Industrial Classification Research Articles

A Two-Stage Optimisation of Ship Hull Structure Combining Fractional Factorial Design Technique and NSGA-II Algorithm

The intricate nature of ships and floating structures presents a significant challenge for ship designers when determining suitable structural dimensions for maritime applications. This study addresses a critical research gap by focusing on a three-cargo hold model for a multipurpose cargo ship. The complex composition of these structures, including stiffening plates, deck plates, bottom plates, frames, and bulkheads, necessitates thorough structural analysis to facilitate effective and cost-efficient design evaluation. To address this challenge, the research utilises FEMAP-integrated NX NASTRAN software (2021.2) to assess hull girder stress. Furthermore, a novel approach is introduced, integrating the Design of Experiments (DOE) principles within Minitab 21.4.1 software to identify critical parameters affecting hull girder stress and production costs. This method determined the top five key parameters influencing hull girder stress: Hatch coaming plate, Hatch coaming top plate, Main deck plate, Shear strake plate, and Bottom plate, while also highlighting key parameters that impact production costs: the inner bottom plate, Inner side shell plate, Bottom plate, Web frame spacing, and Side shell plate. Ship design optimisation is then carried out by incorporating regression equations from Minitab software into the Non-dominated Sorting Genetic Algorithm II (NSGA-II), which is managed using Python software (PyCharm Community Editon 2020.3.1). This optimisation process yields a significant 10% reduction in both ship weight and production costs compared to the previous design, achieved through prudent adjustments in plate thickness, web frame positioning, and stiffener arrangement. The optimally designed midship section undergoes rigorous validation to ensure conformity with industry standards and classification society regulations. Necessary adjustments to inner bottom plates and double bottom side girders are made to meet these stringent requirements. This research offers a comprehensive framework for the structural optimisation of ship hulls, potentially enhancing safety, sustainability, and competitiveness within the maritime engineering industry.

Efficiency of labor allocation: An observation in Indonesian Standard Industrial Classification (ISIC)

This study aims to analyze the efficiency and elasticity of the large and medium manufacturing industry sector in Indonesia from 2012 to 2020. The analysis focuses on the 24 subsectors within the 2-digit Indonesian Standard Industrial Classification (ISIC) principal groups 10 to 33. The efficiency level of the manufacturing industry sector was analyzed using the Cobb-Douglas production function model with linear logarithmic transformation. During the period 2012–2020, the production efficiency of the 24 ISIC 2-digit subsectors increased by an average of 445%, or 4 times higher than before. The ISIC 2-digit subsector (24 subsectors) had output elasticity values smaller than one (EL < 1) or inelastic 45 times. This indicates that the marginal value added of labor was lower than the average value added of labor. Therefore, the additional allocation of labor in large and medium industries tends to reduce the average value added of labor. The computer industry, electronic and optical goods computer, electronic and optical goods had the highest efficiency index in order, according to the data. This can be achieved by focusing on industries such as ISIC 17 (paper and paper goods industry), ISIC 31 (furniture industry), and ISIC 29 (motor vehicle, trailer and semi-trailer industry). To improve the efficiency of labor allocation, it is necessary to optimize the use of labor resources, increase productivity, and remain competitive in the market. To improve efficiency and elasticity, it is recommended to implement a robust workforce planning strategy that aligns labor resources with production needs. Additionally, matching the skills and abilities of the workforce with job roles is crucial. Finally, identifying tasks that can be automated using technology can also increase efficiency.

Systematic Risk Analysis of Listed Financial Institutions from the Perspective of Complex Network

Based on the complex network theory, this paper studies the systemic financial risks in China’s financial market. According to the industry classification of the China Securities Regulatory Commission in 2012, the daily closing prices of 45 listed financial institutions are collected and the daily return rates of each financial institution are measured according to the logarithmic return rate calculation formula. In this paper, the risk spillover value ΔCoVaR is used to measure the contribution degree of each financial institution to systemic risk. Finally, the relationship between the risk spillover value ΔCoVaR and the node topology index of the risk transmission network is investigated by using a regression model, and some policy suggestions are put forward based on the regression results.

