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As the world's largest manufacturing country, the rapid growth of China's manufacturing industry has historically relied on factor input. To achieve high-quality development of the manufacturing industry, China must accelerate the transformation from extensive factor input to innovation. The purpose of this study is to further explore the impact mechanisms of technological development policy on the high-quality development of the manufacturing industry. It selected data from 30 provinces and cities in China spanning from 2011 to 2021 for the study. Initially, it established a benchmark regression model to verify the positive impact of technological development policies on the high-quality development of the manufacturing industry. Subsequently, a mediation effect model was used to analyze the role of high-tech industry development in mediating this impact, and a moderation effect model was applied to study the moderating effect of the level of infrastructure informatization. Additionally, taking the industrial structure as a threshold variable, a panel threshold effect model was employed to explore the impact of technological development policy on the high-quality development of the manufacturing industry. It was found that enhancing the level of high-tech industries further facilitates the high-quality development of the manufacturing industry through technological development policy. The level of infrastructure informatization positively moderates the impact of technological development policy on the high-quality development of the manufacturing industry. Moreover, the industrial structure exhibits a threshold effect in this impact.

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The integration of modern service and advanced manufacturing industries represents deep industrial reform and profound change in quality concepts, mechanisms, and practices. Integrating these two industries is important for achieving high-quality development and is an area of interest for policymakers and academia. This study analyzes the data of 11 regions in 2021 in the Guangdong-Hong Kong-Macao Greater Bay Area using the fuzzy set qualitative comparative analysis method from the configuration perspective. This study explores the effects of multiple factors, such as economic efficiency, technological innovation, and the policy environment, on improving the coupling coordination level of the modern service and advanced manufacturing industries, and analyzes the path selection of industrial integration development. Overall, economic efficiency, technological innovation, and policy environment factors are not necessary conditions for the integration development of the modern service and advanced manufacturing industries. The multiple impacts of economic efficiency, technological innovation, and policy environment form a multi-restructuring path that drives the integration of the two industries. Owing to the economic development level and resource endowment of different regions, the path dictating the degree of integration between the two industries varies by region. From a holistic perspective, all regions should strive to coordinate the multiple conditions of economic efficiency, technological innovation, and policy environment to break through objective constraints such as the government's conditions and external environment based on their own organizational conditions and resource endowments. Additionally, all regions should strengthen exchanges, cooperation, and integration mechanisms and implement complementary development and distinctive development in core cities in the Greater Bay Area. The results clarify the factors affecting industrial convergence in the Greater Bay Area, enriching existing research methods and laying a foundation for a decision-making theory of industrial integration development.

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To help the manufacturing industry achieve high-quality development, it is urgent to identify the factors that affect the development of regional manufacturing. Compared to previous regression models, this article attempts to discover the nonlinear effects of different factors on regional manufacturing industry development (RMID) and their future impact trends. Based on the theory of new structural economics, we used order parameter analysis to examine the impact of environmental pollution and technology on RMID. The results indicate that: (1) The half of the cities promote industrial growth, but there are still three other situations: development slow down (3/21), a slight downward trend (5/21), and recession (2/21). (2) The two-thirds of cities adopt green development to promote industrial growth, while the development of other cities slows down (3/21), and some cities have a slight downward trend (4/21). The conclusion is as follows: (1) Through comparison, it is found that the impact of environment and technology on the RMID remains roughly synchronous, but currently the environmental promotion effect is greater. (2) We have found four technological development paths and can extend three green development models, effectively promoting RMID's green technology development. These suggestions will lay the foundation for promoting RMID.

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Musculoskeletal disorders (MSD) encompass a variety of conditions affecting muscles, joints, and nerves. In Portugal, MSDs are the most prevalent occupational health problem in companies. Based on the relevance of work-related MSD (WMSD), this study aims to assess the prevalence of MSD complaints in a needle manufacturing industry in Northern Portugal, following a cross-sectional approach. Thus, 526 workers from five departments (i.e., operator, tuning, maintenance, administration, and logistics) answered a sociodemographic questionnaire and the Nordic Musculoskeletal Questionnaire (NMQ). Within the last 12 months, females exhibited a higher frequency of complaints than males across all body parts except for ankles/feet. The body parts eliciting the most percentage of complaints for both genders include the lower back (54.2%), neck (42.2%), shoulders (39.0%), ankles/feet (38.2%), and wrists/hands (35.7%). No significant association was found between Body Mass Index (BMI) and body part complaints. Tuners reported the highest complaint rate, with occupations as substantial predictors of complaints in certain body parts. Likewise, complaints tend to increase with age. The findings advocate for ergonomic interventions that are gender-, age-, and job-sensitive.

