Smart manufacturing scheduling: A literature review

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.     

Data-driven dynamic bottleneck detection in complex manufacturing systems

Production (throughput) bottlenecks are the critical stations defining and constraining the overall productivity of a system. Effective and timely identification of bottlenecks provide manufacturers essential decision input to allocate limited maintenance and financial resources for throughput improvement. However, identifying throughput bottleneck in industry is not a trivial task. Bottlenecks are usually non-static (shifting) among stations during production, which requires dynamic bottleneck detection methods. An effective methodology requires proper handling of real-time production data and integration of factory physics knowledge. Traditional data-driven bottleneck detection methods only focus on serial production lines, while most production lines are complex. With careful revision and examination, those methods can hardly meet practical industrial needs. Therefore, this paper proposes a systematic approach for bottleneck detection for complex manufacturing systems with non-serial configurations. It extends a well-recognized bottleneck detection algorithm, “the Turning Point Method”, to complex manufacturing systems by evaluating and proposing appropriate heuristic rules. Several common industrial scenarios are evaluated and addressed in this paper, including closed loop structures, parallel line structures, and rework loop structures. The proposed methodology is demonstrated with a one-year pilot study at an automotive powertrain assembly line with complex manufacturing layouts. The result has shown a successful implementation that greatly improved the bottleneck detection capabilities for this manufacturing system and led to an over 30% gain in Overall Equipment Effectiveness (OEE).     

The HORSE framework: A reference architecture for cyber-physical systems in hybrid smart manufacturing

In the Industry 4.0 era, manufacturers strive to remain competitive by using advanced technologies such as collaborative robots, automated guided vehicles, augmented reality support and smart devices. However, only if these technological advancements are integrated into their system context in a seamless way, they can deliver their full potential to a manufacturing organization. This integration requires a system architecture as a blueprint for positioning and interconnection of the technologies. For this purpose, the HORSE framework, resulting from the HORSE EU H2020 project, has been developed to act as a reference architecture of a cyber-physical system to integrate various Industry 4.0 technologies and support hybrid manufacturing processes, i.e., processes in which human and robotic workers collaborate. The architecture has been created using design science research, based on well-known software engineering frameworks, established manufacturing domain standards and practical industry requirements. The value of a reference architecture is mainly established by application in practice. For this purpose, this paper presents the application and evaluation of the HORSE framework in 10 manufacturing plants across Europe, each with its own characteristics. Through the physical deployment and demonstration, the framework proved its goal to be basis for the well-structured design of an operational smart manufacturing cyber-physical system that provides horizontal, cross-functional management of manufacturing processes and vertical control of heterogeneous technologies in work cells. We report on valuable insights on the difficulties to realize such systems in specific situations. The experiences form the basis for improved adoption, further improvement and extension of the framework. In sum, this paper shows how a reference architecture framework supports the structured application of Industry 4.0 technologies in manufacturing environments that so far have relied on more traditional digital technology.     

Systematic literature review on augmented reality in smart manufacturing: Collaboration between human and computational intelligence

Smart manufacturing offers a high level of adaptability and autonomy to meet the ever-increasing demands of product mass customization. Although digitalization has been used on the shop floor of modern factory for decades, some manufacturing operations remain manual and humans can perform these better than machines. Under such circumstances, a feasible solution is to have human operators collaborate with computational intelligence (CI) in real time through augmented reality (AR). This study conducts a systematic review of the recent literature on AR applications developed for smart manufacturing. A classification framework consisting of four facets, namely interaction device, manufacturing operation, functional approach, and intelligence source, is proposed to analyze the related studies. The analysis shows how AR has been used to facilitate various manufacturing operations with intelligence. Important findings are derived from a viewpoint different from that of the previous reviews on this subject. The perspective here is on how AR can work as a collaboration interface between human and CI. The outcome of this work is expected to provide guidelines for implementing AR assisted functions with practical applications in smart manufacturing in the near future.     

