Increase Operational Efficiency Research Articles

Optimalisasi Dashboard Pemesanan Makanan Online Menggunakan Looker dan JavaScript

Advances in information technology have enabled rapid development in internet-based services, including online food ordering applications. This growth demands efficient data management and analysis systems to improve user experience and operational performance. This research focuses on developing an optimal online food ordering dashboard using Looker and JavaScript. Research methods include needs identification, literature study, needs analysis, design, and implementation. The research results show that the dashboard developed is able to manage and analyze order data effectively, identify trends, predict customer needs, and increase operational efficiency. This dashboard visualizes important information such as number of orders based on age, gender, income, as well as customer behavior analysis. In doing so, service providers can gain better insight into consumer behavior and ordering trends, supporting more informed and strategic decision making. The results of this study contribute to the literature on the use of data visualization technologies in the online food service sector.

Effect of information quality on supply chain performance through information sharing: Evidence from construction industry in Indonesia

The construction industry is an economic sector that requires strong collaboration between various parties in the supply chain. This study aims to analyze the effect of information quality on supply chain performance through information sharing in the construction industry. This study uses a quantitative approach by collecting data through surveys of developers, consultants, contractors and suppliers involved in the process of building office and apartment buildings in Jakarta. The collected data was analyzed by using Structural Equation Modeling – Partial Least Squares (SEM-PLS 4.0). Out of 100 respondents, 22% were developers, 19% were consultants, 35% were contractors, and 24% were suppliers. The findings show that there is a positive effect of information quality on information sharing. In addition, information sharing practices are also proven to mediate the effect of information quality on supply chain performance. These findings emphasize the importance of information quality in improving supply chain performance through information sharing. These findings can also contribute to the literature on supply chain management, with a particular focus on the construction industry context. The practical implication of this research is that with quality information quality and effective information sharing, companies can carry out better planning and scheduling, reduce uncertainty, and increase operational efficiency.

Integration of Artificial Intelligence (AI) within SmartLynx Airlines to Increase Operational Efficiency

This article investigates the current and potential future applications of AI in the SmartLynx airlines. The airline industry is a complex system that requires efficient and effective management of various operations, including revenue management, flight operations, customer service, and baggage handling. The use of Artificial Intelligence (AI) has now emerged as a promising solution to address the challenges faced by many other airlines. The aim of this research is to analyze SmartLynx Airlines' operational efficiency in flight operations segments of the Latvian aviation industry and to develop recommendations for improving the current operational strategy for the company through the integration of AI-supported tools with conventional flight operation tools. Research tasks are: (1) to conceptualize theoretical aspects of the use of AI in the aviation industry; (2) to perform empirical research regarding current operational issues and study the use of AI in SmartLynx Airlines to improve these issues; (3) to work out recommendations. The current research employs the quantitative approach – a survey of SmartLynx employees of various departments.

Strategy to Increase the Competitiveness of MSME Businesses in the Era of Digitalization for Rural Communities in the Medan Region of North Sumatra

The era of digitalization presents opportunities and challenges for Micro, Small and Medium Enterprises (MSMEs) in rural areas. To increase competitiveness in this era, an appropriate and comprehensive strategy is needed. This abstract discusses strategies that can be implemented to increase the competitiveness of MSMEs in rural areas, with a focus on aspects of using digital technology to reach wider markets, increase operational efficiency, and build brand image. This can be done through the use of social media, e-commerce and other digital platforms to market products and services. Use of online accounting and bookkeeping applications to manage finances more effectively. The research method used is a qualitative method by collecting interviews, observing challenges and opportunities in the era of digitalization in the North Sumatra region. By using digital technology, increasing human resource capacity, strengthening networks and collaboration, and getting conducive infrastructure and policy support, MSMEs in rural areas can develop and contribute to the national economy.

Bridging Human Expertise with Machine Learning and GIS for Mine Type Prediction and Classification

This paper introduces an intelligent model that combines military expertise with the latest advancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes, by predicting mined areas and classifying them by mine type, difficulty and priority of clearance. The model is based on direct input and validation from field decision-makers for their practical applicability and effectiveness, and accurate historical demining data extracted from military databases. With a survey polling the inputs of demining experts, 95% of the responses came with an affirmation of the potential of the model to reduce threats and increase operational efficiency. It includes military-specific factors that factor in the proximity to strategic locations as well as environmental variables like vegetation cover and terrain resolution. With Gradient Boosting algorithms such as XGBoost and LightGBM, the accuracy rate is almost 97%. Such precision levels further enhance threat assessment, better allocation of resources, and around a 30% reduction in the cost and time of conducting demining operations, signifying a strong synergy of human expertise with algorithmic precision for maximal safety and effectiveness in demining.

