Supply chain management and Industry 4.0: conducting research in the digital age Introduction In essence, Industry 4.0[1] enables an automated creation of goods and services as well as supply and delivery, which functions largely without human intervention. Industry 4.0 is happening now (Vogel-Heuser and Hess, 2016, Sprovieri, 2019) and describes the trend toward automation and data exchange in manufacturing technologies and processes which include among others cyber-physical systems (CPS), industrial Internet of Things (IIoT), cloud computing, cognitive computing and artificial intelligence (AI). Decision making is predominantly decentralized, and system elements (e.g. production plants or transport vehicles) make autonomous, targeted decisions. A digital manufacturing enterprise is not only interconnected, but also communicates, analyzes and uses information to further drive intelligent actions back into the physical world. Industry 4.0 will change how supply chains are designed and operated, yet research on promises and impacts of Industry 4.0 on supply chain management (SCM) is still scarce (Holmström and Partanen, 2014; Hofmann and Rüsch, 2017). We refer to SCM in the new era of Industry 4.0 as "SCM 4.0." In SCM 4.0, the digital and autonomous linkages within and between companies become a focal point of SCM (Stölzle et al., 2017). SCM 4.0 represents a new stage of development in SCM, in which the coordination of materials, information and financial flows in corporate networks is largely automated and permeated with digital technologies. This Special Issue is thus dedicated to exploring the abundant research opportunities associated with SCM 4.0 and laying down a foundation for future research on this important emerging topic. The idea is to fill gaps in the existing supply chain theory and explore the areas that are likely to be impacted by the combination of knowledge, traditional and emerging technologies. SCM 4.0 will over time manifest substantially different from conventional SCM.

Despite the anticipated benefits and the numerous announcements of pilot cases, we have seen very few successful implementations of blockchain technology (BCT) solutions in supply chains. Little is empirically known about the obstacles to blockchain adoption, particularly in a supply chain's interorganizational setting. In supply chains, blockchains' benefits, for example, BCT‐based tracking and tracing, are dependent on a critical mass of supply chain actors adopting the technology. While previous research has mainly been conceptual and has lacked both theory and empirical data, we propose a theory‐based model for interorganizational adoption of BCT. We use the proposed model to analyze a unique in‐depth revelatory case study. Our case study confirms previous conceptual work and reveals a paradox as well as several tensions between drivers for and against (positive and negative determining factors, respectively) of BCT adoption that must be managed in an interorganizational setting. In this vertical context, the adoption and integration decision of one supply chain actor recursively affects the adoption and integration decisions of the other supply chain actors. This paper contributes midrange theory on BCT in supply chain management (SCM), future research directions, and managerial insights on BCT adoption in supply chains.

With the emergence of distributed ledger technology (DLT), numerous practitioners and researchers have proclaimed its beneficial impact on supply chain transactions in the future. However, the vast majority of DLT initiatives are discontinued after a short period. With the full potential of DLT laying far down the road, especially managers in supply chain management (SCM) seek for short-term cost-saving effects of DLT in order to achieve long-term benefits of DLT in the future. However, the extant research has bypassed grounding long-term as well as short-term effects of DLT on supply chain transaction with empirical data. We address this shortcoming, following an abductive research approach and combining empirical data from a multiple case study design with the corresponding literature. Our study reveals that the effects of DLT on supply chain transactions are two-sided. We found six effects of DLT solutions that have a cost-reducing or cost avoidance impact on supply chain transactions. In addition, we found two effects that change the power distribution between buyers and suppliers in transactions and a single effect that reduces the dependency of supply chain transactions on third parties. While costreducing and avoidance as well as dependency-reducing effects are positive effects, the change in power distribution might come with disadvantages. With these findings, the paper provides the first empirical evidence of the impact of DLT on supply chain transactions, which will enable managers to improve their assessment of DLT usage in supply chains.

PurposeThe purpose of this paper is to develop a waste framework for motor carrier operations by adapting the classical 7 waste framework, and furthermore, to validate it by collecting empirical data from several motor carrier operators.Design/methodology/approachThe chosen approach includes three steps, starting with analyzing qualitative data from a literature review and an interview study. The interviewees were experts from carrier operations, the lean field, carrier technology providers and carrier service buyers. The findings were validated with qualitative and quantitative studies at five motor carrier operators.FindingsThe finding of this paper is a waste framework adapted to motor carrier operations that has been based on the classical 7 waste framework. This provides a structured framework of inefficiencies found in motor carrier operations.Originality/valuePrevious literature is scarce on both holistic approaches to describing waste in carrier operations and in‐depth studies of day‐to‐day transport operations. It is also a novel approach to use a waste framework for transport operations.