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The Industry 5.0 concept has placed human needs at the heart of industrial processes. This raises the question of how new technologies can enhance employee decision-making processes and influence the evolution of team autonomy. Recent studies have shown that the best way to measure these impacts is to conduct experiments in complex and realistic environmental settings. However, the main methods cannot satisfy this requirement while controlling the events and associated variables, whereas a set of use cases can. Therefore, a model should be defined to generate and structure these use cases while validating their relevance. Following the decomposition of the global research objective and case-definition recommendations, this study proposes a framework for designing complementary use cases to evaluate the impact of new technologies on emerging autonomy models in a structured, realistic, and global manner. Based on widely recognised related work, the 6-step framework helps define a coherent context specifying the business process model, agent, autonomy, technologies to be implemented, their fields of action, detailed variable collection protocol, and experimental setup. A cross-analysis of existing cases from the literature and empirical use of the framework validated the relevance of the model in designing experimental environments that are close to real-world settings. [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]

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]

The fourth industrial revolution tempts manufacturing companies to transform into smart factories, which in turn affects the human work. We examine this complexity from the perspective of human factors and ergonomics (HF/E) by analysing the organisational capabilities needed when seeking and selecting new technology solutions, and designing and implementing them into local application. This qualitative study focuses on manufacturing companies that have pioneered adopting high technologies in their processes. Empirical material was collected from 15 Finnish manufacturing companies of different sizes. Particular attention is paid to how HF/E are present when companies (1) sense new opportunities for technological development of their production, (2) seise these opportunities through design actions, and (3) shift the solutions into use. Analyses revealed that companies, regardless of their size and the technology being implemented, do not fully understand the fundamentals and value of HF/E, resulting in technology and production-oriented solutions in which human skills and capabilities are often neglected. A novel road map is proposed for integrating HF/E expertise and knowledge when seeking, selecting, and utilising new production technologies. This study increases understanding of the role of HF/E when adopting new technologies in manufacturing. The road map improves possibilities for fact-based decision-making. [ABSTRACT FROM AUTHOR]

Technologies such as Autonomous Guided Vehicles (AGVs) and the Internet of Things (IoT) increasingly disrupt traditional manufacturing and production systems. However, there is a scarcity of empirical studies synthesising and evaluating the impact of disruptive technologies on existing manufacturing systems. This study examines the impact of AGVs applying IoT on Flexible Manufacturing Systems (FMS) through a case study demonstrating the integration of AGVs with IoT in a manufacturing company. As a concept, FMS was conceived decades ago; this study uses socio-technical systems theory to elaborate the concept of FMS into the current context. Key themes uncovered from the literature review include (i) AGVs in warehouse systems, (ii) AGV scheduling and routing, (iii) Human-machine interface, and (iv) integrating and controlling AGVs/IoT. The case study demonstrates how AGVs can create smart, flexible manufacturing systems by taking the following steps: (a) problem identification, (b) performance measurement, (c) designing the proposed solution, (d) evaluate IoT systems, (e) implementation of the new solution, and (f) future improvements. The study concludes with specific recommendations to implement Industry 4.0 in manufacturing companies. [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]

Digital enabled agility reflects the transformative outset of an organisation to first upgrade its business processes digitally towards facilitating real-time information processing/capabilities. Second, tapping from real-time capabilities, digital enabled agility maximises an agile response's efficacy in enhancing the sophistication of rapidity, flexibility, and sensing involved. In this backdrop, driven by newer IT infrastructure and disruptive technology, this research accentuates on Industry 4.0 digitalisation to understand how a manufacturer can gain digital enabled agility. An empirical survey of manufacturers within the premise of Industry 4.0 is conducted. Drawn from the findings, the capabilities of traceability and visibility can be facilitated by Industry 4.0 that would improve information sharing and information quality in real-time. These capabilities are key propellants to unlock the digital extent of agility. This research however details further how this scope of agility is an extension to that of the scope of typical agility in terms of its ability to handle expansive information processing needs and dynamic coordination/resource orchestration. Insights are weaved under the purviews of (a) organisational information processing view and (b) the manufacturer who is coordinating contemporary dynamic production and allied value chain requirements. [ABSTRACT FROM AUTHOR]

