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Digital Twins (DTs) hold significant promise in addressing the challenges faced by food supply chains (FSCs). This paper aims to provide critical insights into the potential for Digital Twins to meet the key challenges of the FSC and establish a comprehensive conceptual framework for their implementation. Following a systematic literature review (SLR), the study identified 81 peer-reviewed, high-quality papers published over the last decade (2012–2023). The typology-driven thematic analysis emphasises the emergent nature of DTs within FSCs, highlighting their key characteristics including monitoring, real-time simulation, and scenario analysis. The identified characteristics, applications, implementation drivers and barriers of Digital Twin form the basis for a novel conceptual framework for implementing DTs in FSCs. Leveraging insights from Innovation Adoption Theory and the Technology-Organization-Environment (TOE) framework, the study outlines a structured five step implementation process divided into three stages. Notably, technology assessment and performance evaluation emerge as two innovative steps necessary for the successful implementation of DTs specifically, not previously considered by the theory. The study identifies promising avenues for future research. These findings provide invaluable guidance to researchers and practitioners seeking to embrace the potential of Digital Twin within the food industry. [ABSTRACT FROM AUTHOR]

This paper offers an in-depth examination of the ergonomics of human-centred assembly systems in Industry 4.0, where manual tasks remain essential. The use of advanced technologies such as motion capture (MOCAP) and virtual reality (VR) is analysed as ways to enhance system efficiency and improve worker well-being. The paper highlights the importance of optimising assembly system performance while considering both economic and human factors. Metrics to assess ergonomic risk and productivity are discussed based on human-centred technologies, and existing operational research models are explored to analyse how human factors could be considered in optimising system performance. Additionally, the paper explores potential future directions and how they could play a role in Industry 4.0. [ABSTRACT FROM AUTHOR]

In this study, we develop a new paradigm, Circular Lean Six Sigma 4.0 (CLSS4.0) to promote manufacturing sustainability. This paper aims to provide a practical and holistic view of the drivers and barriers that can help companies design an integrated CLSS4.0 model. The paper is based on a qualitative exploratory study using multiple case studies within 12 Moroccan manufacturing firms conducted through semi-structured interviews with top executive managers. The results show that the drivers are related to expected operational and environmental performance, increasing customer requirements, gaining competitive advantage and market growth while barriers are related to insufficient tangible (finance, human and equipment) and intangible (skills and techniques) resources, data privacy, technical issues and management support. The proposed framework identifies the assessment of drivers and barriers and their attributes as a starting point for managers to lead the CLSS4.0 transformation, thereby contributing to its successful implementation. To the best of our knowledge, this study is among the very first studies to discuss the CLSS4.0 drivers and barriers. It could be useful to managers as a diagnostic tool to assess their ability to implement CLSS4.0 before investing in the initiative. [ABSTRACT FROM AUTHOR]

Rapid advances in new-generation information technologies have been the main driving force for the transformation of manufacturing enterprises in Industry 4.0. Digital twin (DT), as a key technology to promote intelligent manufacturing, has shown great potential for manufacturing enterprises to create an industrial intelligence-driven production equipment through in-depth integration of cyber-physical systems. However, the lack of a systematic effective DT modelling method with a supporting evaluation metric is the most important factor restricting the application of DT in manufacturing enterprises. To bridge the gap, this paper proposes a novel multi-level modelling and fidelity evaluation (MLM&FE) method of DT for creating smart production equipment in manufacturing enterprises, which could help enterprises establish an industrial intelligence-driven production environment to quickly respond to changes in the customised global market, thus greatly improving competitiveness of the enterprises. Specifically, this paper firstly designs a reference framework for DT-enhanced smart production equipment, on which an MLM&FE architecture is proposed. Then, key implementation methodologies and tools for MLM&FE are introduced from the perspective of data space modelling, virtual space modelling, knowledge space modelling, model integration and evaluation. Finally, the developed smart production equipment prototype demonstrates the feasibility and effectiveness of DT MLM&FE. [ABSTRACT FROM AUTHOR]

In recent years, the way that maintenance is carried out has evolved due to the incorporation of digital tools and Industry 4.0 concepts. By connecting to and communicating with their production system, companies can now gather information about the current and future health of the equipment, enabling more efficient control through a process called predictive maintenance (PdM). The goal of PdM is to reduce unplanned downtimes and proactively address maintenance needs before failures occur. However, it can be challenging for industrial practitioners to implement an intelligent maintenance system that effectively manages data. This paper presents a methodology for developing and implementing a PdM system in the automotive industry, using open standards and scalable data management capabilities. The platform is validated through the presentation of two industry use cases. [ABSTRACT FROM AUTHOR]

Validated quantitative models for lean supply chain planning (LSCP) are still scarce in the literature, particularly because conventional push systems have not been widely integrated and tested with pull systems in sustainable and resilient environments in the Industry 4.0 context. Hence the main contribution of this paper is to develop an optimisation model that is able to contribute to the LSCP with the combination of push and pull strategies. Here we present an integrated just-in-time (JIT) production system with material requirement planning (MRP) for a SC that takes a traditional five-level structure based on a mixed-integer linear programming model (MILP) dubbed as LSCP 4.0. The model is able to simultaneously plan the production and inventory of materials and finished goods to satisfy demand from forecasts and firm orders. The selection of alternative suppliers as a proactive measure to face disruptive events is also considered. Furthermore, sustainable practices are included in the objective function for profit maximisation by considering CO2 emissions. This proposal is tested in the footwear sector. The results demonstrate that the combined use of JIT and MRP through a quantitative approach improve performance in leanness, sustainability and resilience by decreasing the bullwhip effect at different SC levels. [ABSTRACT FROM AUTHOR]

