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Companies with manufacturing systems that are more responsive and resilient will be able to survive or even gain market shares in the face of the unpredicted variable of an outbreak similar to the COVID-19 pandemic. Motivated by an industrial company restructuring its manufacturing system with the layout of fixed-position assembly islands (FPAI) during the COVID-19 pandemic, this paper introduces the synchronization-oriented reconfiguration of FPAI under Graduation Intelligent Manufacturing System (GiMS). Inspired by the graduation ceremony, a novel manufacturing mode-Graduation Manufacturing System (GMS) with ticket-based reconfigurable structures, is designed for organizing production operations with simplicity and resilience for the layout of FPAI. The IIoT and digital twin-enabled GiMS is developed for transforming real-time visibility in operations to support the reconfiguration of the manufacturing system. A synchronization-oriented reconfiguration mechanism is proposed to achieve the synchronous interaction among changing customer demand, island configuration, and production activities allocation rapidly and cost-effectively. Cloud services integrating the proposed reconfiguration mechanism are developed for managers and onsite operators for supporting the successful reconfiguration implementation with enhanced operational visibility. Through the case study of an industrial company, the effectiveness of the proposed concept and approach is verified.

In production, poor ergonomic environments not only lead to increased workloads and health hazards for employees but also tend to reduce efficiency and quality. Recently, the human-cyber-physical (HCP) system has been proposed and widely studies to meet human capabilities and limitations. However, most existing frameworks are still not adaptive enough to integrate humans into a smart production environment due to a lack of real-time individual human factors digitalization. This research proposes an HCP workstation model for comprehensive assembly resources digitalization and autonomous interaction by CPS enabling technologies. Based on this, an adaptive control system has been developed for proactive ergonomic risk mitigation. Computer vision is deployed for real-time individual ergonomic evaluation and a prototype has been set up for functional validation.

PurposeThe purpose of this paper is to develop an intelligent manufacturing system for transforming production management and operations to an Industry 4.0 manufacturing paradigm.Design/methodology/approachA manufacturing mode-Graduation Manufacturing System is designed for organizing and controlling production operations. An Industrial Internet of Things (IIoT) and digital twin-enabled Graduation Intelligent Manufacturing System (GiMS) with real-time task allocation and execution mechanisms is proposed to achieve real-time information sharing and production planning, scheduling, execution and control with reduced complexity and uncertainty.FindingsThe implementation of GiMS in an industrial company illustrates the potential advantages for real-time production planning, scheduling, execution and control with reduced complexity and uncertainty. For production managers and onsite operators, effective tools, such as cloud services integrates effective production and operations management strategies are needed to facilitate their decision-making and daily operations at the operational level.Originality/valueThis paper presents an Industry 4.0 paradigm-GiMS, which aims to explore Industry 4.0 technologies opportunities on operations and production management, especially on production planning, scheduling, execution and control.

Manufacturing flexibility is strategically important to firms for improving competition in the fast-changing market. This research inspired by the assembly cells system, which significantly appears rapid response, high flexibility and efficiency on the high-mix, low-volume (HMLV) environment. However, due to the non-repetitive operations caused by various products, high dependency on workers’ skills and inevitable exceptions and disruptions in manual operation, the complexity and uncertainty within the assembly cell will cause the system disturbance propagation and amplification. This paper presents a reconfigurable assembly system consisted of multiple intelligent assembly workstations (iWorkstation), named W-RAS. Based on a proposed human-cyber-physical system (HCPS) framework, the innovated iWorkstation can assist workers with differences in abilities and characteristics. Computer vision technology is applied to monitor and analyze the operation process and status for work measurement and ergonomic assessment. This real-time information visibility and traceability can support feedback-based optimal management and enhance coordination among the iWorkstations for enhancing the adaptability and reconfigurability of W-RAS. Finally, an operational mechanism is proposed for multiple iWorkstations collaboration and a numerical example shows the benefits in shortening the lead time of various orders.

The layout of fixed-position assembly islands is widely used in the equipment manufacturing industry. In this configuration, the product remains at one assembly island for its entire assembly period, while required workers, equipment and materials are moved to the island according to the assembly process and production plan. Such configuration is not only suitable for producing large, bulky, heavy or fragile products, but also offers considerable flexibility and competitive operational efficiency for products with medium variety and production volumes. However, inherently limited space, complex assembly processes, and high dynamic of material, equipment and manpower flows increase the complexity and chaotic dynamics with massive human interventions in fixed-position assembly islands. For overcoming these challenges, inspired by graduation ceremony, this paper proposes a novel manufacturing mode-Graduation Manufacturing System (GMS), in which three kinds of tickets are designed to organise and manage production activities in fixed-position assembly islands. Under the context of Industry 4.0, the pillars of Assembly 4.0 are discussed. Finally, based on the principles of Assembly 4.0, by integrating Internet of Things (IoT), Industrial Wearable (IW), and Machine Learning (ML) technologies, the framework of Graduation Intelligent Manufacturing System (GiMS) is developed.