The rise of industry 4.0 has led to the implementation of new technology-based manufacturing standards (TMSs). This study proposes a new contract to support a manufacturer, Haier, in diffusing its home-country TMSs into host countries with technology gaps. The manufacturer conducts the diffusion by operating two-sided networks (i.e. consumer and supplier networks) and dual sourcing (i.e. placing orders from two suppliers in different countries). A procurement contract is designed to coordinate the two-sided networks; it defines a market-pricing and part-procurement problem to maximise the scale of customers and local suppliers, respectively. These suppliers decide whether to accept the contract after considering their production conditions and technology gaps. A part-supply contract is executed if the procurement contract is accepted. Surplus parts are procured from the manufacturer's home country if the local supply is insufficient. The coupling of procurement and supply contract (PSC) design is inherent, while a hierarchical collaborative optimisation (HCO) model based on Stackelberg game theory is formulated. The HCO model deploys a multi-objective bi-level programming decision structure to examine the PSC coupling. A nested non-dominated sorting genetic algorithm II is designed to address this HCO model, while a case study of refrigerator TMS diffusion is analysed for verification. [ABSTRACT FROM AUTHOR]

As a manufacturing power in the era of Industry 4.0, the Chinese traditional manufacturing industry urgently needs innovation and upgrading. In this process, the pressure on the traditional manufacturing industry is mainly financial shortages. The government can act as a Booster to add power to the upgrading by providing financial support. We build a three-tier game model consisting of Raw Material Suppliers, a Traditional Manufacturer, and an Original Equipment Manufacturer to explore the upgrading conditions and construct three settings: Agency, Consignment, and Design. We find that the government subsidy can serve as an effective way to ease enterprises' financial burden and improve the innovation level, which is more evident when the traditional manufacturer innovates. In addition, when the market competition and the government subsidy are both at a moderate level, the combined effect of the two factors is positive, and the condition is the best to upgrade. Encouragingly, the social welfare under Design will be the highest when the government subsidy is slightly great. Finally, excessive subsidy hinders upgrading and aggravates market competition, even if the traditional manufacturer enjoys exorbitant profits. HIGHLIGHTS The upgrading on the manufacturing industry in the era of Industry 4.0 The right of product design in supply chain models The cross-influence of the government subsidy and market competition on industrial upgrading is introduced The conditions to help upgrade the manufacturing industry are given in the era of Industry 4.0 [ABSTRACT FROM AUTHOR]

A supply and demand mismatch, or imbalance of the amount of supplies in the market, is always an issue and can happen all the time. Capacity sharing is an effective way to address this problem, and the capacity sharing platform facilitates the optimal matching between multiple capacity buyers and sellers. In the context of Industry 4.0, many industries are adopting intelligent algorithms to assist in decision-making. This paper presents an optimal or near-optimal matching algorithm to cope with a large volume of capacity-sharing problems. The fairness of the matching solution is captured by including three objectives from platform, sellers and buyers. In this paper, a 2-dimensional crossover and an order-first mutation are developed and employed with genetic algorithms (GA), including GA and NSGA-II. Additionally, a novel repair mechanism is proposed by considering various constraints to transform infeasible solutions into feasible ones. Two matching schemes are studied based on whether orders from buyers can be split or not. The results show that both algorithms based on traditional GA and NSGA-II are effective for different schemes. In addition, it is found that GA has better performance in the case of 'more sellers' and NSGA-II shows better performance in the 'more buyers' case. [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]

Nowadays many processes in the food industry are monitored in an automatic way, with the purpose of minimising the need for workforce and of ensuring the proper control of the quality and safety of the foodstuff. All the sensors share data with a centralised management unit, where often a Manufacturing Execution System collects and evaluates them. As reported in recent research, however, a further step that can be undertaken, exploiting Industry 4.0 enabling technologies, is the implementation of digital twin approaches, with the additional aim to prevent possible issues during production. In line with these considerations, this work aims at showing two different digital twin models intended for improving the control of as many real food systems. Liquid and powder fluids are taken as examples for highlighting the differences in the optimization of the two food processes, as well as for fully exploring the potential of the digital twin approach. Finally, based on the real data taken from two pilot plants, a framework for the selection of the best digital twin tool in the food sector is delineated. [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]

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]

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]

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]