Supporting disassembly processes through simulation tools: A systematic literature review with a focus on printed circuit boards

Several strategies have been detected in the extant literature to understand how Circular Economy (CE) can be pursued. Considering all the End-of-Life (EoL) management practices detected, disassembly processes have been identified as strategic. However, only scattered attempts have explored how digital technologies (specifically, simulation) can support the CE adoption, by focusing on disassembly processes. This research detects, through a systematic literature review, how so far simulation approaches have been proposed and applied in the extant literature to foster the disassembly process with a focus on Printed Circuit Boards (PCBs). At this purpose, the main roles played by simulation to ease the disassembly process, also through the support of Industry 4.0 (I4.0) technologies, have been detected. Based on the results obtained, two roles of simulation to support the disassembly process (Sequence planning and process optimization and Training) seem the most interesting from the twofold CE/I4.0 perspective, combining up to date technologies with traditional simulation approaches. Discussed in a deeper and sector-independent way, results provide directions about the lifecycle phases to be explored through simulations, the technologies to be involved, and both the final purpose and the type of simulation approach to be adopted. Finally, the gaps needing for further contributions, raised from the adoption of a Digitized Disassembly, are listed, and the main technologies so far employed to cope with them, through a Virtual/Augmented Disassembly-oriented Simulation perspective, have also been detected and provided as a guide for those who want to approach the hybrid simulation/disassembly research context.     

Optimizing smart manufacturing systems by extending the smart products paradigm to the beginning of life

The research objective of this work is to enhance the perception of, sensing in, and control of smart manufacturing systems (SMS) by leveraging active sensor systems within smart products during the manufacturing phase. Smart manufacturing utilizes rich process data, usually collected by the SMS (e.g., machine tools), to enable accurate tracking and monitoring of individual products throughout the process chain. However, until now, the to-be-manufactured product itself has not contributed to the sensing and compilation of product and process data. More specifically, data measured from the product’s structure during its own fabrication. In this paper, we discuss and evaluate the opportunity to actively use the capabilities of smart products within a SMS in terms of technical and economic feasibility. This opportunity emerged only recently with the advancements in smart products engineering. In this research, we developed a smart product prototype and evaluated it on a SMS testbed (CPlab) with eight distinct, fully-connected manufacturing processes. The results of the conducted experiments show the possibility to uniquely identify two distinct ‘fingerprints’ of manufacturing processes solely based on data provided by sensors within the smart product itself. The sensor data was collected directly from the smart product before manufacture was completed, yet after the intended sensor functionality during the product’s use phase was activated. The capability to automatically, accurately, and reliably identify process signatures and even inform the optimization of manufacturing parameters creates new opportunities for improvements in quality, scheduling, and seamless transparency across the whole value chain.     

Analysis of fractal manufacturing systems framework towards industry 4.0

Currently, ways to approach the design of Cyber Physical Systems in Industry 4.0 are under development. Emerging concepts of Smart Factories require the development of specialized knowledge; new working methods are also needed to manage the transition from conventional industry to industry 4.0. To achieve this objective, fractal theory could provide the appropriate knowledge and tools. Fractal systems applied to manufacturing have been widely used over the last decades to design complex adaptive systems: it allows the introduction of resilience requirements (capacity to react to changes in a turbulent environment) and to reduce the complexity of its structure, operation and management. In order to know the potential and the possibility of applying fractal theory to the design of systems in Industry 4.0, this article reviews the publications that develop fractal systems for manufacturing engineering. The review includes contributions published between 1985 (approximate date of the first works on the theory applied to manufacturing engineering) and 2019. The objective is to gather those strategies, methodologies and successful case studies that can be useful for the approaches of Industry 4.0 and to define a set of future lines of work for the adaptation of the fractal theory to the new challenges posed by Industry 4.0.     

Digital twin-based designing of the configuration,motion, control,and optimization model of a flow-type smart manufacturing system

Digital twins can achieve hardware-in-the-loop simulation of both physical equipment and cyber model, which could be used to avoid the considerable cost of manufacturing system reconfiguration if the design deficiencies are found in the deployment process of the traditional irreversible design approach. Based on the digital twin technology, a quad-play CMCO (i.e., Configuration design-Motion planning-Control development-Optimization decoupling) design architecture is put forward for the design of the flow-type smart manufacturing system in the Industry 4.0 context. The iteration logic of the CMCO design model is expounded. Two key enabling technologies for enabling the customized and software-defined design of flow-type smart manufacturing systems are presented, including the generalized encapsulation of the quad-play CMCO model and the digital twin technique. A prototype of a digital twin-based manufacturing system design platform, named Digital Twin System, is presented based on the CMCO model. The digital twin-based design platform is verified with a case study of the hollow glass smart manufacturing system. The result shows that the Digital Twin System-based design approach is feasible and efficient.     