Optimizing waste management strategies through artificial intelligence and machine learning - An economic and environmental impact study

Applying artificial intelligence (AI) and machine learning (ML) techniques to optimize waste management strategies, focusing on enhancing economic efficiency and reducing environmental impact, is vital. The study utilized ML models to analyze and forecast waste generation trends, assess the viability of various waste management methods, and develop optimization models for resource allocation and operational efficiency. The research employs the World Bank’s comprehensive waste management dataset. After rigorous data preprocessing, including cleaning and feature selection, a variety of ML techniques, such as regression models, classification algorithms like Support Vector Machines (SVM), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and optimization algorithms, including linear programming, are applied. Unlike other research, this study achieved 85 % accuracy on predictive analytics models for forecasting waste generation trends, primarily attributed to integrating more diverse data sets, including socio-economic factors. Also, the optimization resource allocation achieved a 15 % increase in operational efficiency. These findings provide significant insights for policymakers and urban planners, suggesting that integrating ML in waste management can lead to more sustainable and cost-effective practices. This paper demonstrates the transformative potential of ML in optimizing waste management strategies, offering a pathway towards more sustainable and economically viable waste management solutions globally.

Internal Control and Administrative Management of Agricultural Companies in the Majes Distric

Abstract: The problem addressed in this study is the relationship between internal control and administrative management in agricultural companies in the Majes district in 2022. The objective was to determine how these factors correlate and affect organizational performance. The research was conducted using a non-experimental and descriptive correlational design. Data were collected from 28 companies using validated surveys and questionnaires, processed with SPSS v28 software. The results showed that 60.7% of the companies had high levels of internal control and administrative management, while 39.3% did not reach an optimal level. The Pearson correlation test yielded a value of 0.893, indicating a significant and direct relationship between the two variables. This suggests that better internal control management is associated with greater efficiency in administrative management. It is concluded that strengthening internal control can significantly improve administrative management, which is crucial for the sustainability and competitiveness of agricultural companies in Majes. Implementing effective internal control measures can reduce risks and increase operational and administrative efficiency.

Integration of dental therapists in safety net practice increases access to oral health care in Minnesota.

This study evaluates the impact of introducing dental therapists (DTs) into Children's Dental Services (CDS), a large non-profit organization in Minnesota. The aim is to assess the effect of DTs in improving access and reducing dental care costs in Minnesota by analyzing the trends in dental care delivery and procedures performed by CDS dentists and therapists. Using 2009 to 2021 data from CDS, the study compares trends in patient volume, types of procedures, salary data and payments by dentists, DTs, and registered dental hygienists (RDH). Return on investment (ROI) trends are calculated using salary and revenues for each provider type. After introducing DTs at CDS and implementing mobile clinics, the number of patients served and volume increased steadily, demonstrating increased access. DTs provided an increasing proportion of fluorides, sealants, and extractions through 2020. Interestingly 2021, there was a decrease for DTs, possibly due to Covid related workforce shortages. ROI analysis showed that DTs' ROI dramatically rose, eventually surpassing dentists, while RDHs maintained a constant ROI. Dentists' ROI also initially increased after adopting DTs in the practice. Integrating DTs at CDS improved access by expanding mobile clinics, increasing patient volume, and redistributing procedures, while demonstrating a positive return on investment. Results suggest that adopting the DT model may be a promising practice for other organizations seeking to improve access to dental care, increase operational efficiency, and boost the dental care team's ROI.

VOSviewer Analysis of Studies on Software Selection in the Logistics Sector

Abstract Outsourcing in logistics is an important step to reduce costs and increase operational efficiency. In this sense, it is possible to come across outsourcing in many areas of logistics processes. One of the outsourcing steps is the decision to select the software. Choosing the right software can affect the market share of the organization and the implementation time, effort, and cost. The findings of this research will help the marketing and sales teams of software product companies to develop key points and also enable end users to make informed decisions in selecting software packages for the organization. In this study, the studies in the literature were examined through the Scopus database, and co-citation analysis was performed using the VOSviewer program. As a result of the analysis, the articles analyzed were divided into four clusters and explained.