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Volatile organic compounds （VOCs） from the wooden furniture-manufacturing industry are an important emission source. To study the emission characteristics of VOCs from the wooden furniture-manufacturing industry and associated environmental impacts, nine typical wooden furniture manufacturers in China were selected to carry out sample collection and VOCs detection. The maximum incremental reactivity （MIR） method and secondary organic aerosol （SOA） formation potential method were used to quantify the corresponding contributions to the generation of O3 and SOA. The results showed that： ① The concentrations of VOCs emitted from different types of coating exhaust gas were different. The emission concentration of VOCs in solvent-based coating exhaust gas was significantly higher than that in water-based coating exhaust gas and ultra-violet （UV） coating exhaust gas, and the VOCs emission concentrations ranged between 2.82 - 155.37, 1.13 - 104.45, and 0.57 - 1.15 mg·m-3, respectively. ② The main organic group in solvent-based coating exhaust gas was esters, accounting for 45.88%, and butyl acetate （31.07%） was the main VOCs species. The main organic group in water-based coating exhaust gas and UV coating exhaust gas was alcohols, and the main VOCs species in water-based coating exhaust gas and UV coating exhaust gas were both ethanol, accounting for 46.63% and 34.32%, respectively. ③ The OFP of VOCs emitted by solvent-based coating, water-based coating, and UV coating were 149.23, 50.90, and 1.87 mg·m-3, respectively, and the primary contributing components of OFP of different types of coating were m/p-xylene （26.61%）, ethanol （36.35%）, and ethanol （23.98%）, respectively. ④ The SOA of VOCs emitted by solvent-based coating, water-based coating, and UV coating were 0.76, 0.25, and 0.01 mg·m-3, respectively. The SOA generation of various types of coating was dominated by aromatics（96.35%-98.96%）, and the main active compounds were toluene, ethylbenzene, and xylene. ⑤ Comparing the environmental impact of exhaust gas from solvent-based coating, water-based coating, and UV coating, it was found that the OFP and SOA generated by the VOCs emitted from solvent-based coating were much higher than those for water-based coating and UV coating. Therefore, the implementation of water-based coating and UV coating substitution strategy from the source could effectively reduce VOCs emissions and abate OFP and SOA productions.

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To identify the effect and mechanism of servitization level and digital transformation on the performance of listed sporting goods manufacturing enterprises in China, we construct an index to measure the degree of digital transformation using data from 31 sporting goods manufacturing firms listed on Shanghai and Shenzhen A-shares and the New Over-the-Counter Market in China. The study uses the proportion of service business income in enterprise operating income to quantify servitization level, by analyzing the semantic expression of national digital economy policy and collecting digital category keywords from enterprise annual reports using crawler technology. Thereafter, we analyze the impact of servitization extent and digital transformation on company outcomes as well as if digital evolution acts as a moderating factor between servitization and company outcomes. The findings indicate that the extent of servitization reduces the outcomes of publicly traded sporting commodities production companies, showing a servitization paradox occurrence. Digital transformation degree has a positive U-shaped impact on enterprise performance and a weak positive moderating effect on servitization level and enterprise performance.

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Industrial symbiosis, a promising approach for sustainable industrial practices, has garnered attention in recent days for its ability to enhance resource efficiency, minimize waste, and preserve the environment through collaborative exchanges among industries. In emerging economies like Bangladesh, integrating industrial symbiosis in the manufacturing industries offers the potential to balance economic growth with environmental sustainability. However, this integration encounters various barriers that complicate the implementation. Despite research on industrial symbiosis in robust economies, studies on emerging and developed economies are still scarce. To date, no research has yet investigated the barriers hindering the performance of industrial symbiosis in the Bangladeshi apparel manufacturing sector. To address this gap, this study integrates the Bayes theorem and the Best-Worst Method to identify and prioritize barriers to the Bangladeshi apparel manufacturing sector. From extensive literature reviews and expert validation, 17 barriers were identified. Findings reveal the "lack of technology and infrastructure readiness" as the most significant barrier, followed by "lack of inter-company cooperation" and "lack of management support". Conquering these barriers empowers emerging economies to fortify the apparel manufacturing sector's resilience, resource efficiency, and environmental performance while fostering sustainable development via circular economy practices. This study is expected to guide policymakers and stakeholders in crafting targeted strategies for promoting steady growth and sustainable development in the apparel manufacturing sector of emerging economies like Bangladesh.