Selecting manufacturing partners in push and pull-type smart collaborative networks

The idea of Collaborative Manufacturing, also known as the Production Networks or Social Manufacturing, has been around for more than 25 years. It is a production concept based on non-hierarchical collaboration among enterprises often referred as Virtual Enterprise (VE). Despite many scientific research and projects with this topic, it is difficult to find an example of fully operational non-hierarchical production network anywhere in the world. However, that fact could be changed very soon. Namely, the new industrial revolution, called Industry 4.0, encourages industrial enterprises to adopt information-communication technology (ICT) and Internet of Things (IoT) into their production systems, thus creating Cyber-Physical Production System (CPPS). From the aspect of production networks, CPPS represents crucial infrastructure, or a missing link between enterprises. Now, with CPPS in place, non-hierarchical networking and collaboration becomes possible through Smart Collaborative Production Networks. In this research, the concept of information system for Smart Collaborative Production Networks was developed and called ‘VENTIS’. Although the idea of the concept is to manage the collaboration inside Virtual Enterprise, in this research, a special focus has been put on manufacturing planning phase in which optimization problem known as the Partner Selection Problem (PSP) occurs. Since the PSP in manufacturing phase is far more complex than partner selection during the collaborative product development phase, new research premises regarding the Virtual Enterprise type have been set. Two types of Virtual Enterprise business models – Push-type and Pull-type – have been defined in this research. If VE is Push-type, HUMANT algorithm is used to solve PSP that occurs in that case. If VE is Pull-type, a special procedure, inspired by phenomenological reduction, has been established in which set of a priori created VEs is compared with theoretically ‘the best’ VE and theoretically ‘the worst’ VE. Enterprises’ data of production network from Dalmatia (Split-Dalmatia County, Croatia) is used as a Case Study to present ‘VENTIS’ concept and to present the procedure for creation of sustainable Virtual Enterprise.     

Understanding Service-Oriented Architecture (SOA): A systematic literature review and directions for further investigation

Service-Oriented Architecture (SOA) has emerged as an architectural approach that enhances the service delivery performance of existing traditional systems while still retaining their most important features. This approach, due to its flexibility of adoption, has gained the attention of both academic and business entities, especially in the development of world-leading technologies such as Cloud Computing (CC) and the Internet of Things (IoT). Although many studies have listed the success factors of SOA, a few minor failures have also been reported in the literature. Despite the availability of rich material on SOA, there is a lack of systematic reviews covering the different aspects of the SOA concept in Information Systems (IS) research. Therefore, the central objective of this study is to review existing issues of SOA and share the findings with the academia. Hence, a systematic literature review (SLR) was conducted to analyse existing studies related to SOA and the factors that led to SOA success and failure from 2009 to 2019. To completely cover all SOA-related research in the IS field, a two-stage review protocol that included automatic and manual searching was applied, resulting in 103 primary studies. The articles were categorised into four research themes, namely: SOA Adoption, SOA Concepts, SOA Impact, and SOA Practice. The result shows that the academic research interest on SOA increased recently with most of the articles covering SOA Practice followed by SOA Adoption. Moreover, the findings of this review highlighted SOA Governance, SOA Strategy, Financial Issues and Costs, and Education and Training as the most significant factors of SOA adoption and implementation. Consequently, the outcomes will assist professionals and experts in organisations as well as academic researchers to focus more on these factors for successfully adopting and implementing SOA.     

Lean production in complex socio-technical systems: A systematic literature review

Although lean production (LP) is usually associated with complexity reduction, it has been increasingly applied in highly complex socio-technical systems (CSS) (e.g. healthcare), in which the complexity level cannot be reduced below a certain (high) threshold. This creates a paradoxical situation, which is not well understood in theory and can be underlying the frustrating results of many lean implementations. This article presents a systematic literature review of how LP has dealt with complexity, both in theory and in practice, from a complexity science perspective. The review was based on 94 papers, which were analyzed according to seven criteria: how the concept of complexity is being used in lean research; the complexity level of the studied systems; the compatibility between the methodological approach and the nature of complexity; how complexity is managed by LP; barriers to LP in CSS; side-effects of LP in CSS; and whether complexity is always detrimental to LP. A research agenda is also proposed.     

