Manufacturing Industry Research Articles

Fast-Charging Lithium-Ion Batteries Enabled by Magnetically Aligned Electrodes.

With the increasing popularity of electric transportation over the past several years, fast-charging lithium-ion batteries are highly demanded for shortening electric vehicles' charging time. Extensive efforts have been made on material development and electrode engineering; however, few of them are scalable and cost-effective enough to be potentially incorporated into the current battery production. Here, we propose a facile magnetic templating method for preparing LiFePO4 (LFP) cathodes with vertically aligned graphene sheets to realize fast-charging properties at a practical loading of 20 mg cm-2. Graphene sheets decorated with Fe3O4 nanoparticles can be responsive to an external magnetic field and can maintain their vertical alignment during the electrode fabrication process. The vertically aligned graphene provides the magnetized LFP electrodes (m-LFP) with simultaneously improved electron and lithium-ion transport properties, achieving 110 and 76 mA h g-1 at 3C and 4C, respectively. Furthermore, magnetized Fe3O4 (m-Fe3O4) anodes were also prepared via the magnetic templating method to vertically align the Fe3O4 nanosheets inside, which outperforms the conventional graphite anodes at a high rate of 3C. Finally, by pairing the magnetized LFP cathode and Fe3O4 anode, we demonstrate the simultaneous fast-charging properties and good cycling stability in the m-LFP||Fe3O4 full cells. This study not only provides an effective methodology for achieving vertically aligned structures which can potentially be incorporated into industrial manufacturing but also brings insightful considerations for designing scalable fast-charging energy storage systems.

In-situ quality monitoring during embedded bioprinting using integrated microscopy and classical computer vision

Despite significant advances in bioprinting technology, current hardware platforms lack the capability for process monitoring and quality control. This limitation hampers the translation of the technology into industrial GMP-compliant manufacturing settings. As a key step towards a solution, we developed a novel bioprinting platform integrating a high-resolution camera forin-situmonitoring of extrusion outcomes during embedded bioprinting. Leveraging classical computer vision and image analysis techniques, we then created a custom software module for assessing print quality. This module enables quantitative comparison of printer outputs to input points of the computer-aided design model's 2D projections, measuring area and positional accuracy. To showcase the platform's capabilities, we then investigated compatibility with various bioinks, dyes, and support bath materials for both 2D and 3D print path trajectories. In addition, we performed a detailed study on how the rheological properties of granular support hydrogels impact print quality during embedded bioprinting, illustrating a practical application of the platform. Our results demonstrated that lower viscosity, faster thixotropy recovery, and smaller particle sizes significantly enhance print fidelity. This novel bioprinting platform, equipped with integrated process monitoring, holds great potential for establishing auditable and more reproducible biofabrication processes for industrial applications.

Digital transformation and business model innovation: the relevance of strategic orientations under varying conditions of competitive intensity

PurposeThe COVID-19 pandemic disrupted existing business models, forcing managers in the manufacturing industry to look for new strategies that could help their firms bounce forward. The situation calls for a rethink, redesign, and development of new business models (BMs) using digital capabilities. Drawing from the dynamic capability theory, this paper investigates how digital transformation (DT) influences business model innovation (BMI) through technology orientation (TO). The paper further examined the moderating role of competitive intensity (CI) in the DT-TO link.Design/methodology/approachThe model was tested using survey data from 208 senior managers in manufacturing firms in Ghana. SPSS 23 and structural equation modeling (SEM) were used for the analyses.FindingsThe results revealed that both DT and technology orientation directly influence all four dimensions of BMI. The result further showed that technology orientation indirectly mediates the relationship between DT and all four dimensions of BMI. The findings further showed that the DT-BMI link is amplified at varying levels of competitive intensity.Originality/valueAlthough recent research has highlighted the pertinence of embracing DT to foster innovation, to the best of the authors’ knowledge, this study among the first few attempts to shed light on the role of DT for sensing, seizing, and (re) configuring firms’ resources to renovate manufacturing business models to stay competitive. In addition, to date, to the best of the authors’ knowledge, no study exists that has examined the conditions and mechanism through which optimal BMI can be achieved through DT. The paper offer practical guidance to managers of manufacturing firms by developing an actionable framework to effectively leverage digital transformation for business model innovation through enhanced technology orientation, offering clear guidelines for assessing and aligning organizational capabilities with digital strategies.

