Global Manufacturing Industry Research Articles

Research on Key Technologies of Artificial Intelligence for Intelligent Manufacturing

Intelligent manufacturing, as the main form of future manufacturing, is the highland of a new round of competition in the global manufacturing industry, which will fundamentally change the production and manufacturing methods and technological and economic paradigms formed by human beings since the industrial revolution. At present, the intelligent manufacturing industry is still in the early stage of development, which is characterized by three typical characteristics: continuous growth of market size, strong promotion by national governments and diversification of competition subjects. At present, China's manufacturing industry is facing transformation, and the manufacturing industry is bound to need to transform to intelligent manufacturing. Industrial robots are the main content of current intelligent manufacturing, how to fully apply artificial intelligence technology to the manufacturing of industrial robots, so that its manufacturing efficiency, manufacturing accuracy and other product attributes are improved, to maximize its economic benefits, has a particularly important application value. The application of intelligent manufacturing and robots can not only promote the faster development of manufacturing, but also reconstruct new forms of manufacturing. Based on this, the paper first summarizes the application technology of intelligent manufacturing, and then analyzes the key technology of robot application for reference to relevant workers.

Cloud 3D Printing Information Research

3D printing technology, as a major technological change in the global manufacturing industry, has gradually become the trend of The Times. As a service-oriented intelligent manufacturing system, cloud manufacturing has been widely studied by scholars at home and abroad in recent years. This paper takes cloud manufacturing as the carrier, studies the informatization of 3D printing service under cloud environment, elaborates the construction of 3D printing service information model in detail, and instantiates through Agent mapping model. This paper creates an implementation path for cloud 3D printing to provide on-demand precision manufacturing services.

PERENCANAAN PENJADWALAN PREVENTIF MAINTENANCE MESIN COLD PRESS PADA PT. XYZ

The development of the manufacturing industry is currently experiencing rapid progress in line with the development of the global manufacturing industry. In line with this, it certainly has an impact on producers or suppliers. Companies are required to always provide satisfaction to consumers or customers, namely by providing services and providing products of the highest quality. Good products are of course produced through the best process as well. Increasing productivity in the production system is an absolute thing that must be done. To achieve production productivity, namely by increasing or rehabilitating production machines at the company. In producing a product, maintenance activities are needed. Maintenance is an activity to maintain the reliability of a facility so that the facility can function properly and is ready to function. Regular maintenance is expected to extend the life of production machines so they don't get damaged and don't interfere with production activities.

A Human-Centric Design Method for Industrial Centrifugal Pump Based on Digital Twin

The worldwide demand for customized centrifugal pumps in numerous industries is intensifying steadily, posing significant challenges to the traditional design model characterized by extended design cycles, numerous errors, and elevated development costs. To address these issues, this paper introduces an intelligent collaborative design methodology for industrial centrifugal pumps grounded in digital twin (DT) technology. The methodology aims to handle the diversified types and intricate design requirements of industrial pumps and is specifically tailored to predict pump performance through a detailed multi-physics model. This model encapsulates various physical processes, including fluid dynamics, thermodynamics, and structural mechanics, and incorporates the designer’s professional knowledge and experience to support enhanced decision-making. Furthermore, the application of intelligent parametric models has facilitated the interconnected design of pump components. This advancement has led to significant improvements such as shortening production cycles, reducing errors and costs, and enhancing design efficiency and quality. Consequently, the paper demonstrates that adopting this intelligent collaborative design approach can greatly benefit the industrial centrifugal pump sector by meeting the growing customization demands with increased efficiency and precision. These findings underscore the potential of DT technology in revolutionizing traditional pump design methodologies, advancing the intelligent progression of industrial centrifugal pump design and manufacturing, and satisfying the expanding demands of the global manufacturing industry.

