Manufacturing Sectors
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2021 ◽  
Vol 5 (6) ◽  
pp. 588
Andy Sucipto ◽  
Carunia Mulya Firdausy

The purpose of this study was to determine the effect of interest rate, exchange rate, and inflation on the Non-Performing Loans of manufacturing sectors that were financed by Conventional Commercial Banks in Indonesia. The sample used in this study consisted of 8 business sectors, namely mining, manufacturing, wholesale and retail trade, accommodation, transportation & warehousing, finance, property, electricity, gas, and water. This study used a panel data regression analysis method with a random effect model (REM) approach. The results showed that the interest rate and inflation had no significant effects on the NPL of the manufacturing sector understudied, while the exchange rate had a significant effect on the NPL. However, the coefficient of determination was found to be 36.11 percent. This indicates that other variables affect the NPL of manufacturing sectors financed by the conventional bank in Indonesia. Tujuan penelitian ini adalah untuk mengetahui pengaruh suku bunga, nilai tukar dan inflasi terhadap Non-Performing Loan sektor manufaktur yang dibiayai oleh Bank Umum Konvensional di Indonesia. Sampel yang digunakan dalam penelitian ini terdiri dari 8 sektor usaha yaitu pertambangan, manufaktur, perdagangan grosir dan eceran, akomodasi, transportasi & pergudangan, keuangan, properti, listrik, gas dan air. Penelitian ini menggunakan metode analisis regresi data panel dengan pendekatan random effect model (REM). Hasil penelitian menunjukkan bahwa suku bunga dan inflasi tidak berpengaruh signifikan terhadap NPL sektor manufaktur yang diteliti, sedangkan nilai tukar berpengaruh signifikan terhadap NPL. Namun, koefisien determinasi diperoleh sebesar 36,11 persen. Hal ini menunjukkan bahwa terdapat variabel lain yang mempengaruhi NPL sektor manufaktur yang dibiayai oleh bank konvensional di Indonesia.

2021 ◽  
Vol 14 ◽  
pp. 90-93
Yitong Xie

Since December 2019, the growth of global trade has slowed significantly during the years when the "COVID-19" was raging. Through a review of the literature, we explore the significant impact of the outbreak on the Chinese economy, mainly in the service and manufacturing sectors already e-commerce, with both negative and positive effects on these industries.

2021 ◽  
Kathiroli Raja ◽  
Krithika Karthikeyan ◽  
Abilash B ◽  
Kapal Dev ◽  
Gunasekaran Raja

Abstract The Industrial Internet of Things (IIoT), also known as Industry 4.0, has brought a revolution in the production and manufacturing sectors as it assists in the automation of production management and reduces the manual effort needed in auditing and managing the pieces of machinery. IoT-enabled industries, in general, use sensors, smart meters, and actuators. Most of the time, the data held by these devices is surpassingly sensitive and private. This information might be modified,
stolen, or even the devices may be subjected to a Denial of Service (DoS) attack. As a consequence, the product quality may deteriorate or sensitive information may be leaked. An Intrusion Detection System (IDS), implemented in the network layer of IIoT, can detect attacks, thereby protecting the data and devices. Despite substantial advancements in attack detection in IIoT, existing works fail to detect certain attacks obfuscated from detectors resulting in a low detection performance. To address the aforementioned issue, we propose a Deep Learning-based Two Level Network Intrusion Detection System (DLTL-NIDS) for IIoT environment, emphasizing challenging attacks. The attacks that attain low accuracy or low precision in level-1 detection are marked as challenging attacks. Experimental results show that the proposed model, when tested against TON IoT, figures out the challenging attacks well and achieves an accuracy of 99.97%, precision of 95.62%, recall of 99.5%, and F1-score of 99.65%. The proposed DL-TLNIDS, when compared with state-of-art models, achieves a decrease in false alarm rate to 2.34% (flagging normal traffic as an attack) in IIoT.