The Potential of Using SDGSAT-1 TIS Data to Identify Industrial Heat Sources in the Beijing–Tianjin–Hebei Region

It is crucial to detect and classify industrial heat sources for sustainable industrial development. Sustainable Development Science Satellite 1 (SDGSAT-1) thermal infrared spectrometer (TIS) data were first introduced for detecting industrial heat source production areas to address the difficulty in identifying factories with low combustion temperatures and small scales. In this study, a new industrial heat source identification and classification model using SDGSAT-1 TIS and Landsat 8/9 Operational Land Imager (OLI) data was proposed to improve the accuracy and granularity of industrial heat source recognition. First, multiple features (thermal and optical features) were extracted using SDGSAT-1 TIS and Landsat 8/9 OLI data. Second, an industrial heat source identification model based on a support vector machine (SVM) and multiple features was constructed. Then, industrial heat sources were generated and verified based on the topological correlation between the identification results of the production areas and Google Earth images. Finally, the industrial heat sources were classified into six categories based on point-of-interest (POI) data. The new model was applied to the Beijing–Tianjin–Hebei (BTH) region of China. The results showed the following: (1) Multiple features enhance the differentiation and identification accuracy between industrial heat source production areas and the background. (2) Compared to active-fire-point (ACF) data (375 m) and Landsat 8/9 thermal infrared sensor (TIRS) data (100 m), nighttime SDGSAT-1 TIS data (30 m) facilitate the more accurate detection of industrial heat source production areas. (3) Greater than 2~6 times more industrial heat sources were detected in the BTH region using our model than were reported by Ma and Liu. Some industrial heat sources with low heat emissions and small areas (53 thermal power plants) were detected for the first time using TIS data. (4) The production areas of cement plants exhibited the highest brightness temperatures, reaching 301.78 K, while thermal power plants exhibited the lowest brightness temperatures, averaging 277.31 K. The production areas and operational statuses of factories could be more accurately identified and monitored with the proposed approach than with previous methods. A new way to estimate the thermal and air pollution emissions of industrial enterprises is presented.

A Robust Semi-Supervised Fisher Discriminant Analysis for Industrial Fault Classification

A Robust Semi-Supervised Fisher Discriminant Analysis for Industrial Fault Classification

Pass‐through to export prices: Evidence from Australia†

AbstractAustralia is a leading exporter of primary and agricultural commodities like iron ore, coal, wool and beef. Industry stakeholders point out that exchange rate fluctuations remain a key determinant of export revenue. This article investigates the exchange rate pass‐through to export prices using the recently proposed impulse responses by the local projections technique. Employing export prices in all destination markets and trade‐weighted exchange rates, there is substantial evidence that 17 SITC industry and sub‐industry level prices experience moderate to high exchange rate pass‐through to export prices. These industry categories include sectors like minerals and natural resources products, which are homogenous in nature, and Australia enjoys a comparative advantage in the export of such goods. However, this competitive edge does not necessarily lead to higher export prices, as currency invoicing plays an important role in determining the extent of the pass‐through effect. Our findings have important policy implications, especially regarding the invoicing currency strategy for exporters selling homogenous items.

Harnessing big data for sustainable urban management: A novel approach to gridded urban GDP dataset development

To effectively address the challenges posed by rapid global urbanization, cities must utilize sound scientific principles and methodologies. A key element is the development of robust socioeconomic indicators, such as Gross Domestic Product (GDP). This study introduces a novel framework for creating a spatially disaggregated urban GDP dataset, using a downscaling approach and integrating data from a vast range of sources, including 3.5 million industrial entities across 835 sub-sectors within 41 major sectors. The framework enhances accuracy by leveraging the benefits of Pollutant Discharge Permits (PDP) and National Enterprise Credit Information Publicity (NECIP) for pinpointing geographic locations, industry classifications, and reliable statistical data for urban GDP. Additionally, the dataset is regularly updated using publicly accessible data. Validation of the dataset was conducted using multi-level statistical data and the NOx hot grid index derived from TROPOMI NO2 column measurements. Results indicate that the dataset accurately reflects the spatial distribution of major industrial sectors. The gridded urban GDP dataset for China, with a high spatial resolution of 0.1° × 0.1°, reveals spatial heterogeneity among sectors, with the mining sector, concentrated in resource-rich areas, making the most significant contribution to this heterogeneity. Other sectors show a strong spatial correlation, with the gravity center located in central China. This study provides a valuable resource for sustainable urban resource management within Earth system boundaries and offers critical data, insights, and methodological guidance for tackling urbanization challenges.

