Hasil untuk "Manufacturing industries"

Alan T. L. Chan, E. Ngai, K. Moon

Abstract Responsiveness to customers and markets is an indispensable requirement for all industries, particularly the fashion industry. The present study attempts to address this issue by employing a resource-based perspective as a lens for exploring the major antecedents and consequences of supply chain agility at both the strategic and operational levels. Drawing on a review of the extant literature, we argue that two organizational flexibility factors – strategic flexibility and manufacturing flexibility – are the critical antecedents to supply chain agility. In addition, supply chain agility, strategic flexibility, and manufacturing flexibility are all significant factors in firm performance. A conceptual framework for the arguments was developed and tested through an empirical study of selected industrial practitioners. Data from a sample of 141 garment manufacturers were analyzed using structural equation modeling. The results reveal that both strategic flexibility and manufacturing flexibility positively influence supply chain agility. However, strategic flexibility has a direct and significant influence on firm performance while manufacturing flexibility does not. Furthermore, supply chain agility plays an important role in mediating the effects of both strategic and manufacturing flexibilities on firm performance. The findings of the present study add to the understanding of supply chain management, with a focus on supply chain agility in the fashion manufacturing industry.

Suqing Zeng, Zhihan Tong, Xiaona Li et al.

Abstract Plastic foams play a crucial role across various industries and building constructions, due to their lightweight structure, thermal insulation properties, and energy absorption capabilities. However, the escalating global demand for petrochemical-based foams is raising significant environmental concerns. Here, we report an all-cellulose molecular foam through an ethanol-induced cellulose molecular programmed assembly. This cellulose molecular foam features a honeycomb-like gradient porous structure, exhibits a high compressive modulus of 11.8 MPa, demonstrates a high thermal stability up to 264.1 °C, and maintains a low thermal conductivity of 0.047 W m−1 K−1. Additionally, it supports diverse shaping processes including casting, molding, and continuous manufacturing. Due to its molecular-level reversible design, all-cellulose foam is both recyclable and biodegradable, offering a potential substitute for conventional petrochemical foams in numerous building and industrial applications. Furthermore, a life cycle assessment reveals that all-cellulose foam significantly reduces carbon emissions, affirming its environmental benefits and positioning it as a promising, eco-friendly alternative.

Peng LI, Yan LIU, Chenyu TANG et al.

This paper evaluates 10 critical high-tech mineral resources—rare earths, lithium, cobalt, indium, germanium, tantalum, chromium, high-purity quartz, graphite, and fluorite—to provide an in-depth analysis of the severe challenges and structural contradictions China faces in ensuring their supply security. These minerals serve as fundamental pillars underpinning national technological innovation, the development of strategic emerging industries, and defense capabilities. Their global demand is rapidly growing, driven by the worldwide transition toward green, low-carbon technologies and industrial upgrading. However, China faces multifaceted pressures stemming from inherent constraints in domestic resource endowment, significant vulnerabilities across the supply chains, and critical dependencies on external actors within key segments of the industrial chain. Specifically, China exhibits a high degree of external reliance on most of these high-tech minerals, sourcing imports from a concentrated set of countries. This concentration exposes the supply chain to considerable risks arising from geopolitical tensions and policy volatility in resource-exporting nations. Domestically, the development of these resources is often hampered by a “triple-low” challenge: low ore grades, low recovery rates during processing, and low comprehensive utilization efficiency. Furthermore, the structure of related industrial chains is imbalanced. While China holds comparative advantages in upstream activities such as raw material extraction and primary processing, it critically depends on technological leaders such as the United States, Japan, and Germany in high value-added downstream sectors, including the production of advanced materials and core components. This dependency creates pronounced “bottleneck” or “chokepoint” risks that threaten the resilience and autonomy of China’s high-tech industries. To address these complex challenges, this paper proposes pathways and strategies for constructing a robust supply guarantee system for high-tech mineral resources, tailored to China’s specific national conditions. The proposed framework encompasses multiple, interconnected dimensions. It emphasizes the necessity of improving overall resource governance and refining market mechanisms to enhance stability and efficiency. Concurrently, a major focus is on driving technological innovation to achieve breakthroughs in areas such as environment-friendly extraction methods, efficient recycling technologies, and advanced preparation of high-end materials, which are crucial for overcoming the existing “triple-low” predicament and moving up the value chain. The strategies also advocate for proactive industrial upgrading to rebalance the industrial chain structure by fostering domestic capabilities in high value-added manufacturing segments and reducing downstream vulnerabilities. On the international stage, the study emphasizes the necessity of pursuing a proactive and diversified global engagement strategy. This involves expanding and securing resource partnerships through diplomatic and strategic investment initiatives, actively participating in shaping global resource governance frameworks, and mitigating risks associated with supply concentration. Finally, the paper highlights the critical need to establish a comprehensive, integrated domestic safeguarding system that synergistically combines exploration, development, strategic stockpiling, and emergency response mechanisms. These proposed measures primarily aim to provide a theoretical foundation and actionable policy insights for achieving a stable, secure, environmentally sustainable, and resilient supply of high-tech mineral resources, which is indispensable for supporting China’s long-term strategic development and technological advancement in an increasingly competitive global landscape.

