Violetta Giada Cannas

Purpose: This study aims to investigate the impact of Lean Production (LP) and Industry 4.0 (I4.0) on the adoption of Circular Economy (CE) within manufacturing firms, also considering the potential moderating effect of I4.0 on the link between LP and CE. By doing so, it addresses the limited and contrasting empirical evidence on how operational practices facilitate the adoption of CE. Design/methodology/approach. This study adopted the Practice-Based View (PBV) as a theoretical lens and employed a quantitative research design. Data were collected via a survey of 151 manufacturing companies operating in Italy and analyzed using hierarchical multiple regression analysis to test the conceptual model. Findings: The empirical results confirm that both LP and I4.0 are significantly and positively associated with CE adoption. I4.0 technologies have a stronger individual impact on CE adoption than LP. However, the hypothesized moderating effect of I4.0 on the LP–CE relationship is not supported. In other words, the implementation of technologies may not contribute to a positive variation in the level of CE adoption that is already achieved due to the implementation of LP practices. Hence, managers should pursue distinct CE strategies aligned with the unique capabilities of LP and I4.0, respectively. Originality/value: This research contributes to the operations management and CE literature by providing empirical evidence on the role of LP and I4.0 in enabling CE adoption. It offers valuable insights for practitioners and policymakers aiming to foster CE adoption in manufacturing, dealt with the links adopting the PBV, challenging the presumed moderating effect of I4.0, and providing actionable recommendations.

Recent years have seen a growing interest among academics and practitioners in the approaches of Industry 4.0 (I40), Lean Production (LP), and Circular Economy (CE). Scientific studies have largely examined these approaches separately, or in a dual, pairwise combination. More recent research has also shown how I40 technologies and LP practices affect the implementation of CE strategies. In particular, it has been noted that I40 technologies and LP practices mutually not only enhance each other's efficacy but also have a positive impact on CE strategies. Despite this evidence, many of the existing works leave a critical gap in our knowledge about an integrated perspective among these three approaches. In other words, a more synergistic interaction among the I40 technologies, LP practices, and CE strategies is not yet well explored in the existing academic literature and needs to be developed. To address this research gap, this study leverages a multiple case study analysis of six companies operating in the manufacturing sector that operate with I40 technologies, LP practices, and CE strategies. Our results confirm that I40 technologies and LP practices foster each other and enable CE strategies. In addition, our empirical analysis adds to the existing studies that the synergistic interaction among the three approaches lies in the fact that the implementation of one approach triggers another one sequentially. In other words, the implementation of I40 technologies contributes to the activation of LP practices, which in turn enable the adoption of CE strategies. The evidence of our results has been visualized in empirically based framework that highlights for scholars and managers how manufacturing companies can optimize their transition pathway towards CE through I40 technologies and LP practices and paving thus the way for a more sustainable and effective industrial environment.

Lean Production (LP) has been widely and successfully employed in mass production situations, showing high capabilities in reducing non-added-value activities, providing process stability, high qualitative production outputs and competitive production lead times. In engineer-to-order (ETO) situations, instead, the high customisation and variability of the context bring complexity and make it very difficult to fully employ the potential of LP. Over the years several studies focused on this issue, studying the application of LP practices to ETO situations. However, recent literature reviews underlined that there is a lack of research addressing the issue of whether to adopt, adapt or reject LP practices in ETO situations, and there is still an ongoing debate on this field. To fill the gaps identified in the existing literature, this study aims to study what LP practices are implemented in ETO situations, as well as how, using multiple case study research. An original data set was constructed using a purposely defined research protocol using structured interviews. The findings of this study show what LP practices are implemented successfully in ETO situations and what, on the other hand, are not easily implemented. Also, the study analyses how LP practices are implemented and what adaptations they must undergo to be effective within the ETO situations.

Artificial intelligence (AI) is increasingly considered a source of competitive advantage in operations and supply chain management (OSCM). However, many organisations still struggle to adopt it successfully and empirical studies providing clear indications are scarce in the literature. This research aims to shed light on how AI applications can support OSCM processes and to identify benefits and barriers to their implementation. To this end, it conducts a multiple case study with semi-structured interviews in six companies, totalling 17 implementation cases. The Supply Chain Operations Reference (SCOR) model guided the entire study and the analysis of the results by targeting specific processes. The results highlighted how AI methods in OSCM can increase the companies' competitiveness by reducing costs and lead times and improving service levels, quality, safety, and sustainability. However, they also identify barriers in the implementation of AI, such as ensuring data quality, lack of specific skills, need for high investments, lack of clarity on economic benefits and lack of experience in cost analysis for AI projects. Although the nature of the study is not suitable for wide generalisation, it offers clear guidance for practitioners facing AI dilemmas in specific SCOR processes and provides the basis for further future research.