For manufacturing companies, quality management is a key feature for increasing the competitiveness, productivity, profitability, and sustainability of their systems. Quality improvement (QI) methods aim to achieve high-quality parts without reducing performance. The Industry 4.0 framework brought technological developments that cannot be used by traditional QI methods, such as Six Sigma, Lean, Lean Six, the Theory of Constraints, and Total Quality Management, which are widely used in manufacturing companies. The need for higher manufacturing sustainability and market requirements has led to the search for alternative QI methods with superior performance to traditional QI methods such as Zero-Defect Manufacturing (ZDM). The current paper is a position paper with a goal to present the ZDM approach and providing a clear definition about ZDM to align everyone in one common understanding of ZDM. Many researchers and manufactures are skeptical about ZDM, therefore, numerous argumentative questions have been created and answered, to convince them why they should migrate from traditional QI methods to ZDM. The migration to ZDM has already started, to support this statement numerous facts from the literature have been presented. Finally, several directions were identified, demonstrating that there is still plenty of room for research in several domains. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, many production scenarios utilise sensors to monitor manufacturing processes, resulting in massive data that can be leveraged to build machine-learning models to improve production efficiency and quality. In semiconductor manufacturing, where many sensors are employed to monitor the production process, the resulting multi-sensor data poses challenges for constructing a virtual metrology (VM) model to assess wafer quality. Furthermore, building separate prediction models for each sensor can result in a loss of redundancy present in the multi-sensor data. Therefore, this paper proposes a multi-source AdaBoost with a cross-weight sampling method for predicting the critical dimension required in VM. Compared with the existing AdaBoost algorithm in regression, the proposed method adapts well to multi-source data by incorporating a cross-weight sampling distribution. Thus, when updating the sampling weight distribution, the impact of both individual and multi-source data is considered to re-sample high-quality observations. Based on the numerical results from the synthetic datasets and case study in semiconductor manufacturing, the proposed method outperforms benchmark methods. Additionally, owing to the redundancy of multi-sensor data, the proposed method demonstrates robust performance regardless of the noise level in the semiconductor VM data. [ABSTRACT FROM AUTHOR]

In the context of great changes in the food industry, the aim of this research is to investigate if and how the implementation of 4.0 enabling technologies can enhance the economic, environmental and social sustainability of the food sector. A systematic literature review, using a combination of 12 keywords, was carried out on Scopus database with defined inclusion and exclusion criteria in order to answer four selected research questions. Overall, 50 relevant papers were retrieved and analysed by Mendeley and Excel with descriptive statistics. VOSviewer was used for co-occurrence and co-authorship analysis. Results illustrate that the interest in the topic has grown, in particular in Italy, and resume the benefits achievable by the implementation of 4.0 technologies in food industries. Social impacts are new job positions, ergonomic design of workplaces, changes in educational institutions, improved nutrition and better animal welfare. Positive aspects are related even to economic growth, improving food chain performances and decreasing companies' costs. Finally, it allows energy, water, CO2 emissions and food savings. Overall, the work provides a helpful overview to food manufacturers and producers, recommending the introduction of I4.0 technologies to positively influence the sustainable development of the sector and remain competitive in the market. [ABSTRACT FROM AUTHOR]

Demand shocks and fluctuations underscore the need for new approaches to coordinate collaboration between firms to scale up production. This paper proposes an approach to formalise product and process requirements via a collaboration ontology and applies semantic reasoning techniques for process formation. Our approach contributes to production research by providing flexibility in coordinating firms engaged in demand-driven collaboration. The proposed approach has four core dimensions: (1) The Collaboration ontology builds on a set of product assembly requirements, process steps, their input/output resources and semantic rules; (2) the ontology reasoner derives resource dependencies between the steps; (3) the java tool interprets resource dependencies as possible transitions in Business Process Management Notation (BPMN); (4) a workflow engine executes the generated product assembly process. The approach and the ontology were validated in an industrial aerospace tendering scenario demonstrating its practical relevance for firms seeking demand-driven collaborations to react to production changes. Finally, we position and explain our contributions to the body of knowledge in collaborative production engineering. [ABSTRACT FROM AUTHOR]

Following the recent advances in Industry 4.0 and the upcoming Industry 5.0, the use of multiple UAVs for indoor tasks has risen, particularly in real-time remote monitoring, wireless coverage, and remote sensing. As a result, UAVs can be viewed as proactive problem solvers and can support Internet of Things (IoT) platforms by collecting and monitoring data cost-effectively and efficiently, leading to better decision-making. Moreover, sophisticated drone operations require specialised software and data processing abilities. However, the utilisation of drones has been mainly focused on outdoor environments, thus creating a literature gap regarding indoor navigation and operation. Therefore, the design and development of a method for remote planning and control of drones based on the utilisation of AR is presented in this paper. The proposed method is based on the utilisation of drones for remote monitoring. The suggested approach involves engineers designing a sequence of actions and transmitting them wirelessly to the drone, eliminating the need for human intervention. Thus, the proposed method contributes towards enabling engineers visualise the drone path with the use of Augmented Reality and provides the flexibility of adding multiple way points. The applicability of the developed framework is tested in a laboratory-based machine shop. [ABSTRACT FROM AUTHOR]

Modern manufacturing is the world's largest and most automated industrial sector. The rise of Industry 4.0 technologies such as Big Data, Internet of Things (IoT) devices, and Machine Learning has enabled a better connection with machines and factory systems. Data harvesting allowed for a more seamless and comprehensive implementation of the knowledge-based decision-making process. New models that provide a competitive edge must be created by combining the Lean paradigm with the new technologies of Industry 4.0. This paper presents novel computer-based vision models for automated detection and classification of damaged packages from intact packages. In high-volume production environments, the package manual inspection process through the human eye consumes inordinate amounts of time poring over physical packages. Our proposed three different computer-based vision approaches detect damaged packages to prevent them from moving to shipping operations that would otherwise incur waste in the form of wasted operating hours, wasted resources and lost customer satisfaction. The proposed approaches were carried out on a data set consisting of package images and achieved high precision, accuracy, and recall values during the training and validation stage, with the resultant trained YOLO v7 model. [ABSTRACT FROM AUTHOR]

