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1.
Scientific African ; : e01547, 2023.
Article in English | ScienceDirect | ID: covidwho-2165828

ABSTRACT

The lagging behind intelligent technologies and the COVID-19 pandemic together have impacted the emerging economy particularly the manufacturing sector in sub-Saharan countries. This paper systematically discusses intelligent manufacturing technologies with an aim to map out their importance and industrial applicability and to show their significance to contain COVID-19 pandemic. Intelligent Manufacturing Systems (IMS) is then adapted as a post COVID-19 recovery and growth opportunity to ensemble to production processes of manufacturing industry in the sub-Saharan countries. Proposition of a Triple Helix Collaboration Eco-system that delineate a recursive contribution of Government(s), academia, and industry accompanies the IMS adoption. The intention is to shape the existing industrial challenges through networking in the area of intelligence technologies. While proposing the Eco-system, a post COVID-19 recovery and growth opportunity and intra-Africa scientific collaborations are taken into account.

2.
2022 International Conference on Cloud Computing, Performance Computing, and Deep Learning, CCPCDL 2022 ; 12287, 2022.
Article in English | Scopus | ID: covidwho-2137315

ABSTRACT

The huge pressure of market demand and competitive environment makes supply chain finance the choice of most enterprises. The emergence of public health emergencies such as the COVID-19 epidemic has made it particularly urgent to improve the risk management capabilities of the pharmaceutical industry's supply chain in a transitional period. In-depth exploration of the key factors affecting the financial credit risk of pharmaceutical companies' supply chain, and the construction of a high-accuracy forecast model is of great significance to the stability of the macroeconomy. Combining the characteristics of the pharmaceutical manufacturing industry, this paper builds a financial credit risk assessment system for the pharmaceutical supply chain. On the basis of Factor Analysis and Random Forest variable screening, the AdaBoost algorithm is used to build the prediction model. By comparing basic machine learning models such as SVM model, decision tree, logistic regression, Bayesian classifier, BP neural network, and integrated learning models such as Random Forest, Bagging meta-estimator, GBM, and XGBoost, the study found that the AdaBoost model has higher accuracy. And through the data forecast in 2020, the superiority and effectiveness of the model for credit risk assessment in the pharmaceutical industry are further verified. According to the prediction results, this paper finds that the epidemic has no obvious negative impact on pharmaceutical manufacturing enterprises and proposes suggestions from the perspectives of the government and enterprises for reference. © 2022 SPIE.

3.
Regionologiya-Regionology Russian Journal of Regional Studies ; 30(3):602-623, 2022.
Article in Russian | Web of Science | ID: covidwho-2121619

ABSTRACT

Introduction. The COVID-19 pandemic has had a strong negative impact on demographic processes in Russian regions. Mortality has increased significantly, the life expectancy has declined, and the natural decline in the population over the two pandemic years has reached ultra-high indicators. The article aims to analyze the dynamics of the mortality growth in the regions of Siberia for 2020-2021 and assess the impact of demographic, socio-economic, medical and infrastructure factors on it. Materials and Methods. The study is based on official statistical data for individual constituent entities of Russia published by Rosstat (Russian Statistics Agency). The regression and correlation analysis was used to identify the most significant factors that determined the increase and interregional differences in mortality displacement rates. Results. For the Siberian regions the significant factors that determine the increase and interregional differences in mortality displacement include the median age of the population;proportion of children in the age distribution structure;population of the regional capital;as well as the share of extractive and manufacturing industries in regional employment pattern. The median age of 39 and above, the low proportion of minors, and the specialization of a particular region in the manufacturing industry (implying more intensive contacts of employees) contribute to a significant increase in mortality during the pandemic. Regions of Siberia with a relatively low rate of mortality displacement have a younger age distribution structure and economic specialization in the extraction of minerals (contacts of the labor force are rather less frequent). The factors of urbanization level, average per capita income of the population, poverty incidence, general unemployment rate, number of hospital beds, and the number of doctors do not show a significant relationship with the increase in mortality. Discussion and Conclusion. The analysis established the causes of a significant increase in mortality in the Siberian regions. The leading role in it was played by demographic factors and economic specialization of the region. The results obtained can be used in the implementation of social and demographic policy aimed at maintaining the population health in regions with different demographic and socio-economic situations.

