Operational Data Research Articles

Metadata schema for virtual building models in digital twins: VB schema implemented in GPT-based applications

A virtual building model (VBM) is a virtual entity that represents the physical behavior of a target building mathematically within a digital twin environment. The creation and synchronization of a VBM are achieved by utilizing various interrelated virtual sub-models, including behavior, correction, and distance models. To achieve continuous digital twinning, it is essential to manage the VBM with virtual sub-models. However, these metadata schemas have limitations in describing VBMs representing operational building behaviors within the concept of building digital twins (DTs). Therefore, this study proposes a novel metadata schema, termed the virtual building model metadata schema (VB schema), to represent and manage VBMs in DT-built environments. The VB schema is established according to the mathematical and semantic ontology of the in-situ modeling and calibration approach for constructing and correcting virtual models during building operations, and it is linked to physical entities, data, and applications within DTs. Specifically, it involves: (1) determining classes for operational data and virtual models; (2) establishing relationships for interactions between model and data entities, between model classes, between model and physical entities, and between model and applications; (3) defining properties for each class of models; and (4) extending into the exiting metadata schema of Brick. To demonstrate the proposed VB schema, a virtual model describing supply pressure behaviors in a central heating system was developed and represented using the VB schema for DT-enabled building operations. Additionally, the VB schema was utilized for implementing generative pre-trained transformer (GPT)-based DT applications, which highlights its benefits in enhancing ontology comprehension of DTs in the context of VBMs, improving autonomous problem-solving capabilities in real building systems, and providing better interpretation of application results compared to cases where only the Brick schema was used. The VB schema is expected to enable continuous and autonomous in-situ management of VBMs for intelligent building services within the DT.

Fault Detection Technology for Ultrapure Water Plant using Virtual Sensors for Pressure Control Valve and Indicators in Return Line

Objectives:This study aims to propose a technology that utilizes virtual sensors to quickly and accurately diagnose faults in valves and measurement devices that may occur in the return line of an ultrapure water plant.Methods:The empirical equations were derived, and the error range was calculated using operational data collected from three pieces of equipment (pressure control valve, pressure transmitter, and flow transmitter) in the recovery system of the ultrapure water process. Based on this, a virtual sensor is established.Results and Discussion:It was confirmed that the utilization of 24-hour data collected from the operation of the ultrapure water plant was enough to establish virtual sensors and could also estimate monthly data with high accuracy. In Phase 1, the fault diagnosis technology was found to estimate values more accurately and had a meaningful margin of error compared to Phase 2.Conclusion:It was possible to develop a fault detection technology using the virtual sensor derived from the empirical equation. The technology showed high performance in detecting anomalies in the flow meter and pressure control valve opening but had limitations in estimating consistently pressure values. Additionally, considering that there was no issue in estimating monthly data using the equations derived using 24 hours operational data. The fault detection algorithm could be used continuously if the empirical equations are updated by collecting a day's data when operating conditions change.

직장 내 갑질과 이직의도와의 영향 관계에서 회복탄력성의 조절효과 연구 - 호텔 F&B 종사원을 중심으로

Objective : This study tested the moderating effect of resilience in the relationship between workplace incivility and turnover intention by targeting hotel F&B employees. Methods : This study conducted a survey using an online questionnaire from June 1 to June 30, 2024. Then, this study conducted an empirical analysis using the SPSS statistical program on a total of 252 subjects. Results : The analysis results are as follows:First, workplace incivility was found to have a significant effect on turnover intention. Second, resilience (self-regulation ability, interpersonal ability, and positivity) had a moderating effect in the relationship between workplace incivility and turnover intention. Implications : Based on these results, this study attempted to provide basic data for efficient human resource management operation in hotel companies.

