Tommaso Rossi

The years 2021 and 2022 showed that maritime logistics are prone to interruptions. Ports especially turned out to be bottlenecks with long queues of waiting vessels. This leads to the question of whether this can be (at least partly) mitigated by means of better and more flexible terminal operations. Digital Twins have been in use in production and logistics to increase flexibility in operations and to support operational decision-making based on real-time information. However, the true potential of Digital Twins to enhance terminal operations still needs to be further investigated. A Delphi study is conducted to explore the operational pain points, the best practices to counter them, and how these best practices can be supported by Digital Twins. A questionnaire with 16 propositions is developed, and a panel of 17 experts is asked for their degrees of confirmation for each. The results indicate that today’s terminal operations are far from ideal, and leave space for optimisation. The experts see great potential in analysing the past working shift data to identify the reasons for poor terminal performance. Moreover, they agree on the proposed best practices and support the use of emulation for detailed ad hoc simulation studies to improve operational decision-making.

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.

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.

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 impact of Industry 4.0 and its opportunities are expected to be significant for manufacturers. A lack of empirical studies creates the need for academic contributions on the critical success factors of Industry 4.0 implementations and their resultant improvements for manufacturing businesses. This research uses case studies of eight implementations of Industry 4.0 technologies in Italy to supplement existent literature. An original data set was constructed using a purposely defined research protocol using plant visits and structured interviews. Continuous improvement/lean management emerged as a critical success factor for implementation, together with quality and flexibility-based competition, top management leadership, establishment of inter-functional teams, conducting of preparatory activities, project planning and training activities. Incremental/evolutionary and radical/revolutionary improvements in business model elements are possible outcomes of implementations, while addressing customers' needs emerges as an antecedent to radical/revolutionary improvements. Managers will benefit from understanding how to achieve successful implementations and business improvements.

Purpose: Lean and agile are essential supply chain management (SCM) strategies that enhance companies' performance. Previous studies have reported the capabilities of different SCM strategies to enhance performance; however, the emergence of Industry 4.0 technologies has bred focus on the possibility of attaining more levels of operational performance. Despite being demonstrated helpful at enabling supply chain (SC) strategies, the literature linking Industry 4.0 with SCM strategies is still in its infancy. Thus, this work investigates the degree to which “Industry 4.0 technologies” enable the implementation of lean and agile practices and subsequently assesses the potential performance implications of integrating Industry 4.0 technologies with the SC operations. Design/methodology/approach: The work employs an exploratory case study approach using empirical data from selected organisations drawn from an Estonian manufacturing cluster and digital solution providing companies. The data collected via interviews were used to assign numerical scores and subsequently aggregated across the five cases for the research variables of interest. The work is crowned with a model grounded on the cross-case analysis to depict which technologies impact each of the lean and agile practices. Findings: The analysis enabled comprehension of the potential impact and level of importance of the main Industry 4.0 technologies on lean and agile practices and ultimately the potential implication on performance. The findings revealed that the technologies have a high impact on the practices. Although the impacts are of varying degrees, the analysis provides means to identify the technologies with the most significant impact on lean and agile SCM and the sets of practices with the greatest likelihood of being enabled by various digital technologies. Practical implications: The work presents various lean and agile practices that practitioners can deploy to operations, alongside the technologies that could support the implementation of the practices towards achieving the various performance measures. Also, it provides some guides for the digital solution providing companies towards understanding the SCM practices that can be improved upon by various digital technologies. This enables them to have more saleable proposals for intending companies who might be sceptical about transiting into the digital operation phase. Originality/value: This is the first attempt to empirically address the connection between Industry 4.0 technologies and the integrated lean and agile strategies despite literature backing of the complementary nature of the two SCM strategies.

As the world becomes globalised, companies fight for survival by connecting their in-house processes with external suppliers/customers. To remain competitive, companies must integrate innovative capabilities like 'industry-4.0 technologies' with their operation and supply chain (SC) strategies. The integration of various strategies has been investigated with the associated effect on performance; however, studies on how industry 4.0 technologies might support integrated strategies are still incipient. This work investigates the hierarchical relationships of 'industry 4.0 technologies' with lean and agile strategies. Adopting the 'Interpretive Structural Modelling (ISM)' technique to present a model depicting the linkage, the work also classifies the technologies and practices according to their 'driving' and 'dependency' powers. The findings revealed that the technologies have a high affinity to enable the implementation of lean and agile strategies. Among the nine technologies included in the study, 'Cyber-Physical-System', 'Internet-of-Things', 'Cloud-Computing', and 'Big-Data-Analytics' have the highest driving powers, signifying their higher affinity with the practices. Meanwhile, all the practices have a high enough affinity to be influenced by the technologies, except for a few (3/16 of lean and 2/9 of agile) that possess affinities too low to be driven by these technologies. The theoretical and managerial impacts of the research are also emphasised.

As competition continues swinging from individual 'company level' to 'supply chain (SC) level' courtesy of diverse and erratic customer behaviour in this globalisation era, survival of such SC is hinged on careful adoption of strategies aligned with organisational goals. Lean and agile are critical strategies because the former ensures efficient use of resources while the latter involves matching supply with demand in turbulent/unpredictable markets. Careful adoption of both strategies will lead to SC performance improvement. This paper aims at advancing their understanding by conducting an intensive scientific literature review to unravel the state of art of lean-agile SC by examining the procedure of knowledge conceiving, transmission, and development from a dynamic viewpoint. To achieve this, a dynamic cum quantitative review technique named 'systematic literature network analysis (SLNA)' is applied. The investigation enabled unravelling research directions/emerging themes in the lean-agile field thereby supporting researchers/newcomers to target specific themes to explore.

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.