4.
International Journal of Business and Society ; 23(2):1169-1189, 2022.
Article in English | Scopus | ID: covidwho-2026618

ABSTRACT

This paper aims to develop a model for predicting corporate bankruptcy for SMEs in the Portuguese manufacturing industry where this question remains rather unaddressed. Using profitability, activity, liquidity, leverage, and solvency ratios, it was added the size and age variables, for a group of 208 firms, including 49 bankrupt firms and 159 active firms, during the years 2011 to 2015. The logit model allowed us to estimate a model with 82.3% of predictive capacity. The most important variables identified were profitability, solvency, and size. Estimations only with the data closest to the bankruptcy date improved predictive capacity. It is evidenced that financial and non-financial variables can predict bankruptcy probability. A possible future approach would be to analyze a larger sample. Also, a larger period could be considered, allowing to test either the effects of the 2007/8 crisis or the effects of the recent economic turmoil related to Covid-19. Important for both corporate managers and investors. Conclusions may be disclosed regarding the influence that economic turmoil certainly has on corporate defaults and bankruptcies allowing its extension to other countries. The contribution of this paper is to find the best specification for a bankruptcy prediction model applied to the Portuguese manufacturing industry SMEs. This paper also contributes to the existing literature by using non-financial variables and analyzing a sector still unexplored in Portugal, albeit its conclusions can be extended to other countries. © 2022, Universiti Malaysia Sarawak. All rights reserved.

5.
Sustainability ; 14(17):10657, 2022.
Article in English | ProQuest Central | ID: covidwho-2024189

ABSTRACT

In the knowledge era, intellectual capital (IC) has been recognized as the determinant of firm performance. The main goal of the current study is to analyze the relationship between IC and its elements and financial performance of Chinese manufacturing small and medium-sized enterprises (SMEs). We also examine whether industry type has an impact on this relationship. This study uses the data of 588 Chinese listed SMEs in the manufacturing industry between 2015 and 2020 and employs the modified value-added intellectual coefficient (MVAIC) model to assess IC. The results show that IC improves SMEs’ financial performance, and physical and human capitals are the main contributor. In addition, the impact of IC and its elements on the financial performance of Chinese manufacturing SMEs is different in different types of industries. Specifically, capital-intensive SMEs have a greater impact of IC on financial performance than labor- and technology-intensive SMEs;labor-intensive SMEs have a higher efficiency of physical capital, while technology-intensive SMEs have higher human capital efficiency. The findings could help SMEs’ managers improve corporate performance by the effective utilization of their IC.

6.
Sustainability ; 14(16):10054, 2022.
Article in English | ProQuest Central | ID: covidwho-2024129

ABSTRACT

In light of global environmental concerns growing, environmental awareness within firms has become more important than before, and many scholars and researchers have argued the importance of environmental management in promoting sustainable organizational performance, especially in the context of supply chains. Thus, the current study aimed at identifying the impact of the components of green intellectual capital (green human capital, green structural capital, green relational capital) on green supply chain performance in the manufacturing sector in Jordan, as well as identifying the moderating role of big data analytics capabilities. To achieve this aim, we developed a conceptual model of Structural Equation Modelling-Partial Least squares and tested through the Smart-PLS software on a sample of 438 respondents. Empirical results showed that each of the components of green intellectual capital and big data analytics explains 71.1% of the variance in green supply chain performance and that all components of green intellectual capital have a statistically significant impact on green supply chain performance. The results also revealed that the relationship between green relational capital and green supply chain performance is moderated through big data analytics capabilities. Finally, this study made a theoretical and managerial implications to the supply chain literature and industry.

7.
Energies ; 15(17):6104, 2022.
Article in English | ProQuest Central | ID: covidwho-2023313

ABSTRACT

The carbon emissions of sectors and households enabled by primary inputs have practical significance in reality. Considering the mutual effect between the industrial sector and the household, this paper firstly constructed an environmentally extended semi-closed Ghosh input–output model with an endogenized household sector to analyze the relationship between carbon emissions and the Chinese economy from the supply-side perspective. The structural decomposition analysis and the hypothetical extraction method were remodified to identify the supply-side driving effects of the changes in carbon emissions and investigate the net carbon linkage. The results show that the electricity, gas, and water supply sector was the key sector with the highest carbon emission intensity enabled by primary inputs. The household sector had an above 93% indirect effect of the enabled intensity, with its enabled intensity dropping significantly by more than 55% from 2007 to 2017. The operating surplus and mixed income caused 3214.67 Gt (34.17%) of the enabled emissions in 2017. The supply-side economic activity, measured by the value added per capita, was the main factor of the carbon emission growth, mainly attributed to the development of the manufacturing sector and the electricity, gas, and water supply sector. The emission intensity and allocation structure both brought a decrease in carbon emissions. The electricity, gas, and water supply sector and the manufacturing sector were the major sources of the supply-induced cross-sectoral input emissions, while the commercial and service sector and the household sector were the top source of supply-induced cross-sectoral output emissions. This paper sheds light on the policies of the carbon emission abatement and the adjustment of the allocation structure from the perspective of supply.