Successful pilot implementation of mailing lethal means safety devices to veterans calling the Veterans Crisis Line

IntroductionVeterans are at greater risk for suicide than non-veterans; veterans who call the Veterans Crisis Line are at even higher risk. Firearms and poisoning are among the most common methods by which people die by suicide in the United States and access to those lethal means are risk factors for suicide. The United States Department of Veterans Affairs’ Veterans Crisis Line conducted a six-month pilot to enhance lethal means safety counseling conversations by mailing lethal means safety devices (cable gun locks and/or medication takeback envelopes) to veteran callers.Materials and methodsVeterans Crisis Line responders were selected based on quality assurance ratings, received training, and passed a knowledge check prior to participating. Veterans were eligible if they were calling for themselves and had access to firearms and/or surplus medications. The pilot was assessed using operational data and qualitative interviews with responders to assess their experience, barriers and facilitators, and suggestions for improvement.ResultsResponders documented 8,323 calls from 7,005 unique phone numbers; 10.8% were eligible for cable gun locks and 8.7% were eligible for medication takeback envelopes. Responders offered cable gun locks to 652 veterans and medication takeback envelopes to 522 veterans. A total of 465 cable gun locks and 567 medication takeback envelopes were mailed to 307 veterans. Operationally, there was little impact of the pilot on call handle time. Five responders participated in qualitative interviews. They reported feeling comfortable incorporating mailing devices into their work and reported that response from veterans was positive. Their most frequent suggestion for improvement was additional training.DiscussionResults demonstrate that mailing these devices to veterans was feasible and acceptable. Call handle time results show that the Veterans Crisis Line would not need additional personnel to manage changes in call handle time associated with offering devices to all veteran callers. Full implementation of this program will require updates to procedures and policies, training, documentation system changes, additional logistical support for mailing, and a plan for ongoing evaluation.

Artificial Intelligence Assisted Surgical Scene Recognition: A Comparative Study Amongst Healthcare Professionals.

This study aimed to compare the ability of a deep-learning platform (the MACSSwin-T model) with healthcare professionals in detecting cerebral aneurysms from operative videos. Secondly, we aimed to compare the neurosurgical team's ability to detect cerebral aneurysms with and without AI-assistance. Modern microscopic surgery enables the capture of operative video data on an unforeseen scale. Advances in computer vision, a branch of artificial intelligence (AI), have enabled automated analysis of operative video. These advances are likely to benefit clinicians, healthcare systems, and patients alike, yet such benefits are yet to be realised. In a cross-sectional comparative study, neurosurgeons, anaesthetists, and operating room (OR) nurses, all at varying stages of training and experience, reviewed still frames of aneurysm clipping operations and labelled frames as "aneurysm not in frame" or "aneurysm in frame". Frames then underwent analysis by the AI platform. A second round of data collection was performed whereby the neurosurgical team had AI-assistance. Accuracy of aneurysm detection was calculated for human only, AI only, and AI-assisted human groups. 5,154 individual frame reviews were collated from 338 healthcare professionals. Healthcare professionals correctly labelled 70% of frames without AI assistance, compared to 78% with AI-assistance (OR 1.77, P<0.001). Neurosurgical Attendings showed the greatest improvement, from 77% to 92% correct predictions with AI-assistance (OR 4.24, P=0.003). AI-assisted human performance surpassed both human and AI alone. Notably, across healthcare professionals surveyed, frame accuracy improved across all subspecialties and experience levels, particularly among the most experienced healthcare professionals. These results challenge the prevailing notion that AI primarily benefits junior clinicians, highlighting its crucial role throughout the surgical hierarchy as an essential component of modern surgical practice.

Multimodal Operation Data Mining for Grid Operation Violation Risk Prediction

With the continuous expansion of the power grid, the issue of operational safety has attracted increasing attention. In power grid operation control, unauthorized operations are one of the primary causes of personal accidents. Therefore, preventing and monitoring unauthorized actions by power grid operators is of critical importance. First, multimodal violation data are integrated through information systems, such as the power grid management platform, to construct a historical case database. Next, word vectors for three types of operation-related factors are generated using natural language processing techniques, and key vectors are selected based on generalized correlation coefficients using mutual information, enabling effective dimensionality reduction. Independent component analysis is then employed for feature extraction and further dimensionality reduction, allowing for the effective characterization of operational scenarios. For each historical case, a risk score is derived from a violation risk prediction model constructed using the Random Forests (RF) algorithm. When a high-risk score is identified, the K-Nearest Neighbor (KNN) algorithm is applied to locate similar scenarios in the historical case database where violations may have occurred. Real-time violation risk assessment is performed for each operation, providing early warnings to operators, thereby reducing the likelihood of violations, and enhancing the safety of power grid operations.

