Smart manufacturing scheduling: A literature review

[EN] 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 work was supported by the Spanish Ministry of Science, Innovation and Universities project entitled 'Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0) ' (RTI2018-101344-B-I00) , the European Union H2020 research and innovation programme with grant agreement No. 825631 "Zero Defect Manufacturing Platform (ZDMP) " and the European Union H2020 research and innovation programme with agreement No. 958205 "In-dustrial Data Services for Quality Control in Smart Manufacturing (i4Q) ".Serrano-Ruiz, JC.; Mula, J.; Poler, R. (2021). Smart manufacturing scheduling: A literature review. Journal of Manufacturing Systems. 61:265-287. https://doi.org/10.1016/j.jmsy.2021.09.0112652876

Smart digital twin for ZDM-based job-shop scheduling

[EN] 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.The research that led to these results received funding from the European Union H2020 Programme with grant agreement No. 825631 Zero-Defect Manufacturing Platform (ZDMP) and Grant agreement No. 958205 Industrial Data Services for Quality Control in Smart Manufacturing (i4Q), and from the Spanish Ministry of Science, Innovation and Universities with Grant Agreement RTI2018-101344-B-I00 "Optimisation of zero-defects production technologies enabling supply chains 4.0 (CADS4.0)"Serrano Ruiz, JC.; Mula, J.; Poler, R. (2021). Smart digital twin for ZDM-based job-shop scheduling. IEEE. 510-515. https://doi.org/10.1109/MetroInd4.0IoT51437.2021.948847351051

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

[EN] 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.The research leading to these results received funding from the European Union H2020 Program with grant agreements No. 825631 "Zero-Defect Manufacturing Platform (ZDMP)" and No. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)", and from Grant RTI2018-101344-B-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe".Serrano-Ruiz, JC.; Mula, J.; Poler, R. (2021). Smart Master Production Schedule for the Supply Chain: A Conceptual Framework. Computers. 10(12):1-24. https://doi.org/10.3390/computers10120156124101

Development of a multidimensional conceptual model for job shop smart manufacturing scheduling from the Industry 4.0 perspective

[EN] Based on a scientific literature review in the conceptual domain defined by smart manufacturing scheduling (SMS), this article identifies the benefits and limitations of the reviewed contributions, establishes and discusses a set of criteria with which to collect and structure its main synergistic attributes, and devises a conceptual framework that models SMS around three axes: a semantic ontology context, a hierarchical agent structure, and the deep reinforcement learning (DRL) method. The main purpose of such a modelling research is to establish a conceptual and structured relationship framework to improve the efficiency of the job shop scheduling process using the approach defined by SMS. The presented model orients the job shop scheduling process towards greater flexibility, through enhanced rescheduling capability, and towards autonomous operation, mainly supported by the use of machine learning technology. To the best of our knowledge, there are no other similar conceptual models in the literature that synergistically combine the potential of the specific set of Industry 4.0 principles and technologies that model SMS. This research can provide guidance for practitioners and researchers¿ efforts to move toward the digital transformation of job shops.The research leading to these results received funding from the European Union H2020 Programme ,Belgium with grant agreements No. 825631 "Zero-Defect Manufacturing Platform (ZDMP) ", No. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q) " and 872548 "Fostering DIHs for Embedding Interoperability in Cyber-Physical Systems of European SMEs (DIH4CPS) ", from Grant RTI2018-101344-B-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe" and the Regional Department of Innovation, Universities, Science and Digital Society of the Generalitat Valenciana entitled "Industrial Production and Logistics Optimization in Industry 4.0" (i4OPT) (Ref. PROMETEO/2021/065).Serrano-Ruiz, JC.; Mula, J.; Poler, R. (2022). Development of a multidimensional conceptual model for job shop smart manufacturing scheduling from the Industry 4.0 perspective. Journal of Manufacturing Systems. 63:185-202. https://doi.org/10.1016/j.jmsy.2022.03.0111852026

Digitally enhanced quality management for Zero-Defect Manufacturing

Though the idea of Zero Defect Manufacturing is not new, it remains a disruptive concept that is able to entirely reshape the manufacturing ideology. Existing literature suggests that Zero Defect Manufacturing can be implemented in two different approaches – namely product- (defective parts) and / or process-oriented (defective equipment) approaches. The recent onset of Industry 4.0 presents organizations with a plethora of technologies that promise to further enhance the quality of both products and processes, but also adds a third dimension to Zero Defect Manufacturing - people. Therefore, in this paper, we add the people-oriented approach as a third dimension to Zero Defect Manufacturing and draw on practical insights to present a framework for digitally enhanced quality management.publishedVersio