This review examines which Industry 4.0 (I4.0) technologies are suitable for improving Lean Six Sigma (LSS) tasks and the benefits of integrating these technologies into improvement projects. Also, it explores existing integration frameworks and discusses their relevance. A quantitative analysis of 692 papers and an in-depth analysis of 41 papers revealed that 'Analyze' is by far the best-supported DMAICs phase through techniques, such as Data Mining, Machine Learning, Big Data Analytics, Internet of Things, and Process Mining. This paper also proposes a DMAIC 4.0 framework based on multiple technologies. The mapping of I4.0 related techniques to DMAIC phases and tools is a novelty compared to previous studies regarding the diversity of digital technologies applied. LSS practitioners facing the challenges of increasing complexity and data volumes can benefit from understanding how I4.0 technology can support their DMAIC projects and which of the suggested approaches they can adopt for their context. [ABSTRACT FROM AUTHOR]

Technological transformations based on 4.0 technologies are a reality. Despite this, there are few studies on their growth-enhancing role at regional level. This paper aims to fill this gap. By conceptually unpacking the two main technological transformations taking place – Industry 4.0 and the digital service economy – and identifying empirically the regions where they prevail, the paper examines whether the two transformations, despite of their profound different nature, are both conducive to regional growth in the period 2007–2018. Empirical results interestingly show that Industry 4.0 and automation technology adoption are associated with regional economic growth especially in those regions where such specific transformation prevails; differently, the effects from digitalisation spread all over European regions and regions where the digital service economy transformation prevails do not enjoy significant growth advantages with respect to others. Targeted regional policies are therefore called for by each transformation. [ABSTRACT FROM AUTHOR]

The objective of this paper is to examine the use and applications of reinforcement learning (RL) techniques in the production planning and control (PPC) field addressing the following PPC areas: facility resource planning, capacity planning, purchase and supply management, production scheduling and inventory management. The main RL characteristics, such as method, context, states, actions, reward and highlights, were analysed. The considered number of agents, applications and RL software tools, specifically, programming language, platforms, application programming interfaces and RL frameworks, among others, were identified, and 181 articles were sreviewed. The results showed that RL was applied mainly to production scheduling problems, followed by purchase and supply management. The most revised RL algorithms were model-free and single-agent and were applied to simplified PPC environments. Nevertheless, their results seem to be promising compared to traditional mathematical programming and heuristics/metaheuristics solution methods, and even more so when they incorporate uncertainty or non-linear properties. Finally, RL value-based approaches are the most widely used, specifically Q-learning and its variants and for deep RL, deep Q-networks. In recent years however, the most widely used approach has been the actor-critic method, such as the advantage actor critic, proximal policy optimisation, deep deterministic policy gradient and trust region policy optimisation. [ABSTRACT FROM AUTHOR]

Innovations and advancements in technology create new opportunities to run and maintain manufacturing plants, which we refer to as digitalised manufacturing. This development is recognised as a socio-technical system (STS) change, where a change in the production system's goals, technology, processes, people, or environment may lead to ripple effects between those sub-systems. Despite this, technology development and technology use cases account for most of the research within digitalised manufacturing, while little attention has been devoted to leadership practices considering digitalised manufacturing from a socio-technical perspective. This paper focuses on the maintenance organisation, whose mission in a company is to keep production systems functional. We aim to describe leadership in industrial maintenance from an STS perspective. This is a unique interview study where twenty maintenance managers from Swedish manufacturing industry offer their perspective on the changing leadership within maintenance, providing a unique insight into the challenges facing leaders of maintenance in digitalised manufacturing. We frame the empirical findings using an STS framework and propose an overall consideration model for leadership that supports the development of a functional maintenance organisation in the face of pervasive digitalisation. [ABSTRACT FROM AUTHOR]

Previous research on automated guided vehicles (AGVs) and Industry 4.0 has mainly focused on technical aspects, while the humans who work with AGVs as well as the influences on the organisation have received limited attention. This paper aims to explore human- and organisation-related challenges in the introduction of AGVs in production facilities. Two cases are examined and analysed using the Human, Technology, and Organisation (HTO) model, which highlights the interactions between the three subsystems: human, technology, and organisation. The paper shows that several human- and organisation-related challenges arise when introducing AGVs, such as supporting the AGVs with new work procedures for managing AGV errors, determining what different operators need to know about AGVs, and developing acceptance among employees. The identified challenges complement the technical focus in previous research. Actions are proposed to facilitate the introduction of AGVs to avoid or manage the identified challenges. The findings of this study can help managers involved in the introduction of AGVs to consider human- and organisation-related aspects to improve AGV performance and employee well-being. [ABSTRACT FROM AUTHOR]

Industry 4.0 was presented more than a decade ago as the fourth industrial revolution, aiming to significantly raise the level of sophistication of interconnected technologies and thus increase manufacturing industries' profits. However, because the technology-driven narrow focus of Industry 4.0 on performance and profit fails to explain how to increase prosperity for all the stakeholders involved, the European Commission has introduced the concept of Industry 5.0. This vision overcomes the weaknesses of Industry 4.0 by paying explicit attention to outcomes for humans in the system and establishing an environment to create human-centric, resilient, and sustainable systems. Considering these developments, this position paper and editorial introducing the special issue of the International Journal of Production Research elaborates on the transition from Industry 4.0 to 5.0 through 10 papers focusing on the human-centric pillar of Industry 5.0 and its impacts on production and logistics system design and management. This work presents guidance for a more systemic approach needed in future research: to include empirically grounded works and data-driven multimethod approaches that consider diversity in system operators and human factors demands holistically in order to incorporate ethical implications missing from Industry 4.0 – in the pursuit of Industry 5.0 systems. [ABSTRACT FROM AUTHOR]