2021 ◽  
Vol 1206 (1) ◽  
pp. 012010
Rohit Sharma ◽  
Ubaid Ahmad Khan

Abstract In order to incorporate agile manufacturing (AM) in materials and systems, the manufacturing sectors have drivers to face obstacles. Agility is generally accepted for satisfying diverse consumer demands as a new strategic principle in the automotive industry. There has now been a prerequisite for evaluating AM in industry. An organization’s effectiveness relies on their ability to find and pay special attention to the crucial success drivers to achieve a high level of efficiency. This paper suggests a number of Agile Manufacturing Drivers (AMDs) to evaluate AM that is deemed suitable to the production industry. In order to prioritise performance drivers, the analytical hierarchy process (AHP) approach is used to summarise the perspective of an expert. The proposed AMDs are believed to encourage and assist the manufacturing sector in producing agile products to achieve higher efficiency so as to improve competition.

2021 ◽  
Vol 11 (5) ◽  
pp. 149-156
Drumil Newaskar ◽  
Shubham Gandhi ◽  
Preet Aligave

Additive manufacturing is a revolutionary technology because of its ability to creates objects by adding material layer by layer rather than removing material from a block or by moulding procedure. Additive manufacturing has been around for more than three decades but still, traditional manufacturing is the dominant method for manufacturing. COVID-19 pandemic has been a torment globally and has brought distress and instability to the global economy. Due to this, the manufacturing sectors are badly affected. In this time of crisis, additive manufacturing has played a major role. This paper discusses the upsurge of Additive manufacturing due global COVID-19 pandemic and its worldwide impact on supply chain management.

2021 ◽  
Vol 29 (4) ◽  
Abba Kyari Buba ◽  
Othman Ibrahim

This preliminary survey investigates and validates the measurement model of factors influencing decision makers’ intentions to adopt Green information technology (Green-IT) in manufacturing sectors in Nigeria. The Norm Activation Model (NAM) and Theory of Planned Behaviour (TPB) were used to explore the factors that could influence decision-makers’ intention in adopting Green-IT. Using constructs from the NAM and TPB, this survey proposes a model for identified behavioural factors. A quantitative research approach with a data collection and analysis plan using a cross-sectional survey design was adopted. A sample of 30 decision-makers in the top three manufacturing industries in Nigeria was selected using a purposive sampling procedure for participation in the study. The data collected was analysed using Partial Least Square Structural Equation Modelling (PLS-SEM) to test the proposed model. The model was validated in two phases: (i) Initial Measurement Model and (ii) Modified Measurement Model. Findings revealed that Green-IT Attitude, Subjective Norm, Ascription of Responsibility, Awareness of Consequences, Personal Norm, Environmental Concern, and Perceive Behavioural Control were the key elements of the behavioural intention model to adopt Green-IT, with 31 indicators having factor loadings of >0.5, adequate internal consistency reliability, CR > 0.7, and Cronbach’s Alpha, >0.7. The result revealed convergent validity, and acceptable discriminant validity was assessed using AVE > 0.5 and Fornell-lacker’s criterion. The results from the full-scale study would contribute to developing a context-specific model to examine Green-IT adoption in developing nations.

Atayi Abraham Vincent ◽  

This study tried to investigate the impact of agriculture and manufacturing on economic growth using time series data from (1987-2019). To analyze the link between the variables, the researchers utilized the ADF to test for stationarity, the Ordinary Least Square Method, the Error Correction Model, and the Granger Causality Test. The result shows that, the coefficient of agricultural output has a positive sign, indicating a favorable association. The AGRQ coefficient is (0.045142), implying that a 5% change in AGRQ will result in a 5% change in Manufacturing Value Added. At the 0.05 percent level, the finding is statistically significant, with a probability of (0.0000). The coefficient of determination R-Squared (R2) is 0.817974, indicating that variations in the explanatory variables account for nearly 82 percent of the variation in Manufacturing Value Added. The ECM's coefficient (-1) is (0.619202). The coefficient indicates that the short run adjustment annually offsets 62% of the system's disequilibrium in order to restore long-run equilibrium. This means that the system will reach equilibrium at a 62 percent rate the following year. At the 5% level of significance, Granger causality demonstrates that there is no causal link between Manufacturing Value Added and Real Gross Domestic Product. However, manufacturing value-added and agricultural output have a one-way relationship. The study recommends that government must urgently expand the Nigerian agricultural sector by allocating more financing to the industry and ensuring that the funds are used wisely and to further support increased industrial productivity and expansion, the government should work to strengthen its incentives to the manufacturing sector.