Optimizing Inventory Control Using Min-Max Method for Sustainable Manufacturing Process

Inventory plays an important role in a company's production process, especially in the sustainable manufacturing industry. The inventory of raw materials such as rayon, polyester, and cotton is an essential element that needs to be controlled to maintain a smooth production process. This research aims to plan and control raw materials through the min-max method, with a focus on evaluating inventory control to identify and overcome existing problems in the raw material warehouse at a yarn and textile manufacturing company. The results show that each type of raw material has a different reorder level, which guides the company in avoiding the risk of shortage or excess stock. By applying the right reorder level, the company can improve its production efficiency and inventory management. This research contributes to the practice of inventory control in the sustainable manufacturing industry, which supports operational stability and minimizes resource wastage. The implications of the findings could expand the application of min-max method-based inventory control in other industries to support operational sustainability.

Unlocking Employee Potential: Exploring the Influence of Identity Fu-sion and Pro-group Motivation on Productivity

This study observes the potential of identity fusion and pro-group motivation to improve employees’ productivity in the textile manufacturing industry in a time of health crisis. This follows explicitly to observe whether the identity-fused employees are pro-group motivated and exert more effort than non-identity-fused employees. The mode of inquiry of this study is a real effort experiment with pre-determined incentives and conducted as an online ex-periment for 152 employees selected randomly from one of the major Sri Lankan apparel industry networks. The findings from the experimental survey revealed that the majority of employees with fused identities are more pro-social, and pro-group motivated and those who are pro-group motivated exerted more effort than the subjects who are not motivated. Also, identity-fused subjects exert more effort than non-identity-fused subjects. Thus, the outcomes prove that identity fusion and pro-group motivation are effective in improving the productivity of employees.

The Coordinated Development and Identification of Obstacles in the Manufacturing Industry Based on Economy–Society–Resource–Environment Goals

Given the deficiencies in the definition of connotation, the construction of index systems, and the coordination of targets within the research on sustainable development in the manufacturing industry, an evaluation index system for sustainable development has been established. This system includes economic benefits, social benefits, resource management, and environmental goals and is built upon a clear definition of the concept’s connotation. The CRITIC–entropy–TOPSIS–CCDM approach is employed for the computation of the coordinated development level of the manufacturing industry. To identify the main factors influencing the coupling coordination degree (CCD) from a mechanistic and compositional point of view, a logarithmic mean divisia index (LMDI) is used. Furthermore, the obstacle degree model analyzes the factors that restrict subsystem development. The results show the following. (1) The coordinated development level of the Chinese manufacturing industry has been maintained at 0.6–0.7, while the CCD of Hainan, Qinghai, and Xinjiang remains to be enhanced. (2) The key factor affecting the CCD is the coupling degree. The evaluation value of the economy and employment system determines the trend of coordinated development in the regional manufacturing industry. (3) The economic and employment scenarios in most provinces (cities) led to a significant decrease in the CCD compared to the baseline scenario, with average growth rates of −10.55% and −12.69%. This suggests that policymakers’ priorities significantly influence the CCD. The research presents a theoretical framework for assessing the sustainability of the manufacturing industry, offering valuable insights to guide the industry towards more sustainable practices.

Enterprise risk management manufacturing industry quality determinants

Goal: Large manufacturing companies face significant risks due to importing raw materials and exposure to fluctuating exchange rates. Enterprise Risk Management (ERM) is vital in this sector to manage its distinctive risks. This study finds ERM factors in Indonesia and Malaysia that affect company value and empirically evaluates risk management in their organizations. Design / Methodology / Approach: The factors that influence the quality of a company's ERM examined in this study include auditor quality, ownership concentration, board monitoring, gender on the board of commissioners, gender on the board of directors, and human capital. This study's population is 2018-2020; 300 Indonesian and 252 Malaysian manufacturing companies contributed 552 research observations. Results: This study shows that a qualified auditor, board monitoring, gender ratio on the board of commissioners, and human capital may uncover ERM implementation issues. On the other hand, higher share ownership does not affect ERM. Implications for practitioners and suggestions for future researchers are also described. Limitations of investigation: The crucial role of Enterprise Risk Management (ERM) in mitigating these unique industry risks, this study underscores the limitations inherent in factors such as auditor quality, ownership concentration, board monitoring, and gender diversity within corporate leadership. Practical implication: The necessity for manufacturing companies to prioritize specific aspects of Enterprise Risk Management (ERM) in their organizational strategies. Originality / Value: This research illuminates the nuanced interplay of factors shaping Enterprise Risk Management (ERM) efficacy within the dynamic context of the manufacturing industry.