Despair and hope: Is the retail community pharmacy workforce in danger of becoming a monopsony labor market?

ObjectiveMergers of big chain retail community pharmacies can affect the competitiveness of the pharmacy workforce to negotiate better wages and work conditions. However, it is unclear whether these types of mergers are generalizable to the U.S. pharmacy workforce. We should observe this effect when comparing annual wage trends between retail community pharmacy workers and nonretail community pharmacy workers. In the absence of this effect, annual wage trends would be similar. To examine this theory, annual wage trends for community pharmacy workers were compared with hospital pharmacy workers between 2012 and 2022. Design, setting and participantsA serial cross-sectional study was performed to compare the annual wages between retail community pharmacy workers and hospital pharmacy workers between 2012 and 2022 using data from the U.S. Bureau of Labor and Statistics (BLS). Pharmacy workforce was categorized as pharmacists, pharmacy technicians, and pharmacy aides (clerks) and grouped into retail or hospital pharmacy settings based on the North American Industry Classification System. Pharmacy workers’ annual wages were based on the U.S. BLS Quarterly Census of Employment and Wages data. Outcome measuresAnnual wages. ResultsBetween 2012 and 2022, statistically significant annual wage reduction was greater among pharmacists in the retail than pharmacists in the hospital setting by −$1974 (95% CI −$2921 to −$1026) per year. However, these trends were not statistically significant among pharmacy technicians and pharmacy aides. Pharmacy technicians in the retail and hospital settings had a 3.4% and 7.0% increase in average annual wages, respectively. Pharmacy aides in the retail and hospital settings had a 16.8% and 21.6% increase in average annual wages, respectively. ConclusionAlthough pharmacists’ annual wages decreased, it is unclear whether this was caused by the monopsony labor market. These findings suggest that there may be inefficiencies in the retail community pharmacy labor market, which may stimulate policies to improve pharmacy workforce conditions and patient safety.

Surveillance of noise exposure level in industrial enterprises-Jiangsu Province, China, 2022.

Occupational noise exposure is the most prominent problem in industrial enterprises in Jiangsu Province. Since 2019, China has established the National Surveillance System for Occupational Hazards in the workplace to grasp the current occupational hazards in critical industries, including occupational noise. According to the Work Plan for Surveillance of Occupational Hazards in the Workplace (2022) issued by the National Health Commission of the People's Republic of China, the noise exposure level of 3,142 enterprises in our province was monitored, the median and interquartile range (IQR) were calculated, and the distribution of noise exposure level was described by industry classification, enterprise-scale and ownership type of the enterprise. The railway, shipping, aerospace, and other transportation equipment manufacturing industries exhibited the highest proportion (42.9%) of individual noise exposure levels exceeding 85 dB(A), followed by the motor vehicles manufacturing industry (36.4%). The proportion of individual noise exposure levels exceeding 85 dB(A) was higher in medium and small enterprises, with rates of 28.1 and 28.6%, respectively. The highest proportion of personal noise exposure levels exceeding 85 dB(A) was observed in Hong Kong, Macao and Taiwan investment enterprises (37.5%), followed by incorporated companies (34.6%) and limited liability companies (28.1%), the lowest was state-owned enterprises(15.5%). The areas with excessive noise are primarily concentrated in grinding, welding, machining, cutting, and other related operations, accounting for 61.2% of the total. Among these operations, grinding accounts for 29.8%. The highest environmental noise and individual noise intensity were found in sandblasting and grinding positions, with individual noise intensities of 115.5 dB(A) and 108.4 dB(A), respectively. The noise exposure risk is so high that cannot be ignored in the manufacturing industry, especially in Hong Kong, Macao, and Taiwan investment enterprises, incorporated companies and medium and small enterprises.

Forecasting the Electricity Consumptions of PLN UP3 Cengkareng using Deep Learning

The consumption of electrical energy for the community every year has increased including the electricity consumption of PLN UP3 Cengkareng customers. Therefore, PLN UP3 Cengkareng must supply electricity to customers in all categories such as Social Category, Household Category, Business Category, Industry Category and Government Category. With customer needs that continue to increase, it is necessary to forecast future electricity needs, so that PLN UP3 Cengkareng can provide the required electrical power. For this reason, it is necessary to predict the electricity demand. This research was conducted to forecast the electricity demand of UP3 Cengkareng by using the Deep Learning Model Long Short-Term Memory (LSTM). The data set used in this study was taken from the PLN UP3 Cengkareng information system, for 10 years, the period from 2012 to 2021. The data used is divided into 2 categories, namely 70% training data and 30% testing data. The results obtained from this prediction are 96,689, with an average neuron value of 32 and an epoch value of 10.