Micromachines ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1293
Andrea Abeni ◽  
Alessandro Metelli ◽  
Cristian Cappellini ◽  
Aldo Attanasio

Ultraprecision micromachining is a technology suitable to fabricate miniaturized and complicated 3-dimensional microstructures and micromechanisms. High geometrical precision and elevated surface finishing are both key requirements in several manufacturing sectors. Electronics, biomedicals, optics and watchmaking industries are some of the fields where micromachining finds applications. In the last years, the integration between product functions, the miniaturization of the features and the increasing of geometrical complexity are trends which are shared by all the cited industrial sectors. These tendencies implicate higher requirements and stricter geometrical and dimensional tolerances in machining. From this perspective, the optimization of the micromachining process parameters assumes a crucial role in order to increase the efficiency and effectiveness of the process. An interesting example is offered by the high-end horology field. The optimization of micro machining is indispensable to achieve excellent surface finishing combined with high precision. The cost-saving objective can be pursued by limiting manual post-finishing and by complying the very strict quality standards directly in micromachining. A micro-machining optimization technique is presented in this a paper. The procedure was applied to manufacturing of main-plates and bridges of a wristwatch movement. Cutting speed, feed rate and depth of cut were varied in an experimental factorial plan in order to investigate their correlation with some fundamental properties of the machined features. The dimensions, the geometry and the surface finishing of holes, pins and pockets were evaluated as results of the micromachining optimization. The identified correlations allow to manufacture a wristwatch movement in conformity with the required technical characteristics and by considering the cost and time constraints.

2021 ◽  
pp. 097215092110501
Sakshi Aggarwal ◽  
Debashis Chakraborty ◽  
Ranajoy Bhattacharyya

Over the last decade, trade policy reforms have significantly influenced the internationalization of Indian manufacturing firms, leading to deeper participation of the country in global value chains (GVCs) and international production networks (IPNs). With the growing participation of foreign firms in the value chain, the domestic value-added (DVA) content embodied in Indian exports have displayed a declining trend. Recently, in 2020, India has decided to launch the ‘Atmanirbhar (self-reliant) Bharat Abhiyan’, which, in principle, aims to consolidate the manufacturing sector, leading to increasing DVA embodied in exports (DVA-content), apart from employment generation. The current article attempts to analyse the drivers of India’s DVA in exports for select manufacturing industries over 2000–15 by using the OECD trade in value added (TiVA) database. The empirical results reveal that sectoral DVA content is positively influenced by both domestic capital and foreign direct investment (FDI), and labour skill intensity, but negatively influenced by the presence of unskilled workers. Moreover, FDI inflows in sectors characterized by high skill-intensity and high-relative growth rate play a crucial role in influencing DVA content. Finally, the presence of larger and more capital-intensive firms is found to be a major driver of DVA. On the policy front, therefore, the empirical results underline that export promotion policies alone will not be able to resolve employment worries, a major concern in India, as vast numbers of unskilled and low-skilled workers trapped in the agricultural sector or working in unorganized and micro-industries fail to figure in the country’s export value addition. A concerted effort towards labour skill enhancement as well as technology transfer is necessary for exports to play a more positive role.

Biswajit Mohapatra ◽  
Sushanta Tripathy ◽  
Deepak Singhal ◽  
Rajnandini Saha

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