Disentangling the effects of nonrenewable energy consumption on CO2 emissions in Canada: The moderating role of construction and manufacturing

The role of industrial sectors, including construction (CONS) and manufacturing (MFG), in mitigating carbon dioxide (CO2) emissions is often overlooked. The response of these indicators in environmental sustainability is gaining critical attention among scholars and policymakers. Therefore, this research aims to address this issue by investigating the impact of nonrenewable energy consumption (NREC) under the moderating effects of CONS and MFG on Canada's CO2 emissions from 1980 to 2021, utilizing both traditional autoregressive distributed lags (ARDLs) and dynamic ARDL simulation methods. The findings reveal that NREC, CONS, and economic growth (GDP) are significant drivers of emissions in both the short and long run. Meanwhile, MFG reduces emissions in the long run with no significant short-run impact. Further analysis using Generalized Kernel-based regularized least squares (gKRLS) and frequency domain causality (FDC) tests confirmed these results. Moreover, examining the moderating role of CONS and MFG exhibits significant long-run positive moderating effects on the NREC-CO2 relationship, with MFG having a more substantial impact than CONS. However, both sectors show insignificant adverse moderating effects in the short run. Robustness analysis using quantile regression (QREG) and simultaneous quantile regression (SQREG) demonstrates that GDP and MFG consistently mitigate CO2 emissions across all quantiles, with stronger effects at higher emissions levels. These results underscore the importance of targeted renewable energy policies that balance economic growth with environmental sustainability.

Composite Forms in the RЕЕ2O3–ZrO2–TiO2 System for Minor Actinides (Am, Cm) and REE Immobilization

The choice of efficient methods for the immobilization of high-level waste (HLW) resulting from the reprocessing of spent nuclear fuel (SNF) is an important scientific and practical task. The current policy of managing HLW within a closed nuclear fuel cycle envisages its vitrification into borosilicate (B-Si) or alumina–phosphate (Al-P) glasses. These wasteforms have rather limited waste loading and can potentially impair their retaining properties on devitrification. The optimal solution for HLW immobilization could be separating radionuclides into groups using dedicated capacious durable matrices. The phases of the Nd2O3–ZrO2–TiO2 system in this respect are promising hosts for the REE (rare earth elements: Nd, Ce, La, Pr, Sm, Gd, Y) –MA (MA: Am, Cm) fraction of HLW. In this manuscript, we present data on the composition of the samples analyzed, their durability in hot water, their behavior under irradiation, and their industrial manufacturing methods.

An exploration of digital servitization business models in manufacturing SMEs

ABSTRACT Digital servitization, the integration of digital technologies into service-based business models, has gained significant traction in the industrial manufacturing sector. However, there is limited understanding of the conditions under which small and medium-sized enterprises (SMEs) develop specific digital servitization business models, owing partly to the lack of an appropriate business model typology. In this study, we develop a generalizable and operationalizable classification of digital servitization business models and identify the environmental and organizational factors that drive such business model innovation. We apply a multinomial logistic regression framework to analyze a dataset of 326 responses from UK manufacturing SMEs, providing insight into how the seven studied factors differentiate among SMEs following different digital servitization business models and more traditional ones (pure product or “just” servitized). The study contributes to the scarce empirical research on digital servitization in SMEs and provides implications for policy and practice.

Engendering the Manufacturing Sector in Ethiopia: Uncovering the Controversy of Empowerment

Female workers have long been the face of the textile and garment manufacturing industry around the globe. They are considered as not only having naturally “nimble fingers,” but also being naturally more “docile and willing” to work in difficult situations. There is an underlying assumption that employment of women in the manufacturing industry will increase their job opportunities, enhance their access to and/or control over valuable resources, and empower them in decision-making. The development of the textile and garment manufacturing industry in Ethiopia has unquestionably opened employment opportunities for women constituting more than 80% of the labor force. By filling in an existing gap in research through addressing the workers’ self-perception of empowerment (referred to as mebkat in Amharic), this paper examines the emic views of how women perceive and experience empowerment. This is juxtaposed with an external definition of empowerment, as it is defined by the government and its international development partners, which presumes a positive correlation between employment and empowerment. Based on the case study of female factory workers in Hawassa Industrial Park in Southern Ethiopia and a closer analysis of research participants’ understanding of empowerment, this paper argues that Ethiopia’s budding manufacturing sector has not delivered on its promises.

