Operational Workflow Research Articles

Collaborative Exploration of Storm-Scale Probabilistic Guidance for NWS Forecast Operations

Abstract The operational utility of the NOAA National Severe Storm Laboratory’s storm-scale probabilistic Warn-on-Forecast System (WoFS) was examined across the watch-to-warning time frame in a virtual NOAA Hazardous Weather Testbed (HWT) experiment. Over four weeks, 16 NWS forecasters from local Weather Forecast Offices, the Storm Prediction Center, and the Weather Prediction Center participated in simulated forecasting tasks and focus groups. Bringing together multiple NWS entities to explore new guidance impacts on the broader forecast process is atypical of prior NOAA HWT experiments. This study therefore provides a framework for designing such a testbed experiment, including methodological and logistical considerations necessary to meet the needs of both local office and national center NWS participants. Furthermore, this study investigated two research questions: 1) How do forecasters envision WoFS guidance fitting into their existing forecast process? and 2) How could WoFS guidance be used most effectively across the current watch-to-warning forecast process? Content and thematic analyses were completed on flowcharts of operational workflows, real-time simulation interactions, and focus group activities and discussions. Participants reported numerous potential applications of WoFS, including improved coordination and consistency between local offices and national centers, enhanced hazard messaging, and improved operations planning. Challenges were also reported, including the knowledge and training required to incorporate WoFS guidance effectively and forecasters’ trust in new guidance and openness to change. The solutions identified to these challenges will take WoFS one step closer to transition, and in the meantime, improve the capabilities of WoFS for experimental use within the operational community. Significance Statement A first-of-its-kind experiment brought together forecasters from local weather forecast offices and national centers to examine the experimental Warn-on-Forecast System’s (WoFS’s) potential applications across watch-to-warning scales. This experiment demonstrated that WoFS can provide great benefit to forecasters, though a few challenges remain. Benefits provided by WoFS frequently overlap roles and responsibilities at local and national scales, suggesting the potential for enhanced cross-office collaboration. The challenges anticipated for WoFS operational use are far fewer than the benefits, and some solutions to these challenges are now being implemented. Finally, the mixed-methods experimental framework described herein also provides guidance for future collaborative experiments in testbed research that examine impacts of new technologies across NWS entities.

Leveraging accessibility modeling to improve housing equity for low-income assisted renters: A case study in Bridgeport, Connecticut

The Housing Choice Voucher Program (HCVP) in the United States assists low-income households with a rent voucher to be redeemed in the private rental market. However, public housing authorities in certain states, such as Connecticut, may impose geographic restrictions that we coin as the “same-jurisdiction rule,” when implementing the HCVP. In practice, this constraint is translated to the requirement that assisted households must lease up within the same jurisdiction, which may eventually restrict access to high-quality housing or prevent relocation to higher-opportunity neighborhoods. To identify ways for policy improvement, we adopt the two-step floating catchment area method to evaluate the accessibility to HCVP-eligible housing in Bridgeport, Connecticut with and without the same-jurisdiction rule. The results indicate that when the geographic restrictions are lifted, there is a 9.23 % increase in median accessibility, and the increase is more significant in low-accessibility areas, which are often lower-opportunity neighborhoods. The results suggest that removing the same-jurisdiction rule can result in more housing options and thus improve housing equity for assisted renters. Since the HCVP and other rental assistance programs are spatial-driven, we call for establishing an operational workflow to corroborate the rigor of these programs to improve housing equity for low-income households.

Optimization of a Hydrogen Exchange-Mass Spectrometry Robotic Liquid Handler Using Tracers.

Hydrogen exchange-mass spectrometry (HX-MS) is a valuable analytical technique that can provide insight into protein interactions and structure. The deuterium labeling necessary to gain this insight is affected by many physical and chemical factors, making it challenging to achieve high reproducibility. Poor precision during dispensing, transfer, and mixing of solutions during the experiment contributes substantially to the overall variability. While the use of a robotic liquid handler can potentially improve precision, its operation must be optimized. We observed poor precision in data collected using a robotic liquid handler to perform HX-MS. In this work, we describe how we were able to improve that system's precision considerably based on tracking performance using caffeine, caffeine-d3, and caffeine-d9 as tracers for the sample, label, and quench to report on each operation of the liquid handling workflow. The insights gained about liquid handler performance and the three-tracer approach can aid in optimizing HX-MS workflow operations, whether performed manually or when using a liquid handling system. Additionally, these tracers can be incorporated as internal tracers during an experiment to report on the labeling and quench operations of each sample throughout the run and, if desired, be used to implement an uptake correction described previously.