Artificial intelligence (AI) is increasingly considered a source of competitive advantage in operations and supply chain management (OSCM). However, many organisations still struggle to adopt it successfully and empirical studies providing clear indications are scarce in the literature. This research aims to shed light on how AI applications can support OSCM processes and to identify benefits and barriers to their implementation. To this end, it conducts a multiple case study with semi-structured interviews in six companies, totalling 17 implementation cases. The Supply Chain Operations Reference (SCOR) model guided the entire study and the analysis of the results by targeting specific processes. The results highlighted how AI methods in OSCM can increase the companies' competitiveness by reducing costs and lead times and improving service levels, quality, safety, and sustainability. However, they also identify barriers in the implementation of AI, such as ensuring data quality, lack of specific skills, need for high investments, lack of clarity on economic benefits and lack of experience in cost analysis for AI projects. Although the nature of the study is not suitable for wide generalisation, it offers clear guidance for practitioners facing AI dilemmas in specific SCOR processes and provides the basis for further future research. [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]

The Internet of Things has received enormous attention. Despite the lucrative potential, this study tries to look beyond the hype to understand how IoT impacts firm performance. Based on the information processing theory, we intend to explore how IoT influences the financial performance of firms. Firms, which realise the urgent need to adopt IoT might encounter a diverse set of challenges, like whether adoption will be profitable, the level of commitment they should devote, and whether to adopt as a first or second-mover. We want to comprehend such adoption challenges on the Indian manufacturing firm's financial performance. This study uses panel data from BSE 500 listed manufacturing firms and conducts multivariate analysis to understand the relationship between the IoT implementation proxies: IoT adoption, first-mover advantage, and firm performance in the light of IoT commitment and experts. The result indicates that IoT implementation has an insignificant effect on manufacturing firm performance. Meanwhile, the consideration of IoT commitment and experts jointly can enable a manufacturing firm to reap significantly positive financial outcomes. In other words, the IoT commitment and expertise positively moderate the relationship. The finding is useful to the decision-makers to address the firm-level adjustment before the mere introduction of IoT. [ABSTRACT FROM AUTHOR]

Scheduling is a complex task due to the need to optimise multiple competing objectives and react to unpredictable events that may occur during production execution. The strategy of predictive-reactive scheduling can be used to reconcile the conflict between the original schedule and the current shop floor situation. This study seeks to present an inventory data-driven predictive-reactive production scheduling model that supports the evolving concepts of the Industry 4.0. Periodically, a machine learning technique provides predictive scheduling considering a best-case scenario according to an established Key Performance Indicator (KPI). Then, material non-availability causes disruptions in production, which triggers the Simulation-Based Optimization (SBO) method to handle these events. Thus, SBO provides a reactive schedule with the best set of priority rules to sequence jobs on each machine according to the data on the shop floor. This model was validated with a real case study using data collected from a metal-mechanical company. Considering the service level KPI, the results showed that the model is able to find a better solution in the compared scenarios. Therefore, even in a dynamic and stochastic scenario, with machine breakdowns, quality problems, raw material delays, and accuracy issues, the model proved efficient in mitigating these variations' effects. [ABSTRACT FROM AUTHOR]

The circular economy has become a promising solution to electronic product reuse. The advanced technology of Industry 4.0 drives the recycling platform (RP) to carry out a variety of new business modes, which are important ways to realise a circular economy. In this article, we consider a monopolistic RP under trade-in for new mode, leasing mode, or their mixed mode, respectively. We develop a discrete-time game with infinite periods and use the Markov decision and Bellman equation to obtain the consumer's perfect equilibria consumption strategies. By comparing different modes under RP's profits, consumer surplus, and environment impact perspectives, we find that when the durability of leased refurbished product exceeds that of the resold refurbished ones, the mixed mode is better than the trade-in for new mode, which can achieve more platform profits, more consumer surplus, and less environment impact. By considering the three perspectives together, the mixed mode is not always better than the other two modes. The mixed mode is optimal only when the durability of leased refurbished one is relatively high. We also conduct extensive numerical research, show optimal service mode under three perspectives, and extend the effects of different qualities of recycled used products on optimal mode. [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]

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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]

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]

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]

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]

