One of the most complex domains in the field of industrial process planning that a professional can face is the job shop. Machines, parts, jobs, routing, orders, batches, machining phases, set-up times, machining times, or delivery times, among others, make up a very unique issue, known as the job shop scheduling problem (JSSP) which, in real environments, acquires a remarkable level of complexity. For a first-time industrial engineering student taking on the challenge of gaining a deeper understanding of the JSSP, theoretical training is essential to establish the basic concepts, but practice can be an eye-opener in terms of grasping the true dynamics of the process, although technological and economic restrictions may put a brake on this endeavour. In this regard, one of the Industry 4.0 enabling technologies that researchers and managers have shown most interest in over the last decade is the digital twin (DT), which allows objects or processes to be virtually replicated to simulate their characteristics and behaviour during the interaction with the surrounding environment. The virtualisation of processes with a high degree of fidelity is not restricted today to the scope of research laboratories or industrial plants, as open source libraries such as SimPy, a process simulation environment based on the Python programming language, facilitate the implementation of DTs, also in educational contexts with limited means, boosting the possibilities of practical laboratory training to a new level that brings it closer, from the perspective of its study, to the experience acquired within real environments. This article proposes the use of a DT programmed in SimPy to carry out simulation practices of the JSSP in two of the subjects of the Master Degree in Industrial Engineering and Logistics (MIEL) currently taught at the Alcoy Campus of the Universitat Politècnica de València: "Quantitative Methods" and "Production Management", so that, on the one hand, students learn to use SimPy as a simulation tool and, on the other hand, they use it to experiment with scheduling priority rules for the allocation of jobs to manufacturing resources. This learning project is based on cooperative learning (CL) and project-based learning (PBL) methodologies and is expected to upgrade student understanding and stimulate their motivation during the learning process.

An integral application of the enabling technologies of Industry 4.0 in the job-shop scheduling problem (JSSP) must contemplate the automation and autonomy of the involved decision-making processes as a goal, which is the main purpose of the smart manufacturing scheduling (SMS) paradigm. In a real production context, uncertainty acts as a barrier that hinders this goal being met and, therefore, any SMS model should integrate uncertainty generators in one way or another. This paper proposes an ontological framework that identifies and structures the entities shaping the joint domain formed by the job-shop scheduling process in its itinerary toward the SMS paradigm, the sources of uncertainty that it faces, and the interrelationship type that link these entities. This ontological framework will serve in future research as a conceptual basis to design new quantitative models that, from a holistic perspective, will address the stochasticity of manufacturing environments and incorporate the management of disturbances into the realtime resolution of automatic and autonomous job-shop scheduling.

Manufacturing companies increasingly become “smarter” as a result of the Industry 4.0 revolution. Multiple sensors are used for industrial monitoring of machines and workers in order to detect events and consequently improve the manufacturing processes, lower the respective costs, and increase safety. Multisensor systems produce big amounts of heterogeneous data. Data fusion techniques address the issue of multimodality by combining data from different sources and improving the results of monitoring systems. The current paper presents a detailed review of state-of-the-art data fusion solutions, on data storage and indexing from various types of sensors, feature engineering, and multimodal data integration. The review aims to serve as a guide for the early stages of an analytic pipeline of manufacturing prognosis. The reviewed literature showed that in fusion and in preprocessing, the methods chosen to be applied in this sector are beyond the state-of-the-art. Existing weaknesses and gaps that lead to future research goals were also identified.

This paper focuses on the investigation of a new efficient method for solving machine scheduling and sequencing problems. The complexity of production systems significantly affects companies, especially small- and medium-sized enterprises (SMEs), which need to reduce costs and, at the same time, become more competitive and increase their productivity by optimizing their production processes to make manufacturing processes more efficient. From a mathematical point of view, most real-world machine scheduling and sequencing problems are classified as NP-hard problems. Different algorithms have been developed to solve scheduling and sequencing problems in the last few decades. Thus, heuristic and metaheuristic techniques are widely used, as are commercial solvers. In this paper, we propose a matheuristic algorithm to optimize the job-shop problem which combines a genetic algorithm with a disjunctive mathematical model, and the Coin-OR Branch & Cut open-source solver is employed. The matheuristic algorithm allows efficient solutions to be found, and cuts computational times by using an open-source solver combined with a genetic algorithm. This provides companies with an easy-to-use tool and does not incur costs associated with expensive commercial software licenses.