Decentralized federated learning based on blockchain: concepts, framework, and challenges

Decentralized federated learning integrates advanced technologies, including distributed computing and secure encryption methodologies, to facilitate a robust and efficient mechanism for safeguarding data privacy and security during collaborative model training endeavors. The incorporation of blockchain technology into Federated Learning provides a transformative framework characterized by its inherent decentralization and data immutability, making it a focal point of contemporary research inquiry. The literature on the integration of blockchain technology with federated learning frameworks is presently deficient in comprehensive summary works. Such summaries are essential for advancing understanding of the implementation challenges and for guiding future research efforts in this domain. Therefore, in this work, we first summarize a typical decentralized federated learning framework based on blockchain and describe its operational workflow and its applications in the fields of the Internet of Things, the Internet of Vehicles, etc. A systematic summary of the challenges confronting this framework and an analysis of the solutions proposed to address these challenges are provided. Finally, this work provides insight into the possible future research directions.

Perancangan Dan Implementasi Bot Telegram Untuk Pemetaan Lokasi ODP Dan Akses Informasi Di Telkom Akses Kota Samarinda

Facing the dynamic landscape of the telecommunications industry, this research delves into key challenges in the operational efficiency of network management. The approach involves designing and implementing an innovative Telegram bot, specifically crafted to map the locations of Optical Distribution Points (ODP). This bot enables technicians to easily access information by entering the ODP name through the Telegram platform. The research methodology employs a descriptive method and a development method using the waterfall SDLC (Software Development Life Cycle) concept, suitable for small-scale projects. Unified Modeling Language (UML), including Use Case Diagrams and Activity Diagrams, aids in the system design. The author conducted an in-depth identification of field technicians' requirements, storing ODP data in Google sheets, including ODP name, geographical coordinates, address, partner, (GPON), and status. The Telegram bot implementation displays location information linked directly to Google Maps, providing essential details such as address, partner, type (GPON), and ODP status. This Telegram bot implementation demonstrates a significant positive impact, contributing to the effectiveness and empowerment of field technicians in managing ODP data. Discussions involving bot success evaluation and potential work effectiveness underscore positive outcomes. The research anticipates that all technicians at PT Telkom Akses Samarinda can utilize this bot to enhance their operational workflows.

Artificial Intelligence, the Digital Surgeon: Unravelling Its Emerging Footprint in Healthcare - The Narrative Review.

Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI's impact on healthcare and to identify areas for further development and focus. The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas. Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare. The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.

Designing the future of prenatal care: an algorithm for a telemedicine-enhanced team-based care model.

This study provides a conceptual exploration of an innovative telemedicine-enhanced team-based care (TETC) model, tailored to prenatal care, integrating a multidisciplinary team approach with advanced telemedicine technologies. The algorithm developed for TETC aims to optimize communication and coordination among healthcare professionals, including obstetricians, midwives, nutritionists, and mental health experts. This cohesive team structure ensures a comprehensive care plan encompassing all facets of maternal and fetal health. Leveraging telemedicine tools like video conferencing and digital health records, the model supports remote consultations and coordinated care, proving particularly advantageous during pandemics or in regions with limited healthcare access. Central to the TETC model is patient-centered care, focusing on personalized care plans attuned to the individual needs, health status, and socioeconomic backgrounds of pregnant women. This approach not only enhances accessibility and convenience by diminishing the necessity for physical consultations but also ensures continuity of care throughout pregnancy. This continuity is crucial for consistent health parameter tracking and early risk identification. The paper discusses the model's design, operational workflow, and ethical and legal considerations, providing implementation guidelines and potential applications. The TETC model, rooted in current technological capabilities and healthcare frameworks, underscores the need for close collaboration with healthcare professionals to adhere to medical standards and address real-world requirements effectively.

