The growing digitization of manufacturing processes is revolutionizing the production job-shop by leading it toward the Smart Manufacturing (SM) paradigm. For a process to be smart, it is necessary to combine a given blend of data technologies, information and knowledge that enable it to perceive its environment and to autonomously perform actions that maximize its success possibilities in its assigned tasks. Of all the different ways leading to this transformation, both the generation of virtual replicas of processes and applying artificial intelligence (AI) techniques provide a wide range of possibilities whose exploration is today a far from negligible sources of opportunities to increase industrial companies' competitiveness. As a complex manufacturing process, production order scheduling in the job-shop is a necessary scenario to act by implementing these technologies. This research work considers an initial conceptual smart digital twin (SDT) framework for scheduling job-shop orders in a zero-defect manufacturing (ZDM) environment. The SDT virtually replicates the job-shop scheduling issue to simulate it and, based on the deep reinforcement learning (DRL) methodology, trains a prescriber agent and a process monitor. This simulation and training setting will facilitate analyses, optimization, defect and failure avoidance and, in short, decision making, to improve job-shop scheduling.

Recently, many novel paradigms, concepts and technologies, which lay the foundation for the new revolution in manufacturing environments, have emerged and make it faster to address critical decisions today in supply chain 4.0 (SC4.0), with flexibility, resilience, sustainability and quality criteria. The current power of computational resources enables intelligent optimisation algorithms to process manufacturing data in such a way, that simulating supply chain (SC) planning performance in real time is now possible, which allows relevant information to be acquired so that SC nodes are digitally interconnected. This paper proposes a conceptual framework based on a digital twin (DT) to model, optimise and prescribe a SC’s master production schedule (MPS) in a zero-defect environment. The proposed production technologies focus on the scientific development and resolution of new models and optimisation algorithms for the MPS problem in SC4.0.

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.

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.

Facility layout planning (FLP) involves a set of design problems related to the arrangement of the elements that shape industrial production systems in a physical space. The fact that they are considered one of the most important design decisions as part of business operation strategies, and their proven repercussion on production systems’ operation costs, efficiency and productivity, mean that this theme has been widely addressed in science. In this context, the present article offers a scientific literature review about FLP from the operations management perspective. The 232 reviewed articles were classified as a large taxonomy based on type of problem, approach and planning stage and characteristics of production facilities by configuring the material handling system and methods to generate and assess layout alternatives. We stress that the generation of layout alternatives was done mainly using mathematical optimisation models, specifically discrete quadratic programming models for similar sized departments, or continuous linear and non-linear mixed integer programming models for different sized departments. Other approaches followed to generate layout alternatives were expert’s knowledge and specialised software packages. Generally speaking, the most frequent solution algorithms were metaheuristics.

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.

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.

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.

This article identifies the advances, advantages, limitations, requirements and current methodologies in implementing the strategic Industry 4.0 (I4.0) initiative. It focuses on all research works mainly on production planning. To do so, it proposes a taxonomy of the principles of I4.0 design terms that contemplates the following classification aspects: interconnection/connectivity, decentralised decision making, technical assistance, the human factor, intelligence/awareness, interoperability, information transparency, technology, organisation, conceptual frameworks and production planning. It also presents the models, algorithms, heuristics and meta-heuristics of the components used in relation to an I4.0 setting. Finally, a considerable number of reference conceptual frameworks is analysed, which allow the term I4.0 to be defined.

Industry 4.0 (I4.0) implies a group of technologies, organisational concepts and management principles to improve the performance of manufacturing companies or supply chains driven by production cost optimisation, mass customisation requirements, connectivity and digitisation of factories. The purpose of this paper is to relate Iberian Peninsula advances in I4.0 from Spanish and Portuguese research works published in CJOR papers. Hence this paper reviews the Spanish and Portuguese operations research (OR) and industrial engineering-based papers published in CJOR from 2011, when the I4.0 concept emerged, to the present-day. Here 47 papers are reviewed according to classification criteria based on the following elements: (1) objectives; (2) application context; (3) modelling approach; (4) development or software tool; (5) I4.0 technologies. The main outcomes, limitations and further research are also identified for recent papers. Finally, research trends and future directions in industrial engineering, OR and I4.0 are discussed.

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.

This paper presents a new innovative framework to support smart manufacturing quality assurance. More specifically, the i4Q framework provides an IoT-based Reliable Industrial Data Services (RIDS), a complete suite consisting of 22 innovative Solutions, able to manage the huge amount of industrial data coming from cheap 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-optimization. i4Q RIDS includes simulation and optimization tools for manufacturing line continuous process qualification, quality diagnosis, reconfiguration and certification for ensuring high manufacturing efficiency, leading to an integrated approach to zero-defect manufacturing. This paper presents the main principles of the i4Q framework and the relevant industrial case studies on which it will be evaluated.