Facing fierce competition in mass customization, rapid response to dynamic changes is strategically important for manufactures to increase opportunity of survival in high-mix, low-volume (HMLV) environment. The traditional dedicated manufacturing system for mass production is required to become modular, scalable and reconfigurable for increasing flexibility and responsiveness to cope with the changing market. Motivated by a hybrid assembly cell lines (HACL) in real-life, a distributed assembly cells system, inspired by swarm intelligence (SI) concept and swarm robotics, has been proposed, named swarm assembly system (SAS). In order to apply and reap the benefits of SI to SAS, three main components are designed by referring to swarm robot system and they are human-cyber-physical system holon (HCPS Holon), intelligent assembly workstation (iWorkstation) and operational framework of SAS. Computer vision technologies are employed to monitor and analyze real-time worker status and operation information within the iWorkstation for closed-loop adaptive control. The hybrid operational framework of SAS provides the basis for communication between iWrokstations and global decision making. Several scenarios are designed to show its potential benefits in robustness, reconfigurability and scalability, resulting in enhancing responsiveness in HMLV environment.

Assembly islands with fixed-position layout refers to a configuration in which the product remains at a fixed site and requires workers moving among different islands. This kind of production pattern is widely applied in heavy industrial manufacturing. However, due to the complexity and highly dynamic level of material and manpower flows, it is difficult to manage the production process in fixed-position assembly island. Graduation intelligent Manufacturing System (GiMS) is viewed as a promising system for tackling production with high complexity and uncertainty. To explore the application of GiMS in fixed product (minibus) assembly. This paper presents a demonstrative case study of minibus assembly under GiMS. IoT technologies are adopted to create smart manufacturing environment, and cloud-based services are developed for facilitating decision-making and daily operation. In addition, the implementation of three kinds of smart tickets in the case gives an explicit illustration of how it works.

Online advertising, one typical application of recommendation system, calls for effective and accurate recommendations of keywords. Extreme sparse and large scale data makes online advertising a challenging problem. To achieve better performance and accuracy of the recommendation, a better model with a short turnaround time is needed. In this paper, we address the problem of personalized online advertising for extreme sparse and large scale data. We develop a novel machine learning model (Similarity Powered Pairwise Amplifier Network, SPPAN for short). The complexity of this model (a.k.a. the number of parameters) grows with the amount of observed data, which makes it suitable to extremely sparse data. The training algorithm based on gradient descent makes it easy to parallelize. The similarity model combines the user neighborhood and item neighborhood ideas in collaborative filtering smartly, obtaining a cost-effective way to handle large scale data. The proposed framework is evaluated on a large set of real-world data set from a large internet company (expressed by "Company A"). The experiment results demonstrate that the proposed SPPAN model can greatly improve the prediction and recommendation accuracy on that extreme sparse data set compared with existing approaches.

Industry 4.0 connotes a new industrial revolution with the convergence between physical and digital spaces, which is revolutionizing the way that production operations are managed. The requirement of increased productivity, improved flexibility and resilience, and reduced cost in Industry 4.0 manufacturing calls for new paradigms that comply with the changing of production and operations management. In this paper, a concept of synchroperation, which is defined as “synchronized operations in an agile, resilient and cost-efficient way, with the spatiotemporal synchronization of men, machines and materials as well as data-driven decision-making, by creating, establishing and utilizing cyber-physical visibility and traceability in operations management”, is proposed as a new paradigm of production and operations management for Industry 4.0 manufacturing. A Hyperconnected Physical Internet-enabled Smart Manufacturing Platform (HPISMP) is developed as a technical solution to support manufacturing synchroperation. Graduation Intelligent Manufacturing System (GiMS) with divide and conquer principles is proposed to address the complex, stochastic, and dynamic nature of manufacturing for achieving synchroperation. An industrial case is carried out to validate the effectiveness of the proposed concept and method. This article provides insight into exploring production and operations management in the era of Industry 4.0.