Hospital-Level Care at Home for Acutely Ill Adults in Rural Settings: Proof of Concept.

Residents in rural areas face barriers to accessing acute care. Rural home hospital (RHH) or delivery of acute care at home could represent an important clinical care model. This study assessed the feasibility and acceptability of RHH as a substitute to traditional hospital care. Patients were cared for by a remote RHH attending physician and an RHH registered nurse deployed to the home. The study team conducted daily check-ins with RHH clinicians to assess workflows for completion. Surveys assessed patient experience and qualitative interviews assessed perceived acceptability, safety, and quality of care. We completed qualitative analysis of the interviews and coded qualitative data into domains and subdomains through an iterative process. RHH was successfully deployed to three acutely ill patients in rural Utah. RHH admission, daily care, and discharge processes were accomplished for each patient. From qualitative analysis, we identified four domains: (1) Perceived comfort level during RHH admission, (2) Perceived safety during RHH admission, (3) Perceived quality of care during RHH admission, and (4) Perception of RHH workflows. We found acute care was delivered to rural homes with satisfactory patient and clinician experience. Team dynamics, technology build, robust clinical and operational workflows, and care coordination were important to a successful admission. Learnings from this study can inform program design and training for RHH teams and startup for larger RHH evaluation. Home hospital care is expanding rapidly in the United States and RHH could represent an important clinical care model.

Incorporating Artificial Intelligence Into Athletic Training Education: Developing Case-Based Scenarios Using ChatGPT

Context ChatGPT is an AI-based large language model platform capable of mimicking human language patterns by responding to user queries. Although concerns over AI-generated content exist in health care and higher education, the ChatGPT platform can assist athletic training educators in creating academic content to alleviate aspects of their academic workload and responsibilities. Objective To provide a brief historical overview of ChatGPT, accepted positives and negatives, and examples of how athletic training educators can use ChatGPT for case-based scenario contention creation. Background The initial development of ChatGPT began in 2018, with its public launch in November 2022. ChatGPT uses machine and in-context learning to replicate human language expression. Since its public launch, students, researchers, educators, clinicians, governments, and companies have sought to determine how to incorporate ChatGPT into their workflow operations. Data Synthesis Athletic training educators can incorporate ChatGPT into their academic content creation methodology. With the capability of ChatGPT, athletic training educators have the potential to facilitate athletic training student learning more efficiently. Results ChatGPT can ease the academic workload of athletic training educators while being a beneficial clinical tool that athletic training students may use in their future workplace settings. Recommendation(s) Athletic training educators should consider using ChatGPT or a similar AI-based large language model platform when developing education content and simulation scenarios. Conclusions ChatGPT can lessen athletic training educator workload and potentially facilitate athletic training student learning.

Utilizing blockchain technology in enhancing supply chain efficiency and export performance, and its implications on the financial performance of SMEs

This study examines the intricate relationships among Blockchain Technology utilization, Supply Chain Efficiency, Export Performance, and the Financial Performance of Small and Medium-sized Enterprises (SMEs). The research aims to elucidate the impact of technology adoption on various operational and financial aspects within the SME context. Employing a quantitative research design, data was collected from a diverse sample of SMEs across industries. The relationships were analyzed using statistical techniques, and the hypotheses were tested to uncover the implications of Blockchain Technology integration on SMEs' performance dimensions. The findings reveal that the adoption of Blockchain Technology significantly enhances Supply Chain Efficiency, underscoring its potential for optimizing operational workflows. However, the direct impact of technology on SME Financial Performance is not established, suggesting the importance of a holistic approach to financial growth. Moreover, the positive association between Blockchain Technology and Export Performance highlights the pivotal role of technology in fostering international trade success. Theoretical implications underscore the intricate interplay between technology adoption, operational efficiencies, and financial outcomes in SMEs. Managerially, the study advocates for SMEs to strategically integrate technology within their supply chain management practices to achieve enhanced efficiency and market competitiveness. Limitations include the potential for contextual variations and measurement biases. Future research can delve deeper into the moderating factors that influence the relationship between technology and financial performance in SMEs. The novelty of this study lies in its comprehensive examination of the interrelationships between these factors within the SME context.