Emerging Trends on Hybrid Artificial Intelligence Framework for Transforming Heavy Machinery with Robots and Energy Efficiency

Today, heavy machinery can be equipped with robots operated by artificial intelligence (AI) to streamline operations, decrease human labor, and increase efficiency. Intelligent technologies like these can boost productivity by carrying out routine tasks with extreme accuracy. Since robots reduce risks to human workers, robots with AI algorithms and sensors could be used in hazardous environments. In certain contexts, including building site management, mining, or handling hazardous materials, safety must take precedence above everything else. Analytics from this study, aided by AI, have opened the door to predictive maintenance procedures. To detect equipment problems, the Hybrid Artificial Intelligence Framework (HAIF) examines sensor data in conjunction with historical patterns. The objective is to avoid expensive failures and downtime. Optimization of equipment utilization, energy consumption, and fuel consumption can be achieved by applying AI-driven machine learning algorithms. Several advantages arise from energy waste, including cost avoidance and enhanced operational efficacy. Robots can do exact alignments, measurements, and operations with the help of AI vision technologies. This precision significantly impacts the manufacturing, agricultural, and construction industries. Artificial intelligence has the potential to optimize the allocation of resources, the use of heavy equipment, and supply networks by evaluating massive amounts of data.

The Digital REadiness Assessment MaturitY (DREAMY) framework to guide manufacturing companies towards a digitalisation roadmap

Digital technologies are considered the main drivers for the transformation of the manufacturing industry. To effectively navigate this wave of transformation, companies need to understand their current maturity state regarding implemented technologies and organisational processes across various functions. However, many organisations struggle to assess their digital status quo. Addressing this gap, this paper proposes a framework to help manufacturing companies become aware of their digital maturity level and guide them toward a digitalisation roadmap. The framework consists of a maturity model, the Digital Readiness Assessment MaturitY (DREAMY), inspired by the Capability Maturity Model Integration (CMMI), and a digitalisation roadmap tool. The research follows the design research methodology, combining a literature review, expert consultations, and validation through multiple industrial cases to develop, test, and refine the framework. The proposed framework enables companies to assess their current digital maturity, identify strengths and weaknesses, and discover opportunities for improvement, supporting a conscious transition toward Industry 4.0 and the forthcoming Industry 5.0 paradigm.

Evolving Trends in Haryana's Food Processing Sector: Growth, Structure, and Linkages

The present piece of study tries to highlight the status and structure of the food manufacturing industry of the state Haryana. The status of the industry has been analyzed in terms of its structure, growth and contribution in the total manufacturing sector of Haryana. The study utilizes ASI data (2008-22) and NIC classification to evaluate sub-sector performance. The food manufacturing sector is dominated by the grain milling industry followed by manufacturing of other food products units including bakery products, confectionary, prepared foods and many more. Fruit and Vegetables processing units, prepared animal feed manufacturing units and meat processing units remains few, limiting their contribution in the sector. Moreover, the industry has shown a very inconsistent growth trend when compared to the manufacturing sector. Higher working capital intensity of the industry in comparison to raw material intensity indicates creation of more forward linkages by the sector in terms of technological advancements and have revealed comparatively lesser backward linkages.

Enantioselective Synthesis of Chiral 2,2-Difluoro-spiroindanone-dihydroquinazolinones by CPA-Catalyzed Cyclization Reactions.

The synthesis of chiral gem-difluorinated spiro-heterocyclic compounds continues to be a significant challenge in organic chemistry due to their widespread applications across various fields. Therefore, efficient asymmetric approaches for the synthesis of gem-difluorinated spiroindanone-dihydroquinazolinones are particularly valuable, especially in the industrial manufacturing of chiral fluorinated drugs. Herein, we developed the CPA-catalyzed asymmetric enantioselective cyclization reactions of gem-difluoroalkyl 1,3-indandiones with anthranilamides to achieve various chiral 2,2-difluoro-spiroindanone-dihydroquinazolinones in good to high yields with excellent enantioselectivities. Moreover, the mechanism experiment proved that the gem-difluoro substitution pattern promotes the reaction.

Environmental effects of foreign direct investment in India: pollution haven or pollution halo?

Purpose There is controversy over whether foreign direct investment (FDI) increases or reduces environmental degradation in host countries resulting in pollution havens or pollution halos. Based on the concept of scale, technology and composition effects, this paper aims to examine the causal relationship between FDI and carbon dioxide (CO2) emissions in India. Design/methodology/approach This paper analyzes panel data of the three most polluting industries between 2005 and 2021 by conducting a Granger causality test. Findings The results provide evidence of pollution havens in the manufacturing and transportation industry, and in the metallurgy and chemical sectors within the manufacturing industry. Research limitations/implications FDI inflows and CO2 emissions are characterized by large regional variations in India. Hence, future studies of the pollution haven vs pollution halo effect in India could therefore use state-level or even district-level data to test for regional variations. Practical implications This paper provides policy recommendations such as increasing the absorptive capacity of local firms to strengthen the technique effect, which would help India combat climate change. Social implications Increasing the absorptive capacity of local firms through incentives such as subsidies and environmental requirements in public contracts can lead to job creation in the green technology sector. This can provide new employment opportunities, especially in R&D and sustainable technology fields, boosting the local economy. Originality/value The study adds to the understanding of the endogenous relationship between FDI and environmental degradation, the importance of lagged feedback responses and the impact of industry- and sector-specific influences on this relationship.

