Rossella Pozzi

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.

The urgency of addressing global challenges such as climate change, resource scarcity, and environmental degradation has positioned the Circular Economy (CE) as a crucial strategy for sustainable development. Industry 4.0 (I4.0) technologies have been recognized as key enablers of CE. However, a significant knowledge gap exists regarding how organizations can effectively integrate these technologies with CE strategies. Existing integration frameworks often focus narrowly on specific industries, technologies, or the traditional 3R model, neglecting the broader 10R framework and, hence, offering limited guidance. This paper addresses these gaps by developing a comprehensive 10R-based integration framework, providing practical guidance on how I4.0 technologies can support the full range of CE strategies. Using a literature review based on keywords’ clusters analysis, this study explores how I4.0 can support both implementation and decision-making in the CE 10Rs, providing a practical guide for businesses and supporting a broader shift towards sustainable business models. The results show that IoT, Big Data, and Digital Twins effectively support Rs related to smarter product use and manufacturing processes. Additive Manufacturing, Augmented/Virtual Reality, and Cognitive Twins are crucial in extending the lifespan of products or components. IoT, Artificial Intelligence, Blockchain, and human-robot collaboration can improve recycling practices and material recovery. The study reveals that while ’Reduce’ and ’Recycle’ dominate the literature, integrating I4.0 technologies with lesser-explored strategies like ’Reuse,’ ’Repurpose,’ ’Refurbish,’ and ’Remanufacture’ offers significant potential for future research. It also stresses the need to assess the energy and environmental impacts of I4.0 technologies themselves in the CE context.

I n the current healthcare setting, challenges related to increasing demand for care, scarcity of resources, and the need to ensure high quality standards require a rethinking of organizational and management models. The introduction of advanced methodologies such as Lean Management and Operational Excellence offers concrete tools to optimize processes, reduce waste and make the most of available resources, with the support of organizational change and training initiatives to ensure their effective adoption. The article presents a practical overview of these methodologies, highlighting how they can facilitate the transformation of healthcare processes.

The pharmaceutical sector is experiencing rapid growth in global medicine usage and spending. As the sector expands, so do risks. Blockchain technology promises significant benefits for addressing key issues in the pharmaceutical supply chains, such as enhancing traceability, preventing counterfeit drugs, and securing sensitive data. However, its adoption is not without criticism, facing challenges such as high implementation costs, regulatory uncertainties, and even skepticism about its purported benefits. This study provides the first empirical analysis of blockchain adoption challenges and countermeasures in the pharmaceutical industry. The research aims to identify key challenges and propose effective solutions to support broader implementation. A three-step methodology was employed: a systematic literature review to identify challenges, followed by a Delphi study to assess their relevance, and finally, a questionnaire collecting practical countermeasures. Seventeen major challenges and eight sets of countermeasures were identified and prioritized, with "IT security" being ranked as the most critical challenge and "Education and training" as the most effective countermeasure as it tackles more pressing challenges. The results were then analyzed using the Technology-Organization-Environment (TOE) framework and the Stakeholder Theory. The use of these complementary frameworks allowed to shed light on how different stakeholders can address the different challenges based on their roles, emphasizing the importance of their collective and collaborative efforts. This integration of theoretical frameworks provides valuable practical insights for addressing blockchain-related challenges and accelerating its adoption. Moreover, this research compares the pharmaceutical sector with other application areas, extending insights into blockchain adoption across industries.

For decades researchers have been facing the issue of adapting the economic production quantity (EPQ) to the case of multi-item production contexts characterised by a single (shared) resource with finite capacity. The economic lot scheduling problem (ELSP), which is still of interest to researchers, has addressed this issue. A recent attempt by Rossi et al. (Omega 71:106–113, 2017) addressed the problem while avoiding scheduling. Notwithstanding their relevance, these approaches present limitations in adapting the EPQ model to multi-product ‘pull’ production systems. The present work attempts to overcome these limitations through the development of a methodology based on the equation proposed by Mallya (1992) and restricting items production frequencies to define feasible solutions while avoiding scheduling. The feasibility and performance of the proposed model are evaluated through its application to well-known benchmarking instances (Bomberger’s, Eilon’s and Mallya’s problems) and a large set of test problems.

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.

Drug distribution performed through hospital pharmacies facilitates public expenditure savings but incurs higher social costs for patients and caregivers. The widespread presence of community pharmacies could support patient access while also improving drug distribution. The implementation of prescriptive data analyses as constrained optimization to achieve specific objectives, could be also applied with good results in the healthcare context. Assuming the perspective of the Italian National Healthcare Service, the present study, built upon existing research in this field, proposes a decision support tool that is able to define which self-administered drugs for chronic diseases should be distributed by community pharmacies, answering to critical challenges in the case of future pandemics and healthcare emergencies, while also providing suggestions for the institutional decision-making process. Moreover, the tool aids in determining the optimal setup of the drug distribution network, comparing centralized (hospital pharmacies) and decentralized (community pharmacies) approaches, as well as their economic and social implications.

GoogLeNet is a pre-trained Convolutional Neural Network (CNN) that allows transfer learning and has achieved high recognition rates in image classification tasks. A Recurrence Plot (RP) is an imaging method that depicts the recurrence of the state space system using coloured points and lines in 2D images. This work contributes to facilitating time series feature extraction by proposing a method that applies the GoogLeNet to time series images obtained with RP. The developed method is tested using simulated time series and selected time series from the M3 competition dataset. The results shows that the transfer learning approach allowed the extraction of business time series features by means of a GoogLeNet fine-tuned using 100 simulated time series. The combination of GoogLeNet and RPs outperforms the alternative and easier combination of GoogLeNet and plots of the time series and support the convenience of the RP transformation step. This application of deep learning techniques to business time series imaging offers opportunity for further developments.