Risks arising from the effect of disruptions and unsustainable practices constantly push the supply chain to uncompetitive positions. A smart production planning and control process must successfully address both risks by reducing them, thereby strengthening supply chain (SC) resilience and its ability to survive in the long term. On the one hand, the antidisruptive potential and the inherent sustainability implications of the zero-defect manufacturing (ZDM) management model should be highlighted. On the other hand, the digitization and virtualization of processes by Industry 4.0 (I4.0) digital technologies, namely digital twin (DT) technology, enable new simulation and optimization methods, especially in combination with machine learning (ML) procedures. This paper reviews the state of the art and proposes a ZDM strategy-based conceptual framework that models, optimizes and simulates the master production schedule (MPS) problem to maximize service levels in SCs. This conceptual framework will serve as a starting point for developing new MPS optimization models and algorithms in supply chain 4.0 (SC4.0) environments.

Industry 4.0 is a new revolution that is introducing a paradigm shift in the industry. Automation, decentralization, and modulation are concepts that are becoming significantly more relevant, changing the industry. In the motor industry, the use of Automated Guided Vehicles (AGVs) is essential to improve efficiency of intralogistics processes, since they allow automation of essential material transportation for this process, providing a better quality of service. However, some AGVs do not have sensory capabilities that can provide data, in real time, regarding their status. Even though these AGVs do not possess those capabilities, it is necessary to find solutions that would allow the data to be acquired for the monitoring, without an investment in more advanced AGVs. This paper provides a contribution to the development of an AGV fleet managing system, where it will be possible to monitor the battery transportation process, providing a quantitative and qualitative analysis of the entire process, as well as the detection of failures/anomalies that may occur during the same. This solution will bring a greater capacity for optimization and efficiency, improving aspects related to automotive production. All this work is part of the European project BOOST 4.0, which was validated at Volkswagen Autoeuropa.

This paper presents a complete solution consisting of sustainable IoT-based Reliable Industrial Data Services (RIDS) able to manage the huge amount of industrial data coming from cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. The i4Q Framework guarantees data reliability with functions grouped into five basic capabilities around the data cycle: sensing, communication, computing infrastructure, storage, and analysis and optimisation. With the i4Q RIDS, factories will be able to handle large amounts of data, achieving adequate levels of data accuracy, precision and traceability, using it for analysis and prediction as well as to optimise the process quality and product quality in manufacturing, leading to an integrated approach to zero-defect manufacturing. The i4Q Solutions efficiently collect the raw industrial data using cost-effective instruments and state-of-the-art communication protocols, guaranteeing data accuracy and precision, reliable traceability and time stamped data integrity through distributed ledger technology and provide simulation and optimisation tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency and optimal manufacturing quality.

A lean supply chain (LSC) is a set of organizations directly linked by upstream and downstream value streams between processes that work collaboratively to reduce costs and waste. Currently, supply chains (SCs) have been put to the test as the world has had to face a series of unprecedented disruptions in demand and supply caused by the COVID-19 pandemic. In this paper, a detailed study of constructs and multistructural components was carried out to develop a conceptual reference model that merges Industry 4.0 (I4.0) digital technologies with lean manufacturing tools to reduce waste and minimize costs in the lean supply chain planning (LSCP) context. The main theoretical contribution of this conceptual proposal is to establish a structured relation among the lean, agile, sustainable, resilient and flexible paradigms to improve SC performance by implementing I4.0 enabling technologies. The proposed conceptual model, dubbed as LSCP 4.0, is applied and validated with a case study in a large footwear company. It can help decision-makers and researchers to improve the planning and management of digital SC production processes, even with unexpected disruptions.