A human-in-the-loop manufacturing control architecture for the next generation of production systems

In recent years, the introduction of Industry 4.0 technologies in the manufacturing landscape promoted the development of smart factories characterised by relevant socio-technical interactions between humans and machines. In this context, understanding and modelling the role of humans turns out to be crucial to develop efficient manufacturing systems of the future. Grounding on previous researches in the field of Human-in-the-Loop and Human Cyber-Physical Systems, the paper aims at contributing to a deep reflection about human-machine interaction in the wider perspective of Social Human-in-the-Loop Cyber-Physical Production Systems, in which more agents collaborate and are socially connected. After presenting an evolution of manufacturing control organisations, an architecture to depict social interactions in smart factories is proposed. The proposed architecture contributes to the representation of different human roles in the smart factory and the exploration of both hierarchical and heterarchical data-driven decision-making processes in manufacturing.     

Mobilising information systems scholarship for a circular economy: Review,synthesis, and directions for future research

One of today's grand societal challenges is to replace the current ‘take‐make‐waste’ economic model with a circular economic model that allows a gradual decoupling of economic activities from the consumption of finite virgin resources. While circular economy (CE) scholars have long lauded digital technologies such as sensors, distributed ledgers, or platforms as key enablers, our own community has not fully explored the potentials of information systems (IS) for a CE. Considering recent technological advances in software and hardware and our history of helping address wicked challenges, we believe the time is ripe to mobilise IS scholarship for a CE. Our findings from an interdisciplinary literature review show that research has primarily examined IS potentials for increasing efficiency of isolated intra‐organisational processes while neglecting the larger sustainability potential of IS to establish circular material flows—that is, slow down and close material loops across entire product lifecycles. In response, we propose directions for IS research that develop our knowledge of how IS can help understand and enact circular material flows to intensify and extend use of products and components and recycle waste materials. Our directions offer pathways to building and evaluating the problem‐solution pairing that could characterise a prolific CE‐IS relationship.     

Production planning and worker training in dynamic manufacturing systems

Production planning is a vital activity in any manufacturing system, and naturally implies assigning the available resources to the required operations. This paper develops and analyzes a comprehensive mathematical model for dynamic manufacturing systems. The proposed model integrates production planning and worker training considering machine and worker time availability, operation sequence and multi-period planning horizon. The objective is to minimize machine maintenance and overhead, system reconfiguration, backorder and inventory holding, training and salary of worker costs. Computational results are presented to verify the proposed model.     

Applications of agent-based systems in intelligent manufacturing: An updated review

Agent technology has been recognized as a promising paradigm for next generation manufacturing systems. Researchers have attempted to apply agent technology to manufacturing enterprise integration, enterprise collaboration (including supply chain management and virtual enterprises), manufacturing process planning and scheduling, shop floor control, and to holonic manufacturing as an implementation methodology. This paper provides an update review on the recent achievements in these areas, and discusses some key issues in implementing agent-based manufacturing systems such as agent encapsulation, agent organization, agent coordination and negotiation, system dynamics, learning, optimization, security and privacy, tools and standards.     

Integrated yet distributed operations planning approach: A next generation manufacturing planning system

Present paper envisages the need for an innovative operations planning system to handle the challenges and opportunities offered by next industrial revolution called Industry 4.0 or smart manufacturing. In specific, to embrace the increasing level of automation in manufacturing industries, the obligation of joint consideration of multiple operations functions is realized. On the other hand, quick response to dynamic conditions created by machine failures, change in demand, uncertainty in supply, etc., is important in captivating the advantages of the digitization in industries. Easing out the computational complexity, imposed by the integration of multiple functions, therefore, becomes an important aspect of next generation manufacturing planning systems. Consequently, in this paper, an agent-based approach is engineered around the opportunities offered by modern digital factory viz., intelligence at the shop-floor and ubiquity of wireless communications. While intelligence at shop-floor allows distributing the decision-making tasks to various functional agents, the communication among the agents makes it feasible to incite integrated view through the coordination agent. The approach is demonstrated for a representative industrial environment of an automotive plant. Further, comparison over conventional approaches, computational comparison, effect of degree of integration, and performance of the approach under dynamic conditions are investigated. Finally, the approach is comprehensively evaluated to analyze its robustness and implications in various manufacturing settings. This extensive investigation shows that the proposed operations planning system has capability to apprehend the benefits from next generation intelligent factory.     

