Hasil untuk "Management information systems"

Maryam Alavi, D. Leidner

Yuanxin Zhang

Pascal programming facility used to customize the package, while Chapter 12 gives specific examples of customizing and Chapter 13 discusses various applications, including numerical applications, standard formats, a sample MARC database, a directory database, library housekeeping systems and Online Public Access Catalogues (OPACs). Finally, Chapter 14 provides a list of items for further reading, information on CD-ROM using CDS/ISIS and on extended roman and non-roman scripts, and a list of useful addresses. This book will be warmly welcomed by all who have struggled with the implementation of CDS/ISIS or have hesitated to

Didik Kurniawan, Farhan Oktaviansyah Hidayat, Binsar Hakim Aritonang et al.

Cross Equatorial Northerly Surges or CENS are an important atmospheric phenomenon influencing weather variability over the Maritime Continent. These surge events frequently generate hazardous hydrometeorological conditions, including heavy rainfall and surface cooling, posing risks to maritime activities and coastal regions. This study presents a climatological analysis of atmospheric dynamics anomalies associated with CENS over the Western Maritime Continent using a 30 year dataset covering the period from 1991 to 2020. Atmospheric anomalies in precipitation rate, outgoing longwave radiation, relative humidity, and maximum temperature are analyzed using NCEP NCAR Reanalysis data. Active CENS events are identified based on meridional wind speed thresholds during the boreal winter season from November to March, resulting in 170 active CENS days. The results indicate that CENS events are consistently associated with enhanced precipitation, reduced outgoing longwave radiation, increased low level relative humidity, and widespread surface cooling. These anomalies reflect intensified convective activity driven by the transport of cold and moist air masses from the Northern Hemisphere. Maximum temperature decreases by up to 4.5 degrees Celsius due to the combined effects of cold air advection and increased cloud cover that suppresses incoming solar radiation. By adopting a multi decadal climatological framework, this study provides new insights into persistent atmospheric responses to CENS that are not fully captured by shorter term or event based analyses. The climatological baseline established here improves understanding of large scale drivers of extreme rainfall and atmospheric instability over western Indonesia and offers valuable information for enhancing weather forecasting, early warning systems, and maritime risk management.

Abdullah Addas, Abdullah Addas, Muhammad Nasir Khan et al.

IntroductionThe regional disparity in higher education access can only be met when there are strategies for sustainable development and diversification of the economy, as envisioned in Saudi Vision 2030. Currently, 70% of universities are concentrated in the Central and Eastern regions, leaving the Northern and Southern parts of the country with limited opportunities.MethodsThe study created a framework with sensors and generative adversarial networks (GANs) that optimize the distribution of medical universities, supporting equity in access to education and balanced regional development. The research applies an artificial intelligence (AI)-driven framework that combines sensor data with GAN-based models to perform real-time geographic and demographic data analyses on the placement of higher education institutions throughout Saudi Arabia. This framework analyzes multisensory data by examining strategic university placement impacts on regional economies, social mobility, and the environment. Scenario modeling was used to simulate potential outcomes due to changes in university distribution.ResultsThe findings indicated that areas with a higher density of universities experience up to 20% more job opportunities and a higher GDP growth of up to 15%. The GAN-based simulations reveal that redistributive educational institutions in underrepresented regions could decrease environmental impacts by about 30% and enhance access. More specifically, strategic placement in underserved areas is associated with a reduction of approximately 10% in unemployment.DiscussionThe research accentuates the need to include AI and sensor technology to develop educational infrastructures. The proposed framework can be used for continuous monitoring and dynamic adaptation of university strategies to align them with evolving economic and environmental objectives. The study explains the transformative potential of AI-enabled solutions to further equal access to education for sustainable regional development throughout Saudi Arabia.

Eskedar Gizat Desalegn, Maria João Coelho Guedes, Jorge Filipe Da Silva Gomes et al.