8.
IISE Annual Conference and Expo 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2012915

ABSTRACT

The semiconductor industry has faced supply chain manufacturing shortages that ultimately led to a worldwide chip shortage during the COVID-19 pandemic. These chip manufacturers use sophisticated and advanced manufacturing machinery in their fabs to manufacture chips. As experienced during the pandemic, manufacturing unavailability is often due to the lack of critical manufacturing-related spare parts. This thesis evaluates the effectiveness of machine learning algorithms to identify significant factors contributing to manufacturing part outages (i.e., zero-bin) to keep manufacturing equipment running at total capacity within the organization. We propose clustering methods to segment the data and use logistic regression, logistic lasso regression, and kNN approaches to identify important factors for those parts that could go to zero-bin. Extant research applies classic inventory management strategies based on expenditure, criticality, or usage to manage their parts' inventory throughout the year. Instead, the proposed methods explore whether predefined, static inventory parameters can predict whether a spare part reaches zero bin. To demonstrate the viability of this approach, we present a case study using one year's worth of data from a leading chip manufacturing company. Based on the modeling approaches, a lasso-based logistic regression proved the best predictive model amongst the five clusters with lead-time, current quantity available, days on inventory (usage remained relevant), and the part's reorder point being the most significant parameters. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.

9.
Pharmaceutical Technology ; 2022:s30-s32, 2022.
Article in English | EMBASE | ID: covidwho-2006922
10.
Sustainability ; 14(15):9307, 2022.
Article in English | ProQuest Central | ID: covidwho-1994178

ABSTRACT

Services export plays a significant role in the world economy and benefits businesses and countries. In the service sector, higher education service has become vital for countries’ economic sustainability. The flux of international students has created global business opportunities and trade networks. However, past studies have largely focused on exports within the manufacturing industry rather than service exports, particularly on the inward export service industry. Therefore, the present study was conducted to investigate the relationship between business technology strategy, cultural sensitivity, and export performance in the higher education service industry. Data were collected from directors of international offices of 137 Malaysian higher education institutions. PLS-SEM was used for data analysis using the SmartPLS 3.2.8 software. The findings reveal that business technology strategy is positively related to cultural sensitivity and inward export performance. Furthermore, the study found that business technology strategy mediates the relationship between cultural sensitivity and inward export performance. The findings can help researchers in understanding factors that influence the inward export performance of higher education institutions. Since business technology strategy plays a mediating role in the inward export performance of higher education, this study recommends that Malaysian higher education institutions (HEIs) are equipped with the latest education-related technologies in order to increase their internationalization performance.

11.
Energies ; 15(15):5341, 2022.
Article in English | ProQuest Central | ID: covidwho-1993957

ABSTRACT

Manufacturing facilities use about 35% of the domestic energy in the United States every year. Implementing an effective energy management system (EnMS) is one of the most important approaches to improve energy efficiency. However, the implementation of EnMS is low for many countries (including the US) and even for energy-intensive sectors. The reasons for the low implementation rate of energy management systems had been investigated by multiple researchers, but very few studies have focused on the barriers and challenges of implementing ISO 50001-based energy management systems. To contribute to this understudied area, this paper discusses the implementation and outcomes of the first Better Plants 50001 Ready Virtual In-plant Training. This paper first provides an overview of 50001 Ready and the 50001 Ready Navigator Tool. Then, it provides details on this training event and its outcomes. Finally, it discusses findings from the responses to 40 live polling questions about the status of the 25 tasks of the 50001 Ready Navigator for participating companies, key components of the participating manufacturing companies’ energy management systems, and challenges and barriers that these companies are facing. The findings suggest that although many companies understood the importance of an effective energy management system, about half of them do not understand the required resources for building energy management systems, and most of them have only just begun establishing these systems and need more assistance and resources in multiple areas. More specifically, more assistance is necessary for the following: (1) improving corporate management’s understanding of the time and resources needed to build an EnMS as well as the benefits;(2) creating linear regression models for more accurate energy performance tracking;(3) understanding energy use, collecting and analyzing energy performance data;(4) optimizing equipment operational controls, and creating action plans.