A quick look at the recent advances, current state of utilization and expected future usage of artificial intelligence (AI) in the global textile manufacturing industry

A significant part of the current textile manufacturing industry around the world is managed and governed by individuals who are not sufficiently trained in information technology. While future textile engineers and technologists may come out sufficiently equipped to understand IT and its implications to the textile manufacturing industry, those running the industry today can benefit through simple elaboration of potentially useful IT tools and the many benefits they offer to the textile industry. This is especially true in the case of technologies that surfaced within the last two decades and are already making a sizable impact on products, production processes and the bottom lines of manufacturing industries. Artificial intelligence (AI) and related technologies fall under the category of rapidly emerging technologies that carry the potential to significantly re-shape the global manufacturing and service industries. This paper makes an attempt to describe these technologies and their potential benefits in such a way that textile industry leaders who are not IT experts can understand them to the extent of driving themselves to enthusiastically adopt them.

Can digital service trade promote the high-quality development of global manufacturing? – existence and mechanism

PurposeThis thesis deeply studies the impact mechanism of digital service trade on the high-quality development of the manufacturing industry from the aspects of technological innovation and industrial structure.Design/methodology/approachIn this thesis, 40 countries from 2010 to 2020 were selected as samples, and the panel fixed-effect model and intermediary effect model were used to empirically analyze the impact path of digital service trade on the high-quality development of global manufacturing.FindingsOverall, digital service trade has a positive impact on the high-quality development of the global manufacturing industry. Through the analysis of the intermediary effect mechanism, it is found that digital service trade can further positively affect the high-quality development of the global manufacturing industry by promoting technological innovation and industrial structure upgrading.Research limitations/implicationsBased on the empirical results, targeted countermeasures and suggestions are given in this paper.Practical implicationsThrough the test of national heterogeneity, it is found that in developing countries, digital service trade mainly acts on the high-quality development of the manufacturing industry by promoting industrial structure upgrading.Social implicationsIn developed countries, digital service trade mainly promotes the high-quality development of manufacturing through technological innovation; from the perspective of industry heterogeneity, the three service industries of information and communication technology (ICT), other business services and property have the intermediary effect of technological innovation and industrial structure.Originality/valueThis manuscript suggests that trade in digital services should be promoted as a national trade priority.

Systematic knowledge modeling and extraction methods for manufacturing process planning based on knowledge graph

Process knowledge is an important part of intelligent manufacturing, which has become the trend of global manufacturing industry. However, process planning in manufacturing heavily relies on experience and historical knowledge that cannot be effectively reused. Also, process knowledge is complicatedly structured, widely distributed, and weakly associated, which makes it more difficult to be shared and reused. Moreover, existing methods of organizing process knowledge are labor-intensive, inefficient and inevitable to extensive manual annotation. To overcome the above problems, a systematic method of knowledge graph construction for process knowledge is presented. First, through key concepts and relationships analysis, a domain ontology for process knowledge is proposed. Second, a pattern-based bootstrapping framework with a 2-level masking technique is established to perform process knowledge extraction, which does not require manual annotation and avoids overfitting issues. Third, a meta path-based question answering over knowledge graph approach is presented to support process planning, which can effectively capture keywords of the input question, present the intention and match the designed path. Finally, taking the production process of radiator as a research object, a corresponding manufacturing process knowledge graph is constructed and applied to process planning scenario, and experiments validate the feasibility of the proposed methods.

The Impact of Financial Resources, Natural Ecological Orientation, and Environmental Innovation Capabilities on Environmental Management Accounting Across Kenyan SMEs