Abstract Organizational agility is the ability of an organization to swiftly and efficiently respond to changes in the organization’s environment. However, the literature demonstrates the interchangeability of agility, flexibility, adaptability, and versatility. Therefore, confusion and conceptual overlap persist. As a result, this study aimed to provide further conceptual clarity about organizational agility by synthesizing organizational agility, flexibility, adaptability, and versatility. A systematic review of 40 articles published in business and management-related journals between 1991 and 2022 in ABI/INFORMS, Since Direct, Emerald databases are employed. Findings from thematic analysis and content analysis using Leximancer text mining analysis show that versatility, adaptability, and flexibility are closely connected with their focus on coping with change in the business environment. However, agility is distinct due to its emphasis on organizational ability, capability, and changeability, as well as how it conceptualizes these attributes. This review contributes to developing organizational agility theory and practice by disentangling organizational agility from related concepts. Specifically, it contributes to scientific communication by referring to the same phenomena as organizational agility. Finally, the study concludes by highlighting future research directions.

khoirul Adib, Maya Rini Handayani, Wenty Dwi Yuniarti et al.

Pemilihan Presiden di Indonesia seringkali menjadi pemicu perubahan dramatis dalam dinamika opini publik, terutama di era digital yang dipenuhi dengan suara yang tersebar di media sosial. Penelitian ini bertujuan untuk memetakan perubahan sentimen publik pasca-pemilihan Presiden dengan menggunakan analisis media sosial, dengan fokus pada aplikasi X yang memiliki 24 juta pengguna aktif di Indonesia. Metode Support Vector Machine (SVM) digunakan untuk menganalisis dan mengklasifikasikan sentimen dengan akurat berdasarkan kata tweet yang sedang tren setelah pemilihan Presiden. Penelitian ini bertujuan untuk memberikan pemahaman yang lebih dalam tentang perubahan opini publik pasca-pemilihan presiden, dengan menggambarkan dinamika sentimen masyarakat yang tercermin dalam media sosial. Kontribusi dari penelitian ini adalah pemetaan yang akurat tentang perubahan opini publik, yang dapat memberikan wawasan yang berharga bagi pembuat kebijakan, analis politik, dan praktisi media sosial dalam merespons kebutuhan masyarakat di era digital ini. Hasil pengujian dengan menggunakan 3850 dengan karateristik dataset dengan menggunakan tiga kelas kata tweet yang sedang tren dari platform X menunjukkan tingkat akurasi tertinggi pada klasifikasi "Pemilu Damai" dengan 97.3%, "Hak Angket" dengan 96.5%, dan "Pemilu Curang" dengan 94.0%.

Omar Djoukbala, Salim Djerbouai, Saeed Alqadhi et al.

Soil erosion significantly impacts dam functionality by leading to reservoir siltation, reducing capacity, and heightening flood risks. This study aims to map soil erosion within a Geographic Information Systems (GIS) framework to estimate the siltation of the K'sob dam and compare these estimates with bathymetric observations. Focused on one of the Hodna basin’s sub-basins, the K'sob watershed (1477 km2), the assessment utilizes the Revised Universal Soil Loss Equation (RUSLE) integrated with GIS and remote sensing data to predict the spatial distribution of soil erosion. Remote sensing data were pivotal in updating land cover parameters critical for RUSLE, enhancing the precision of our erosion predictions. Our results indicate an average annual soil erosion rate of 7.83 t/ha, with variations ranging from 0 to 224 t/ha/year. With a typical relative error of about 13% in predictions, these figures confirm the robustness of our methodology. These insights are crucial for crafting mitigation strategies in areas facing high to extreme soil loss and will assist governmental agencies in prioritizing actions and formulating effective soil erosion management policies. Future studies should explore the integration of real-time data and advanced modeling techniques to further refine these predictions and expand their applicability in similar environmental assessments.

MA Nan, CAO Shanshan, BAI Tao et al.