Industry 4.0 aims to establish highly flexible production, enabling effective and efficient mass customisation of products. Modelling techniques and simulation of production processes are among the core techniques of the manufacturing industry that facilitate flexibility and automation of a shop floor in the era of Industry 4.0. In this paper, we present an approach to support production process modelling and process model management. The approach is based on Model-Driven (MD) principles and comprises a Domain-Specific Modelling Language (DSML) named Multi-Level Production Process Modelling Language (MultiProLan). MultiProLan uses a set of concepts to specify production process models suitable for automatic instruction generation and execution of the instructions in a simulation or on a shop floor. By using MultiProLan, process designers may create process models independent of the specific production system. Such process models can either be automatically enriched by matching and scheduling algorithms or manually enriched by a process designer via MultiProLan's modelling tool. In this paper, we also present an application of our approach in the assembly industry to showcase its dynamic resource management, generation of production documentation, error handling and process monitoring. [ABSTRACT FROM AUTHOR]

The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new technologies emerged (including software and connected equipment) that digitise manufacturing systems. These technologies bring new vitality and opportunities to the manufacturing industry, but they also bring new challenges. This paper focuses on the impact of Industry 4.0 on production planning approaches and software. We first propose a digital twin framework that integrates production planning systems and frontier technologies. The frontier technologies that may impact production planning software are the internet of things, cloud manufacturing, blockchain, and big data analytics. Second, we provide a state-of-the-art on the application of each technology in the production planning, as well as a detailed analysis of the benefit and application status. Finally, this paper discusses the future research and application directions in the production planning. We conclude that Industry 4.0 will lead to the construction of data-driven models for production planning software. These tools will include models built accurately from data, account for uncertainty, and partially actuate the decision autonomously. [ABSTRACT FROM AUTHOR]

Before and aftermath of COVID-19 has created multiple waves of digital transformation adoption in supply chain management. The power of potential benefits is realised as a multitude however the transformation phases are not sustainable. This editorial special issue has edited papers of scholarly research contributions to the theory and practice of digital transformation aspects in the field of logistics and supply chain management. Based on the summary of each contribution and an extended literature review on the theme, this editorial paper proposes a novel framework that could facilitate the logistics industry at times of disruptive technologies when firms are struggling to cope with the challenges created due to the COVID-19 pandemic and the recovery. [ABSTRACT FROM AUTHOR]

This paper aims to address in-depth the state of the art of current literature relating to digital supply chain management. A set of 100 papers deduced from the most relevant scientific databases from 2005 to 2020 has been analyzed and synthesised. The analysis and evaluation of these papers have enabled identifying the difference between traditional and digital supply chains, the emerging technologies' impact on supply chain performance, and the current state of digital supply chain management using the SWOT matrix. Besides, it provides a holonic roadmap framework for future study. This work's originality lies in the holonic architecture considering the digital supply chain's hierarchy multiscale and multilevel (business strategies). This holonic architecture supports supply chain performance under the industry 4.0 ecosystem by considering strategic, tactical, and operational strategies. Therefore, the developed framework helps to enable the necessary transition towards the achievement of digital supply chain performance based on the holonic architecture. The proposed study is beneficial for both practitioners and academics as it describes the critical aspects of the supply chain transformation and represents a solid background that outlines the necessity of incorporating a strategical viewpoint for digital supply chains adoption. [ABSTRACT FROM AUTHOR]

Process automation and mass customisation requirements of modern manufacturing systems are driven by artificial intelligence (AI). As AI derives decisions from data, securing the data against tampering is crucial to prevent ensuing operational risks. Additionally, manufacturing systems necessitate collaboration, transparency, and trust among participants while preserving a competitive advantage. Thus, we position blockchain, an enabler of transparent and secure operations, as a security solution for AI-assisted manufacturing systems. In this conceptual viewpoint paper, we present a framework to integrate blockchain in AI-assisted manufacturing systems. We highlight the special needs of manufacturing BCs over generic BCs. We delineate the ways in which manufacturing can be a beneficiary of the synergy between AI and BC. We discuss how BC and AI can accelerate early-phase product design, collaboration, and manufacturing processes and secure supply chains against counterfeit products and for ethical consumerism. Lastly, we identify the needs of modern manufacturing systems and cite a few examples of organisational failures to underscore the importance of security while delineating the significant challenges in adopting blockchain-based solutions in the manufacturing industry. [ABSTRACT FROM AUTHOR]

Interaction between humans and robots in the workplace garners interest in recent years due to the introduction of Industry 4.0 and Industry 5.0 frameworks. A scoping review was performed aimed at investigating the effect of robot design features on their human counterparts. In the analysis of the 32 identified articles, the robot design features used in the literature are shown along with the effects on the operators. Results showcased the many to many relationships between robot design features and effects on operators. Robot appearance, for example, and capabilities play a role in the operators' perception and expectations of their capabilities based on the task and subsequently perceived reliability and safety. Communication capabilities between operators and robots is an integral part for teamwork and performance as it can affect work processes. The paucity of papers empirically addressing human robot interaction as a system is consistent with results from previous literature, indicating the need for more research. The results of this investigation can prove useful in the form of advice to designers and practitioners, such as the operator's involvement in implementation, knowledge on robots' capabilities and training. Research gaps identified are discussed, as well as future research directions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) provides new opportunities to improve manufacturing lines' performance and in-plant logistic processes. The digital milk-run system represents the new frontier to optimize material handling strategies but is still not fully exploited to address material distribution depending on the time slots required by the manufacturing lines. Therefore, to fill this gap, this paper investigates the actual integration of the milk-run system with an IoT system. An analytical model for planning a dynamic routing strategy for tugger trains to deliver the materials to different workstations of a production line has been developed. The proposed model provides a materials distribution consistent with the time slot required by the manufacturing line, ensuring the minimisation of the total distance of the routes. An algorithm developed in Python is proposed to solve the NP-hard problem (nondeterministic polynomial time problem). The model has been applied to a real case of a worldwide automotive company to validate and prove its efficacy and efficiency. Indeed, compared to the current in-plant logistic strategy, (i) the inventory stock of each workstation was ensured, (ii) the average utilization rate of the tugger trains' fleet was improved, and (iii) the daily path was minimized. [ABSTRACT FROM AUTHOR]