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Objectives. Associations between shift-work, musculoskeletal symptoms and absenteeism are poorly investigated in the manufacturing industry. This study aimed to investigate associations between working schedule, musculoskeletal symptoms and days of absenteeism among pulp and paper industry workers. Methods. Musculoskeletal symptoms of 904 workers were assessed through the Nordic Musculoskeletal Questionnaire. χ2 tests assessed associations between being a day-worker or shift-worker, the prevalence of musculoskeletal symptoms and days of absenteeism. Results. A significant association was found between working schedule and symptoms in the lower back in the last 12 months, with shift-workers presenting higher prevalence than day-workers (p = 0.022). Significant associations were also found between days of absenteeism and symptoms in the shoulders (p = 0.002), which mostly led to absenteeism of 100-365 days; elbows (p < 0.001), wrists/hands (p = 0.045) and ankles/feet (p = 0.042), which produced absenteeism mostly of 25-99 days; and dorsal region (p = 0.001), which mainly led to absenteeism of 10-24 days. No associations were found between working schedule and days of absenteeism (p = 0.265). Conclusion. Shift-work is associated with increased prevalence of lower back symptoms, but seems not to influence days of absenteeism. Shoulders seem to be the region leading to higher days of absenteeism, followed by elbows, wrists/hands, ankles/feet and the dorsal region.

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Current studies do not provide a consensus on whether digital technology innovation can reduce enterprise carbon intensity despite the rise of the digital economy. This paper examines the role and influence pathway of digital technology innovation on enterprise carbon intensity using data from A-share listed enterprises in China's manufacturing industry from 2012 to 2021. The findings indicate that (1) digital technology innovation has been found to significantly reduce enterprise carbon intensity, as confirmed by numerous robustness and endogeneity tests. However, its inhibitory effect on carbon intensity shows a marginal decreasing trend. (2) In the heterogeneity analysis, it was found that digital technology innovation significantly reduces the carbon intensity of consuming coal, coke, kerosene, and diesel. From various perspectives, including enterprise, industry, and external environment, there are significant differences in the carbon reduction effects of digital technology innovation. (3) The analysis of impact paths reveals that digital technology innovation can affect enterprise carbon intensity through three paths: improving productivity, enhancing green innovation efficiency, and adjusting energy consumption. (4) Upon further analysis, it was discovered that the spillover effect of digital technology innovation is more pronounced in the industry cohort of enterprises. Additionally, digital technology innovation plays a positive role in enhancing enterprise ESG performance. The paper's findings offer empirical evidence and decision-making references for the government to develop reasonable policies for reducing carbon emissions, promoting green and low-carbon enterprise transformation, and actively and steadily achieving the goal of carbon peaking and carbon neutrality.

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The manufacturing sector is the main battlefield of energy saving and carbon reduction in China, and vigorously promoting energy saving and carbon reduction in manufacturing and enhancing the green development level are the key links to support China's realization of the dual-carbon goal. The article adopts the SBM-GML model to measure the level of green development of the manufacturing industry in China. Based on this, it analyzes the spatio-temporal characteristics and the evolution law of the level of green development of the manufacturing industry by using the Dagum Gini Coefficient and Kernel Density Estimation. Using a spatial econometric model to explore the influencing factors of the level of green development of the manufacturing industry. The study finds that the green development level of the manufacturing industry has achieved remarkable results in recent years, but there are differences in the development level of each region. The regional differences in the level of green development of the manufacturing industry are significant. The optimization of manufacturing structure is a key factor influencing the level of green development of the manufacturing industry, and there is a positive spatial spillover effect of manufacturing structure optimization. However, The green development of the manufacturing industry shows a negative spatial spillover effect. The article proposes optimization paths based on the requirements of dual-carbon targets and regional characteristics, which is an important inspiration and reference for the green development level of the manufacturing industry in the world.