A Multi-UAV cooperative mission planning method based on SA-WOA algorithm for three-dimensional space atmospheric environment detection

Abstract In the application of rotorcraft atmospheric environment detection, to reflect the distribution of atmospheric pollutants more realistically and completely, the sampling points must be spread throughout the entire three-dimensional space, and the cooperation of multiple unmanned aerial vehicles (multi-UAVs) can ensure real-time performance and increase operational efficiency. In view of the problem of coordinated detection by multi-UAVs, the region division and global coverage path planning of the stereo space to be detected are studied. A whale optimization algorithm based on the simulated annealing-whale optimization algorithm (SA-WOA) is proposed, which introduces adaptive weights with the Levy flight mechanism, improves the metropolis criterion, and introduces an adaptive tempering mechanism in the SA stage. Path smoothing is subsequently performed with the help of nonuniform rational B-spline (NURBS) curves. The comparison of algorithms using the eil76 dataset shows that the path length planned by the SA-WOA algorithm in this paper is 10.15% shorter than that of the WOA algorithm, 13.25% shorter than the SA planning result, and only 0.95% difference from the optimal path length in the dataset. From the perspective of planning time, its speed is similar to WOA, with a relative speed increase of 27.15% compared to SA, proving that the algorithm proposed in this paper has good planning performance. A hardware system platform is designed and built, and environmental gas measurement experiments were conducted. The experimental results indicate that the multi-UAV collaborative environment detection task planning method proposed in this paper has certain practical value in the field of atmospheric environment detection.

Application of a supporting catheter in surgery for forearm arteriovenous fistula construction.

This study explored the feasibility of a supporting catheter combined with modified end-to-side anastomosis in the operation of radio-cephalic arteriovenous fistula (RC-AVF) and evaluated the clinical application value of this technique. Sixty patients underwent RC-AVF operations in our hospital from January 2022 to June 2022. All the patients were treated with modified end-to-side AVF anastomosis and divided into the control group or the test group depending on whether a supporting catheter was applied. The clinical data of 60 cases were analysed retrospectively. Intraoperative related indices, the first time the fistula was used, the success rate of first puncture, the blood flow of first dialysis, the maturity condition of fistula, the size of anastomosis, the diameter of radial artery and drainage vein, the blood flow of brachial artery 8 weeks after operation and the incidence of complications within 6 months after operation were compared between the two groups. Compared with that in the control group, the time spent on the vascular anastomosis in the test group was significantly shortened (p<0.05). The blood flow of the first dialysis, the size of the anastomosis, the diameter of the drainage vein, the blood flow of the brachial artery 8 weeks after the operation and the incidence of complications within 6 months after operation were significantly different between the two groups (p<0.05). In the RC-AVF operation, using a supporting catheter can not only increase operation efficiency by reducing surgical injury and difficulty of vascular anastomosis, but also improve postoperative prognosis. RC-AVF is worth promoting in clinical practice.

Pengendalian Biaya Tenaga Kerja dalam Organisasi Sektor Publik: Studi tentang Pengelolaan Pegawai Tidak Tetap (PTT) dan Alternatif Outsourcing

This article discusses the management of labor costs in the public sector with a focus on the management of non-permanent employees (PTT) and outsourcing alternatives. This research highlights problems in personnel management, especially employee expenditure costs that must be borne by government agencies. This study reviews cost control systems, labor costs, and methods for managing honorary staff costs in government. The findings show that outsourcing is more efficient than recruiting PTT or temporary workers as a solution to control labor costs. Additionally, this paper recommends Activity Based Costing (ABC) method for public sector organizations to provide valuable information related to detailed cost data. This research shows that decentralizing recruitment in local governments can improve efficiency, effectiveness and fairness, but can also lead to corruption and disparities in the distribution of human resources. Cost control, outsourcing, and the use of Activity-Based Costing (ABC) can help public sector organizations manage costs and increase operational efficiency. To achieve better governance, operational efficiency and effective cost control, public sector organizations need to consider the various strategies and methods described in this research.