According to [Dolgui, Alexandre, and Jean Marie Proth. 2010. Supply Chain Engineering: Useful Methods and Techniques. Vol. 539. Springer.], advancing tactical levels in production systems has led to the disappearance of static scheduling in favour of dynamic scheduling. Additionally, the evolving challenges in the supply chain paradigm have significantly impacted the organisation of production systems. This shift has moved scheduling issues from the tactical to the strategic level, resulting in linear organisations encompassing scheduling decisions. [Proth, Jean Marie. 2007. "Scheduling: New Trends in Industrial Environment." Annual Reviews in Control 31 (1): 157–166. .] emphasised that real-time scheduling in production systems has become a pivotal area of research. He presented several open problems for researchers to address in this context, including (1) the development of real-time algorithms capable of handling multiple operations on the same product and unrelated resources, (2) adapting previous schedules with certain modifications, (3) addressing unforeseen actions that arise randomly in real-time planning, and (4) exploring cyclic scheduling problems with size limits as alternative solutions to heuristic approaches. This paper reviews the evolving trends in light of J.M. Proth's predictions and advice within the aforementioned domains. [ABSTRACT FROM AUTHOR]

Industry 4.0 (I4.0) brings a set of challenges that need to be settled to enable the expected industrial revolution. Cyber-Physical Systems (CPS) and Autonomous Robots are two of mainstream I4.0 technologies and can be combined with other Enabling Technologies to overcome main I4.0 challenges. This Systematic Literature Review (SLR) aimed to understand how these Enabling Technologies were combined with CPS and Autonomous Robots to achieve fully featured I4.0 solutions. The 5C is a CPS architecture which was used as a measurement to CPS compliance. After filtering and processing 15,144 papers from six different academic search engines, it was found that full 5C compliant solutions are rare to find; that not all enabling technologies were integrated; and most of existing research focused in implementing practical solutions, but comparison to conventional approaches is scarce. We conclude that there is a need for further research to accomplish full 5C I4.0. [ABSTRACT FROM AUTHOR]

In disrupted environments, the concept of digitalisation, viability, and sustainability have attracted considerable attention for the last decade. Digital, lean, and green capabilities have stimulated enormous advances in the development of industries. This paper discusses the current state of the art literature regarding sustainable and digital supply chain management performance. It reviews the relationship between digital technologies, lean, green, sustainability, and supply chain performance. A literature review approach, combined with a bibliometric analysis was adopted for robust field analysis. A set of 86 papers has been analysed and synthesised. The analysis of these papers has enabled the identification of the impact of Industry 4.0 technologies on the supply chain performance; the relationship of lean, green, and sustainability capabilities and digital supply chain; and the implications of the digital transformation of those capabilities. It presents a roadmap model integrating lean and green for viable, sustainable, and digital supply chain performance. The study is useful for academics and practitioners as it provides a roadmap framework and a solid background for integrating lean and green in the digital supply chain to achieve sustainable and viable performance as it outlines the necessity for incorporating the sustainability and viability performances in the digital supply chains. [ABSTRACT FROM AUTHOR]

At the core of every high-performing warehouse is an efficient order picking (OP) system. To attain such a system, policy choices should be carefully aligned with subjects responsible for the actual picking within the established system. Despite recent advancements in automating the picking process due to Industry 4.0, human operators will continue to play a crucial role in the future of ware-housing. However, unlike robots, human operators have specific skills, conduct, and perceptions, which are only partly accounted for in current planning models. This review adopts a multimethod approach to identify and analyse how these phenomena are currently integrated into OP planning problems. In addition, we assess the relevance and adequacy of human factors modelling in academic literature with practice-based insights gathered via semi-structured interviews. This leads to five major human factors integration constructs and dedicated recommendations on how to refine them. We then take the analysis one step further and make suggestions on how to integrate these constructs with leading research methodologies in the context of Industry 5.0. The results highlight the prevalent need to increasingly account for psychosocial phenomena and their impact on operational performance. Future research opportunities provide a substantiated foundation to assist in human-centric work design. [ABSTRACT FROM AUTHOR]

With the great opportunities created by the new advances in Industry 4.0, many manufacturers are testing and investing in new equipment and infrastructure to deploy these technologies. However, there are a huge number of small and medium-sized manufacturers (SMMs) that are lagging behind due to the lack of in-house R&D capabilities and workforce shortage and/or financial constraints to afford such investment. Additionally, application of theoretical production research in SMMs often confront challenges such as low data availability and data quality, etc. In this paper, we describe a case study at a local medium-sized manufacturer of electromechanical devices for industrial, consumer, and medical applications, who was struggling to meet ever-growing market demand, and apply a novel approach of production system modelling to overcome the challenge of unavailability of the operation up-and downtime data. Specifically, the parametric model of the production system is identified using several system performance metrics derived based on the parts flow data of the in-process buffer. With the mathematical model constructed, the system bottleneck is analysed anda number of improvement scenarios are explored that can potentially enhance the system throughput. Finally, model sensitivity is analysed by calculating the deviation of the model-predicted performance metrics to those produced by a reference nominal model. This analysis demonstrates that the model constructed using our proposed approach is robust even when the system parameters vary from the baseline ones. [ABSTRACT FROM AUTHOR]

This paper analyses extant literature on how Industry 4.0 impacts Social Capital in Buyer-Supplier Relationships. We conduct a systematic literature review and identify 36 academic articles that are analysed in the research process. The study uncovers strategic changes Industry 4.0 implies for Social Capital in Buyer-Supplier Relationships. These include transformations in cognitive, structural and relational capital in terms of a shared vision, social interaction and trust. Therein, Social Capital in Buyer-Supplier Relationships is needed and further invested in aspects like common decision-making, information sharing and cross-company integration in Industry 4.0 contexts. We propose that Industry 4.0 implementation does require and foster Social Capital in Buyer-Supplier Relationships and that two diametrically opposed elementary forms of Buyer-Supplier Relationships co-exist in an Industry 4.0 context. The systematic literature review is the first to analyse the extant body of literature on Buyer-Supplier Relationships in Industry 4.0 to synthesise detailed transformations against the backdrop of Social Capital. It provides a comprehensive overview of the current state of research and develops several suggestions for future research and managerial practice, for example, concerning the role of humans in strategic tasks in Industry 4.0. [ABSTRACT FROM AUTHOR]