[Significance]The rapid development of artificial intelligence and automation has greatly expanded the scope of agricultural automation, with applications such as precision farming using unmanned machinery, robotic grazing in outdoor environments, and automated harvesting by orchard-picking robots. Collaborative operations among multiple agricultural robots enhance production efficiency and reduce labor costs, driving the development of smart agriculture. Multi-robot simultaneous localization and mapping (SLAM) plays a pivotal role by ensuring accurate mapping and localization, which are essential for the effective management of unmanned farms. Compared to single-robot SLAM, multi-robot systems offer several advantages, including higher localization accuracy, larger sensing ranges, faster response times, and improved real-time performance. These capabilities are particularly valuable for completing complex tasks efficiently. However, deploying multi-robot SLAM in agricultural settings presents significant challenges. Dynamic environmental factors, such as crop growth, changing weather patterns, and livestock movement, increase system uncertainty. Additionally, agricultural terrains vary from open fields to irregular greenhouses, requiring robots to adjust their localization and path-planning strategies based on environmental conditions. Communication constraints, such as unstable signals or limited transmission range, further complicate coordination between robots. These combined challenges make it difficult to implement multi-robot SLAM effectively in agricultural environments. To unlock the full potential of multi-robot SLAM in agriculture, it is essential to develop optimized solutions that address the specific technical demands of these scenarios.[Progress]Existing review studies on multi-robot SLAM mainly focus on a general technological perspective, summarizing trends in the development of multi-robot SLAM, the advantages and limitations of algorithms, universally applicable conditions, and core issues of key technologies. However, there is a lack of analysis specifically addressing multi-robot SLAM under the characteristics of complex agricultural scenarios. This study focuses on the main features and applications of multi-robot SLAM in complex agricultural scenarios. The study analyzes the advantages and limitations of multi-robot SLAM, as well as its applicability and application scenarios in agriculture, focusing on four key components: multi-sensor data fusion, collaborative localization, collaborative map building, and loopback detection. From the perspective of collaborative operations in multi-robot SLAM, the study outlines the classification of SLAM frameworks, including three main collaborative types: centralized, distributed, and hybrid. Based on this, the study summarizes the advantages and limitations of mainstream multi-robot SLAM frameworks, along with typical scenarios in robotic agricultural operations where they are applicable. Additionally, it discusses key issues faced by multi-robot SLAM in complex agricultural scenarios, such as low accuracy in mapping and localization during multi-sensor fusion, restricted communication environments during multi-robot collaborative operations, and low accuracy in relative pose estimation between robots.[Conclusions and Prospects]To enhance the applicability and efficiency of multi-robot SLAM in complex agricultural scenarios, future research needs to focus on solving these critical technological issues. Firstly, the development of enhanced data fusion algorithms will facilitate improved integration of sensor information, leading to greater accuracy and robustness of the system. Secondly, the combination of deep learning and reinforcement learning techniques is expected to empower robots to better interpret environmental patterns, adapt to dynamic changes, and make more effective real-time decisions. Thirdly, large language models will enhance human-robot interaction by enabling natural language commands, improving collaborative operations. Finally, the integration of digital twin technology will support more intelligent path planning and decision-making processes, especially in unmanned farms and livestock management systems. The convergence of digital twin technology with SLAM is projected to yield innovative solutions for intelligent perception and is likely to play a transformative role in the realm of agricultural automation. This synergy is anticipated to revolutionize the approach to agricultural tasks, enhancing their efficiency and reducing the reliance on labor.

Prabath Jayathissa, Roshan Hewapathirana

This review underscores the vital role of interoperability in digital health, advocating for a standardized framework. It focuses on implementing a Fast Healthcare Interoperability Resources (FHIR) server, addressing technical, semantic, and process challenges. FHIR's adaptability ensures uniformity within Primary Care Health Information Systems, fostering interoperability. Patient data management complexities highlight the pivotal role of semantic interoperability in seamless patient care. FHIR standards enhance these efforts, offering multiple pathways for data search. The ADR-guided FHIR server implementation systematically addresses challenges related to patient identity, biometrics, and data security. The detailed development phases emphasize architecture, API integration, and security. The concluding stages incorporate forward-looking approaches, including HHIMS Synthetic Dataset testing. Envisioning FHIR integration as transformative, it anticipates a responsive healthcare environment aligned with the evolving digital health landscape, ensuring comprehensive, dynamic, and interconnected systems for efficient data exchange and access.

Demetrius Gulewicz, Uduak Inyang-Udoh, Trevor Bird et al.