12.
Applied Sciences ; 12(15):7534, 2022.
Article in English | ProQuest Central | ID: covidwho-1993921

ABSTRACT

In order to cope with the changing era of the innovative management paradigm of the manufacturing industry, it is necessary to advance the construction of smart factories in the domestic manufacturing industry, and in particular, the 3D design and manufacturing content sector is highly growthable. In particular, the core technologies that enable digital transformation VR (Virtual Reality)/AR (Augmented Reality) technologies have developed rapidly in recent years, but have not yet achieved any particular results in industrial engineering. In the manufacturing industry, digital threads and collaboration systems are needed to reduce design costs that change over and over again due to the inability to respond to various problems and demands that should be considered when designing products. To this end, we propose a VR/AR collaboration model that increases efficiency of manufacturing environments such as inspection and maintenance as well as design simultaneously with participants through 3D rendering virtualization of facilities or robot 3D designs in VR/AR. We implemented converting programs and middleware CPS (Cyber Physical System) servers that convert to BOM (Bill of Material)-based 3D graphics models and CPS models to test the accuracy of data and optimization of 3D modeling and study their performance through robotic arms in real factories.

13.
Pharmaceutical Technology ; 46(1):42-43, 2022.
Article in English | EMBASE | ID: covidwho-1976086
14.
Applied Sciences ; 12(14):6986, 2022.
Article in English | ProQuest Central | ID: covidwho-1963683

ABSTRACT

Meat 4.0 refers to the application the fourth industrial revolution (Industry 4.0) technologies in the meat sector. Industry 4.0 components, such as robotics, Internet of Things, Big Data, augmented reality, cybersecurity, and blockchain, have recently transformed many industrial and manufacturing sectors, including agri-food sectors, such as the meat industry. The need for digitalised and automated solutions throughout the whole food supply chain has increased remarkably during the COVID-19 pandemic. This review will introduce the concept of Meat 4.0, highlight its main enablers, and provide an updated overview of recent developments and applications of Industry 4.0 innovations and advanced techniques in digital transformation and process automation of the meat industry. A particular focus will be put on the role of Meat 4.0 enablers in meat processing, preservation and analyses of quality, safety and authenticity. Our literature review shows that Industry 4.0 has significant potential to improve the way meat is processed, preserved, and analysed, reduce food waste and loss, develop safe meat products of high quality, and prevent meat fraud. Despite the current challenges, growing literature shows that the meat sector can be highly automated using smart technologies, such as robots and smart sensors based on spectroscopy and imaging technology.

15.
Pharmaceutical Technology ; 45(11):34-40, 2021.
Article in English | EMBASE | ID: covidwho-1935337
16.
Sustainability ; 14(11):6688, 2022.
Article in English | ProQuest Central | ID: covidwho-1892975

ABSTRACT

This paper focuses on the path of China’s participation in global value chain reconstruction and concludes three ways to reconstruct the global value chain: embedding in the global value chain, reconstructing the national value chain, and leading the regional value chain. Based on the value-added accounting system and the latest statistics of the TiVA database, we construct an index system for the path selection of global value chain reconstruction and put forward a more suitable path for different manufacturing industries in China. According to the VRCA index and ranking of each type of manufacturing industry, our study concludes that: transportation equipment manufacturing tends to embed in global value chains;textiles, clothing, leather, and related manufacturing;wood products, paper products, and printing;chemical and non-metallic mineral products;base metals and metal products;computer, electronic, and electrical equipment manufacturing;machinery and equipment manufacturing;and other manufacturing industries tend to dominate the regional value chains;and food and beverage manufacturing and tobacco industries tend to restructure national value chains. Finally, our paper gives suggestions and prospects for path upgrading;promoting the integrated development of e-commerce and the manufacturing industry can enhance the competitive advantages of China’s manufacturing industry and achieve path upgrading and optimization. Furthermore, the two-way nesting of the “Belt and Road” regional value chain and global value chain can help China’s manufacturing industry eliminate the dilemma of low-end lock-in and upgrade from the original low-end dependent embedding mode to the middle high-end hub embedding mode.

17.
Sustainability ; 14(11):6493, 2022.
Article in English | ProQuest Central | ID: covidwho-1892963

ABSTRACT

Main aim: This paper examines the main topics of research in the literature studying the topic of sustainability in small and medium-sized enterprises (SME), and aims at presenting a future research agenda. Method: We conducted a systematic literature review based on articles published between 2000 and 2020. From an initial set of 88 papers taken from WoS in the period under analysis, 42 papers were effectively analyzed. Main results: The results of an in-depth reading reveal four clusters representing the main topics of research in the field: sustainability and SMEs’ performance;green and environmental management issues;social and cultural issues and their impact on sustainability policies;values, skills, and capabilities. Key findings suggest that the following angles of research appear to be underexplored: theoretically grounded research;research using large samples;articles examining sustainability reporting;research looking into non-manufacturing sectors;work examining settings in developing countries;research undertaking international comparisons;articles exploring the complementarity between the literature on sustainability in SMEs and on family-owned businesses;and the influence of the social and cultural context on SMEs’ engagement with sustainability. Main contribution: This paper offers insights to academia, practitioners, and policy makers to help SMEs engaging with sustainability and may assist also the latter to develop strategies to improve SMEs’ social and environmental reporting. Given the current pandemic crisis, and the urgency for sustainable business practices, we expect to contribute to expanding knowledge in this field of research.