New evidence on patella resurfacing in modern total knee arthroplasty for all inflammatory arthritis in a mixed Asian population

BackgroundTraditional teaching advocates for routine patella resurfacing (PR) during total knee arthroplasty (TKA) in patients with inflammatory arthritis. However, evidence on this topic remains limited in the Asian population. This study aims to evaluate the postoperative outcomes and complication of patella resurfacing (PR) during primary total knee arthroplasty (TKA) in Asian patients with inflammatory arthritis. MethodsA retrospective analysis was conducted using registry data from our institution. Patients with inflammatory arthritis who underwent primary TKA from August 2017 to December 2021 were included in the study. Patients were divided into two groups - patella resurfaced (PR, n = 25) and non-resurfaced patella (PNR, n = 31) groups. Demographics, operative data, patient reported outcome measures, and complications were compared. ResultsPreoperative range of motion (ROM) (PR:95.3 ± 23.6° vs PNR:105 ± 19.5°, p = 0.106), Knee Society Scoring System Knee score (KS-KS) (PR:44.6 ± 17.4 vs PNR:49.3 ± 17.7, p = 0.331) and Knee Society Function Score (KS-FS) (PR:40.9 ± 27.3 vs PNR:47.7 ± 27.0, p = 0.325) and Oxford Knee Score (OKS) (PR:25.0 ± 7.8 vs PNR:23.3 ± 10.3, p = 0.525) were similar in both groups. Outcomes between PR and PNR groups at the one-year mark in terms of ROM (PR:112.7 ± 18.1 vs PNR:114.6 ± 16.5, p = 0.455), OKS (PR:41.5 ± 3.9 vs PNR:41 ± 4.7, p = 0.954), KS-KS (PR:84.5 ± 13.2 vs PNR:89.2 ± 9.3, p = 0.095) and KS-FS (PR:73.6 ± 18.3 vs PNR:78.7 ± 19.3, p = 0.173) were also similar. Intraoperative complication (PR:0/25 vs PNR: 3/31 (9.6 %), p = 0.245) and re-operation rates (PR: 1/25 (4 %) vs PNR: 1/31 (3.2 %), p = 0.877) were also similar in both groups. ConclusionThis study conducted on a mixed Asian population with inflammatory arthritis demonstrated that the one-year postoperative ROM and functional outcomes, complication, and re-operation rates between PR and PNR groups were similar. Hence, routine patella resurfacing on all Asian patients with inflammatory arthritis undergoing TKA may not be necessary.

Numerical simulation and intelligent prediction of a 500 t/d municipal solid waste incinerator

After classifying municipal solid waste (MSW), understanding how changes in its characteristics affect the incineration process becomes essential. Optimizing and adjusting operating parameters is crucial to ensure the smooth operation of MSW incinerators. In this study, we developed a three-dimensional numerical simulation model for a 500 t/d MSW incinerator, based on a two-fluid model that accounts for grate motion. We examined the effects of load and moisture on the combustion process. Using the results from the numerical simulation and on-site operational data, we established a machine learning model to predict incinerator conditions based on load, waste particle size, and moisture content. The average relative errors for the training and test sets were approximately 0.33 % and 0.44 %, respectively. Finally, we compared the influence of different operating parameters on flue gas temperature. Our numerical simulation model and intelligent prediction method offer valuable guidance for optimizing existing MSW incinerators and designing new ones.