Risk-based performance assessment from fully manual to human-robot teaming in pressurized tank inspection operations

The use of robotics in the process industry has shown a stable, increasing trend, according to survey reports, reducing the exposure of operators to critical operations, mainly during maintenance. Moving from full manual (FM) to human-robot teaming (HRT) operations is expected to reduce the risks for operators and increase operational efficiency. The methods used to assess system performances require an adaptation. Traditional risk assessment techniques are no longer adapted to analyze the new way of working; the previous methods proposed for HRT systems have been criticized for their fragmentation, complexity, and lack of validation.This study proposed an integrated framework, including the qualitative and quantitative stages, to investigate the risk-based system performance and compare the full manual and human-robot teaming scenarios in pressurized tank inspection operations. The outputs demonstrated that the integrated framework worked for FM and HRT system performance assessment, considering multiple element types and their interdependencies, generating knowledge that can be exploited to reduce the system's risk and choose among different operational alternatives.

Optimizing the Security of Letter of Credit Transactions: Application of Blockchain Technology in Reducing the Risk of Fraud in Banking

Letter of Credit (LC) transactions play a vital role in facilitating international trade, but are vulnerable to various forms of fraud such as forgery of documents and manipulation of information. In the banking context, ensuring the security of LC transactions is an urgent challenge. This research aims to investigate the potential of applying blockchain technology in improving the security of LC transactions and reducing the risk of fraud in the banking sector. This research is included in descriptive quantitative research. This research was conducted at Banks in DKI Jakarta. The population in this research are customers who use Letter of Credit transactions. The sampling technique in this research is purposive sampling so that in this research the research sample was 100 customers who used letter of credit transactions. The data analysis technique in this research uses Partial Least Square (PLS). The research results show that blockchain technology has great potential to increase the security of LC transactions through aspects such as high data security, transparency and auditability, as well as automation through smart contracts. With proper implementation, blockchain technology can help reduce the risk of fraud, increase operational efficiency, and build trust in international trade. This study contributes to the understanding of the potential application of blockchain technology in the global financial context and highlights the importance of cross-sector collaboration to address transaction security challenges.

Reshaping 3PL Operations: Machine Learning Approaches to Mitigate and Manage Damage Parameters

In the third-party logistics (3PL) environment, it is very important to reduce damage parameters, increase operational efficiency and reduce costs. This study aims to develop strategies for reshaping 3P operations by analyzing the parameters involved in damage control with machine learning. The logistics sector is gradually growing in the world and the potential of the sector is better understood over time. Damage to products in the logistics sector, especially during transportation and storage, not only causes financial losses but also affects customer productivity and operational efficiency. With the use of artificial intelligence techniques, it is possible to determine consumer expectations, predict damage losses, and develop innovative strategies by applying machine learning algorithms. At the same time, options such as driverless vehicles, robots used in storage and shelves, and the easy use of big data within the system, which have emerged with artificial intelligence, minimize errors in the logistics sector. Thanks to the use of artificial intelligence in the logistics sector, businesses are more efficient. This study includes an estimation study in the field of error parameters for the logistics service sector with machine learning methods. In the application, real data of a 3PL company for the last 5 years is used. For the success of 3PL companies, warehousing and undamaged delivery of products are of great importance. The fewer damaged products they send, the more they increase their value. The company examined in the study kept its damage data and wanted it to be analyzed so that it could take precautions accordingly and follow a more profitable path. For this reason, the study focuses on data on errors and damages. This study shows what kind of problems can occur in such a company and how the 3PL company can evaluate the problems to increase customer service quality and cost efficiency.

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.

Sustainable Transportation in The Brazilian Agroindustrial Supply Chain: A Literature Review