Antonio Sartal, R. Bellas, A. Mejías et al.

Today’s society is becoming aware that a new economic model of production and consumption must take into account its environmental and social impact. Industries are under increasing pressure from stakeholders to be transparent in reporting the environmental and social impacts of their operations. In this context, sustainable manufacturing must minimize negative environmental impacts and consumption of energy and natural resources, while also being socially responsible and economically viable. That is why the sustainable manufacturing concept is gaining increasing attention both in the research community and in organizations, especially in the industrial sector. However, even today, there is a great diversity of interpretations and ideas associated with this term. Accordingly, this article first presents an overview of the main concepts related to sustainable manufacturing, and metrics to evaluate organizations’ sustainability performance, and then an outlook of current trends. Our work highlights the consistencies and inconsistencies in the research community related to the interpretations of sustainable manufacturing and Industry 4.0, as well as the lack of consensus about the true social impact of Industry 4.0. However, the positive ecological and economic impacts of sustainable manufacturing seem fairly widespread. In this way, sustainable manufacturing practices seem to be reinforced by initiatives within the fourth stage of industrialization – the so-called Industry 4.0 – which offers great opportunities for sustainable manufacturing, thanks to digital transformation.

Gautam Dutta, Ravinder Kumar, Rahul Sindhwani et al.

Manufacturing excellence is critical to our nation’s economy. Indian Government’s National Manufacturing Policy, drafted in 2011, is being revamped to include the aspects of Industry 4.0. Initiatives, both led and assisted by government and industries, are being launched to catalyze and transform India’s manufacturing competencies. This paper aims to study the functional areas which can potentially leverage Industry 4.0 technologies and help India’s manufacturing establishments to transform. It does so in context of the aspirations of India’s small and medium discrete manufacturing establishments (SMME) towards adopting digital technologies for the identified functional areas. The study draws its context from the relevant literature review intended to examine the academic articles published until the end of September 2018, followed by a maturity assessment survey of Indian SMMEs to establish priority areas,The literature survey has been complemented with a maturity survey of more than 250 of Indian SMMEs to establish adoption gaps by comparing proficiency and sophistication of their present status and proposed adoption aspirations by 2020. The assessment of the organizational aspirations and gap areas identified is expected to indicate which of the Industry 4.0 elements can be adopted by them.,The maturity survey undertaken throws up several insights – Indian SMME community’s self-assessment indicates operational measurements followed by manufacturing and design interventions as the aspired transformation cycle. The survey indicates that manufacturers would like to make changes to their design and manufacturing strategies based on performance metrics; therefore, they need to first capture real-time machine data, analyze and then incorporate the resulting improvements in manufacturing and design decisions in that order.,The maturity assessment method itself is in evolution stage, and future correlations with benefits will strengthen observations. Industry 4.0 being relatively new initiative for India, availability of country-specific academic literature is limited. The maturity assessment survey undertaken across organizations of North, West and South India therefore carries the risk of not reflecting the views of a wider population. The current maturity, or the lack of it, of proficiency and readiness of India’s SMMEs with respect to digital technologies may also be a barrier to self-examine.,This research is expected to provide insight into priorities to be adopted for digital-centric transformation by Indian SMMEs. It is expected to facilitate policymakers and influencers from government and industry to help frame policies that facilitate the adoption of digital technologies by Indian SMMEs and facilitate India’s technical education community to adopt skill development programs to support industry. It is expected to provide guidance to India’s academic institutions to rejig their curriculums to help bridge the critical skills gap that exists between newly inducted engineering professionals and industry.,Digitalization is expected to foster lean and therefore support sustainability initiatives. Digitalization is expected to help create new, alternative sources of employment which are more relevant to emerging times and foster unlearning the past and relearning of new skills. This emerging diversity of engineering applications resulting from digitalization is expected to also support the larger and poorer agricultural community of India and help the sector to become more efficient and productive, which in turn will reduce economic alienation of a large section of Indian society.,Industry 4.0 has been identified as the transformational initiative for India’s manufacturing competitiveness. Indian manufacturing sector needs to urgently implement the digital technologies and improve their performance and remain relevant in this dynamic market. This research will help guide them to frame their respective digital strategies and be successful. This research will help government and industry influencers to plan and execute their interventions.