A census of the green economy: measuring marijuana employment, businesses, and payroll in four states

ABSTRACT The recent legalization of cannabis across many U.S. states affords economists an opportunity to observe the birth, development, and consolidation of a new industry. The present study characterizes the disposition of this nascent sector of the economy in California, Colorado, Oregon, and Washington, the first four states to permit recreational use. Using the method of generalized synthetic controls, we identify marijuana related business activity which would otherwise remain hidden due to the tendency of such firms to obfuscate the nature of their trade when reporting to government sources. We find that roughly 68,000 marijuana-related jobs have emerged in these states as of 2020, with a combined annual payroll of roughly 6 billion dollars. Across these states, the scale of the marijuana business rivals that for alcoholic beverages, and would approach the size of a 3-digit NAICS (North American Industry Classification System) sub-sector if viewed as a single industry.

AI Model for Industry Classification Based on Website Data

This paper presents a broad study on the application of the BERT (Bidirectional Encoder Representations from Transformers) model for multiclass text classification, specifically focusing on categorizing business descriptions into 1 of 13 distinct industry categories. The study involved a detailed fine-tuning phase resulting in a consistent decrease in training loss, indicative of the model’s learning efficacy. Subsequent validation on a separate dataset revealed the model’s robust performance, with classification accuracies ranging from 83.5% to 92.6% across different industry classes. Our model showed a high overall accuracy of 88.23%, coupled with a robust F1 score of 0.88. These results highlight the model’s ability to capture and utilize the nuanced features of text data pertinent to various industries. The model has the capability to harness real-time web data, thereby enabling the utilization of the latest and most up-to-date information affecting to the company’s product portfolio. Based on the model’s performance and its characteristics, we believe that the process of relative valuation can be drastically improved.

The Effect of Company Size, Industry Classification, and Audit Tenure on Audit Report Lag

This study aims to empirically prove the effect of company size, industry classification, and audit tenure on audit report lag with complexity of operations as moderating variable of State or Regional Owned Enterprises (Badan Usaha Milik Negara/Daerah/BUMN/D) companies listed on the Indonesian Stock Exchange in 2020-2022. This study employs quantitative approach involving secondary data of the companies’ annual report published on the official website of the Indonesia Stock Exchange collected through documentation. The samples selected through purposive sampling method include 111 observational data analyzed by multiple linear regression and moderated regression analysis. The results of the study show that company size has a negative effect and industry classification has a positive effect on audit report lag, with complexity of operations being able to moderate the effect of both variables. However, the results of this study cannot prove the existence of an effect between audit tenure and audit report lag, and complexity of operations is unable to moderate the effect of audit tenure on audit report lag.

Sustainability reporting: How consistency and interdependence in financial and managerial accounting enhance eco‐controls

Sustainability reporting requires consistency and interdependency of financial accounting and managerial accounting. Accounting is closely entangled with the eco control (EC) in organisations. The quality of (sustainability) reporting and the disclosures is only as good as the quality and consistency of accounting data captured. Therefore, accounting choices and practises have an important role to play in sustainability reporting. This study explores the mechanism through which accounting informs EC. Through the independent and serial use of two levers, first ‘interdependency’ between financial accounting and management accounting and second, though the use of ‘digitalized information’ as a moderating variable, it is probed as to how EC are shaped. An empirical investigation is undertaken by carefully curating primary data, analysed through partial least squares structural equation modelling technique to inform broader debates in the ‘sustainability reporting’ literature concerning the mechanisms through which EC, are influenced by accounting mechanics, choices and disclosures. Sound EC contributes to sustainability accounting and governance to meet needs of the firms direct and indirect stakeholders without comprising the firm's ability to meet the needs of future stakeholders. The study established that the interdependency between the financial accountants and the management accountants output, mediates the effect of a consistent financial language on EC practises and this effect is independent of the firm size or the industry category. This article brings to the fore, the interplay of FA and MA in informing the environmental controls. The under researched perspective addressed by this study is that EC are highly interdependent with the consistency and comprehensiveness of accounting that informs it which in turn is effected by the cooperation and coordination (interdependency) between FA and MA information providers.