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INTRODUCTION: The National Institute for Occupational Safety and Health (NIOSH) established the Revised NIOSH Lifting Equation (RNLE) for manual lifting risk assessment. The objectives of this study were to determine the characteristics of physical factors using the RNLE and to explore additional factors to RNLE by modifying it to an Individual Lifting Equation (ILE). METHODS: This cross-sectional study was conducted in the manufacturing industry of three states in Malaysia among manual lifting workers. A questionnaire was administered, which comprised the sociodemographic characteristics and Nordic Musculoskeletal Questionnaire (NMQ) assessing low back pain (LBP). The RNLE dataset includes a load constant and six manual lifting variables collected from observational ergonomic risk assessment. The RNLE was modified to ILE by incorporating age, gender, and BMI. The equations' Lifting Index (LI) computed provides an overall manual lifting risk estimate. RESULTS: There were 165 participants, with a mean age of 28 years, and 108 (65.5%) were male. Most participants had a BMI within the normal range (60 (36.4%)) or were classified as overweight (54 (32.7%)). The lifting horizontal location showed the highest risk estimates, with the lowest mean multiplier value of 0.55. In contrast, age and BMI had the lowest risk estimates, with mean multiplier values of 0.99 and 0.98, respectively. Among the participants, LI values of one or less, indicating very low risk, were observed in 58 (35.1%) for RNLE and 39 (23.6%) for ILE. Additionally, RNLE and ILE showed figures of 11 (6.7%) and 20 (12.1%), respectively, signifying a very high risk of LI exceeding three. CONCLUSION: Studying the lifting factors and equation multipliers from RNLE is critical for evaluating the risk estimates of manual lifting. Exploring the ILE based on individual characteristics is appropriate to support the ergonomic program. Further study is needed to validate the ILE as an accurate screening tool for determining LBP risk estimates.

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Controlling volatile organic compounds (VOCs) emitted from the automobile manufacturing industry requires establishing VOCs emission factors (EFs) and source profiles refinedly. In this study, 41 samples involved 32 VOCs discharge links were collected from three factories. The EFs and VOCs source profiles were estimated by the material balance method and weighted average method, respectively. The ozone formation potential (OFP) of the 110 VOCs species were calculated by the maximum incremental reactivity (MIR). According to estimations, the ranges of EFs were 0.23-1.66 kg VOCs/SUV car and 2.14-14.86 g VOCs/m2 painted area. EFs of six materials were firstly estimated, which are electrophoretic primer (152.31 ± 97.39 g VOCs/SUV car, 0.97 ± 0.38 g VOCs/m2 painted area), sealant (48.39 ± 26.20 g VOCs/SUV car, 0.46 ± 0.25 g VOCs/m2 painted area), floating coat (87.40 ± 75.63 g VOCs/SUV car, 0.86 ± 0.74 g VOCs/m2 painted area), colored paint (127.24 ± 168.24 g VOCs/SUV car, 1.25 ± 1.66 g VOCs/m2 painted area), varnish (205.46 ± 218.14 g VOCs/SUV car, 2.01 ± 2.15 g VOCs/m2 painted area), and cleaning solvent (328.54 ± 404.94 g VOCs/SUV car, 3.23 ± 3.98 g VOCs/m2 painted area). OVOCs (37.40-51.60 %) and aromatics (36.40-37.00 %) were the dominant components. n-Butyl acetate, 1,2,4-trimethylbenzene, undecane, n-hexanal, acetone, 1,2,3-trimethylbenzene, 1,3,5 -trimethylbenzene, m/p/o-xylene, 3-ethylbenzene, and 4-ethylbenzene were the major VOCs species, accounting for 68 % of total VOCs in the automobile manufacturing industry. Considering the OFP values of species, 1,2,4-trimethylbenzene, 1,3,5-trimethylbenzene, 1,2,3-trimethylbenzene, m/p-xylene, acetaldehyde, methyl ethyl ketone are the key active species that should be prioritized for control.

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Objective: Three occupational health risk assessment methods were used to assess the occupational health risk of noise exposed posts in an automobile manufacturing enterprise. According to the results, the selection of risk assessment methods and risk management of such occupational noise enterprises were provided. Methods: Form April to November 2021, The occupational health field survey was carried out in an automobile manufacturing industry in Tianjin. The occupational health MES risk assessment method, occupational health risk index risk assessment method and Australian occupational hazard risk assessment method were used to evaluate the occupational health risk of noise-exposed posts in this enterprise, and the evaluation results of different methods were analyzed and compared. Results: The average value of L(Aeq, 8 h) in the four workshops of automobile manufacturing industry was 82.95 dB (A) , and the noise detection exceeding rate was 22.41% (26/116) . The LAeq, 8h and exceeding rate noise of welding workshop were higher than those of other workshops (χ(2)=23.56, 32.94, P<0.01) . The three occupational health risk assessment methods have the same risk assessment results for the four major workshops. The assembly and painting workshops are level 4 risk (possible risk) , and the stamping and welding workshops are level 3 risk (significant risk) . Conclusion: Occupational noise has certain potential hazards to workers in automobile manufacturing enterprises. Therefore, in the future work, corresponding organizational management measures should be taken to improve the working environment and reduce the actual exposure level of workers in order to protect the health of occupational workers.

