the design of a blockchain-based collaborative framework for resource sharing using smart contracts. These are particularly well-suited for supporting operations in broader networks or ecosystems beyond supply chains with established collaborations and hierarchies. Based on a systematic literature review, a demonstrator framework was developed for stakeholder interactions through a procurement and distribution unit backed with blockchain technology. The framework consists of (a) network architecture to demonstrate partner interactions; (b) rules for network working principles based on supply collaboration requirements; (c) UML diagram to define smart contract interaction sequence; and (d) algorithm for smart contract network verification and validation. Applicability of these smart contracts was verified by deployment on an Ethereum blockchain. The demonstrator framework ensures quality and data authenticity in supply networks, so it is useful for effective resource utilisation in networks where outsourcing and production surpluses are major issues. 

One of the technical solution that enables the transition towards data-driven smart in-house logistics is IoT based cloud solution. Despite the noticeable progress in the associated technologies, there are limited access to the empirical studies regarding the contribution of implementation of these platforms in the realization of data-driven smart in-house logistics. Related to this issue, the aim of this paper is two folded. The first one is to figure out the requirements that should be posed by in-house logistics system owners to platform service providers. To address this matter, this paper reviewed some of the earlier works, which identified the evaluation criterion of IoT based cloud platforms. To accomplish the first aim, the specific requirements of in-house logistics systems on cloud platforms are highlighted. The second one is to evaluate the implementation process of an IoT based cloud platform within an in-house logistics testbed. The latter led us to identify the contribution of IoT based cloud services in the realization of data-driven in-house logistics. The results show that implementation of IoT based cloud platform can contribute to these areas: real-time track and trace, visibility to supply chain partners, planning and order management, and machine state monitoring.

One of the technical solution that enables thetransition towards data-driven smart in-house logistics is IoTbased cloud solution. Despite the noticeable progress in the associated technologies, there are limited access to the empirical studies regarding the contribution of implementation of theseplatforms in the realization of data-driven smart in-houselogistics. Related to this issue, the aim of this paper is two folded.The first one is to figure out the requirements that should beposed by in-house logistics system owners to platform service providers. To address this matter, this paper reviewed some ofthe earlier works, which identified the evaluation criterion ofIoT based cloud platforms. To accomplish the first aim, the specific requirements of in-house logistics systems on cloud platforms are highlighted. The second one is to evaluate the implementation process of an IoT based cloud platform within an in-house logistics testbed. The latter led us to identify the contribution of IoT based cloud services in the realization ofdata-driven in-house logistics. The results show that implementation of IoT based cloud platform can contribute tothese areas: real-time track and trace, visibility to supply chain partners, planning and order management, and machine state monitoring.

Nowadays, digital twins exist everywhere in various fields. However, an analysis of existing applications in manufacturing and logistics revealed that many entirely apply the concept. To identify when a complete implementation of the concept is beneficial, we analyse the need and the implications within production logistics. This study also presents an architecture supporting integrating a digital twin into production logistics and a corresponding application scenario. Based on this, we have derived practical applications. Each application is applied to different situations, and actual benefits can overcome the limitations of the previous studies. 

Digitalisation and automation of existing processes are key factors for competitive industry, but still logistics operations are often dominated by manual work. A shift towards higher degree of automation within existing infrastructure is often challenged by high cost and complex processes, thus a return-on-investment is hardly achievable within decent time. The experience has shown that it is hard to assess all restrictions and interactions between new and old components before any new equipment or infrastructure is implemented and put into operation. This paper presents and discusses if the usage of digital twins representing and simulating a physical part can support the related assessing and decision-making processes. In this context, this paper presents a production logistics test-bed includes physical devices, an IoT-infrastructure and simulation software for innovation as well as operational management purposes.

Digitalisation and automation of existing processes are key competitive factors for industry. Still, logistic operations often comprise manual effort, because the movement of goods and material places stringent requirements on the interactions between different systems, human-computer/robot-interaction as well as on changes in the operative processes. In general, the introduction and up-take of new enabling technologies, like the IoT, in complex systems evolved over decades, are challenging. The experience has shown that it is hard to assess all restrictions and interactions between new and old components before any new equipment or infrastructure is implemented and put in operation. This paper presents and discusses the usage of digital twins for supporting the decision-making processes in two different areas: Workstation design and logistics operation analysis. The results are based on tests and experiments carried out in a production logistics test-bed that includes physical devices, an IoT-infrastructure and simulation software. The digital twin is realised in a combination of using Unity and the simulation software IPS. The primary results show that there is no one-size fit all in terms of granularity of the underlying simulation model as well as for the reduction of reality in the digital twin, but the results also indicate that a context-aware digital twin supports the decision-making within a given scope.