The Possible Factors that Influence the Stability of Binary Star System

Binary star systems are a crucial subject in astrophysics, as they provide insights into stellar formation, evolution, and dynamics. Despite extensive research, there remains a gap in understanding the factors influencing the longterm stability of such systems. This paper discusses the factors that may affect the stability of binary star systems by analyzing and exploring the physical laws and formulas of the operation of binary star systems and using the operation data of specific binary star systems as evidence. It can be concluded that the factors affecting the stability of a binary star system may be as follows: 1. Mass ratio of two stars, 2. Stellar spacing, 3. Orbital eccentricity, 4. Influence of stellar evolution stage, 5. The influence of external bodies. Future research should further explore the combined effects of these factors in more complex systems to better understand binary star stability in various astrophysical environments. Additionally, understanding how these factors interact in multi-body systems could provide deeper insights into the evolution of complex stellar systems.

The Supervising Role of the Apron Movement Control (AMC) Unit on Aviation Security and Safety at Pattimura Ambon International Airport

Security and safety are crucial factors in aviation activities, considering the sensitivity of air transportation that requires assurance of safety at all times. The success of the AMC unit's tasks and responsibilities is measured by their ability to optimize supervision, thus creating safe and secure conditions, in accordance with applicable regulations. This study aims to evaluate the role of the AMC unit in ensuring security and safety in the airside area of Pattimura Ambon International Airport and to identify obstacles in supervision in the area. The approach used in this study is descriptive qualitative. The data collected consists of primary and secondary data. Primary data is obtained through observation and interviews, while secondary data comes from literature studies and photographs related to the problem being studied. Data analysis techniques involve data reduction, data presentation, and drawing conclusions. To ensure the validity of the data, triangulation techniques are used. The research was conducted in March 2024. The results of the study indicate that the AMC unit at Pattimura Ambon International Airport has carried out supervision well in accordance with the standard operating procedures PM-IK (Quality Procedures and Work Instructions). Supervision covers four components of the work area: Airport Operation Control Center (AOCC), Data Entry, Field Inspection, and Aviobridge Operations. Supervision is carried out through inspections every two hours for FOD and fuel spills, supervision of aircraft movements and engine running up/idle, GSE vehicle speed, and worker compliance with the use of Personal Protective Equipment. The AMC unit strives to implement the 3S+1C principle (Safety, Security, Service, and Compliance). The obstacles faced include a shortage of personnel, which causes an increase in workload and less than optimal supervision, as well as violations by service users or workers in the airside area, which have the potential to cause accidents.

Comparative insights into type non-A non-B vs type B acute aortic syndromes and their blood pressure management: a monocentric, retrospective, 11-year study

Abstract Background Acute aortic syndromes (AAS) are classified either as type A, if the ascending aorta is involved, or as type B, if only the descending aorta is; AAS affecting the aortic arch, alone or in combination with the descending aorta, have recently been classified as type non-A-non-B (NANB). Few and discordant data are available about the differences between type B and type NANB AAS, especially in terms of presentation, management and prognosis. Little evidence also exists about the effects of blood pressure (BP) management, peaks and variability on in-hospital outcomes in such context. In particular, the largest international AAS registry suggests a comparable course and prognosis, while a recent meta-analysis indicates a poorer clinical outcome with higher rate of complications, need of invasive treatment and mortality in the case of type NANB AAS. Methods We conducted a monocentric, observational, analytical, retrospective registry study including all patients ≥ 18 years old admitted to our tertiary referral hospital with a diagnosis of type B or type NANB AAS from January 2011 to December 2021. Epidemiological, clinical, operative and outcome data were analyzed by comparing type B and type NANB AAS patients. We also focused on systolic and diastolic BP, in terms of both peak values as well as BP variability (BPV); BPV was defined as the difference between the peak and the through value on a certain day. Blood pressure variables were analyzed in relationship with mortality and the failure of an initial conservative management. Results 274 type B and 44 type NANB AAS patients were enrolled. The only significant differences between the two groups were a higher rate of pre-operative complications in type NANB patients (p=0.002) resulting in a higher rate of invasive management (p=0.005), particularly as failure of an initial conservative strategy (p=0.018). However, comparable rates of post-operative complications and in-hospital mortality (overall 7%) were observed. In the overall population, lower limb ischemia, ST segment depression, and post-operative visceral ischemia were associated with higher in-hospital mortality. Peri-aortic effusion was independently associated with failure of conservative management, whereas diagnosis of intramural hematoma (as opposed to other subtypes of AAS) appeared to be protective. Among all BP variables (including anti-hypertensive treatment intensity and daily systolic or diastolic BP peaks and variability), only peak systolic BP on day 5 was found to be non-independently associated with a higher risk of in-hospital mortality and failure of an initially conservative strategy. Conclusions In our study type NANB AAS were more often complicated and treated invasively than type B AAS, while having the same rates of post-operative complications and overall mortality. BP peaks had only a minor effect on outcomes, while BPV and BP management intensity did not have any significant impact.OutcomesBlood Pressure