Within the scheduling framework, the potential of digital twin (DT) technology, based on virtualisation and intelligent algorithms to simulate and optimise manufacturing, enables an interaction with processes and modifies their course of action in time synchrony in the event of disruptive events. This is a valuable capability for automating scheduling and confers it autonomy. Automatic and autonomous scheduling management can be encouraged by promoting the elimination of disruptions due to the appearance of defects, regardless of their origin. Hence the zero-defect manufacturing (ZDM) management model oriented towards zero-disturbance and zero-disruption objectives has barely been studied. Both strategies combine the optimisation of production processes by implementing DTs and promoting ZDM objectives to facilitate the modelling of automatic and autonomous scheduling systems. In this context, this particular vision of the scheduling process is called smart manufacturing scheduling (SMS). The aim of this paper is to review the existing scientific literature on the scheduling problem that considers the DT technology approach and the ZDM model to achieve self-management and reduce or eliminate the need for human intervention. Specifically, 68 research articles were identified and analysed. The main results of this paper are to: (i) find methodological trends to approach SMS models, where three trends were identified; i.e. using DT technology and the ZDM model, utilising other enabling digital technologies and incorporating inherent SMS capabilities into scheduling; (ii) present the main SMS alignment axes of each methodological trend; (iii) provide a map to classify the literature that comes the closest to the SMS concept; (iv) discuss the main findings and research gaps identified by this study. Finally, managerial implications and opportunities for further research are identified.

Emerging Industry 4.0 applications require ever-increasing amounts of data and new sources of information to more accurately characterize the different processes of a production line. Industrial Internet of Things (IIoT) technologies, and in particular Wireless Sensor Networks (WSNs), allow a large amount of data to be digitized at a low energy cost, thanks to their easy scalability and the creation of meshed networks to cover larger areas. In industry, data acquisition systems must meet certain reliability and robustness requirements, since other systems such as predictive maintenance or the digital twin, which represents a virtual mapping of the system with which to interact without the need to alter the actual installation, may depend on it. Thanks to the IEEE 802.15.4e standard and the use of Time-Slotted Channel Hopping (TSCH) as the medium access mechanism and IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) as the routing protocol, it is possible to deploy WSNs with high reliability, autonomy, and minimal need for re-configuration. One of the drawbacks of this communication architecture is the low efficiency of its deployment process, during which it may take a long time to synchronize and connect all the devices in a network. This paper proposes an analytical model to characterize the process for the creation of downstream routes in RPL, whose transmission of multi-hop messages can present complications in scenarios with a multitude of interfering nodes and resource allocation based on minimal IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH). This type of multi-hop message exchange has a different behaviour than the multicast control messages exchanged during the synchronization phase and the formation of upstream routes, since the number of interfering nodes changes in each retransmission.

Emerging Industry 4.0 applications require ever-increasing amounts of data and new sources of information to more accurately characterize the different processes of a production line. Industrial Internet of Things (IIoT) technologies, and in particular Wireless Sensor Networks (WSNs), allow a large amount of data to be digitized at a low energy cost, thanks to their easy scalability and the creation of meshed networks to cover larger areas. In industry, data acquisition systems must meet certain reliability and robustness requirements, since other systems such as predictive maintenance or the digital twin, which represents a virtual mapping of the system with which to interact without the need to alter the actual installation, may depend on it. Thanks to the IEEE 802.15.4e standard and the use of Time-Slotted Channel Hopping (TSCH) as the medium access mechanism and IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) as the routing protocol, it is possible to deploy WSNs with high reliability, autonomy, and minimal need for re-configuration. One of the drawbacks of this communication architecture is the low efficiency of its deployment process, during which it may take a long time to synchronize and connect all the devices in a network. This paper proposes an analytical model to characterize the process for the creation of downstream routes in RPL, whose transmission of multi-hop messages can present complications in scenarios with a multitude of interfering nodes and resource allocation based on minimal IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH). This type of multi-hop message exchange has a different behaviour than the multicast control messages exchanged during the synchronization phase and the formation of upstream routes, since the number of interfering nodes changes in each retransmission.

In today’s world, industrial SCs face formidable challenges to efficiently establish tools that lower costs and are competitive in a digitalized environment. Supply chain management (SCM) has been used for planning and controlling physical and information flows, internal and external logistics activities, and processes with other companies, and also for addressing the relationship developed and the processes shared with both customers and suppliers [1]. In this context, a number of approaches like lean manufacturing (LM) and, more recently, Industry 4.0 (I4.0), have been developed to help manufacturers to fulfill these objectives [2]. To date, very few authors have studied the I4.0 technologies that most favor the implementation of LM tools to improve organizational performance, especially with disruption risks like pandemics or other unexpected crises [3–5]. This paper aims to determine the relations between I4.0 technologies and LM practices to provide a lean supply chain management 4.0 (LSCM 4.0) framework. This analysis also provides theoretical arguments that can help researchers and practitioners to develop resilient SCs in situations with disruptive risks because they may affect performance.