The layout of fixed-position assembly islands is widely used in the heavy equipment industry, where the product remains at one assembly island for its entire assembly period, while required workers, equipment, and materials are moved to the island according to the assembly plan. Such layout is not only suitable for producing bulky or fragile products, but also offers considerable flexibility and competitive operational efficiency for products with medium variety and volumes. However, due to inherent complexity of the product, sophisticated assembly operations heavily rely on skilled operators, and the complexity and uncertainty are high and amplified by such massive manual interventions as well as the unique routing patterns of the fixed-position assembly process. Aiming at reducing the complexity and uncertainty, this paper introduces a digital twin-enabled Graduation Intelligent Manufacturing System (DT-GiMS) for fixed-position assembly islands. Inspired by the success of graduation ceremony, an assembly system-Graduation Manufacturing System (GMS) is proposed for fixed-position assembly islands, in which job tickets, setup tickets, operation tickets, and logistics tickets are designed to organize the production activities. Following the concept of digital twin, unified digital representations with appropriate sets of information are created at object level, product level, and system level, respectively. Through Internet of Things (IoT), smart gateway, Web 3D and industrial wearable technologies, vital information including identity, status, geometric model, and production process can be captured and mapped in physical space, and converged and synchronized with their digital representations in twin (cloud) space on a real-time basis. The overall framework of DT-GiMS is presented with physical layer, digital layer, and service layer. Real-time convergence and synchronization among them ensure that right resources are allocated and utilized to the right activities at the right time with enhanced visibility. Considering customer demand and production capacity constraints, real-time ticket pool management mechanisms are proposed to manage production activities in a near-optimal way under DT-GiMS. With the support of cloud-based services provided in service layer in DT-GiMS, managers could easily make production decisions, and onsite operators could efficiently complete their daily tasks with nearly error-free operations with enhanced visibility. A demonstrative case is carried out to verify the effectiveness of the proposed concept and approach.

Drilling through and completing wells through deep and acid environment regions are technically challenging and costly. Based on twin shear unified strength theory (TSUST), a new high collapse (HC) oil country tubular goods (OCTG) collapse strength model, involving the manufacturing imperfections and significant anisotropy of the material, was developed in this paper. Comparisons of numerical calculations with full-scale test collapse data show that the new HC OCTG collapse strength model gets higher calculation accuracy for predicating HC OCTG collapse strength than both American Petroleum Institute (API) Bulletin 5C3 and ISO/TR 10400. Thus, the new HC OCTG collapse strength model will provide a more scientific method and exciting possibility for deep and acid environment wells design and construction.

Drilling and completing wells in deep salt stratum are technically challenging and costing, as when serving in an incomplete borehole in deep salt formation, well casing runs a high risk of collapse. To quantitatively calculate casing remaining strength under this harsh condition, a three-dimensional mechanical model is developed; then a computational model coupled with interbed salt rock-defective cement-casing and HPHT (high pressure and high temperature) is established and analyzed using multiphysics coupling FEM (finite element method); furthermore, open wellbore shrinkage and casing remaining strength under varying differential conditions in deep salt formation are discussed. The result demonstrates that the most serious shrinkage occurs at the middle of salt rock, and the combination action of salt rock creep, cement defect, and HPHT substantially lessens casing remaining strength; meanwhile, cement defect level should be taken into consideration when designing casing strength in deep salt formation, and apart from the consideration of temperature on casing the effect of temperature on cement properties also cannot be ignored. This study not only provides a theoretical basis for revealing the failure mechanism of well casing in deep complicated salt formation, but also acts as a new perspective of novel engineering applications of the multiphysics coupling FEM.

Over the past half-century, manufacturers have strived to achieve “Zero Inventory (ZI)” manufacturing, which puts the burden on suppliers to build their warehouses nearby production lines to meet the strict time delivery requirements of ZI production. Considerable improvements in manufacturing processes have been made. However, this practice in the meanwhile brings excessive warehousing operations along the supply chain. Recently, massive up-front investment has been made by some larger manufacturers for industrial 4.0 and warehouse automation to facilitate warehousing operations, such as put away and picking. However, warehouse operation activities enhanced by automation are still non-value-adding and current practice of redundant handling process implies continuous cost increase with the demand expanding. Therefore, it is of significance to simplify the corresponding operations as much as possible. This paper makes a comprehensive introduction to a new manufacturing paradigm named Zero-Warehousing Smart Manufacturing (ZWSM) from the concept, practices, principles, and core technologies. ZWSM basically aims to avoid traditional non-value-adding warehousing operations such as put away and order picking, as well as to reduce the warehousing space for operations to the minimum. First, prototype layout and basic practices under ZWSM are proposed. Second, synchronization, unitization and uncertainty hedging, which are the key principles to achieve the ZWSM, are explicitly introduced. Third, A Zero-Warehousing Smart Manufacturing Platform (ZWSMP) is developed with the Internet of Things (IoT) enabled infrastructures. Corresponding logistics services are presented to provide information visibility and achieve operations improvement. A case study of the Hong Kong prefabrication construction project is used to demonstrate the materials delivery process among different supply chain partners to provide a basis for zero-warehousing achievement.