Pengaruh Artificial Intelligence dalam Pembuatan Laporan Keuangan

The presence of Artificial Intelligence (AI) has a significant impact on the preparation of financial reports, especially in terms of efficiency and accuracy. This article aims to examine the influence of AI on the process of preparing financial reports that were previously carried out conventionally and the challenges faced by companies in adopting it. This study uses a literature study method to identify trends and findings related to the use of AI in accounting. The results show that AI is able to improve operational efficiency, data analysis, and security, although there are challenges in its implementation, such as human resource adaptation and data security.

Late outcomes of Fontan operation from the EUROFONTAN experience

Abstract Background Fifty years after the first Fontan operation, the persisting challenges and evolving landscape of post-Fontan complications are explored in the EUROFONTAN experience. Aim of the study is the evaluation of long term outcomes of a large cohort of Fontan patients, focusing on age at Fontan and onset of Fontan-associated liver disease (FALD). Methods A retrospective clinical study across 21 european congenital cardiac centers across Europe. Clinical and operative data of patients who underwent Fontan operation between January 1990 and December 2022 were gathered through a RedCap online database. Results 2606 patients were included in this analysis (M/F: 59%/41%, median age and median weight at Fontan of 4.3 y.o (IQR 3.3-6.4), and 16kg (IQR 14-20), respectively. Most frequent cardiac anomaly were tricuspid atresia and pulmonary atresia with intact ventricular septum (781, 30%). Ventricular morphology was left in 1379 patients (53%), and right in 1029 patients (40%). Extra cardiac conduit (ECC) was performed in the majority of patients (2335, 90%), with a median conduit size of 20mm (IQR 18-20). Early post-operative complications were present in 52% (1340), with an early reintervention in 349 patients (249 surgical , 99 catheter driven) At a median follow-up median of 10 years (IQR 4-15), there were 79 late deaths (3%). Adverse events occurred in 49% (1288 , non-surgical in 962 , surgical in 249). The clinical status was stage A in 60% (1288 patients), while only a minority (1.3%) was in status D. Systemic Ventricular function was normal in 1898 patients (73%), while absent or mild Atrioventricular Valve regurgitation was presnet in 86% (1816). Of note, FALD was detected in 388 (15 %) , and sinus rhythm was present in 99%. Statistical analysis shows that late death and adverse events are significantly associated right ventricular morphology of the systemic ventricle (p-value 0.019). Of note, when compared to lateral tunnel, TCPC extracardiac conduit demonstrated lower late mortality (2.3% vs 8.2%, p-value &amp;lt;0.001) and complication rates (60% vs 48%, p-value 0.001). Analyzing age at Fontan, a linear relationship exists between age and late mortality, whereby mortality increases with advancing age (graph 1). However, concerning the development of late adverse events, the risk decreases with age at Fontan up to 7 years; beyond 7 years, the risk increases with advancing age (graph 2). The analysis demonstrated growing FALD-prevalence with increasing time from Fontan completion (significant especially after 10 years). Conclusion current long term outcomes of Fontan operation are highly satisfactory. However, the longer survival is associated to an increasing incidence of late term extracardiac complications (namely FALD) which are more evident if Fontan is performed at older age.Data of Fontan PatientsCorrelation between age at Fontan and AE

What to do with your maintenance records for protection layers?