Nowadays competitiveness in the global manufacturing industry does not rely only on industry location, employment, productivity, and innovation. However, environmental regulations and standards (e.g, European countries (EU) policies and regulations) also have a significant impact on firms' competitive advantage (Dechezleprêtre & Sato, 2017; Agan et al. 2013). This is clearly manifested in exporters' performance from SMEs, especially in developing countries such as Kenya (Massoud et al., 2010), which show instability in the sales of shares in the EU (Osano, 2019). Qian et al., (2015) emphasized the advantages of implementing Environmental Management Accounting (EMA) as desirable environmental information systems export barrier overcome and boosting competitiveness. The determinants influencing Environmental Management Control Systems (EMCS) for superior performance or Sustainable Competitive Advantage (SCA) based on Natural Resource Based View (NRBV) is the subject matter of an ongoing debate (Asiaei et al., 2022). However, there has been increasing attention placed on the impact of Intangible resources (IRs) (Latan et al., 2018), since these resources were regarded as the most likely sources of a company's success because they are difficult to be acquired and replicated (Kamasak, 2017). On the other hand, tangible resources (TRs) (e.g. financial resources) have largely been ignored (Schriber & Löwstedt 2015). Corporations especially SMEs also need to integrate financial resources which are considered intrinsic manifestations of the context in which organizational activities occur (Schriber & Löwstedt 2015). At the same time, Newbert, (2007) confirmed that neither intangible nor tangible resources could have actual value without effective capabilities integration as both resources are considered static in nature. Keywords: Natural Resource Based View, Financial Resources, Natural Ecological Orientation, Environmental Innovation Capabilities. Environmental Management Accounting

Examining Digital Readiness in the Era Of Industry 4.0 in Tunisia

Industry 4.0 is the fourth and most current industrial revolution, which refers to the integration of the digital and physical worlds in a manufacturing context. It is the current trend in organizational automation and data interchange. It denotes intelligent manufacturing processes that combine cyber and physical systems via a collection of technologies such as the Internet of Things, big data, and cloud computing. The transition to Industry 4.0 is predicted to result in significant structural changes, productivity increases, and increased competitiveness in the global manufacturing industry. The use of such technology enables businesses to identify solutions capable of transforming rising complexity into possibilities for assuring long-term competitiveness and profitable growth. Organizations must self-assess their Industry 4.0 readiness in order to survive and prosper in the Fourth Industrial Revolution. Digital readiness enables the workforce to be able to use digital tools consciously, productively for a given purpose, and to adapt quickly to the ever-changing work environment. Following that, the conception or construction of an Industry 4.0-ready model with the basic model parameters is required. The business environment is changing as a result of digitalization, and companies are facing new hurdles in order to advance. The first step in supporting companies is checking their digital readiness and capabilities, as well as setting clear progress goals. The purpose of this paper is to identify the primary aspects for measuring companies' preparation for Industry 4.0, the relationships between these readiness criteria, and how future Ph.D. research should proceed based on the findings. This paper focuses on the literature analysis identifying the reasons, challenges, barriers, and facilitators of digitalization. The research applies triangulation, by combining qualitative and quantitative methodology. The qualitative approach will be applied initially by conducting open interviews, and then, for the quantitative part, with a questionnaire aimed at Tunisian companies and their workforces.

Industrial automation and robotics in AAC industry: Selected models and case studies

AbstractAs the global manufacturing industry entered its fourth revolution (4IR, or Industry 4.0), innovations such as robotics, automation, and artificial intelligence are set to take over and improving the global industries. As the number of active industrial robots worldwide is increasing every day in different applications and industries automation continues to create new types of robots with improved utility and function. In the AAC Industry, these innovations already took place for some time in different process phases and applications such as oiling, autoclaves stacking pins, handling, packing, and so forth. Through this research, advantages commonly attributed to automation and robotics include higher production rates and increased productivity, more efficient use of materials, better product quality, improved safety, shorter workweeks for labor, and reduced factory lead times. Especially reduced scrap and waste through improved accuracy, increased efficiency, and operation around the clock could be emphasized. Based on the accumulated experience of WKB Systems GmbH in the field of the forging industry, selected models, real‐life application case studies, and implementation of different robotics in the AAC industry are discussed in this paper. These examples such as linear robots for autoclaves stacking pins, articulated robots for AAC packing and sorting line, and hybrid systems for handling and packing AAC insulation boards are studied in detail.