Digital twin for supply chain master planning in zero-defect manufacturing

[EN] 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.The research leading to these results received funding from the EuropeanUnion H2020 Program with grant agreement No. 825631 Zero Defect Manufacturing Platform(ZDMP) and with grant agreement No. 958205 Industrial Data Services for Quality Control inSmart Manufacturing (i4Q) and from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-101344-B-I00 Optimisation of zero-defects productiontechnologies enabling supply chains 4.0 (CADS4.0).Serrano, JC.; Mula, J.; Poler, R. (2021). Digital twin for supply chain master planning in zero-defect manufacturing. IFIP Advances in Information and Communication Technology. 626:102-111. https://doi.org/10.1007/978-3-030-78288-7_1010211162

Manufacturing Lead time Reduction towards Zero Defect Manufacturing

Copyright © 2014 Future Technology Press and the authors. Companies seek to reduce manufacturing lead-time in order to reduce the cost of the production; short lead-times are a major source of potential competitive advantage and can help achieve zero-defect manufacturing (ZDM). The quality of a product is dependent on both facilities (or equipment) and the manufacturing process. Any unexpected obstacle or glitch can create longer lead-time, which will prevent the product from promptly reaching the customer. A focus on reducing lead-time, therefore, will ensure an elimination of error, and work towards a zero-defect manufacturing system, which is necessary to increase the reliability and safety of manufacturing set ups and systems. This paper proposes a study on reducing manufacturing lead-time (MLT) in factories in the Kurdistan region of Iraq, where lead-time has become a major issue in the manufacturing industry. The aim is to provide guidance to industry practitioners/technicians on how to reduce MLT. The paper will present a conceptual framework of the causes of excessive lead-time and also describe the relationship between setup time, operation time, and non-operation time in order to find potential methodologies that can reduce lead-time in the manufacturing process. It will also further identify simple strategies for reducing lead-time that can help towards achieving ZDM

Advances in Production Management Systems: Issues, Trends, and Vision Towards 2030

Since its inception in 1978, the IFIP Working Group (WG) 5.7 on Advances in Production Management Systems (APMS) has played an active role in the fields of production and production management. The Working Group has focused on the conception, development, strategies, frameworks, architectures, processes, methods, and tools needed for the advancement of both fields. The associated standards created by the IFIP WG5.7 have always been impacted by the latest developments of scientific rigour, academic research, and industrial practices. The most recent of those developments involves the Fourth Industrial Revolution, which is having remarkable (r)evolutionary and disruptive changes in both the fields and the standards. These changes are triggered by the fusion of advanced operational and informational technologies, innovative operating and business models, as well as social and environmental pressures for more sustainable production systems. This chapter reviews past, current, and future issues and trends to establish a coherent vision and research agenda for the IFIP WG5.7 and its international community. The chapter covers a wide range of production aspects and resources required to design, engineer, and manage the next generation of sustainable and smart production systems.acceptedVersio

Scheduling for Flexible Production: A Case Study of Production Leveling Under Volume Constraints

Flexible manufacturing systems in high-mix low-volume segments offer many challenges in terms of planning and scheduling. The complexity of these systems often requires a systemic approach for which humans are the perfect actors. However, computer systems can support scheduling tasks more effectively due to their capacity to synthesize large amounts of data. This paper describes a system developed for the flexible manufacturing of wooden doors with a wide range of product configurations. This paper proposes a rule-based scheduling method for high-mix production. The method was applied and validated at a wooden door producer. Based on the company's production schedule for a given week, a scheduling program was developed that suggested minor rearrangements for production leveling. As the rule-based approach makes the decisions verifiable, the program was validated at the producer, through a case study of production leveling under volume constraints. The results include the complete elimination of changeovers and the stabilization of throughput, which improved the precision of the delivery time. The producer is integrating the program into their production planning and control system. Thus, the results suggest that the proposed method can be useful for scheduling high-mix low-volume production, and merits further validation in similar environments.acceptedVersio