AbstractCollecting and analyzing operation and maintenance data is essential to process safety. The analysis aims to demonstrate that what is installed in the field achieves the required performance so that adjustments can be made if the required performance is not met. Very large organizations routinely have data collection systems and resources for data analysis. Midsized and smaller organizations collect the same data in various ways but often need help to analyze it. This paper presents examples of American Petroleum Institute (API) 754 Tier 3 and 4 metrics related to protection layers and how to use available plant data to support their analysis.

Evaluation and Long-Term Prediction of Annual Wind Farm Energy Production

A comparison and evaluation of the AEP(Annual Energy Production) of a wind farm were conducted in this study with a feasibility study and using the actual operation data from the S wind farm on Jeju Island from January 2020 to December 2022. The free wind speed data were selected from the data measured from a nacelle anemometer, the correlation equation between wind speed and AEP was obtained, and the annual average wind speed for the past 20 years was predicted using the MCP method. As a result, comparing the AEP from the operation data with that estimated in the feasibility study, we found that the AEP was reduced by approximately 2.40% in 2020 and 12.14% in 2021, and increased by 6.76% in 2022. The wind speeds over the 20-year lifetimes of the wind turbines were obtained, and the AEP that could be generated at the S wind farm indicated that it could be used for operation. In the future, the S wind farm will operate at between 25% and 30% availability for the remaining 17 years of operation. If the availability falls below 25%, there will be a need to check the reasons for the deterioration of wind turbine performance and the frequency of failures.

The Proliferation of Artificial Intelligence in the Forklift Industry—An Analysis for the Case of Romania

This paper investigates the impact of artificial intelligence (AI) on the forklift industry, focusing on logistics and procurement within small and medium-sized enterprises (SMEs) in Romania. Using a mixed-methods approach, including interviews with seven managers from a benchmarked company in the forklift industry (BCFI) and quantitative analysis of operational data, we examine the transformative effects of AI integration. Key findings include a 30% reduction in inventory holding costs due to AI-powered predictive analytics; a 15% decrease in procurement costs through AI-driven supplier evaluation systems; a 25% increase in operational efficiency from AI-optimized route planning; a 40% boost in overall productivity attributed to AI-enabled automation; and a projected 20% reduction in low-skilled labor requirements over the next five years. The study employs environmental, social, and corporate governance (ESG), balanced scorecard (BSC), benchmarking, and activity-based management (ABM) models to analyze risks and implications of AI integration. A case study of a leading Romanian SME in the forklift industry is presented, examining financial strategies using McKinsey’s 7S framework. The paper concludes that while AI offers significant operational benefits, it also presents challenges in workforce transition and ethical considerations that require careful management.

Factors Related to Early Postoperative Modified Blalock-Taussig Shunt Thrombosis and In-Hospital Mortality in Infants

Objective: The modified Blalock-Taussig shunt (mBTs) is used palliatively in cyanotic congenital heart diseases when definitive surgery is not possible. However, factors contributing to shunt thrombosis and in-hospital mortality in infancy remain unclear. The present study aimed to identify those risk factors. Materials and Methods: The authors conducted a retrospective review of medical records from the infants diagnosed with cyanotic congenital heart disease who underwent isolated mBTs procedures at Chiang Mai University Hospital between March 1, 2011 and April 30, 2015. Patient characteristics, operative data, and treatment outcomes were extracted. The primary outcome assessed was shunt thrombosis, with in-hospital mortality within 30 days as the secondary outcome. Risk regression analysis was used to identify factors associated with both outcomes. Results: Forty-nine infants were included in the present study, with a mean weight of 3.81 kilograms (range of 2.05 to 7.70 kg) and an average age of 66 days (range of 3 to 349 days). Early shunt thrombosis occurred in eight patients (16.33%), and five patients (10.2%) died. Multivariable analysis revealed only sepsis as a significant predictor (RR 7.93, 95% CI 1.86 to 33.69). Neither weight at surgery nor shunt size showed a significant association with shunt thrombosis. Prognostic factors for in-hospital mortality in the univariable analysis included prematurity (p=0.01), size of pulmonary artery (p=0.033), preoperative intubation (p=0.043) shunt thrombosis (p=0.002), and body weight at surgery of less than 3 kg (p=0.002). However, multivariable analysis could not demonstrate any difference. Conclusion: Postoperative sepsis emerged as the sole statistically significant predictor for early shunt thrombosis following isolated mBTs. However, larger studies are needed to ascertain prognostic factors for in-hospital mortality in infants with cyanotic congenital heart disease.