Konstantinos Nikiforidis, Alkiviadis Kyrtsoglou, Thanasis Vafeiadis et al.

Explainable Artificial Intelligence (XAI) is crucial for the transition from the fourth to fifth industrial revolution, providing transparency and fostering user confidence in Artificial Intelligence (AI) powered systems. Since 2020, XAI applications demonstrate potential to transform manufacturing. This paper provides an extensive overview of XAI-based applications in Industries 4.0 and 5.0 by highlighting the trends regarding methods used, connecting XAI methods with important parameters and presenting XAI visualization approaches. The survey provides valuable insights for researchers, practitioners and industry leaders as it underscores the potential of XAI in shaping the future of manufacturing by enhancing transparency and user acceptance of AI-powered applications.

Anton Novoshytskyi, Svitlana Bodu, Dmytro Fabritsyiev et al.

The improvement of heat exchange equipment is one of the key directions for increasing energy efficiency in industrial and energy systems. Technologies for manufacturing thin-walled heat exchange elements with a relief surface, which provides heat transfer intensification, are becoming particularly relevant. One of the promising solutions is the use of flat parts with spherical protrusions, the formation of which requires high accuracy and stability of geometric parameters. This study considers the technology of profiling such parts using the bending process with longitudinal tension. Unlike traditional forming methods, particularly stamping and drawing, the proposed approach reduces energy consumption, avoids residual deformations, and increases the repeatability of the profile geometry. The controlled tensile force imposed on the workpiece during the passage of the profiling rollers is a key technological factor. It has been experimentally established that the optimal result is achieved at a stress level of 85–95% of the yield strength of the material. It is in this range that a high-quality relief is formed without waviness in the zones of formation of spherical protrusions and on the edges, and the longitudinal curvature corresponds to the values 85–95% predicted by the tension correction. The design and principle of operation of the experimental equipment – the profiling unit is described, along with its technical characteristics and general appearance. The production of experimental batches of profiles on the experimental unit confirmed the possibility of manufacturing profiles with spherical protrusions by sequential local deformation under the action of longitudinal tension and clarified the technical requirements for the design of an experimental and industrial profiling unit. The proposed technology is confirmed by practical results and has the potential to be implemented in the serial production of heat exchange elements. It can be adapted to the manufacture of parts with various types of relief and used in related mechanical engineering industries, such as aviation, energy, and shipbuilding.

Yanli Shi, Sasa Yang

Abstract Background Against the backdrop of deep specialization within global value chains (GVCs), it is crucial to explore how firms’ participation in global production networks affects their carbon efficiency, a key factor in achieving green growth. Using merged data from the Chinese National Tax Survey Database, the Chinese Customs Trade Statistics Database between 2008 and 2014, and the World Input-Output Database, this paper empirically examines the effect of firms’ position embedded in GVCs on carbon emission efficiency in China’s manufacturing sectors. Results It is found that: (1) Improving firms’ position embedded in GVCs can significantly improve their carbon emission efficiency. (2) This improvement is primarily driven by trade structures optimization and technological innovation. (3) Forward GVCs embeddedness exerts a stronger positive impact on carbon efficiency compared to backward embeddedness. And the carbon efficiency benefits of upgrading to higher positions within GVCs are more pronounced in firms with a higher degree of participation, those engaged in mixed and general trade, firms in high-pollution industries, and those located in non-resource-oriented cities. (4) Participation in GVCs contributes to energy conservation and emission reduction, supporting long-term low-carbon and intensive development of enterprises. Conclusions The findings shed light on the crucial role of GVCs embeddedness in enhancing carbon emission efficiency, offering a solid foundation for understanding how globalization contributes to achieving long-term sustainable development goals.

