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. 

Supply chain visibility (SCV) has been gaining recognition in recent years as a key factor for achieving analytical capabilities and improving supply chain performance. However, levels of SCV implementation lag behind current technological advances. This research was motivated by the lack of visibility in inbound logistics, which limits the possibility of managing deviation, in particular concerning changes in arrival time of incoming goods, in large industrial firms. We addressed this problem by adopting a design science approach. In particular, we followed context–intervention–mechanism–outcome (CIMO) logic to map and analyse material and information flows. The problems areas were successively translated via business and functional requirements into technological solutions. We evaluated alternative technologies using controlled experiments that mimicked real-life situations. This study provides guidance for manufacturing companies aiming to enhance deviation management and predictive capabilities by improving visibility in their inbound logistics and potentially extending visibility to other areas, such as internal and outbound flows.

Production logistics has an important role as a chain that connects the components of the production system. The most important goal of production logistics plans is to keep the flow of the production system well. However, compared to the production system, the level of planning, management, and digitalization of the production logistics system is not high enough, so it is difficult to respond flexibly when unexpected situations occur in the production logistics system. Optimization and heuristic algorithms have been proposed to solve this problem, but due to their inflexible nature, they can only achieve the desired solution in a limited environment. In this paper, the relationship between the production and production logistics system is analyzed and stochastic variables are introduced by modifying the pickup and delivery problem with time windows (PDPTW) optimization model to establish a flexible production logistics plan. This model, taking into account stochastic variables, gives the scheduler a new perspective, allowing them to have new insights based on the mathematical model. However, since the optimization model is still insufficient to respond to the dynamic environment, future research will cover how to derive meaningful results even in a dynamic environment such as a machine learning model. 

The rapid uptake of electric vehicles (EVs) will be vital to decarbonise the transport sector and achieve climate change targets. However, this transition is leading to an increased demand for key battery materials and associated resource challenges and supply-chain risks. On the other hand, discarded EV batteries create business opportunities for second life and recycling. This study presents scenario-driven material flow analysis (MFA) to estimate the future volume of EV battery wastes to be potentially generated in Sweden and future demand for key battery materials, considering potential EV fleet, battery chemistry developments, and end-of-life strategies of EV batteries. Further, we combine MFA with a socio-technical approach to explore how different socio-technical developments will affect both EV battery flows and the underlying systems in the future. Recycling has the potential to reduce primary demand by 25–64% during 2040–2050 based on projected demand, meaning that waste streams could cover a considerable part of the future raw material demands. Second-use of EV batteries can promote circularity yet postpones recycling potentials. From a transition perspective, promoting recycling, second-life use of EV batteries and advanced battery technologies entail system disruption and transformational changes in technology, markets, business models, policy, and infrastructure and user practices. Demand for high-capacity batteries for grid decarbonisation and aviation applications may contribute to the emergence of niche battery technologies. Each scenario highlights the need for effective policy frameworks to foster a circular EV battery value chain.

Visibility across supply chains has been a key concern for organisations for many years, but the tools and information systems to achieve real-time visibility have not been available until recently. In response to uncertainty and complexity, advanced information and communication technologies have been explored for supply chain visibility (SCV). However, managerial perspectives are largely absent from the current literature. In response, this paper systematically documents managerial factors influencing SCV and information that should be collected and shared among supply chain partners for better visibility. A multi-stage Delphi analysis was conducted with 26 supply chain experts from various globally recognised enterprises with manufacturing units located worldwide. The results provide details on prioritised managerial perspectives and experiences within (1) factors affecting SCV (drivers, enablers, challenges and contingencies), (2) SCV content (supplier, internal and customer information) and (3) implications of SCV (capabilities and performance effects). One observation was that forecasting is not deemed as important due to severe disruptions in supply chains. Real-time visibility for better predictability emerged as the top priority. This study is among the few that empirically explores factors influencing supply chain visibility and generates new insights into why barriers can be difficult to overcome in complex supply chain settings.

Supply chain visibility is a concept that is gaining increasing attention. Increasing levels of visibility in the supply chain is considered beneficial in many respects. A systematic literature review identified 47 empirical research papers from international scientific journals. Two main findings emerged from the content analysis. First, the perspectives provided in the extant literature can be grouped into four categories: antecedents, barriers and challenges, drivers, and effects. The effects include both capabilities and operational performance. Second, the few models on supply chain visibility include only antecedents and effects, and do not capture the perspectives of barriers, challenges, or drivers. We present a framework for supply chain visibility driven by insights from the content analysis. The ABCDE framework includes antecedents (A), barriers and challenges (BC), drivers (D), and effects (E), and provides a holistic view of supply chain visibility. The framework can aid managers with per-spectives and factors to consider when discussing, implementing, and improving supply chain visibility and provides researchers with a state-of-the-art review of supply chain visibility. We also propose an agenda for further research.

The study analyzes the application of reinforcement learning (RL) for material handling tasks in Smart Production Logistics (SPL). It presents two contributions based on empirical results of a RL model in dynamic production logistics environment from the automotive industry. Firstly, an architecture integrating the use of RL in SPL. Secondly, the study defines various elements of RL (environment, value, state, reward, and policy) relevant for training and validating models in SPL. The study provides novel insight essential for manufacturing managers and extends current understanding related to research combining artificial intelligence and SPL, granting manufacturing companies a unique competitive advantage.

Traceability has emerged as a prime requirement for a multi-tier and multi-site production. It enables visibility and caters to the consumer requirements of transparency and quality assurance. Textile and clothing industry is one such example that requires traceability implementation to address prevailing problems of information asymmetry and low visibility. Customers find it difficult to access product data that can facilitate ethical buying practices or assure product authenticity. Besides, it is challenging for stakeholders to share crucial information in an insecure environment with risk of data manipulations and fear of losing information advantage. In this context, this study investigates and proposes a blockchain-based traceability framework for traceability in multi- tier textile and clothing supply chain. It conceptualizes the interaction of supply chain partners, and related network architecture at the organizational level and smart contract and transaction validation rules at the operational level. To illustrate the application of the proposed framework, the study presents an example of organic cotton supply chain using blockchain with customized smart contract and transaction rules. It finally demonstrates the applicability of the developed blockchain by testing it under two parameters. The proposed system can build a technology-based trust among the supply chain partners, where the distributed ledger can be used to store and authenticate supply chain transactions. Further, the blockchain-based traceability system would provide a unique opportunity, flexibility, and authority to all partners to trace-back their supply network and create transparent and sustainable supply chain. 

With the increased electrification of transportation, there is a growth in the number of electric vehicles (EV) in use, and hence also discarded EV batteries. It is critical to trace the batteries so that the policy of electrification does not lead to a negative impact on sustainability. To achieve the goals of circular economy, it is necessary to consider the sustainable extended life cycle strategies of reduce, reuse and recycle. Information gathering and sharing through the supply chain is the key driver for enabling the tracking and tracing of materials and services needed. Traceability indicators across the value chain may enable the creation of a comprehensive database that aids the circular economy goals. In this study, we discuss three different circular economy business models and identify the key traceability indicators for enabling circularity in the lithium-ion battery application in the automotive sector. Insights are used to develop a framework for viable EV battery circularity, capturing three key circular economy elements and four traceability characteristics for different circularity types.