In the era of Industry 4.0, the demand fluctuation has become fiercer due to the characteristics of diversification, customisation, and uncertainty. Reconfigurability of manufacturing systems has been proven to be a useful and necessary feature when it comes to handling demand uncertainty. This feature can be achieved through the implementation of reconfigurable manufacturing system (RMS) and delayed reconfigurable manufacturing system (D-RMS). D-RMS is a subclass of RMS that focuses primarily on improving the convertibility of the manufacturing system. The two main phases involved in implementing D-RMS are part family formation and configuration design. Therefore, we proposed a multi-objective joint optimisation method of part family formation and configuration design according to the philosophy of D-RMS. Firstly, we develop a multi-objective joint optimisation model that takes into account investment cost, reconfiguration cost, similarity coefficient, and delayed reconfiguration to optimise the part family and configuration of D-RMS simultaneously. Three types of machine tools namely dedicated machine tools, flexible machine tools, and reconfigurable machine tools are considered in the optimisation model. Secondly, the non-dominated sorting genetic algorithm-III (NSGA-III) is adopted to solve the proposed multi-objective integer programming problem. Finally, numerical experiments are presented to demonstrate the effectiveness of the proposed multi-objective joint optimisation method. [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]

Digital transformation is a major organisational challenge for manufacturing firms due to the extremely low success rate of such transformations to date. Capability Maturity theory suggests that firms need to develop digital transformation capability incrementally by focusing on a 'vital few' improvement priorities for advancing progress. The practitioner literature lacks empirical studies that validate extant capability maturity models (CMM) for digital transformation despite their importance. Moreover, there is a lack of assessment methods, and those that exist do not specify improvement points explicitly, nor prioritise them. Our research aims to address this gap through a systematic, quantitative analysis of digital capability by understanding the deployment of IT-enabled resources. Based on a sample of 302 manufacturing firms, results indicate that the digital transformation stages are punctuated by various resource-capability combinations. Results highlight that strategy- and organisation-related IT-enabled resources are the key drivers of digital transformation. We also observe that as a firm's digital capability grows at each maturity stage, successively greater IT-enabled resources are required to support this in a stepwise function. To succeed, firms should be incentivised and supported to think beyond technology and develop five specific digital capabilities simultaneously. We also indicate the limitations that underlie our empirical work. [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]

Organisations are having difficulty in determining and integrating Industry 4.0 technologies (I4.0T) to improve social dimensions of sustainable performance. We address this issue by showing how to identify the impact degree and path of I4.0T using the United Nations sustainable development goals (SDGs). First, we identify the SDGs associated with I4.0 and establish an evaluation framework. Second, we introduce an evaluation method that integrates hesitant fuzzy sets, rough sets, decision-making, trial evaluation laboratory (DEMATEL) and interpretative structural modelling methods to identify the impact degree and path of I4.0T on SDGs. Third, we confirm the effectiveness of our framework and method using secondary case data from World Economic Forum reports. The case results show that I4.0T have varying direct and indirect influences on SDGs. Policy makers should consider that 'Big Data and Analytics' and 'Cloud Computing' are central I4.0T that link other technologies and SDGs. The prioritisation of I4.0T can lead to contributions to addressing 'Responsible consumption and production' and indirectly improve SDGs. The findings set an initial stage to help sequence investment in various technologies depending on SDGs. We identify sequential paths that can aid in planning for and investing in adopting I4.0T to better achieve SDGs. [ABSTRACT FROM AUTHOR]

In recent years, the emerging Digital Twin (DT) paradigm under Industry 4.0 has been attracting more attention from both practitioners and academia due to its dynamic capabilities. Most DT studies are theoretical and deal with hypothetical analysis, whereas fewer studies are available on real-life empirical cases. Due to dynamic problem-solving capabilities, DT technologies are widely used in performance improvement analysis in food processing companies (FPC) despite limited implication's for business strategies. Our study incorporates the DT technologies with an implementation case study on FPC and accomplishes the real-life problem of the food processing company (FPC). Moreover, the proposed DT research framework demonstrates the various DT implementation stages, such as strategic mapping and physical-virtual space replica, with rigorous analysis. The results show that DT enhances the existing system's machine availability, allocation efficiency, technical efficiency, worker efficiency, utilization rate, effectiveness, step ratio, and throughput rate. The proposed physical-virtual interface model is executed using AnyLogic software with JAVA-enabled programming. [ABSTRACT FROM AUTHOR]

