Production research literature and industry practice have started to pay increasing attention to the Industry 4.0 (I4.0) phenomenon. Scholars and practitioners identified a strong link between this paradigm and the well-known Lean Production (LP) paradigm. Most studies consider LP as a prerequisite of I4.0 and I4.0 as a tool to overcome LP limits and boost its practices. However, so far, these effects have been studied only at a high level, without an in-depth and comprehensive pairwise analysis at a practice-technology level. Moreover, few empirical studies have been carried out on this topic. Our paper attempts to fill these gaps by conducting a multiple case studies research to explain the one-to-one relationships between LP techniques and I4.0 technologies, and vice versa. More specifically, the one-to-one analysis examines the enabling effect of LP on I4.0 and the empowering effect of I4.0 on LP. Based on the empirical analyses, we propose a framework on the relationships between the two paradigms structured into six areas drawn from previous research (i.e. manufacturing equipment and processes, shop-floor management, workforce management, new product development, supplier relationships, customer relationships). Such representation clarifies the interdependence of the two paradigms in the whole supply chain.

The aim of this study is to investigate the body of literature on lean published by the International Journal of Production Research (IJPR), which has been interested in the subject since its dawn. This review adopted a dynamic and quantitative bibliometric method composed of the keywords co-occurrence network and keywords burst detection. The analyses performed on keywords co-occurrence networks highlighted how research in IJPR has addressed research on lean over time and allowed a comparison with the consolidated research streams in literature. The burst detection completed the analysis highlighting the trends and most recent research areas characterising IJPR publications. The outcomes of this study reflected the evergreen relevance of lean; indeed, the latest research trajectories identified in IJPR stressed its link with the increasingly topical issues concerning industry 4.0, sustainability and remanufacturing. The analysis recognised in 'lean Six Sigma', and specifically in its support to the service sector, an under-considered topic, hence a scope that offers room for further study, in accordance with IJPR objectives.

The literature discusses data science (DS) as a very promising set of techniques and tools to support lean production (LP) practices. DS could aid manufacturing companies in transforming massive real-time data into meaningful knowledge, increasing process transparency and product quality information and supporting improvement activities through data-driven decision-making. However, no attempt has been made in the literature to formalise the links between DS and LP practices. Thus, this study aims to overcome this gap by clarifying the DS techniques and tools that can support LP practices and how to apply them. This study employs a quantitative bibliometric method-specifically, a keyword co-occurrence network analysis-on a set of papers extracted from Scopus. The results obtained allowed the researchers to identify a set of DS techniques and tools that can support LP practices and to develop a model to guide their implementation based on the typical improvement implementation stages of the plan-do-check-act cycle. The model shows how to use DS techniques and tools in LP for: identifying areas for improvement and subsequent implementation (plan); enabling a better knowledge and process management (do); identifying/predicting potential problems and employing statistical process control (check); providing remedial actions and effectively applying process improvement (act).

The aim of this study is to investigate the body of literature on digital twins, exploring, in particular, their role in enabling smart industrial systems. This review adopts a dynamic and quantitative bibliometric method including works citations, keywords co-occurrence networks and keywords burst detection with the aim of clarifying the main contributions to this research area and highlighting prevalent topics and trends over time. The analysis performed on citations traces the backbone of contributions to the topic, visible within the main path. Keywords co-occurrence networks depict the prevalent issues addressed, tools implemented and application areas. The burst detection completes the analysis identifying the trends and most recent research areas characterizing research on the digital twin topic.Decision-making, process design and life cycle as well as the enabling role in the adoption of the latest industrial paradigms emerge as the prevalent issues addressed by the body of literature on digital twins. In particular, the up-to-date issues of real-time systems and industry 4.0 technologies, closely related to the concept of smart industrial systems, characterize the latest research trajectories identified in the literature on digital twins. In this context, the digital twin can find new opportunities for application in manufacturing, control and services.

In this decade, manufacturing companies are facing events that disrupt delicate balances and experiencing tangible challenges that cannot be deferred. Among these, the acceleration of technological transformation to follow the introduction of Industry 4.0, the difficulty in sourcing raw materials and accessing different markets due first to the COVID 19 pandemic and more recently to geopolitical tensions and conflicts. In this scenario, traditional supply chain models need to transform into digital supply chains (DSCs), where functional silos are broken down to enable end-toend visibility, agility, collaboration, and resilience to such shocks. The literature on the subject is still immature and many concepts are still vague and undefined. In this regard, this article aims to answer the following questions: (i) What are the main research areas within the topic? (ii) What is the development trajectory of the topic? (iii) What are the main digital technologies that can support DSC capabilities? (iv) What can be its research agenda? For this purpose, this paper aims at reviewing the existing scientific production on DSCs, combining a systematic review with bibliometric tools. The resulting framework can serve as a preliminary guide for companies facing the challenges listed above and can open up future research that can validate the results empirically.