A systematic review of systematic review process research in software engineering

ContextMany researchers adopting systematic reviews (SRs) have also published papers discussing problems with the SR methodology and suggestions for improving it. Since guidelines for SRs in software engineering (SE) were last updated in 2007, we believe it is time to investigate whether the guidelines need to be amended in the light of recent research.ObjectiveTo identify, evaluate and synthesize research published by software engineering researchers concerning their experiences of performing SRs and their proposals for improving the SR process.MethodWe undertook a systematic review of papers reporting experiences of undertaking SRs and/or discussing techniques that could be used to improve the SR process. Studies were classified with respect to the stage in the SR process they addressed, whether they related to education or problems faced by novices and whether they proposed the use of textual analysis tools.ResultsWe identified 68 papers reporting 63 unique studies published in SE conferences and journals between 2005 and mid-2012. The most common criticisms of SRs were that they take a long time, that SE digital libraries are not appropriate for broad literature searches and that assessing the quality of empirical studies of different types is difficult.ConclusionWe recommend removing advice to use structured questions to construct search strings and including advice to use a quasi-gold standard based on a limited manual search to assist the construction of search stings and evaluation of the search process. Textual analysis tools are likely to be useful for inclusion/exclusion decisions and search string construction but require more stringent evaluation. SE researchers would benefit from tools to manage the SR process but existing tools need independent validation. Quality assessment of studies using a variety of empirical methods remains a major problem.     

Cloud services selection: A systematic review and future research directions

Cloud computing has developed in popularity as a large-scale computing paradigm that offers a range of computing resources as a service through the internet on a pay-as-you-go basis. The expansion in demand and commercial availability of cloud services brings new challenges to cloud services selection. Several research studies have been conducted to develop enhanced methodologies to assist service consumers in selecting appropriate services. In this paper, 105 primary studies published during January, 2011 to May, 2022 has been selected using a multi-stage scrutinizing approach. The selected preliminary studies were further classified based on various variables to answer the research questions stated for this work. A systematic review of existing cloud service selection approaches is performed, which are analyzed along eight dimensions: decision-making methods, context, purposes, cloud service performance parameters, simulation/language tools, domain, datasets, and experiment/validation methods. After a thorough review and comparison of these approaches across the above-mentioned dimensions, several open research issues in the current literature have been identified. The contribution of this research is fourfold: focusing on state-of-the-art cloud services selection approaches, highlighting the benefits and drawbacks of various cloud services selection methodologies and their future directions, offering a taxonomy based on a thorough literature study, and identifying nine critical challenges in cloud services selection that require further investigation. This systematic review study is anticipated to benefit both academics and business experts.     

State of the art on quality control for data streams: A systematic literature review

These days, endless streams of data are generated by various sources such as sensors, applications, users, etc. Due to possible issues in sources, such as malfunctions in sensors, platforms, or communication, the generated data might be of low quality, and this can lead to wrong outcomes for the tasks that rely on these data streams. Therefore, controlling the quality of data streams has become increasingly significant. Many approaches have been proposed for controlling the quality of data streams, and hence, various research areas have emerged in this field. To the best of our knowledge, there is no systematic literature review of research papers within this field that comprehensively reviews approaches, classifies them, and highlights the challenges.In this paper, we present the state of the art in the area of quality control of data streams, and characterize it along four dimensions. The first dimension represents the goal of the quality analysis, which can be either quality assessment, or quality improvement. The second dimension focuses on the quality control method, which can be online, offline, or hybrid. The third dimension focuses on the quality control technique, and finally, the fourth dimension represents whether the quality control approach uses any contextual information (inherent, system, organizational, or spatiotemporal context) or not. We compare and critically review the related approaches proposed in the last two decades along these dimensions. We also discuss the open challenges and future research directions.     

Digital twins-based smart manufacturing system design in Industry 4.0: A review

A smart manufacturing system (SMS) is a multi-field physical system with complex couplings among various components. Usually, designers in various fields can only design subsystems of an SMS based on the limited cognition of dynamics. Conducting SMS designs concurrently and developing a unified model to effectively imitate every interaction and behavior of manufacturing processes are challenging. As an emerging technology, digital twins can achieve semi-physical simulations to reduce the vast time and cost of physical commissioning/reconfiguration by the early detection of design errors/flaws of the SMS. However, the development of the digital twins concept in the SMS design remains vague. An innovative Function-Structure-Behavior-Control-Intelligence-Performance (FSBCIP) framework is proposed to review how digital twins technologies are integrated into and promote the SMS design based on a literature search in the Web of Science database. The definitions, frameworks, major design steps, new blueprint models, key enabling technologies, design cases, and research directions of digital twins-based SMS design are presented in this survey. It is expected that this survey will shed new light on urgent industrial concerns in developing new SMSs in the Industry 4.0 era.