Luay Jum’a, Mohand Tuffaha, Ahmed Adnan Zaid

The study investigates the impact of strategic purchasing (SP) on manufacturing firms’ economic, environmental, and social sustainability. It also examines the moderating impact of blockchain technology (BCT) between SP and sustainability performance. The study additionally applied importance-performance map analysis (IPMA) to analyze BCT benefits to sustainability performance in Jordanian manufacturing sector. The study is quantitative in nature, and the data collected through a structured questionnaire from managers at different levels of management within manufacturing firms in Jordan resulted in 316 responses. The results of the study showed that SP has a significant and positive impact on all dimensions of sustainability performance. Additionally, the study revealed that BCT has a significant moderating impact on the relationship between SP and sustainability performance. Finally, the study identified BCT benefits importance that affect each type of sustainability performance along with their priorities. This study made a theoretical contribution by developing a framework that will help researchers gain a better understanding of the SP, BCT, and sustainable performance. Moreover, the study provided practical insights for strategic decision-making in the manufacturing sector. It highlighted SP’s role in sustainability performance and how companies can invest in BCT to improve sustainability. The study warns against generalizability and emphasizes the need to conduct the study in specific industries or countries.

Kunal Manoj Gide, Clara Elise Tranchemontagne, Muhammad Zaryyab Sardar et al.

Additive manufacturing (AM) has advanced significantly, yet challenges remain in producing composites with tailored properties. Stereolithography (SLA), a high-resolution AM technique, struggles to achieve controlled fiber orientation in composite materials. This study addresses this limitation by integrating an in-house electromagnetic filler alignment system into a commercial SLA 3D printer. The system uses electromagnets to align reinforcing fillers at 0° and 90° during printing. Acrylic resin-cobalt powder composites were fabricated and analyzed using optical microscopy, tensile testing, micro-indentation, and scanning electron microscopy (SEM). Microscopy confirmed successful fiber alignment with the electromagnet system. Compared to the control (pure resin) and randomly oriented samples, the aligned composites exhibited lower stiffness but significantly enhanced ductility. Specifically, the strain at failure increased from 1.4 % in the control samples to 7.9 % and 6.8 % in the 0° (perpendicular to loading direction) and 90° (parallel to loading direction) aligned composites, respectively. This marked improvement in strain capacity indicates a clear transition to more ductile behavior, a trend further corroborated by SEM observations. This approach overcomes SLA limitations, enabling controlled filler alignment for enhanced mechanical, thermal, and electrical properties. These advancements hold promise for customized manufacturing in aerospace, automotive, medical, and computing industries.

Blaž Rolih, Matic Fučka, Danijel Skočaj

Surface defect detection is a critical task across numerous industries, aimed at efficiently identifying and localising imperfections or irregularities on manufactured components. While numerous methods have been proposed, many fail to meet industrial demands for high performance, efficiency, and adaptability. Existing approaches are often constrained to specific supervision scenarios and struggle to adapt to the diverse data annotations encountered in real-world manufacturing processes, such as unsupervised, weakly supervised, mixed supervision, and fully supervised settings. To address these challenges, we propose SuperSimpleNet, a highly efficient and adaptable discriminative model built on the foundation of SimpleNet. SuperSimpleNet incorporates a novel synthetic anomaly generation process, an enhanced classification head, and an improved learning procedure, enabling efficient training in all four supervision scenarios, making it the first model capable of fully leveraging all available data annotations. SuperSimpleNet sets a new standard for performance across all scenarios, as demonstrated by its results on four challenging benchmark datasets. Beyond accuracy, it is very fast, achieving an inference time below 10 ms. With its ability to unify diverse supervision paradigms while maintaining outstanding speed and reliability, SuperSimpleNet represents a promising step forward in addressing real-world manufacturing challenges and bridging the gap between academic research and industrial applications. Code: https://github.com/blaz-r/SuperSimpleNet