Exploring Effective Approaches To Minimize Downtime In Final Assembly Line Of Braking Systems

The global automotive manufacturing industry's growth in downtime reductio is substantial, valued at $3272.6 billion USD with a 3.01% growth rate.This growth in downtime reduction underscores the industry's commitment to enhancing efficiency, quality, and overall productivity across its diverse range of operations. Downtime in the braking system assembly line can lead to utilization loss or technical availability loss. In this context, many proactive maintenance strategies are explored but there's limited focus on addressing error-prone machines and utilizing Machine Learning for predicting downtime. The objective is to prioritize downtime reduction through error analysis, critical machine identification, and implementing ML-based solutions.
 This comprehensive research delved into the significance of minimizing downtime in the braking system final assembly line through meticulous data analysis, visualization techniquesand targeted interventions and then identified key issues and achieved tangible improvements in operational efficiency. The analysis revealed significant findings and then employed the Pareto Principle to identify top downtime machines, illustrating their distribution through a Pareto chart of machine defects. Furthermore, Exceptions and Problem Areas were identified utilizing statistical process controls, offering insights into critical error contributors. Notably, a comprehensive exploration of the most prominent downtime machine was undertaken, evidenced by LCL and UCL charts and a Fishbone Diagram detailing causal relationships. The research leverages real-world data involving dates, machine names, and downtime durations to develop a predictive model that aids in proactively managing production disruptions.
 The application of RPN calculations before and after error correction demonstrated a substantial reduction from of 432 to 75, validating the efficacy of the corrective actions. The real-time data was used to build a model that can predict when production machines downtime might happen. This helps us be prepared and manage any possible disruptions in production.The outcome of the project highlights the intrinsic link between downtime reduction and assembly line efficiency, emphasizing the importance of data-driven interventions. This culminated in the resolution of key issues, illustrated by the mitigation of the PCBA Pressin machine and LVDT sensor errors, yielding tangible reductions in downtime and notable productivity improvements. In this direction, exploring AI-driven predictive maintenance holds immense potential for advancing downtime reduction strategies. Leveraging AI algorithms to analyse live data streams from machinery and sensors can enable the detection of patterns indicating imminent failures and can pre-emptively prevent downtime.

The impact of manufacturing servitization on consumption-based carbon rebound effect: A global value chain research perspective

This study investigates the causal effect of manufacturing servitization on the consumption-based carbon rebound effect, employing the empirical approach based on the value-added trade and SBM-DEA model. The results show that improving the servitization level will lead to a significant decrease in the consumption-based carbon rebound effect of the global manufacturing industry. Moreover, the main paths through which the manufacturing servitization inhibits the consumption-based carbon rebound effect are the human capital and government management. We also find that the impact of manufacturing servitization is more substantial in advanced manufacturing and developed economies, which is smaller in manufacturing sectors with higher global value chain positions and lower export penetration levels. These findings suggest that improving the level of manufacturing servitization helps alleviate the consumption-based carbon rebound effect and is conducive to achieving the target of global carbon emission reduction.

Push and Pull Hybridization Manufacturing Strategy in Medical Diagnostic Equipment Industry

Abstract: The global manufacturing industry employs two main strategies for production: push and pull manufacturing systems. Each strategy has its own set of advantages and disadvantages. However, in this paper, a hybrid push and pull system model is proposed, aiming to capitalize on the strengths of both strategies while mitigating their weaknesses. The proposed model specifically focuses on the medical diagnostic equipment industry, where the annual production volume is relatively low, typically under 200 products. The objective of the proposed model is to improve various aspects of the production system compared to the current approaches used in the industry. These improvements primarily revolve around optimizing production line efficiency, reducing the lead time between customer orders and product delivery, and enhancing overall productivity. By incorporating elements from both push and pull systems, the hybrid model aims to achieve these goals more effectively. The results of the study indicate that the proposed hybrid model outperforms the existing production system in terms of time optimization in the production line, minimizing the lead time between customer orders and product delivery, and increasing the productivity of the production line. These findings suggest that the hybrid model can offer significant advantages and improvements over traditional production approaches in the medical diagnostic equipment industry.

