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.

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.

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.

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.

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).

In 2009 a literature review on supply chain management in Engineer-to-Order (ETO) situations was published in the International Journal of Production Economics (Gosling and Naim, 2009). The paper has received more than 200 citations from over 100 international journals. The ETO body of knowledge has been particularly relevant to those seeking to mobilise operations and supply chain concepts within the context of complex innovative engineering work. These are all increasingly pressing concerns for many organisations in the contemporary global economy; hence, it is timely to revisit this body of knowledge. Consequently, this study performs a systematic review of the last decade (2010–2020) ETO studies to identify the major advances revealed and develop a future research agenda. The results show that literature, over the last decade, presented new emerging trends related to: (i) ETO definitions through conceptualisation of the engineering flows and integration of engineering/production flows via the two-dimensional decoupling point; (ii) strategies for decoupling positioning, supply chain integration, planning and control, uncertainty/risk management, industry 4.0, exploration of new business models and system design, design automation and engineering management in ETO situations; (iii) applicability of lean within ETO situations. Finally, the paper suggests guiding research questions in relation to linkages between different disciplinary areas, evaluation of the application of new technologies, guidance for managing transitions between decoupling configurations and understanding of the new servitisation trends in ETO situations. In conclusion, the study highlights four research challenges to address: positive science challenge, comparative research challenge, multidisciplinary research challenge, and prescriptive research challenge.

Engineer-to-Order (ETO) companies are embracing the mass customisation strategy to face the challenges posed by global competition. Product configurators are key enablers of such strategy. Despite the benefits, the actions to perform to manage the challenges of implementing product configurators are still understudied. This paper aims to fill this gap by empirically exploring seven case studies of ETO companies that are embracing a mass customisation strategy and have implemented a product configurator. The results provide a classification of the challenges that ETO companies have to manage in each phase of the implementation of product configurators, and a framework that supports managers in defining the actions necessary for the development and implementation of product configurators. This study, thereby, contributes to the debate on how ETO companies can move towards a mass customisation paradigm.

There is a need for the dairy supply chain to improve its environmental performance. Intermodal rail-road transportation can be a way to reduce CO2 emissions. However, despite technological innovations in the realm of cooling technology, which could enable a shift to intermodal transportation, the use of intermodal rail-road in the dairy supply chain is still low. A blueprint is needed to foster the application of intermodal transportation in the sector. Literature provides little guidance in this sense. Therefore, this paper investigates how to ease the shift to intermodal rail-road transportation in the dairy supply chain through multiple case studies, performed at different stages of the supply chain. A set of enablers of the shift is discussed, along with a blueprint for innovative technology, and logistics and business models. The plan takes into account all the actors of the dairy industry, as well as other players, i.e. technology providers, academia and institutions. This paper enriches literature, thanks to its multi-stage research, providing managers with a practical tool to support the shift to intermodal transportation in the dairy industry. The main limitations lay in the choice of the sample, i.e. only Italian companies and no small retailers and farmers have been involved.

Recent empirical studies have refined our understanding of engineer-to-order (ETO) situations, supporting the existence of different order-fulfilment strategies based on the degree of customer involvement in the engineering and production activities, which differs depending on the strategic fit with the environment in which the company operates. Despite the importance of this finding, limited attempts have been made to comprehensively understand the determinants for this strategic choice in ETO companies. To overcome this gap, this study aimed to investigate the sources of differentiation between the environments that ETO companies can face and the ways of reacting to strategically fit the order-fulfilment strategy. Therefore, this research analysed the existing literature through a contingency theory lens and performed a multiple case-study research in a specific ETO sector, i.e. the machinery industry. The study identified five different order-fulfilment strategies implemented in the machinery industry to provide different product families to the market. For each strategy, the different environment characteristics were defined, and the performance outcome was measured, explaining the rationale for the positioning of the product families in different strategies. The findings of this study have two main contributions. First, the study contributes to theory by deepening and refining the analysis of contingencies for choosing different order-fulfilment strategies in the ETO context. Second, the study provides practical guidelines to ETO companies that want to adapt their order-fulfilment strategies to the unexpected or planned changes in their environment.