Native Enterprise Mobile App Development: Best Practices for Security, Performance, and Scalability

This article explores best practices for developing high-quality native enterprise mobile applications, addressing security, performance optimization, scalability, seamless enterprise integration, and automated testing and compliance. With the enterprise mobile app market projected to reach $430 billion by 2024, organizations must prioritize robust development strategies to enhance operational efficiency and data security. The article discusses key techniques such as implementing strong encryption, leveraging native SDKs, adopting modular architectures, utilizing well-designed APIs, and incorporating AI-driven testing tools. By adhering to these practices, enterprises can significantly reduce the risk of data breaches, improve app performance, ensure scalability for future growth, streamline integration with existing systems, and maintain compliance with industry regulations, ultimately positioning themselves for long-term success in an increasingly mobile-first business landscape.

PERANCANGAN SISTEM ENTERPRISE RESOURCE PLANNING (ERP) TERHADAP PENGADAAN BAHAN BAKU, PENJUALAN, DAN PRODUKSI PADA CV. JAYA LOGAM MENGGUNAKAN SOFTWARE ODOO

CV. Jaya Logam faced various operational challenges caused by ineffective sales and raw material inventory recording systems, leading to production delays, errors in information delivery, and difficulties in monitoring and decision-making. This research was conducted to address these issues through system needs analysis, design, and testing of an Enterprise Resource Planning (ERP) system using Odoo software. The methodological process involved a detailed analysis of system requirements, system design using Unified Modelling Language (UML) diagrams to visualize the system architecture, and system testing using the Black Box Testing method to ensure the reliability and effectiveness of the designed system. The testing results showed that the developed ERP system successfully met all 12 test class parameters, indicating that the system is valid and can be implemented effectively. The implementation of this ERP system at CV. Jaya Logam resulted in significant improvements in operational efficiency and data accuracy, better business process integration, and the provision of real-time information that supports faster and more accurate decision-making processes.

Research on the Impact and Solution Strategies of Blockchain Technology on Data Security and Transparency in Enterprise Digital Transformation

Abstract: This research delves into the profound impact of blockchain technology on enhancing data security and transparency during enterprises digital transformation. By dissecting blockchains core attributesdecentralization, immutability, and advanced securitythis study elucidates how it offers pragmatic solutions to prevalent challenges such as data breaches and opaque information processing. Leveraging an extensive literature review, meticulous case analysis, and expert insights, the article comprehensively illustrates blockchains diverse applications in ensuring data integrity and enhancing transparency across critical sectors, including financial services, supply chain management, and healthcare. Furthermore, the study identifies and deliberates on existing research gaps in blockchain technologys application within digital transformation, proposing a systematic framework to deepen comprehension of its role in this process. Moreover, it conducts an in-depth analysis of the technical, operational, and environmental hurdles hindering blockchain adoption, offering a series of action strategies to enable enterprises to harness their potential effectively amidst the swiftly evolving digital landscape. Through this comprehensive inquiry, the article underscores the strategic significance of blockchain technology in augmenting corporate operational transparency and data security. Additionally, it elucidates how blockchain can bolster corporate governance, compliance, and stakeholder trust. Overall, this synthesis provides a thorough review and profound insights into research findings, underscoring blockchains pivotal role in addressing contemporary digital challenges while furnishing invaluable strategic guidance for industry innovation and advancement. The research outcomes hold substantial implications for practitioners and researchers, signaling the transformative potential of blockchain technology in reshaping enterprise operations and charting a course for future exploration in this pivotal domain.