Manufacturing systems are socio-technical systems, with explicit interactions between humans and technologies in shared workspaces. These shared workspaces could also be called hybrid collaborative manufacturing systems, which involve workers as well as technological equipment and combine the benefits of human workers and new Industry 4.0 technologies, such systems are particularly useful in a context requiring flexibility and adaptability. Furthermore, the new Industry 5.0 approach has the objective to shift toward more human-centric and resilient manufacturing systems. The key problems to solve in the design of collaborative manufacturing systems are the combinatorial assembly line balancing problem and the equipment selection problem. An efficient and sustainable line requires a cost-effective choice of equipment while improving the ergonomics and the safety of workers. Both decisions of balancing workload and the assignment of equipment impact the ergonomics of a collaborative system and present conflicting criteria. To this end, we propose a multi-objective approach, the objectives are the optimisation of the investment costs and the ergonomics with a fatigue and recovery criterion. We propose to linearise the fatigue and recovery to formulate a new Mixed Integer Linear Programming formulation. We developed an exact multi-objective solving algorithm based on the ϵ-constraint to obtain the trade-off between these objectives. We conducted numerical experiments with different instances from the literature with promising results for instances with up to 45 operations. Finally, we discuss insightful managerial conclusions and future research perspectives. [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]

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]

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]

This study examines the effect of Industry 4.0 (I4.0) technologies on the learning process of operators. We collected data from the training of new operators in a quality inspection workstation. Two distinct scenarios were considered: before and after the adoption of I4.0 technologies. Data from 10 operators were collected in each scenario; the quality inspection cycle was repeated by each operator 30 consecutive times. A 2-parameter hyperbolic learning curve model was used to assess the learning process in the two groups. Results indicated that operators supported by I4.0 technologies had a significantly higher learning rate than those performing the same tasks without I4.0 support. No significant difference was found in the final performance level between groups. Our study bridges a theoretical gap in the relationship between I4.0 and learning by directly comparing the effect of digital support on the training of new employees in a manufacturing environment. We also offer arguments to support managerial decisions with regards to I4.0 adopti-on at an operational level. That allows organisations to prioritise their digitalisation efforts so that the training of operators in workstations can be expedited. [ABSTRACT FROM AUTHOR]

Industry 4.0 concepts make it possible to rethink human resources allocation, even for more traditional environments like metal machining. While parts machining on Computer Numerical Control (CNC) machines is automated, some manual tasks must still be executed by operators. The current approach is typically that operators are statically allocated to one or many machines. This causes avoidable bottlenecks. We propose an optimisation model to dynamically assign tasks to the operators with the objective of minimising production delays. Three different scenarios are compared; one representing the current widely used static allocation method and two others that allow more flexibility in the operators' allocation. The dynamic task assignment problem is solved using a constraint programming model. The model was applied to a case study from a high-precision metal manufacturing job shop. Experimental results show that switching from a static allocation to a dynamic one reduces by 76% the average production delays caused by human operators. Supposing more versatile operators under the dynamic allocation leads to further improvements. [ABSTRACT FROM AUTHOR]

Productivity, quality, and flexibility are key production targets pursued by companies that adopt Industry 4.0. However, it is unclear how Industry 4.0 technologies can help achieve these different and sometimes competing targets. This study investigates this relationship through a survey of 92 manufacturers. The study employs Exploratory Factor Analysis to define four main technology arrangements based on 18 Industry 4.0 technologies: Vertical Integration, Virtual Manufacturing, Advanced Manufacturing Processing Technologies, and Online Traceability. Then, independent samples tests were conducted to compare the implementation status of these arrangements when manufacturing flexibility, process quality, and productivity are (or are not) pursued as the main production targets. The results show that Vertical Integration is a general-purpose technology arrangement because it supports all targets. On the other hand, Virtual Manufacturing and Online Traceability are specific-purpose arrangements, adopted especially for flexibility and productivity targets, respectively. Advanced Manufacturing Processing Technologies, in turn, is an integrative-purpose technology arrangement since it is adopted when two competing targets are pursued: productivity and manufacturing flexibility. The study ends with a decision model to implement Industry 4.0 based on the production targets a company may pursue. It shows the interconnection and trade-offs between these production targets and the Industry 4.0 technologies adopted. [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]

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]