Objective: This article aims to discuss the importance of incorporating sustainable transportation practices into the supply chains of the Brazilian agro-industry, highlighting their potential to reduce costs, increase operational efficiency, and enhance companies' reputation in an increasingly sustainability-focused global context. Theoretical framework: Agribusiness plays a vital role in the Brazilian economy, converting agricultural raw materials into higher value-added products and driving economic gains and job creation. However, transportation in this chain has a significant environmental impact, with Brazil heavily reliant on road transport, contributing to carbon emissions. Companies from various sectors are adopting sustainable transportation practices to address these challenges, recognizing the growing importance of sustainability as a business strategy to ensure competitive advantage in the global market. Method: The literature review was conducted in five detailed stages: formulation of the research question, identification and careful selection of relevant studies, analysis and synthesis of data, and description and dissemination of results. Scientific articles, book chapters, and technical reports published between 2015 and 2023 were considered, ensuring a solid and updated basis for analysis. Results and conclusion: The analysis of 102 studies highlighted the relevance of implementing sustainable transportation practices, such as the use of electric vehicles and bioenergy, in reducing greenhouse gas emissions and optimizing logistics costs. Intermodality also emerged as an effective strategy adopted to mitigate environmental impacts and strengthen competitiveness. However, there is a continuous need for future research to understand the nuances of sustainable transportation strategies in different segments and regions of the Brazilian agro-industrial supply chain. Research implications: This study emphasizes the urgency and importance of transitioning to sustainable transportation practices in the Brazilian agro-industry, highlighting the need for government intervention and investment in research to develop approaches tailored to the specific challenges of each sector and region. Additionally, it underscores the importance of public policies that incentivize and promote sustainability in supply chains. Originality/value: It significantly contributes to understanding the complexities of sustainable transportation in the Brazilian agro-industry, providing insights for companies and policymakers interested in promoting sustainability in supply chains. By filling knowledge gaps and presenting innovative perspectives, this study stands out for its original contribution and practical value.

A Survey Paper on Automation of Attendance System through Facial Recognition

Abstract—In recent times almost, everything around us is being automated in some way just to make life easier for us. With the use of machine learning and deep learning we can allow non- living objects to program themselves to self-learn from the available data and perfect itself in the process. The process of recording attendance in educational institutions and workplaces has traditionally relied on manual methods, which are time- consuming, error-prone, and lack the level of security and accuracy desired in today’s digital age. This research presents a technological solution to these challenges. This technology offers a range of compelling advantages. It eliminates the need for manual attendance tracking, reducing the potential for errors and administrative workload. Moreover, it enhances security by ensuring that only authorized individuals can mark their attendance. Real-time data is available for monitoring and generating attendance reports, offering valuable insights into punctuality and attendance patterns. This research provides a comprehensive overview of the system’s architecture, algorithms, and the underlying technology, along with a discus- sion of the ethical and privacy considerations associated with face recognition. By implementing this innovative technology, educa- tional institutions and organizations can streamline attendance management, increase operational efficiency, and contribute to a more secure and productive environment. Keywords: Face recognition, LBPH, Haar-cascade, face detection, Viola n Jones, openCV, Adaboost

Role of Digital Transformation and Innovation in Enhancing the Competitiveness and Sustainability of Hospitality and Tourism Businesses in South Africa

Purpose: The study sought to analyze the role of digital transformation and innovation in enhancing the competitiveness and sustainability of hospitality and tourism businesses&#x0D; Methodology: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.&#x0D; Findings: Digital transformation and innovation are essential drivers for enhancing the competitiveness and sustainability of hospitality and tourism businesses in South Africa. By embracing digital technologies, businesses can improve customer experiences, increase operational efficiency, make data-driven decisions, and adapt to changing market dynamics, ultimately securing their position in the evolving industry landscape.&#x0D; Unique Contribution to Theory, Practice and Policy: Resource-Based View (RBV), Dynamic capabilities theory &amp; Disruptive innovation theory may be used to anchor future studies in the role of digital transformation and innovation in enhancing the competitiveness and sustainability of hospitality and tourism businesses. Encourage research that explores the intersection of digital transformation, innovation, and competitiveness within the hospitality and tourism sector. Promote the adoption of digital technologies and innovation strategies among hospitality and tourism businesses.

GIS-based service network optimisation for location of postal delivery system

ABSTRACT Public postal organisations aim to determine the optimal locations for postal delivery service branches to enhance service quality, increase operational efficiency, reduce costs, and optimise resource utilisation. In this paper, we consider how to enhance the process of identifying suitable sites for these branches in Istanbul, Turkey, through a case study that evaluates land-use categories – and their influence on demand for postal services – within different districts so as to pinpoint the most appropriate locations for service branches. We employ Kernel Density Estimation (KDE) analyses in conjunction with Geographic Information Systems (GIS) to optimise the service network. This optimisation aids in deciding whether to close underperforming branches or to establish new branches in neighbourhoods with higher demand. Turkish postal delivery system statistics serve as the basis for our case study. Consequently, this study provides valuable insights into the selection of postal service branch locations and offers practical implications for improving branch accessibility in relation to spatial variability in demand. Such applied insights can be crucial for administrators and policymakers when making informed decisions regarding long-term location planning and initiatives across various sectors.