Comparative Analysis of NLP-Based Models for Company Classification

The task of company classification is traditionally performed using established standards, such as the Global Industry Classification Standard (GICS). However, these approaches heavily rely on laborious manual efforts by domain experts, resulting in slow, costly, and vendor-specific assignments. Therefore, we investigate recent natural language processing (NLP) advancements to automate the company classification process. In particular, we employ and evaluate various NLP-based models, including zero-shot learning, One-vs-Rest classification, multi-class classifiers, and ChatGPT-aided classification. We conduct a comprehensive comparison among these models to assess their effectiveness in the company classification task. The evaluation uses the Wharton Research Data Services (WRDS) dataset, consisting of textual descriptions of publicly traded companies. Our findings reveal that the RoBERTa and One-vs-Rest classifiers surpass the other methods, achieving F1 scores of 0.81 and 0.80 on the WRDS dataset, respectively. These results demonstrate that deep learning algorithms offer the potential to automate, standardize, and continuously update classification systems in an efficient and cost-effective way. In addition, we introduce several improvements to the multi-class classification techniques: (1) in the zero-shot methodology, we TF-IDF to enhance sector representation, yielding improved accuracy in comparison to standard zero-shot classifiers; (2) next, we use ChatGPT for dataset generation, revealing potential in scenarios where datasets of company descriptions are lacking; and (3) we also employ K-Fold to reduce noise in the WRDS dataset, followed by conducting experiments to assess the impact of noise reduction on the company classification results.

The Relevance of Sectoral Clustering in Corporate Debt Policy: The Case Study of Slovak Enterprises

The processing and transformation of natural resources into completed and semi-finished products is the primary function of industry in each nation’s economy. There is no denying the significance of industry and sectoral classification of the economy, but the slow development and extension of one industry could have resulted in the advancement of other sectors that are now a part of contemporary communities. Since there are statistically significant differences between various industries, numerous authors are currently investigating the impact of the industry on the financial structure of firms, revealing the industry as a crucial determinant of corporate indebtedness. Thus, the main aim of this study is to determine the debt level of a sample of 4237 enterprises operating in the market in the period of 2018–2021 from various sectors using eight debt indicators, as well as to identify relationships between them, which may help to reveal sectors with homogeneous patterns of indebtedness (using the cluster analysis) and thus understand which sectors are the most stable and independent. The Kruskal–Wallis test is then used to determine if there are statistically significant differences between the calculated ratios related to the economic sector. Based on the results, it can be concluded that the choice of financial structure is significantly influenced by the industry. Financial performance and indebtedness indicators are quantitative statistics used to assess, monitor, and forecast company or sectoral financial health. They act as instruments for business insiders and outsiders to assess a company’s performance, particularly in comparison to competitors, and to pinpoint its strengths and weaknesses, making the outputs of this study important for all types of stakeholders.

The spatial and temporal situation of China's digital technology innovation and its influencing factors.

Digital technology innovation is the core driving force for the high-quality development of the digital economy, and in-depth exploration of the regional distribution pattern and formation mechanism of digital technology innovation in China is conducive to the rational layout and coordinated development of the inter-provincial digital economy. Based on the Reference Relationship Table of the Classification of Core Industries of Digital Economy and the International Patent Classification (2023), the patent authorization data of digital technology from 2012 to 2022 were obtained, and the spatiotemporal situation of China's digital technology innovation was analyzed by using ArcGIS software, Dagum's Gini coefficient, and Moran's I index, and the spatial Dubin panel model was used to explore the influencing factors of digital technology innovation. It is found that: (1) the scale and vitality of China's digital technology innovation have increased significantly, and there are obvious spatial differentiation characteristics, and the innovation level of "eastern coastal-central and western interior" is decreasing, forming a cluster distribution pattern in the Yangtze River Delta region, Beijing, Guangdong, and other places, and the degree of agglomeration is decreasing. (2) The overall regional differences in China's digital technology innovation are large, the differences between the East and the West dominate the interregional differences, and the net differences between regions are the main factors leading to regional differences. (3) There is a significant positive spatial correlation between the scale and vitality of digital technology innovation, which has a significant spatial spillover effect. (4) The results confirm that the level of economic development, digital access, financial scientific and technological support, technology market development level, and R&D intensity have a significant positive impact on the scale and vitality of digital technology innovation; The investment in scientific and technological talents has a significant positive impact on the scale of digital technology innovation, but has no significant impact on the vitality of digital technology innovation.