Zero trust architecture logical components and implementation approaches

Zero Trust Architecture (ZTA) is a contemporary cybersecurity approach that challenges the traditional perimeter-based security model. In the zero-trust model, organizations do not automatically trust any user or device, regardless of whether they are inside or outside the corporate network. Instead, it assumes that threats can come from both internal and external sources, and it verifies every user and device attempting to access resources. Here are the key principles of this software development trend: • Identity verification: individuals need to authenticate their identity before gaining access to resources. This often includes multifactor authentication and reliable verification methods. • Least privilege access: users are granted the minimum access required to perform their tasks. Access is limited only to essential elements, reducing the potential impact of a security breach. • Micro-segmentation: involves segmenting the network at a granular level, allowing isolation and protection of individual resources. • Data encryption: encryption is applied both during transmission and at rest to safeguard data from unauthorized access. • No implicit trust: applying the principle of "never trust, always verify," meaning verification is necessary at every stage of access. The article discusses modern challenges and approaches to cybersecurity amidst the rapid development of cloud technologies. Specifically, it analyzes the shift in container usage in software deployment and its impact on the cybersecurity model. Security approaches based on the concept of Zero Trust Architecture (ZTA) are highlighted in the context of new demands and opportunities. The article elaborates on key logical components of ZTA, such as policy mechanism and policy administrator, pointing out their interaction in creating a secure environment. It also provides an overview of data sources used for creating access policy rules and their consideration in ZTA mechanisms. Additionally, approaches to implementing ZTA for operational workflows in corporate environments are proposed: enhanced identity management, logical micro-segmentation, and network-based segmentation. Each of these approaches has its advantages and is considered based on the needs of individual organizations.

Automated and advanced document processing system in the woodworking industry

This article presents a detailed framework for the seamless integration of a new document processing system within the operational workflow of a company. The proposed plan prioritizes both continuity and parallel deployment to ensure minimal disruption during implementation. It includes a thorough decomposition of system use cases, illustrated with block diagrams, along with sample mock-ups of the user interface to facilitate understanding among developers, designers, and technical writers throughout the development process. The primary objectives of this initiative are to boost employee productivity, reduce costs associated with production downtime, and guarantee the system’s scalability to meet evolving document processing demands.

Archiving Archaeological Data: Understanding Current Practice through Case Studies from Established Repositories

The workflows examined are representative of operational workflows around data assessment and data removal that are common to all data repositories, as well as specific strategies of dealing with Microsoft Access Databases, a commonly deposited file format used by archaeologists for assessment and analysis. The paper will present each workflow as a succinct case study with an emphasis on why the decisions have been made to follow a certain method. This will be followed by a discussion on similarities and differences in approach and implementation so as to bring together core recommendations that can be used by others currently building or scoping the development of new digital repositories in the archaeological sector. The paper will link out to copies of the detailed workflows and policies to be deposited within COPTR, a finding aid for helping find tools to deal with practical data management issues.

A Dynamic Model for the Computation of Gesture Types for Image-based Software Agents

<p>Computer vision technology allows the computer to interact with a human operator to quickly complete the interpretation of events and improve the operational workflow of processing events. As computer vision technology continues to evolve, the cost of the equipment continues to increase. Therefore, the stability of the system can be ensured through the design of the middleware and the calculation of the auxiliary functions of the software agents. At present, the recognition of characters for image processing is based on the technology of image recognition, which can provide a more flexible user experience. However, the dilemma of contactless design lies in the processing and calculation of images, which should reduce the inconvenience caused by delays.This article uses a Raspberry Pi as an example of a computing proxy application. After the visual inspection, the verification operation of the software is carried out. Our system is the detection of hand position and movement, and the detection of hand mark position in reconnaissance. In addition, we simultaneously developed management and remote control events and connected to the remote edge computer. After that, we successfully completed the automatic control and serial application of two different edge computing recognition jobs, and verified the image vision computing based on the Raspberry Pi software agent, which can be used for image vision analysis and control applications.</p> <p> </p>

Learning Curve in Robotic Stereoelectroencephalography: Single Platform Experience