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BACKGROUND: The COVID-19 pandemic has led to fear, rumours, and stigma, particularly against those infected with the virus. In Malaysia, the manufacturing industry is particularly vulnerable to COVID-19 clusters, making it critical to assess stigma attitudes among workers. To address this issue, The Workplace COVID-19 Knowledge & Stigma Scale (WoCKSS) was developed specifically for use in the manufacturing industry which served as the sample population for testing this scale. It was developed in the Malay language to ensure alignment with the local context. This study examines the content and face validity of WoCKSS, which can help assess the level of knowledge and stigma associated with COVID-19 among workers. METHODS: The WoCKSS was developed with 20 and 31 items for knowledge and stigma domains, respectively, based on an extensive review of COVID-19 literature. Content validation was conducted by four experts using a content validation form to assess the relevancy of each item to the intended construct. Content Validity Index (CVI) was calculated to measure the agreement between the experts on the relevance of each item to the intended construct. Face validation was then conducted by randomly selecting 10 respondents from the manufacturing industry, who rated the clarity and comprehension of each item using a face validation form. The Item Face Validity Index (I-FVI) was calculated to determine the clarity and comprehension of each question, and only items with an I-FVI ≥ 0.83 were retained. RESULTS: The WoCKSS achieved excellent content validity in both knowledge and stigma domains. Only 19 items from the knowledge domain and 24 items from the stigma domain were retained after CVI analysis. All retained items received a CVI score of 1.00, indicating perfect agreement among the experts. FVI analysis resulted in 17 items for the knowledge domain and 22 items for the stigma domain. The knowledge domain achieved a high level of agreement among respondents, with a mean I-FVI of 0.91 and a S-FVI/UA of 0.89. The stigma domain also showed high agreement, with a mean I-FVI of 0.99 and a S-FVI/UA of 0.86. CONCLUSION: In conclusion, the WoCKSS demonstrated high content and face validity. However, further testing on a larger sample size is required to establish its construct validity and reliability.

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Digital finance is a product of emerging technology-enabled innovation in financial services and has a critical impact on the green transformation of the manufacturing industry. We propose a new efficiency measurement model based on the slacks-based measure (SBM) to measure the efficiency of green transformation on regional manufacturing. Chinese interprovincial data from 2010 to 2019 were obtained for the study. In addition, we estimated the effect of digital finance on green transformation of manufacturing using a benchmark panel model. Finally, considering the regional heterogeneity and spatial effects of green transformation efficiency in the manufacturing industry, we constructed a spatial Durbin model based on an economic-geographic nested spatial weight matrix to analyze the spatial influence of digital finance on green transformation in the manufacturing industry. The results show that the green transformation of the manufacturing industry has significant positive spatial spillover effects owing to the existence of competition, demonstration, and economic correlation effects among regions.

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Objectives. Although various studies have examined the relationship between ergonomic factors and employee well-being, the impacts of ergonomic factors on workers' capability for innovation have not yet been studied in the manufacturing industry. This study advances understanding of the relationship between ergonomic risk factors and employees' capability to innovate and to feel good at work in the manufacturing sector. Methods. The analysis uses the structural equation modeling technique based on cross-sectional data collected from 200 experienced workers in manufacturing industries using self-administered close-ended questionnaires. Results. Findings from this research show that the main ergonomic factors influencing the well-being and innovation capability of employees in the manufacturing industry are neutral awkward posture, psychological risk factors and effective utilization of information and communications technology infrastructures. Thus, ergonomic factors are significantly correlated to the innovation capability of employees. Conclusion. As there have been no studies addressing the integration of ergonomic risk factors and the capability for innovation of employees in the manufacturing industry, this study provides a unique contribution to the body of knowledge. Further research is also required to develop an in-depth understanding of the relationship among components of each ergonomic risk factor, and the well-being and innovation capability of employees.