In a machining line with a Lean manufacturing environment, such as one used for mass production of automotive components, tool life is determined by a tool counter that is set on the safe side to avoid producing a failed product, so the cutting tools are typically underutilised. On the other hand, cutting parameters optimisation, which has a significant impact on tool life, is not feasible due to the rule that prohibits changing the cutting parameters during mass production to maintain the quality standard. To address both issues, this paper proposes a hybrid monitoring and optimisation process that can be carried out concurrently without interfering with production activities. The monitoring process involves two methods, namely threshold limit-based monitoring and machine learning-based monitoring, for ensuring product quality during the optimisation process. Meanwhile, Bayesian optimisation (BO) is used for the optimisation process due to its capability for autonomous search for the optimum condition of an unknown and expensive objective function. The case study demonstrated BO's ability to find the optimal condition with a small number of samples, while the machine learning method used can monitor tool wear and surface roughness with average errors of 5.5% and 7.7%, respectively. [ABSTRACT FROM AUTHOR]

Digital assets are highly vulnerable and always prone to malicious intervention. Identification of causes of such intervention for timely support and assistance remains a key challenge for businesses to remain functional and thrive with the competition. A framework is proposed in this paper for identifying cyber risk, threat, and countermeasure, based on breach databases and textual information processing. Alongside, a multi-objective optimisation of a mixed-integer non-linear problem (MINLP) is made post linearisation to find out a suitable trade-off between cyber risk and investment. The model helps in effective decision-making by finding the proneness of suppliers (as nodes) in the sequence of reducing vulnerability and pairing of categorised factors. The web scrapping and historical databases are processed to extract relationships among categorised factors using natural language processing (NLP). Pareto optimal pairs are obtained to explain the application of the current contribution in terms of risk-cost trade-off. It helps in forming preventive strategies with a suitable amount of investment and the required order of precedence or susceptibility. [ABSTRACT FROM AUTHOR]

The widespread adoption of Industry 4.0 technologies is revolutionising how manufacturing operations are managed and done. This revolution drives manufacturing practitioners to reevaluate their current manufacturing planning and control (MPC) strategies to maintain global competitiveness. The production and intralogistics (PiL) operations within traditional MPC systems are organised separately, which results in inferior overall solutions. PiL operations in a single factory are inherently coupled and interact with each other throughout the entire process, which needs synchronous organisation and operations. This paper introduces a novel concept of operations twins (OT), with vertical twinning and horizontal twining, for achieving PiL synchronisation by leveraging Industry 4.0 technologies and innovative operations management strategies. An Internet-of-Things (IoT)-based vertical twinning method is developed for real-time object-level data collection and information-sharing between PiL. A horizontal twinning mechanism is proposed to support real-time coordination of production and intralogistics operations with real-time information-sharing. A numerical study is carried out, and the results show that OT outperforms the widely used static and dynamic methods regarding the overall stability and typical measures such as makespan, average manufacturing time, and average tardiness under different levels of uncertainties. [ABSTRACT FROM AUTHOR]

In Industry 4.0, the production planning and execution of smart factories (SFs) full of continuously delivered small-batch orders become dynamic and complicated. Traditional centralised manufacture planning is difficult to handle unexpected disturbances. With the aid of new information technologies, resources in SFs become smart and connected to make autonomous decisions. This paper tries to release intelligence of smart connected resources to allocate production tasks and logistics tasks in SFs coordinately and autonomously. The architecture is modelled as an autonomous decision-making manufacturing system with IIoT support, which aims to synchronously allocate manufacturing tasks by the bidding of resources in SFs. Then, a dynamic production-logistics-integrated tasks allocation model is built. The orders makespan and resources utilisation are considered as the objective function, and production resources and logistics resources are integrated to autonomously communicate and interact with each other to bid for dynamic production-logistics integrated operations. To figure out, a reinforcement learning (RL) algorithm is studied, which makes operations decisions for each job step by step based on in-situ data during manufacturing process. Finally, a demonstrative case showed that compared to centralised scheduling system, the RL-based model performs better in handling production-logistics-integrated tasks allocation problem in SFs full of dynamic and small-batch individualised orders. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationship between organizational culture (OC) profiles and the adoption of Industry 4.0 (I4.0) design principles. We surveyed 153 middle and senior managers from Brazil and India, whose manufacturing companies have been implementing I4.0. Participants provided answers regarding the perceived OC characteristics that prevail in their companies, which were assessed based on the Competing Values Framework. We also collected data on the adoption levels of I4.0 design principles, whose analysis was conducted through the utilization of multivariate data analysis techniques. Our results indicated that, depending on the I4.0 design principle, OC profiles (Clan, Adhocracy, Market and Hierarchy) may either corroborate or impair its adoption, allowing the verification of the hypothesized relationships. The understanding of the association between OC profiles and I4.0 design principles provides practitioners with arguments to identify possible problems during their digital transformation. As changes in OC require significant efforts and are usually time-consuming, companies that can anticipate those issues might face a smoother transition towards the Fourth Industrial Revolution, hence, obtaining competitive advantages. [ABSTRACT FROM AUTHOR]

The Fintech and Industry 4.0 paradigms, and the combination of the two, have gained increasing attention from the scientific community in recent years. This paper aims to explore the convergence of these two research themes by framing their interplay through a systematic literature review covering the period of 2017 to August 2021 and involving 78 papers. The bibliometric approach made it possible to extract a large amount of useful information which was previously scattered and to present it in a systematic way. The analysis of the co-occurrence network of keywords identified four distinct clusters, corresponding to the dominant research themes in the area, which suggested the design of a research agenda. The agenda-driven questions indicated which current aspects of Fintech and Industry 4.0 need additional insight. In that regard, the environmental and social responsibility aspects of the firms in the sectors were found of particular importance. Theoretical and practical implications of this study as well as answers provided to a set of initially posed research questions were also discussed. [ABSTRACT FROM AUTHOR]