Learning curve, training, and cost impede widespread implementation of new technology. Neurosurgical robotic technology introduces challenges to visuospatial reasoning and requires the acquisition of new fine motor skills. Studies detailing operative workflow, learning curve, and patient outcomes are needed to describe the utility and cost-effectiveness of new robotic technology. A retrospective analysis was performed of pediatric patients who underwent robotic stereoelectroencephalography (sEEG) with the Medtronic Stealth Autoguide. Workflow, total operative time, and time per electrode were evaluated alongside target accuracy assessed via error measurements and root sum square. Patient demographics and clinical outcomes related to sEEG were also assessed. Robot-assisted sEEG was performed in 12 pediatric patients. Comparison of cases over time demonstrated a mean operative time of 363.3 ± 109.5 minutes for the first 6 cases and 256.3 ± 59.1 minutes for the second 6 cases, with reduced operative time per electrode (P= 0.037). Mean entry point error, target point error, and depth point error were 1.82 ± 0.77 mm, 2.26 ± 0.71 mm, and 1.27 ± 0.53 mm, respectively, with mean root sum square of 3.23 ± 0.97 mm. Error measurements between magnetic resonance imaging and computed tomography angiography found computed tomography angiography to be more accurate with significant differences in mean entry point error (P= 0.043) and mean target point error (P= 0.035). The epileptogenic zone was identified in 11 patients, with therapeutic surgeries following in 9 patients, of whom 78% achieved an Engel class I. This study demonstrated institutional workflow evolution and learning curve for the Autoguide in pediatric sEEG, resulting in reduced operative times and increased accuracy over a small number of cases. The platform may seamlessly and quickly be incorporated into clinical practice, and the provided workflow can facilitate a smooth transition.

Improving Sentinel-1 Flood Maps Using a Topographic Index as Prior in Bayesian Inference

Sentinel-1-based flood mapping works well but with well-known issues over rugged terrain. Applying exclusion masks to improve the results is common practice in unsupervised and global applications. One such mask is the height above the nearest drainage (HAND), which uses terrain information to reduce flood lookalikes in SAR images. The TU Wien flood mapping algorithm is one operational workflow using this mask. Being a Bayesian method, this algorithm can integrate auxiliary information as prior probabilities to improve classifications. This study improves the TU Wien flood mapping algorithm by introducing a HAND prior function instead of using it as a mask. We estimate the optimal function parameters and observe the performance in flooded and non-flooded scenarios in six study sites. We compare the flood maps generated with HAND and (baseline) non-informed priors with reference CEMS rapid mapping flood extents. Our results show enhanced performance by decreasing false negatives at the cost of slightly increasing false positives. In utilizing a single parametrization, the improved algorithm shows potential for global implementation.

Assessment of eruption source parameters using infrasound and plume modelling: a case study from the 2021 eruption of Mt. Etna, Italy

Atmospheric injection of volcanic ash during eruptions is a threat to aviation. Reliable forecast of airborne ash dispersal relies on empirical and numerical models. Key inputs into these models are so-called eruption source parameters such as the rate at which pyroclastic material is ejected from the vent and the maximum height of eruptive columns. Here, we use infrasound data recorded during eruptive activity in June 2021 at Mt. Etna, Italy, to demonstrate its potential for assessment of eruption rates in near-real time. We calculate a time series of flow velocity at the vent using data corrected for topographic scattering, and the effect of vent geometry on the acoustic source radiation. We obtain values of flow velocity of 50–125 m/s during a period of sustained, paroxysmal, activity. We use independent estimates from other ground-based remote sensing data to validate our results. Further, we use the infrasound-derived flow velocities as input into a 1D plume model to estimate the maximum height of the eruption column. Our results suggest that infrasound technology holds promise to assess eruption rates and inform modelling of volcanic plumes. We anticipate that implementation of real-time operational workflows based on infrasound data and plume modelling will impact decision-making and risk mitigation at active volcanoes.