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In this paper, we present a new generation of omnidirectional automated guided vehicles (omniagv) used for transporting materials within a manufacturing factory with the ability to navigate autonomously and intelligently by interacting with the environment, including people and other entities. This robot has to be integrated into the operating environment without significant changes to the current facilities or heavy redefinitions of the logistics processes already running. For this purpose, different vision-based systems and advanced methods in mobile and cognitive robotics are developed and integrated. In this context, vision and perception are key factors. Different developed modules are in charge of supporting the robot during its navigation in the environment. Specifically, the localization module provides information about the robot pose by using visual odometry and wheel odometry systems. The obstacle avoidance module can detect obstacles and recognize some object classes for adaptive navigation. Finally, the tag detection module aids the robot during the picking phase of carts and provides information for global localization. The smart integration of vision and perception is paramount for effectively using the robot in the industrial context. Extensive qualitative and quantitative results prove the capability and effectiveness of the proposed AGV to navigate in the considered industrial environment.

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The container manufacturing industry is the key contributor of industrial volatile organic compounds (VOCs). Emission factors (EFs) and source profiles of container manufacturing industry were comprehensively investigated basing on multiple VOCs discharge links. 17 samples were collected from a typical container manufacturing enterprise based on field measurements. The material balance method and weighted average method were applied to estimate EFs and establish VOCs source profiles. It is found that diluent use (DU) was the largest contributor (39.96 %), followed by intermediate painting spaying (IMPS), primer painting (PP), chassis painting (CP), exterior paint spaying (EPS), and interior paint spaying (IPS). EF of the container manufacturing industry (2.90 kg VOCs/ Twenty-foot Equivalent Units, TEU) was firstly estimated. EFs of six processes were further estimated. The EFs of DU, IMPS, PP, CP, EPS, and IPS were 1.22, 0.74, 0.42, 0.33, 0.20, and 0.00045 kg VOCs/TEU, respectively. EFs of six materials were further estimated. The EF of the diluent was largest (382.74 kg VOCs/t material), followed by water-based epoxy intermediate paint (132.09 kg VOCs/t material), organic-based epoxy zinc-rich priming paint (91.31 kg VOCs/t material). EFs of other paints ranged from 0.0047 to 43.01 kg VOCs/t material. These results suggest that the replacement of lower- VOCs- contained diluent and effective control from diluent consumption are dramatically conducive to VOCs reduction. Source profiles were established at the industry and individual process levels. Aromatics (77.05-98.38 %) were dominant components in all processes, followed by alkane and OVOCs. m/p-Xylene, o-xylene, and ethylbenzene were the key active species that should be prioritized for control. Overall, EFs and source profiles of the container manufacturing industry were firstly proposed, conducing to the systematic formulation of VOCs control strategies.

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BACKGROUND: Small-scale industries (SSI) are the global economy's backbone since most industrial workers are connected. Most of these workers are contractual and temporary without appropriate training. Also, the SSI does not have a standard workplace with an appropriate layout and infrastructure, as they manage with minimum resources. Therefore, the work hazards, i.e., musculoskeletal disorders and fatigue, often go unnoticed as holistic postural risk methodology is still scarce for identifying the awkward postures in SSI. OBJECTIVE: The present study proposes a novel holistic methodology to track and mitigate awkward postural risks in human-physical activities in SSI. To determine the effectiveness of the proposed methodology, a case study is presented in the South Indian Pump industry, wherein a critical workstation with a complex ergonomic work environment is employed. METHODS: An ergonomic evaluation was conducted empirically and numerically in the workplaces using Digital Human Models. In numerical evaluation, three virtual workspaces have been created to redesign the identified crucial workstation, focusing on ergonomics and workflow. RESULTS: The results obtained from the case study are encouraging for to use of the novel methodology in SSI. The case study reports that the proposed design significantly reduced the REBA score and WISHA lifting index by 6 and 1.20, respectively, without significant investment. CONCLUSION: The proposed methodology could encourage research to identify awkward posture in SSI.

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The study investigates the relationship between green production, green technology, waste reduction, energy use, and sustainability. A Partial Least Squares Structural Equation Modeling (PLS-SEM) approach was used for analysis. The data was collected from a sample of companies in the textile industry. The results suggest that green production and technology positively and significantly affect waste reduction and energy use, which mediates the positive relationship between these two factors and sustainability. This study concludes that green production and technology are critical drivers of sustainability and emphasizes the need to prioritize waste reduction and energy use in sustainable manufacturing practices. The study has practical and managerial implications in all production or manufacturing industries and provides a guideline for managers and policymakers to ensure sustainability.