In modern manufacturing systems, operators' role is evolving continuously in relation to the main production paradigms affecting how companies approach their manufacturing and logistics operations. If the introduction of Lean Manufacturing principles has affected workers' well-being directly, the adoption of digital technologies is modifying how factory work is organised and performed. As a result, digitalised and Lean Manufacturing contexts require operators to enhance their skills increasingly to perform several tasks and functions. This paper examines the evolution of operators' role in production by recalling two main work design paradigms that originated in the 1960s: Job Enlargement and Job Enrichment. Indeed, through a literature review and development of a causal loop diagram (CLD), this research points out how the main features of Industry 4.0 and Lean Manufacturing jointly affect the Job Enlargement and Job Enrichment concepts. The study significantly helps reshape work design in next-generation production systems, shedding light on the implications and relationships of the most widespread manufacturing paradigm, i.e. Lean Manufacturing and Industry 4.0, using Job Enrichment and Job Enlargement strategies. A CLD also can be a helpful tool for practitioners to understand what factors to leverage to increase efficiency and worker satisfaction. [ABSTRACT FROM AUTHOR]

This paper proposes an interpretation methodology for the effect of transient process data on quality of injection molded parts. The transient process data measured in the actual processing space have been regarded as the most relevant information to manufacturing processes and product quality. However, its interpretation to pinpoint which feature in the data would affect part quality has traditionally relied on knowledge and understanding of the manufacturing process. The main objective of this method is to reduce the dependency of the transient process data analysis on process knowledge and understanding by using explainable artificial intelligence (XAI). The contribution of the 'section-wise' features in the transient process data to the quality prediction of machine learning (ML) models was investigated for the first time. The interpretation results of the effect of cavity pressure and mold surface temperature on four different quality factors represented reasonable explanations of the characteristics of the polymer materials, product geometry, and molding process. Due to the intermediate relationship of the transient process data with the user-specified process parameters and the resulting quality variables, the interpretation results can be further utilized to optimize the process and provide the optimal transient process data profile for best part quality. [ABSTRACT FROM AUTHOR]

Collaborative robots (cobots) are a type of Industry 4.0 technology designed to support manufacturing workers and create smart working environments (also called as Industry 5.0). However, little is known about how the use of cobots shapes workers' skills. We analyse this in four types of human-cobot interaction: coexistence, synchronism, cooperation, and collaboration. We examine the implementation of cobots by a leading global provider using a qualitative research based on: (i) analysis of reports regarding the implementation of 200 cobots in 138 companies, (ii) interviews with the team and customers, (iii) six-month follow-up of cobot implementation in a manufacturing plant, and (iv) interviews with two cobot competitors. Our findings demonstrate how each type of human-cobot interaction influences workers' skills in various manufacturing activities. We observe that most companies are in early stages of implementation, focusing on worker substitution. However, we identify a range of effects, including deskilling or reskilling, depending on the type of manufacturing activity analysed. The upskilling effect is particularly evident in the most advanced types of human-cobot interaction, regardless of the company's size. As a main contribution, this paper sheds light on how companies can enhance workers' skills through other levels of interaction between workers and cobots. [ABSTRACT FROM AUTHOR]

This systematic literature review synthesises the literature on human centric IN 4.0 and IN 5.0 while exploring driving forces behind the transition from technocentric IN 4.0 to value centric IN 5.0 using the principles of the multiple level perspective (MLP). Works that discuss contextual, regime and niche level factors which impact on the transition were explored. The Covid- 19 pandemic and Climate change are identified as key contextual, 'Landscape', factors impacting the transition while Trust, Mass personalisation and Autonomy are highlighted as key Regime factors. In terms of Niche innovations, Advanced Extended reality technologies, Cobots/ Advanced Robotics, and Advanced AI are often connected with landscape or regime issues. Drawing on MLP theory, the study demonstrates that the transition from IN 4.0 towards IN 5.0 is occurring through a reconfiguration pattern. The paper further emphasises aspects that both practitioners and academics need to be cognisant of in order to affect a transition from IN 4.0 to IN 5.0. [ABSTRACT FROM AUTHOR]

Numerous studies have focused on digital twins (DTs) and their applications. However, a common method for evaluating the performance of DTs is lacking. Digital twins performance assessment mechanism is crucial when improving or monitoring any process or system in a business, such a strategy could serve as a guide to help researchers and practitioners create more effective digital twins. Guided by these challenges, we propose a structured method for measuring the performance and flexibility of digital twins, and our study provides a procedure for quantitative calculation of the performance of DTs based on four key performance indicators (KPIs). Moreover, we propose a new KPI, DTflex, for determining the flexibility of digital twins. We evaluated the performance of DTflex as a KPI to demonstrate its use as a simple tool for designers and practitioners to quickly compare different DT methodologies, which might provide valuable input for the iterative improvement of DTs to increase their efficiency in future applications. The knowledge provided along with the method proposed in this paper is expected to help researchers and practitioners working in different fields to compare DT methodologies and DT performance which will lead to higher efficiency DT solutions and increased re-usage of existing DTs. [ABSTRACT FROM AUTHOR]

This paper analyzes how Industry 4.0 transforms supply chain collaboration and design. Therefore, we identify and analyze 151 academic articles. The study uncovers holistic strategic changes Industry 4.0 and associated technologies imply for cross-company collaboration in supply chains. These include changes in the initiation of buyer-supplier transactions, information exchange and handling, and how Industry 4.0 transforms relationships and cooperation. Subsequently, implications for aspects concerning supply chain design, such as ecosystem development are given. The systematic literature review is the first to analyze the body of literature on supply chains in Industry 4.0 to synthesise how cross-company collaboration is transformed in detail. The paper provides a comprehensive overview of the current state of research and develops several suggestions for future research and managerial practice, such as the role of risk-benefit sharing and information sharing, allowing to develop new forms of collaborative value creation in Industry 4.0. [ABSTRACT FROM AUTHOR]