Effect of Enterprises Resource Planning System on the Sustainability of Non-Governmental Organisations: A Case Study of NGOs in Kinondoni District

The study examined the effect of an Enterprise Resource Planning System (ERP) on Non-Governmental Organization (NGO) sustainability on the three organizational Sustainability pillars of stakeholder engagement, financial viability, and operational efficiency. The research methodology employed in this study involved a sample of 22 NGOs in Kinondoni District, all of which have been operating for over a decade, actively engaged in projects, and have integrated ERP systems into their operations. Maximum variation sampling was used to select NGOs from the target population. A descriptive research design was adopted, and data was collected using structured questionnaires to gather insights into the experiences and perceptions of NGO representatives regarding ERP system implementation and its impact on sustainability. The findings of this research underscore the positive influence of ERP systems on the sustainability prospects of NGOs in Kinondoni District. The study reveals that ERP systems have led to notable improvements in stakeholder interaction, financial viability, and operational efficiency within these organizations. ERP system has enhanced communication with stakeholders, streamlined financial management processes, and optimized operational workflows, ultimately contributing to the long-term sustainability of NGOs. The study offers recommendations for NGOs to emphasize the potential benefits of ERP system adoption and the need for strategic planning to maximize the advantages of these systems. Furthermore, this research contributes to the broader understanding of how technology adoption can enhance the sustainability of organizations in the nonprofit sector in Tanzania.

Application of Support Vector Machine Regression and Partial Least-Square Regression Models for Predicting Sugarcane Leaf Nutrients Content Using Near Infra-Red Spectroscopy

ABSTRACT Near infra-red spectroscopy (NIRS) has been suggested as a rapid, cost-effective, and accurate diagnostic tool for leaf nutrient analysis that could replace more traditional laboratory diagnostics. To ease operational workflows, there would advantage in estimating nutrients using a single method, namely NIRS. This study evaluated the potential of NIRS as a diagnostic method for the measurement of key macro and micronutrients in sugarcane leaf samples. Three hundred and fifty-one sugarcane leaf samples used in quality control reference analysis in Fertiliser Advisory Service (FAS) were used for model calibration. About 35% of the samples were from growers within South Africa, while the remainder were from estates across southern and eastern Africa. Dried and milled leaf material was scanned on a Bruker MPA-NIRS instrument, and spectral pre-processing was performed. Support vector machine regression (SVMR) and partial least squares regression (PLSR) were used for calibrating the estimation models with the test validation(Tval) procedure. The results showed both the PLSR and SVMR model resulted in the best calibration for (K, Ca, Mg)(R2 >87% and the ratio of performance to inter-quartile distance(RPIQ) > 2.1). With a test validation dataset, the SVMR yielded relatively high accuracy(R2 >85% and the RPIQ > 2.5, while PLSR yielded poor accuracy. The SVMR also outperforms Zn(R2 = 0.86; and RPIQ = 2.5) for model calibration. NIR Spectrometry combined with the SVMR technique resulted in a practical option to accurately measure leaf nutrient concentrations and evaluate sugarcane mineral contents accurately. These results indicate that NIRS can replace the current analytical methods used in FAS.

Pemilihan Supplier Barang Elektrikal Dengan Menggunakan Metode AHP (Studi Kasus: PT Petrokopindo Cipta Selaras)

PT. Petrokopindo Cipta Selaras is a corporate entity within the business sector that operates specifically within the service industry. The successful operation of trade is contingent upon the presence of adequate assistance. Nevertheless, the firm has some challenges, particularly in the domain of electrical products procurement. Delays in the delivery of electrical goods from suppliers, specifically PT. X, PT. Y, and PT. Z, impedes the operational workflows of the company. The study employed the Analytic Hierarchy Process (AHP) methodology to conduct the research. This involved constructing a hierarchical or network structure of the problem and conducting a pairwise comparison stage afterward. The findings reveal the key factors that significantly impact the supplier selection process for electrical items at PT. Petrokopindo established a prioritization framework, wherein quality criteria were assigned the greatest weight of 45.6%. This was followed by pricing criteria, which were assigned a weight of 30.3%. The third priority was given to delivery criteria, with a weight of 14.6%. Lastly, payment criteria were assigned the lowest priority, with a weight of 9.4%. PT Z holds the highest weight, specifically 35.2%, in relation to suppliers who demonstrate exceptional performance in terms of pricing criterion, carrying a weight of 20.6%. The supplier PT. The provider PT Y achieved the least outcome, with an overall weight value of 9.3%, while the lowest weight recorded was 3.8%.