Bazigu Alex, Mwebaze Johnson

The increasing demands for sustainable and efficient manufacturing systems have driven the integration of Internet of Things (IoT) technologies into smart manufacturing. This study investigates IoT-enabled systems designed to enhance energy efficiency and resource optimization in the manufacturing sector, focusing on a multi-layered architecture integrating sensors, edge computing, and cloud platforms. MATLAB Simulink was utilized for modeling and simulation, replicating typical manufacturing conditions to evaluate energy consumption, machine uptime, and resource usage. The results demonstrate an 18% reduction in energy consumption, a 22% decrease in machine downtime, and a 15% improvement in resource utilization. Comparative analyses highlight the superiority of the proposed framework in addressing operational inefficiencies and aligning with sustainability goals. The study underscores the potential of IoT in transforming traditional manufacturing into interconnected, intelligent systems, offering practical implications for industrial stakeholders aiming to optimize operations while adhering to global sustainability standards. Future work will focus on addressing identified challenges such as high deployment costs and data security concerns, aiming to facilitate the broader adoption of IoT in industrial applications. Keywords: IoT (Internet of Things), Smart Manufacturing, Energy Efficiency, Resource Optimization, Manufacturing

Carlos Serôdio, Pedro Mestre, Jorge Cabral et al.

In the context of Industry 4.0, this paper explores the vital role of advanced technologies, including Cyber–Physical Systems (CPS), Big Data, Internet of Things (IoT), digital twins, and Artificial Intelligence (AI), in enhancing data valorization and management within industries. These technologies are integral to addressing the challenges of producing highly customized products in mass, necessitating the complete digitization and integration of information technology (IT) and operational technology (OT) for flexible and automated manufacturing processes. The paper emphasizes the importance of interoperability through Service-Oriented Architectures (SOA), Manufacturing-as-a-Service (MaaS), and Resource-as-a-Service (RaaS) to achieve seamless integration across systems, which is critical for the Industry 4.0 vision of a fully interconnected, autonomous industry. Furthermore, it discusses the evolution towards Supply Chain 4.0, highlighting the need for Transportation Management Systems (TMS) enhanced by GPS and real-time data for efficient logistics. A guideline for implementing CPS within Industry 4.0 environments is provided, focusing on a case study of real-time data acquisition from logistics vehicles using CPS devices. The study proposes a CPS architecture and a generic platform for asset tracking to address integration challenges efficiently and facilitate the easy incorporation of new components and applications. Preliminary tests indicate the platform’s real-time performance is satisfactory, with negligible delay under test conditions, showcasing its potential for logistics applications and beyond.

Linfang Tan, Da Gao, Xiaowei Liu

Improving the energy efficiency of enterprises is one of the key means to solve the problem of energy shortage. It is of great significance to investigate how environmental information disclosure (EDI) promotes the green total factor energy efficiency (GTFEE) of enterprises. Based on this, this study calculates the GTFEE of enterprises by combining the database of Chinese manufacturing and the pollutant emission of industrial enterprises and investigates the impact of EDI on the GTFEE of manufacturing industries by using a difference-in-difference model. The following is found: (1) EDI can significantly promote the manufacturing enterprises’ GTFEE, and the results are still valid after a series of robustness tests; (2) Mechanism analysis shows that EDI can improve the GTFEE of manufacturing enterprises by promoting technological innovation and optimizing energy structure; (3) The heterogeneity analysis shows that EID is more positive on firms’ GTFEE in the eastern than western regions. The positive impact is greater for non-state-owned, low-energy consumption, export, and polluting enterprises. The findings of this paper provide a theoretical basis and practical enlightenment for the government to promote the green development transformation of enterprises.

Jihua Chen, Yue Yuan, Amir Koushyar Ziabari et al.

Artificial Intelligence (AI) approaches are increasingly being applied to more and more domains of Science, Engineering, Chemistry, and Industries to not only improve efficiencies and enhance productivity, but also enable new capabilities. The new opportunities range from automated molecule design and screening, properties prediction, gaining insights of chemical reactions, to computer-aided design, predictive maintenance of systems, robotics, and autonomous vehicles. This review focuses on the new applications of AI in manufacturing and healthcare. For the Manufacturing Industries, we focus on AI and algorithms for (1) Battery, (2) Flow Chemistry, (3) Additive Manufacturing, (4) Sensors, and (5) Machine Vision. For Healthcare applications, we focus on: (1) Medical Vision (2) Diagnosis, (3) Protein Design, and (4) Drug Discovery. In the end, related topics are discussed, including physics integrated machine learning, model explainability, security, and governance during model deployment.