18.
Sustainability ; 14(11):6437, 2022.
Article in English | ProQuest Central | ID: covidwho-1892956

ABSTRACT

This study examines the change in labor productivity in Vietnam by means of a Fisher index decomposition and attribution analysis. The results can be summarized as follows. First, the aggregate labor productivity is decomposed into pure labor productivity and structural change from 2007 to 2019. All of the aggregate labor productivity, pure labor productivity, structural change, and interaction terms have increased by 69.83%, 36.74%, 24.20%, and 8.89%, respectively. Second, the percentage change in labor productivity is attributed to 20 sub-industries by pure labor productivity and structural change. The sum of the multi-period attribution of pure labor productivity and structural change shows that the manufacturing industry positively dominates (15.84%) and plays a key role in economic development. The positive pure labor productivity and structural change in the manufacturing industry imply that the structural bonus hypothesis does hold in the industry. The findings also indicate that pure labor productivity, especially in the service industry, should be improved to sustain economic growth.

19.
Journal of Cleaner Production ; : 132608, 2022.
Article in English | ScienceDirect | ID: covidwho-1882160

ABSTRACT

Despite the current slowdown in global economic growth due to the impact of COVID-19, the digital economy is still performing well. Under the background of double carbon, green innovation and intelligent production of manufacturing enterprises have become the general trend of sustainable development. It is particularly important to study the integration of digital technology into green innovation and production processes to improve the performance of digital green innovation and the competitiveness of enterprises. However, the integration of digital technology and green innovation from the perspective of knowledge management has not been fully introduced into current literatures. In this study, hierarchical regression and fsQCA approaches were used to empirically verify the adoption process of digital green innovation activities and the impact of digital green knowledge creation on digital green innovation performance (DGIP), and explores the moderating effects of digital green risk perception (DGRP) and digital green complexity perception (DGCC) through 429 questionnaires from Chinese manufacturing enterprises. In addition, knowledge search is divided into three dimensions: scientific digital green knowledge search (SDGKS), market digital green knowledge search (MDGKS) and supply chain digital green knowledge search (SCDGKS). The results show that: i) SDGKS promotes exploitative digital green knowledge creation (EDGKC). MDGKS has a positive impact on both utilizing digital green knowledge creation (ADGKC) and EDGKC. SCDGKS promotes EDGKC. ii) The relationship between SDGKS and EDGKC is only moderated by DGCC (positive). The relationship between MDGKS and EDGKC is only moderated by DGCC (negative). The relationship between SCDGKS and EDGKC is moderated by DGRP (negative) and DGCC (negative). iii) There is an inverted U-shaped relationship between ADGKC and DGIP. There is a U-shaped relationship between EDGKC and DGIP. The essence of this study is to help manufacturing enterprises find external partners to improve their digital green innovation performance through external knowledge search partner selection. The conclusion of this study has certain theoretical contribution to the clarification of the complex process of digital green innovation. This study provides a theoretical basis for enterprises to select knowledge search partners according to their own environment to carry out digital green innovation activities smoothly. This study has practical value for enterprises to improve competitiveness, better survival and development process under the current environment.

20.
Sustainability ; 14(10):6282, 2022.
Article in English | ProQuest Central | ID: covidwho-1871588

ABSTRACT

Facing the sustainable use of electric power resources, many countries in the world focus on the R&D investment and application of electrochemical energy storage projects (i.e., EESP). However, the high R&D cost of EESP has been hindering large-scale industrial promotion in the energy-intensive manufacturing industry represented by the tobacco industry. Reducing and controlling the R&D cost has become an urgent problem to be solved. In this context, this paper innovatively proposes a multi-technology driven R&D cost improvement scheme, which integrates WBS (i.e., Work Breakdown Structure), EVM (i.e., Earned Value Method), BD (i.e., Big Data), and ML (i.e., Machine Learning) methods. Especially, the influence of R&D cost improvement on EESP application performance is discussed through mathematical model analysis. The research indicates that reducing EESP R&D costs can significantly improve the stability of EESP power supply, and ultimately improve the application value of EESP in energy-intensive manufacturing industries. The R&D cost management scheme and technical method proposed in this paper have important theoretical guiding values and practical significance for accelerating the large-scale application of EESP.

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