Model predictive control has gained popularity for its ability to satisfy constraints and guarantee robustness for certain classes of systems. However, for systems whose dynamics are characterized by a high state dimension, substantial nonlinearities, and stiffness, suitable methods for online nonlinear MPC are lacking. One example of such a system is a vehicle thermal management system (TMS) with integrated thermal energy storage (TES), also referred to as a hybrid TMS. Here, hybrid refers to the ability to achieve cooling through a conventional heat exchanger or via melting of a phase change material, or both. Given increased electrification in vehicle platforms, more stringent performance specifications are being placed on TMS, in turn requiring more advanced control methods. In this paper, we present the design and real-time implementation of a nonlinear model predictive controller with 77 states on an experimental hybrid TMS testbed. We show how, in spite of high-dimension and stiff dynamics, an explicit integration method can be obtained by linearizing the dynamics at each time step within the MPC horizon. This integration method further allows the first-order gradients to be calculated with minimal additional computational cost. Through simulated and experimental results, we demonstrate the utility of the proposed solution method and the benefits of TES for mitigating highly transient heat loads achieved by actively controlling its charging and discharging behavior.

Mesele Damte Argaw, Binyam Fekadu Desta, Zergu Taffesse Tsegaye et al.

Abstract Background The aim of this study was to investigate the quality of immunization data and monitoring systems in the Dara Malo District (Woreda) of the Gamo Administrative Zone, within the Southern Nations, Nationalities, and Peoples’ Region (SNNPR) of Ethiopia. Methods A cross-sectional study was conducted from August 4 to September 27, 2019, in Dara Malo District. The district was purposively selected during the management of a pertussis outbreak, based on a hypothesis of ‘there is no difference in reported and recounted immunization status of children 7 to 23 months in Dara Malo District of Ethiopia’. The study used the World Health Organization (WHO) recommended Data Quality Self-Assessment (DQS) tools. The accuracy ratio was determined using data from routine Expanded Program of Immunization (EPI) and household surveys. Facility data spanning the course of 336 months were abstracted from EPI registers, tally sheets, and monthly routine reports. In addition, household surveys collected data from caretakers, immunization cards, or oral reports. Trained DQS assessors collected the data to explore the quality of monitoring systems at health posts, health centers, and at the district health office level. A quality index (QI) and proportions of completeness, timeliness, and accuracy ratio of the first and third doses of pentavalent vaccines and the first dose of measles-containing vaccines (MCV) were formulated. Results In this study, facility data spanning 336 months were extracted. In addition, 595 children aged 7 to 23 months, with a response rate of 94.3% were assessed and compared for immunization status, using register and immunization cards or caretakers’ oral reports through the household survey. At the district level, the proportion of the re-counted vaccination data on EPI registers for first dose pentavalent was 95.20%, three doses of pentavalent were 104.2% and the first dose of measles was 98.6%. However, the ratio of vaccination data compared using tallies against the reports showed evidence of overreporting with 50.8%, 45.1%, and 46.5% for first pentavalent, third pentavalent, and the first dose of measles vaccinations, respectively. The completeness of the third dose of pentavalent vaccinations was 95.3%, 95.6%, and 100.0% at health posts, health centers, and the district health office, respectively. The timeliness of the immunization reports was 56.5% and 64.6% at health posts and health centers, respectively, while the district health office does not have timely submitted on time to the next higher level for twelve months. The QI scores ranged between 61.0% and 80.5% for all five categories, namely, 73.0% for recording, 71.4% for archiving and reporting, 70.4% for demographic information, 69.7% for core outputs, and 70.4% for data use and were assessed as suboptimal at all levels. The district health office had an emergency preparedness plan. However, pertussis was not on the list of anticipated outbreaks. Conclusion Immunization data completeness was found to be optimal. However, in the study area, the accuracy, consistency, timeliness, and quality of the monitoring system were found to be suboptimal. Therefore, poor data quality has led to incorrect decision making during the reported pertussis outbreak management. Availing essential supplies, including tally sheets, monitoring charts, and stock management tools, should be prioritized in Daro Malo District. Enhancing the capacity of healthcare providers on planning, recording, archiving, and reporting, analyzing, and using immunization data for evidence-based decision making is recommended. Improving the availability of recording and reporting tools is also likely to enhance the data accuracy and completeness of the community health information system. Adapting pertussis outbreak management guidelines and conducting regular data quality assessments with knowledge sharing events to all stakeholders is recommended.