An effective adaptive adjustment model of task scheduling and resource allocation based on multi-stakeholder interests in cloud manufacturing

Cloud manufacturing (CMfg) is a new manufacturing mode emerging in the global manufacturing industry. One of the key issues in CMfg is task scheduling and resource allocation (TSRA), which is to allocate suitable resources for multi-tasks while satisfying the interests of multi-stakeholders. Among them, dynamic TSRA is a challenging but crucial problem to ensure the smooth operation of CMfg system since it involves several exceptions. Under these contexts, this study first analyzes the optimized objectives and conflict situations that may damage the interests of multi-stakeholders in dynamic TSRA process. And then, four adaptive adjustment strategies are designed to deal with these conflict situations and ensure the smooth operation. After that, an adaptive adjustment TSRA model based on multi-stakeholder interests (TSRA-MSI) is proposed. To solve the problem, a multi-objective algorithm called HHO-NSGA2 is proposed by combining the advantages of standard Harris Hawks Optimizer and Non-dominated Sorting Genetic Algorithm-II, which contains several problem-specific optimization strategies. In the numerical experiments, the superiority of HHO-NSGA2 is demonstrated by comparing with other five algorithms in terms of convergence, diversity, and comprehensive performance. Finally, a case study is conducted under the actual auto parts production environment, and the results also demonstrate the effectiveness of the proposed TSRA-MSI model and HHO-NSGA2 algorithm.

Research on Structure Features of Supply Chain from the Perspective of Complex Network

With the rapid development of global manufacturing industry, many manufacturers begin to cooperate with each other. In China, a large number of foreign-funded enterprises in the automobile industry have settled in the Chinese mainland to set up factories and cooperate with local manufacturers. It is a significant task to find a way to optimize the structure of the complex supply network in the manufacturing industry. The paper selects the data on the share ratio relationship between enterprises in the automotive supply chain network published in the China Automotive Industry Yearbook, and selects appropriate software to analyze the topology structure characteristics of the supply chain network. Through numerical analysis, we can conclude that the structure features of supply chain networks can help us identify the hidden dangers of important nodes and point out the problems existing in the supply.

Drivers for Sustainable Supply Chain Management and the Practices in Ethiopian Apparel Industries

Environmental, social, and economic concerns in the apparel manufacturing industries have grown in recent years. The sustainability of the apparel supply chain management in developing countries is characterized by operations highly hazardous to the environment and unethical to society and of important economic value. Studies showed that socioeconomic and cultural differences in different countries affect the success of sustainability practices. Therefore, extensive research is required to measure the current challenges of sustainable practices from environmental, social, and economic perspectives of the apparel industry. Due to unsustainable practices, competitiveness in export and local markets is low in the Ethiopian apparel industries. This paper explores the effect of different factors on the sustainable practices of Ethiopian apparel manufacturing industries. A conceptual framework was formulated to measure sustainable practices. Questionnaires, interviews, and secondary data sources were used for data collection. The data are prepared and pretested for reliability. Integrated interpretive structural modelling and structural equation modelling techniques have been employed for model building and data analyses to see the relationship between the challenges and the existing practices. The proposed model was validated using partial least squares structural equation modelling. The Bootstrapping technique in smart partial least square ensures the reliability of the data for small sample size. The result showed a significant relationship between the factors and sustainable practices. All the environmental, economic, and social perspectives were statistically tested and found significant atpvalues of 0.05. Sustainable practice in the apparel manufacturing supply chain was found to be low with a mean value of three. This performance is far below the average standard in developing and developed countries. This research provides customized areas of intervention from environmental and social aspects that recognize the societal difference towards sustainability awareness and motivation to implement sustainability initiatives in Ethiopian apparel manufacturing industries. This research could provide awareness for business owners and policymakers and help understand the limitations of existing sustainability practices and the need for interventions. Therefore, apparel manufacturing industries could be potential sourcing destinations for global apparel manufacturing industries.

