Violetta Giada Cannas

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.

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.

Predictive maintenance (PM) involves the use of internet of things and machine learning techniques applied to machinery for remote monitoring of different variables to timely detect problems, before they require costly maintenance or generate customers’ complaints. Thus, it minimises the probability that the machine will break, extends its lifecycle and reduces the number of corrective actions. Despite the importance of PM and the growing implementation of Industry 4.0 technologies, a limited number of Italian machinery companies today includes PM systems in their products. Additionally, the topic has received little attention by literature. Consequently, there is a need to identify the barriers to the implementation of PM in the Italian machinery industry. Therefore, the aim of this research is to categorise the challenges to be considered when implementing a PM and propose a set of possible countermeasures. In doing so, the study reviews the existing literature on this topic and empirically explores three cases in the machinery industry. The results of the literature review show a list of barriers to PM implementation that can be related to the machinery industry. Then, the barriers are empirically validated, and inductively extended, and final set of countermeasures is proposed to overcome these challenges, in help of managers that are interested in adopting PM.

The link between Industry 4.0 (I40) and lean manufacturing has recently gained significant popularity in both academia and industry. The implementation of I40 has been proved to be beneficial for lean programs, supporting lean practices and increasing the flexibility of lean. In this context, the present paper introduces i-FAB, a learning factory developed by Università Carlo Cattaneo (LIUC) to demonstrate the benefit of the adoption of I40 technologies in a lean managed assembly system. The paper provides details on the i-FAB lean tools, I40 technologies and the training modules developed for Industrial Engineering and Management students and executive learning programs, showing empirical evidence of the benefits linked to the implementation of I40 technologies in a lean managed assembly system.

The development of innovative simulation and integration technologies, led by Industry 4.0, brought increasing attention to the theme of “Digital Twins” (DT) in manufacturing. As a matter of fact, since 2016 the number of papers related to DT has been strongly growing in the industrial engineering body of literature. Articles, conference papers, and book chapters can be found, presenting models and applications of DT in different manufacturing realities. Also, reviews published from 2018 have analysed the current state-of-the-art and opened interesting future research directions in terms of DT methods, tools, and technological issues. These are key contributions to provide support to the decision-makers in integrating the benefits of different technologies and developing the idea of Smart Factory. However, achieving a successful DT-driven Smart Factory within industrial realities is a demanding task, and nowadays companies still struggle in understanding how to face the challenges related to create and maintain DT. These challenges are not only related to technological barriers, but also to managerial, cultural, and organisational barriers. Nevertheless, a complete overview of the barriers and the consequent enabling factors to DT implementation are still missing in the literature. Hence, the aim of this paper is to present a general overview of the literature on barriers and enablers to DT implementation and understand the current gaps that need to be filled in this research area. In doing so, the study conducts a systematic review of the literature on DT for manufacturing applications, presenting a descriptive and thematic analysis of the existing contributions. By analysing the DT literature, this article develops a taxonomy of the main barriers and enablers for the implementation of DT and presents a research agenda to define future research directions and guide new contributions to the DT knowledge.

Cross-functional coordination among engineering, sales and production departments is known to be beneficial for improving order fulfillment processes. In Engineer-to-Order (ETO) companies, sales, design and production activities are strongly interrelated and sometimes they overlap, thus requiring cross-functional coordination. In these companies, design and production activities can be both partially performed before the customer order arrival. ETO companies pursue different objectives and implement different managerial approaches before and after the customer order decoupling point (CODP). However, despite its relevance for company performance, how ETO companies manage cross-functional coordination and how departments are coordinated before and after the CODP is still understudied. This paper sheds light on this topic by investigating 12 case studies in the Italian machinery industry. Results suggest that the coordination mechanisms used before and after CODP are different, and vary depending on the CODP configuration chosen.

An efficient and effective forecasting of production cycle times (CT) is a critical success factor in semiconductor manufacturing systems (SMS): inaccurate CT forecasts can have a negative impact on production scheduling, causing late deliveries, as well as on the amount of inventories and work-in-progress, which rapidly lose value over time because of the high risk of obsolescence. Therefore, since the 80s, several quantitative techniques have been developed to face this problem. Furthermore, Artificial Intelligence (AI) techniques are gaining importance, despite their potential is still not fully exploited even in the most advanced manufacturing systems. However, a synthetic overview of the techniques to forecast CT in SMS is still missing in the literature. As a result, it is difficult for decision makers to orient themselves and choose, among the many existing ones, the best model for their specific situation, comparing the different performance in terms of accuracy, data required, speed and easiness to use. This paper aims at presenting an overview of the quantitative techniques developed to forecast production CT in SMS. Firstly, a description of the methodology with which the literature review has been carried out is provided. Secondly, a taxonomy of forecasting techniques is proposed. Subsequently, a synthetic description of analytical, simulation, time-series and causal methods is presented. Within statistical techniques, a special focus is deserved to AI ones, since their popularity has dramatically increased in the last years. In particular, the most recent applications of artificial neural networks (ANN) in SMS – namely, hybrid methods and Long-Short-Term-Memory recursive neural networks – are described. Finally, a table with a qualitative comparison between the different methods is proposed.

The dairy industry plays an important role in the agricultural economy of the European Union. Moreover, as a part of the agrifood sector, it is greatly exposed to the challenges of the Sustainable Development. In particular, the literature has recently put a strong emphasis on the need for improving environmental performance of such industry. One of the reasons regards the environmental impact of the logistics processes, especially the transportation of the products. Implementing innovative green supply chain management (GSCM) practices (i.e. the set of techniques used to increase environmental performance of the supply chains) might be a solution. Nevertheless, the practical adoption of GSCM practices is still far from widespread. In fact, the dairy supply chains are plagued by high risk and uncertainty, which often prevent managers to move towards sustainable solutions. The purpose of this research is to understand the main factors influencing the adoption of innovative GSCM practices within the dairy supply chains. In particular, given its relevance, the focus is on the introduction of new sustainable logistics models with the perspective of the entire supply chain. Accordingly, a multiple case studies research in multiple stages (producer, logistics operator, and supermarket) of Italian dairy supply chains has been performed. This allows the researchers to empirically explore the context and collect data from different actors of the supply chain. The findings support the identification of factors affecting the implementation of new sustainable logistics models in the dairy supply chain. They can be classified as KPIs (e.g. delivery and financial performance), technology and features of the logistic model (e.g. track and trace technologies, traveling stock) and exogenous factors (e.g. weather conditions). The factors are different from one stage to the other, due to dissimilar needs. The resulting framework supports companies operating in the dairy industry to implement innovative GSCM practice.

Engineer To Order (ETO) companies have to face the difficulty to manage high uncertainty in customer requirements, and, consequently, to organize all the activities for efficiently and effectively answering customer orders. In these contexts, planning the activities of design, production and assembly departments is a complex task, also given the variability of lead times and the difficulties in forecasting resources workload. Literature has mainly devoted attention to planning tools for standardized make to stock environments, often neglecting ETO companies’ needs. This paper aims to present a methodology for supporting design, production and assembly planning in an ETO context. The methodology combines project requirements planning with lean management tools, i.e. Asaichi and visual control and management tools. It has been successfully applied in a company showing its potential for increasing planning performance, company flexibility and, in the end, delivery performance.