Job Flows Into and Out of Health Care Before and After the COVID-19 Pandemic

Anecdotal evidence suggests that health care employers have faced increased difficulty recruiting and retaining staff in the wake of the COVID-19 pandemic. Empirical research is needed to understand the magnitude and persistence of these changes, and whether they have disproportionate implications for certain types of workers or regions of the country. To quantify the number of workers exiting from and entering into the health care workforce before and after the pandemic and to examine variations over time and across states and worker demographics. This cohort study used US Census Bureau state unemployment insurance data on job-to-job flows in the continental US to construct state-level quarterly exit and entry rates for the health care industry from January 2018 through December 2021 (Arkansas, Mississippi, and Tennessee were omitted due to missing data). An event study design was used to compute quarterly mean adjusted rates of job exit from and entry into the health care sector as defined by the North American Industry Classification System. Data were examined from January to June 2023. The COVID-19 pandemic. The main outcomes were the mean adjusted health care worker exit and entry rates in each quarter by state and by worker demographics (age, gender, race and ethnicity, and education level). In quarter 1 of 2020, there were approximately 18.8 million people (14.6 million females [77.6%]) working in the health care sector in our sample. The exit rate for health care workers increased at the onset of the pandemic, from a baseline quarterly mean of 5.9 percentage points in 2018 to 8.0 (95% CI, 7.7-8.3) percentage points in quarter 1 of 2020. Exit rates remained higher than baseline levels through quarter 4 of 2021, when the health care exit rate was 7.7 (95% CI, 7.4-7.9) percentage points higher than the 2018 baseline. In quarter 1 of 2020, the increase in health care worker exit rates was dominated by an increase in workers exiting to nonemployment (78% increase compared with baseline); in contrast, by quarter 4 of 2021, the exit rate was dominated by workers exiting to employment in non-health care sectors (38% increase compared with baseline). Entry rates into health care also increased in the postpandemic period, from 6.2 percentage points at baseline to 7.7 percentage points (95% CI, 7.4-7.9 percentage points) in the last quarter of 2021, suggesting increased turnover of health care staff. Compared with prepandemic job flows, the share of workers exiting health care after the pandemic who were female was disproportionately larger, and the shares of workers entering health care who were female or Black was disproportionately smaller. Results of this cohort study suggest a substantial and persistent increase in health care workforce turnover after the pandemic, which may have long-lasting implications for workers' willingness to remain in health care jobs. Policymakers and health care organizations may need to act to prevent further losses of experienced staff.

Industry-sensitive language modeling for business

We introduce BusinessBERT, a new industry-sensitive language model for business applications. The key novelty of our model lies in incorporating industry information to enhance decision-making in business-related natural language processing (NLP) tasks. BusinessBERT extends the Bidirectional Encoder Representations from Transformers (BERT) architecture by embedding industry information during pretraining through two innovative approaches that enable BusinessBert to capture industry-specific terminology: (1) BusinessBERT is trained on business communication corpora totaling 2.23 billion tokens consisting of company website content, MD&A statements and scientific papers in the business domain; (2) we employ industry classification as an additional pretraining objective. Our results suggest that BusinessBERT improves data-driven decision-making by providing superior performance on business-related NLP tasks. Our experiments cover 7 benchmark datasets that include text classification, named entity recognition, sentiment analysis, and question-answering tasks. Additionally, this paper reduces the complexity of using BusinessBERT for other NLP applications by making it freely available as a pretrained language model to the business community. The model, its pretraining corpora and corresponding code snippets are accessible via https://github.com/pnborchert/BusinessBERT.

Adoption of classification and cataloging techniques in the maintenance industry

In this article, the author attempts to implement and adopt the idea of library classification and cataloging tools and spares in the maintenance sector To open unexplored sectors of usage and operations. The maintenance sector is crucial in keeping and maintaining equipment, comprehending the various techniques and procedures to ensure the smooth functioning of assets or equipment. Understand different types of servicing and examine the material, tools, and testers' requirements for various repair and maintenance activities. To understand the level of library method implementation in multiple aspects and study the infusion results with relevant examples. Understand the implementation, modification, and adoption of Dewy Decimal Classification and the applicability of library methods in the maintenance sector.