Special Issue: Smart Resilient Manufacturing

During the past decades, the global manufacturing industries have been reshaped by the rapid development of advanced technologies, such as cyber-physical systems, Internet of Things, artificial intelligence (AI), machine learning, cloud/edge computing, smart sensing, advanced robotics, blockchain/distributed ledger technology, etc [...]

The Multiagent Evolutionary Game Research of Enterprise Resource Sharing on a Shared Manufacturing Platform

Shared manufacturing is the application innovation of the digital economy in the field of manufacturing and brings new opportunities for the transformation and upgrading of China and even the global manufacturing industry. In order to explore the strategy selection of enterprises on the shared manufacturing platform, this study constructs a two-party evolutionary game model and analyzes the dynamic relationship between manufacturing enterprise A and manufacturing enterprise B. Furthermore, in order to explore the influence of consumers’ strategies of manufacturing enterprises on the platform, the study constructs a tripartite evolutionary game model of manufacturing enterprise A, manufacturing enterprise B, and consumers. Based on the principles of system dynamics, the paper uses Matlab to simulate the evolutionary game process dynamically and discusses the influence of parameter changes on resource-sharing enthusiasm and strategy selection. The research shows that in the two-party game, manufacturing enterprises are more inclined to adopt the strategy of sharing manufacturing resources. In the tripartite game, consumers’ decision-making will affect the strategic choice of manufacturing enterprises. Consumers tend to establish strong dependence on shared-manufacturing products in the process of sharing manufacturing resources.

Organizational Agility and Sustainable Manufacturing Practices in the Context of Emerging Economy: A Mediated Moderation Model

Since the beginning of the 21st century, agility and sustainability have played a significant role in the global manufacturing industry. The manufacturing paradigm leaning toward green procurement and organizational agility has crossed all levels of sustainability by colossally influencing the firms’ sustainable practices, innovation capacity, and eco-friendly procurements. Integrating sustainable practices in manufacturing is a complex task that demands that global economies conduct comprehensive research on the factors influencing the firms’ sustainable practices. Therefore, the study considers empirical research between organizational agility and sustainable manufacturing practices. The data was collected from 461 respondents working in the manufacturing sector by applying a convenience sampling technique. We utilized structural equation modeling (SEM) for direct and indirect hypothesis testing. The study results revealed that operational, customer, and partnering agility significantly and positively impact sustainable manufacturing practices and green procurement. Green procurement mediates the relationship between operational, customer, partnering agility and sustainable manufacturing practices. The study results showed a significant moderating role of big data between green procurement and sustainable manufacturing practices. The study findings are helpful to managers and policymakers.

Input servitization, global value chain, and carbon mitigation: An input-output perspective of global manufacturing industry

As an emergent pattern of integration between the manufacturing and service industries, manufacturing servitization has become a global trend. While studies have explored the eco-friendly characteristics of servitization, most have overlooked the roles of global value chains (GVCs) in transmitting its carbon mitigation effects. Using cross-country industrial panel data collected from the global manufacturing industry between 2000 and 2014, this study empirically analyzes the multidimensional effects of input servitization on manufacturing carbon emissions from the input-output perspective, with attention to the mediating roles of GVCs in the servitization-emission nexus, including the underlying mechanism and transmission channels. Our results show that the carbon abatement effects of servitization are heterogeneous across different emission intensities. Further, GVC participation implicitly counteracts the carbon abatement effects of servitization, wherein backward GVC participation plays a dominant role. We therefore propose that servitization is a feasible path for coordinating high-quality economic development and resource/environmental constraints.