Digital transformation and pathways for promoting global value position: an empirical study in Chinese manufacturing industries

Abstract Digital transformation offers new avenues for the photovoltaic manufacturing industry and his paper, grounded in value chain theory, collaborative management theory, and value chain cost control theory, examines digital transformation through three key dimensions: process optimization, platform ecosystem development, and data-driven strategies. The results show that digital transformation has effectively streamlined R&D, procurement, production, and sales costs. Notably, the R&D expenditure as a percentage of operating income decreased from around 4.2% to 3.1%. Furthermore, after digital transformation, significant improvements were seen in supplier delivery qualification rates, market share, and customer satisfaction.

Multi-agent behavioral evolutionary game analysis of digital twin technology adoption in manufacturing enterprises

ABSTRACT Digital twin technology is crucial in breaking through the bottleneck of integrating the physical and information worlds in the manufacturing industry. However, manufacturing enterprises lack sufficient economic motivation and subjective willingness to adopt due to concerns about technological challenges and cost inputs. To elucidate the mechanisms that influence the adoption of digital twin technology, this study establishes an evolutionary game model from a stakeholder perspective, dynamically analyzes the strategy selection and system stability conditions of each participant and conducts numerical simulations. The findings reveal that the adoption process of digital twin technology in manufacturing enterprises occurs in phases. The coefficient of product attribute enhancement emerges as a key factor in promoting adoption. Government financial and consumption subsidies further encourage the adoption of digital twin technology and enhance customer preference for twin products and derivative services. The customer digital collaboration coefficient and demand matching coefficient significantly affect customers’ perceived utility benefits, which in turn influence manufacturing enterprises’ decisions to adopt digital twin technology by converting them into spillover benefits. Finally, this study provides targeted recommendations to various stakeholders to further promote the adoption of digital twin technology.

Global initiatives for industry 4.0 implementation and progress within the textile and apparel manufacturing sector: a comprehensive review

ABSTRACT A comprehensive picture on global initiatives to Industry 4.0 (I4.0), technological evolution within fibre-to-apparel manufacturing and its progression so far are reported here. Germany is the global progression leader while other developed countries; France, Italy, Spain, Portugal, and even the UK and USA have generic initiatives for upgraded manufacturing. Their initiatives are I4.0 strategies, funding, platforms, and model projects. Developing countries like Indonesia, Malaysia, Thailand, Brazil, Turkey, Vietnam, India, etc. have also taken I4.0 national initiatives. However, the apparel and textile manufacturing industry lags far behind other sectors, although applying I4.0 technologies in this sector has started. CPS, IOT, Cloud computing, Automatic bobbin changing, RFID, Blockchain, augmented reality, and digital twins, are found in the textile production and fashion retailing. Moreover, advanced machineries offer incorporated automation, remote maintenance and operability, cloud systems, self-optimization, and predictive maintenance facilities. The review identifies that technological gaps between concept and reality hinder commercial application. The readiness evaluation is predominantly grounded in academic literature, which requires field investigation from the start to the end of textile processing. Hence, case studies for empirical data, sustainability mapping for the upgraded technologies and integrated policy development are the future direction in this domain.

A review on recent progress in light metal solid-phase additive manufacturing

Additive manufacturing technology, known for its advantages in rapid layer-by-layer 3D printing, personalized production, and flexible manufacturing, has gained widespread applications in the manufacturing industry. This paper provides an analysis and summary of the recent progress in lightweight metal solid-state additive manufacturing technology and its related process mechanisms. The primary solid-state additive manufacturing technologies currently include ultrasonic additive manufacturing, cold spray additive manufacturing, and friction-based additive manufacturing. The research and application status of solid-state additive manufacturing technology for lightweight metal materials are particularly emphasized, and the characteristics of these three types of solid-state additive manufacturing technologies are compared. In the end, the paper highlights the key directions for future research in solid-state additive manufacturing technology, including additive process mechanisms, simulation study of multi-physics and multi-scale, application of various materials, external field-assisted process modification and optimization through artificial intelligence.