Yifei Li, Jeongwon Park, Guha Manogharan et al.

Recent technological innovations in the areas of additive manufacturing and collaborative robotics have paved the way toward realizing the concept of on-demand, personalized production on the shop floor. Additive manufacturing process can provide the capability of printing highly customized parts based on various customer requirements. Autonomous, mobile systems provide the flexibility to move custom parts around the shop floor to various manufacturing operations, as needed by product requirements. In this work, we proposed a mobile additive manufacturing robot framework for merging an additive manufacturing process system with an autonomous mobile base. Two case studies showcase the potential benefits of the proposed mobile additive manufacturing framework. The first case study overviews the effect that a mobile system can have on a fused deposition modeling process. The second case study showcases how integrating a mobile additive manufacturing machine can improve the throughput of the manufacturing system. The major findings of this study are that the proposed mobile robotic AM has increased throughput by taking advantage of the travel time between operations/processing sites. It is particularly suited to perform intermittent operations (e.g., preparing feedstock) during the travel time of the robotic AM. One major implication of this study is its application in manufacturing structural components (e.g., concrete construction, and feedstock preparation during reconnaissance missions) in remote or extreme terrains with on-site or on-demand feedstocks.

Cuixia Li, Liqiang Liu, Li Shi

Intelligent manufacturing is a new model that uses advanced technologies such as the Internet of Things, big data, and artificial intelligence to improve the efficiency and quality of manufacturing production. As an important support to promote the transformation and upgrading of the manufacturing industry, cloud service optimization has received the attention of researchers. In recent years, remarkable research results have been achieved in this field. For the sustainability of intelligent manufacturing platforms, in this paper we summarize the process of cloud service optimization for intelligent manufacturing. Further, to address the problems of dispersed optimization indicators and nonuniform/unstandardized definitions in the existing research, 11 optimization indicators that take into account three-party participant subjects are defined from the urgent requirements of the sustainable development of intelligent manufacturing platforms. Next, service optimization algorithms are classified into two categories, heuristic and reinforcement learning. After comparing the two categories, the current key techniques of service optimization are targeted. Finally, research hotspots and future research trends of service optimization are summarized.

Xiao Wang, Yutong Wang, Jing Yang et al.

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer ``Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness ``6S" manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber-Physical-Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0.

Jacques Van Damme, Shana Massar, Rohith Acharya et al.

The development of superconducting qubit technology has shown great potential for the construction of practical quantum computers. As the complexity of quantum processors continues to grow, the need for stringent fabrication tolerances becomes increasingly critical. Utilizing advanced industrial fabrication processes could facilitate the necessary level of fabrication control to support the continued scaling of quantum processors. However, these industrial processes are currently not optimized to produce high coherence devices, nor are they a priori compatible with the commonly used approaches to make superconducting qubits. In this work, we demonstrate for the first time superconducting transmon qubits manufactured in a 300 mm CMOS pilot line, using industrial fabrication methods, with resulting relaxation and coherence times already exceeding 100 microseconds. We show across-wafer, large-scale statistics studies of coherence, yield, variability, and aging that confirm the validity of our approach. The presented industry-scale fabrication process, using exclusively optical lithography and reactive ion etching, shows performance and yield similar to the conventional laboratory-style techniques utilizing metal lift-off, angled evaporation, and electron-beam writing. Moreover, it offers potential for further upscaling by including three-dimensional integration and additional process optimization using advanced metrology and judicious choice of processing parameters and splits. This result marks the advent of more reliable, large-scale, truly CMOS-compatible fabrication of superconducting quantum computing processors.

J. Tybout, M. Westbrook

Abstract Did Mexico's recent trade liberalization generate productivity gains? We find that average costs fell in most industries, with tradeable goods producers registering the largest reductions. Among importables, these cost reductions trace partly to improvements in relative productivity. Among exportables, they are due to favorable changes in relative prices, probably because imported intermediate goods became cheaper. Gains due to scale economy exploitation were minor and were not correlated with increases in foreign competition. Hence the results cast some doubt on simulation studies of trade liberalization that stress scale effects as a major source of efficiency gain.