Research in lean production has recently focused on linking lean production to Industry 4.0 by discussing the positive relationship between them. In the context of Industry 4.0, data science plays a fundamental role, and operations management research is dedicating particular attention to this field. However, the literature on the empirical implementation of data science to lean production is still under-investigated and details are lacking in most of the reported contributions. In this study, multiple case studies were conducted involving the Italian manufacturing sector to collect evidence of the application of data science to support lean production and to understand it. The results provide empirical proof of the link and examples of a variety of data science techniques and tools that can be combined to support lean production practices. The findings offer insights into the applications of the traditional lean plan–do–check–act cycle, supporting feedback on performance metrics, total productive maintenance, total quality management, statistical process control, root cause analysis for problem-solving, visual management, and Kaizen.

Digital Twin (DT) implementation in manufacturing plants has attracted increasing attention. Owing to advancements in the use of technologies related to Industry 4.0 pillars, such as the Internet of Things, Big Data analytics, and simulation, the potential of DTs to profoundly impact manufacturing has been recognised. However, DT implementation is challenging. In practice, manufacturing companies that consider DT implementation may encounter several challenges, which can prevent the achievement of its potential benefits and impede its successful realization. Research on this topic lacks empirical evidence and models to guide practitioners to overcome this problem. Therefore, the aim of this study was to map the key challenges related to DT implementation in manufacturing contexts and propose a set of possible countermeasures. To achieve this objective, we conducted a Delphi study involving 15 experts, both practitioners and academics. The process required three rounds. In the first round, the experts were requested to provide a personalized list of potential challenges to DT implementation. In the second round, the experts evaluated the challenges from the literature and their suggested potential challenges, providing a measure of relevance. Furthermore, experts were asked to propose possible countermeasures to these challenges. Finally, a third round achieved consensus. The study identified 18 key challenges divided into four categories and proposed a set of possible countermeasures to overcome these problems. Moreover, a relevance/agreement matrix of the key challenges was proposed to establish a relative impact.

Ten years after the first appearance of the term “Industry 4.0” in the Hannover fair, the advancements of this paradigm are manifold. Among the technologies that constitute Industry 4.0, i.e., Industrial Internet of Things, cloud computing, additive manufacturing, vertical and horizontal integration, big data and analytics, cyber-physical systems, simulation, augmented reality and cyber security, a variety of applications have been developed in relation to products, factories, and cities. From an industrial point of view, the changes at the shop floor and supply chain level will affect the way the supply chain and operations management activities will be conducted. Mapping the path of this growth highlights today’s opportunities and challenges related to Industry 4.0 and helps researchers and practitioners in taking chances and dealing with issues. Hence, the aim of this work is to identify the main trends of evolution of this paradigm by means of a review of literature on the topic. To achieve such a result, this research adopts a dynamic and quantitative bibliometric method including works citations, keywords co-occurrence networks, and keywords burst detection. The aim is to study and analyze the main contributions to this research area and identify prevalent topics and trends over time. The analysis performed on citations traces the backbone of contributions to the topic, highlighted within the main path. Keywords co-occurrence networks depict the prevalent issues addressed, tools implemented, and application areas. The burst detection completes the analysis by identifying the trends and most recent research areas characterizing research on Industry 4.0.

The combination of the industrial paradigms of Circular Economy (CE), Industry 4.0 (I40), and Lean Production (LP) has been debated by academics and practitioners in the last years, demonstrating that I40 technologies and LP enable CE, and that I40 and LP mutually support each other. The analyses conclude that several economic and environmental benefits can be achieved from these synergies. However, given most of the studies in literature focused on the dual combination between these paradigms, there is a need for understanding how all three are related to each other simultaneously. Accordingly, the proposed research defines a model that shows how the circular transition of manufacturing companies can be enhanced through the exploitation of LP practices and the key enabling technologies of I40. To achieve this result, the proposed research conducts a bibliometric review of the literature extracted from Scopus, exploiting a systematic literature network analysis methodology to detect and then analyze clusters of themes. The study observed that employing LP practices and I40 technologies support manufacturing companies towards a more effective circular transition and proposes future research avenues to be addressed by future studies at the intersection between the topics of I40, LP, and CE.

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).