This work aims to explore the potentialities of an end-user personalisation platform in industrial settings. In such a context, stakeholders with different roles and competencies collaborate to manage and control an environment where legacy machines coexist and interact with newer ones. Our goal is to provide a rule-based tool that allows end-users to build personalised solutions to respond quickly to the dynamic needs of factories. We report on a case study in the paper factory domain, in which the industrial aspects identified with expert stakeholders through interviews have been simulated and addressed through an extension of a personalisation platform. A first user test of the resulting environment has been carried out with a representative set of users, and has provided useful and encouraging feedback in terms of the potentialities of the proposed approach in industrial contexts. [ABSTRACT FROM AUTHOR]

Machine tools play a pivotal role in the manufacturing world since their performance significantly affects the product quality and production efficiency. In the era of Industry 4.0, machine tools are expected to have a higher level of accessibility, connectivity, intelligence, adaptivity, and autonomy. With the rapid development and application of various Industry 4.0 technologies, digitalisation and servitisation of machine tools have become a new research trend. However, few review articles on the development of machine tools in the context of Industry 4.0 have been reported. To understand the current status of digitalisation and servitisation of machine tools, this paper provides a systematic literature review combining both bibliometric and qualitative analysis. Our review results provide a comprehensive and in-depth understanding of recent advancements of digitalisation and servitisation of machine tools, including the key enabling technologies, methods, standards, architectures, and applications. Furthermore, we propose a novel conceptual framework of Cyber-Physical Machine Tool (CPMT) as a systematic approach to achieving digitalisation and servitisation of next-generation machine tools. Finally, major research issues, challenges, and future research directions are discussed. This work will help researchers and industrial practitioners spark new ideas for developing the next-generation machine tools in the era of Industry 4.0. [ABSTRACT FROM AUTHOR]

Sawmills are key elements of the forest product industry supply chain, and they play important economic, social, and environmental roles. Sawmill production planning and control are, however, challenging owing to several factors, including, but not limited to, the heterogeneity of the raw material. The emerging concept of digital twins introduced in the context of Industry 4.0 has generated high interest and has been studied in a variety of domains, including production planning and control. In this paper, we investigate the benefits digital twins would bring to the sawmill industry via a literature review on the wider subject of sawmill production planning and control. Opportunities facilitating their implementation, as well as ongoing challenges from both academic and industrial perspectives, are also studied. [ABSTRACT FROM AUTHOR]

Virtual metrology (VM) involves estimating a product's quality directly from production process data without physically measuring it. This enables the product quality of each unit of production to be monitored in real time, while preserving the process efficiency. Initially developed for the semiconductor industry, VM has recently been examined for use in other industrial fields. VM is enabled by components such as quality estimators and drift detectors. It enhances various industrial applications such as machine control and sampling decision systems. The literature lacks a comprehensive summary and a systematic review of the state of the art in VM. To fill this gap, this study followed a systematic methodology to conduct a complete and structured literature review of VM. This paper presents a detailed analysis of the 199 papers that we identified based on our search and selection criteria. Following our methodological framework, we assigned these papers to categories and highlighted shortcomings. Finally, we discussed VM's use in various industrial fields, underlining its potential for every manufacturing industry. This study and the proposed framework are useful for both practitioners and academicians since they raise crucial theoretical and managerial questions as well as provide suggestions for further research on this critical topic. [ABSTRACT FROM AUTHOR]

The transformation of the manufacturing industry over the last two decades has been largely inspired by data. Digitisation has made its mark in different areas of manufacturing, from digital materials and processes to data science in decision-making. The digital manufacturing is evolving towards universal manufacturing that is highlighted in this paper. Different manufacturing initiatives are benchmarked and their relationship to universal manufacturing is demonstrated. The representation of enterprises in the universal manufacturing cloud is discussed. Product- and process-based specifications of digital enterprises are defined. Using these specifications, the enterprise configuration algorithm proposed in the paper is applied for the selection of component models. Two different representations, a node–node and an input–output matrix, of the digital component models are considered. The extended topological sorting algorithm is applied to construct an integrated digital model. [ABSTRACT FROM AUTHOR]

Despite its potentialities, Lean Automation (LA) has not attracted attention for over two decades. However, due to the new opportunities offered by Industry 4.0 technologies, LA revives expectations and enthusiasm. This paper aims to test empirically the association of LA, in the form of integration of Lean practices and Industry 4.0 technologies, and operational performance. The paper used a rigorous, multi-stage empirical method and data from a set of more than 200 manufacturing firms; one representative from each of the studied companies filled in a survey on Lean practices and Industry 4.0 technology bundles, with productivity, delivery, inventory, quality as performance indicators. The extensive survey results helped in identifying underlying components of LA: one focuses on operational stability and includes practices and technologies supporting efficiency along the supply chain; the other focuses on streamlining the flow, on fastening the process to reach the customer. Moreover, it demonstrates the positive correlation between LA and operational performance. Besides its contribution to the body of knowledge on LA, this research paves the base of improving academics and practitioners understanding of the integration of new technologies with Lean Production systems. [ABSTRACT FROM AUTHOR]

This collection on 'Artificial intelligence, big data, algorithms and Industry 4.0 in firms and clusters' is introduced exploring the themes discussed by the nine papers and grouped into three categories to uncover new dynamics and identify future research opportunities for clusters and organizations in these transformative times. The first group explores theoretical aspects of AI and its evolution in social sciences, focusing on industry 4.0, smart cities, big data, and other related topics. The second group examines the role of industrial robots in employment, productivity, and knowledge absorption in industrial districts. The third group discusses innovation in the context of local production systems, AI ecosystems, and the growth and potential of the Metaverse. [ABSTRACT FROM AUTHOR]