Carlos Ceja-Espinosa, Mehrdad Pirnia, Claudio A. Cañizares

This paper presents an Energy Management System (EMS) that considers power exchanges between a set of interconnected microgrids (MGs) and the main grid, in the context of Multi-MG (MMG) systems. The model is first formulated as a centralized optimization problem, which is then decomposed into subproblems corresponding to each MG, using Lagrangian relaxation, and solved through a distributed approach using a subgradient method. The proposed model determines the power exchanges minimizing the operation cost of each MG, considering grid constraints and preserving the privacy of each MG by not revealing their generation cost and demand information. The distributed approach is validated with respect to the centralized problem, and various case studies are presented to demonstrate the performance of the proposed approach, comparing the costs of the MGs operating individually and cooperatively. The results show that all MGs in the MMG system improve their cost as consequence of the power exchanges, thus demonstrating the advantages of interconnecting MGs.

Cameron Gordon

The hierarchical nature of corporate information processing is a topic of great interest in economic and management literature. Firms are characterised by a need to make complex decisions, often aggregating partial and uncertain information, which greatly exceeds the attention capacity of constituent individuals. However, the efficient transmission of these signals is still not fully understood. Recently, the information bottleneck principle has emerged as a powerful tool for understanding the transmission of relevant information through intermediate levels in a hierarchical structure. In this paper we note that the information bottleneck principle may similarly be applied directly to corporate hierarchies. In doing so we provide a bridge between organisation theory and that of rapidly expanding work in deep neural networks (DNNs), including the use of skip connections as a means of more efficient transmission of information in hierarchical organisations.

M. Planer, J. M. Sierchio, for BAE Systems

We explore the impact of environmental conditions on the competency of machine learning agents and how real-time competency assessments improve the reliability of ML agents. We learn a representation of conditions which impact the strategies and performance of the ML agent enabling determination of actions the agent can make to maintain operator expectations in the case of a convolutional neural network that leverages visual imagery to aid in the obstacle avoidance task of a simulated self-driving vehicle.

Paweł Kobis

This paper attempts to classify the main areas of threats occurring in enterprises in the information management processes. Particular attention was paid to the effect of the human factor which is present in virtually every area of information security management. The author specifies the threats due to the IT techniques and technologies used and the models of information systems present in business entities. The empirical part of the paper presents and describes the research conducted by the author on information security in business organisations using the traditional IT model and the cloud computing model. The results obtained for both IT models are compared. (original abstract)

Atif Ahmad, Sean B. Maynard, Sameen Motahhir et al.

Case-based learning is a powerful pedagogical method of creating dialogue between theory and practice. CBL is particularly suited to executive learning as it instigates critical discussion and draws out relevant experiences. In this paper we used a real-world case to teach Information Security Management to students in Management Information Systems. The real-world case is described in a legal indictment, T-mobile USA Inc v Huawei Device USA Inc. and Huawei Technologies Co. LTD, alleging theft of intellectual property and breaches of contract concerning confidentiality and disclosure of sensitive information. The incident scenario is interesting as it relates to a business asset that has both digital and physical components that has been compromised through an unconventional cyber-physical attack facilitated by insiders. The scenario sparked an interesting debate among students about the scope and definition of security incidents, the role and structure of the security unit, the utility of compliance-based approaches to security, and the inadequate use of threat intelligence in modern security strategies.

Şükrü Ozan

How can a text corpus stored in a customer relationship management (CRM) database be used for data mining and segmentation? In order to answer this question we inherited the state of the art methods commonly used in natural language processing (NLP) literature, such as word embeddings, and deep learning literature, such as recurrent neural networks (RNN). We used the text notes from a CRM system which are taken by customer representatives of an internet ads consultancy agency between years 2009 and 2020. We trained word embeddings by using the corresponding text corpus and showed that these word embeddings can not only be used directly for data mining but also be used in RNN architectures, which are deep learning frameworks built with long short term memory (LSTM) units, for more comprehensive segmentation objectives. The results prove that structured text data in a CRM can be used to mine out very valuable information and any CRM can be equipped with useful NLP features once the problem definitions are properly built and the solution methods are conveniently implemented.

Miguel F. Arevalo-Castiblanco, Duvan A. Tellez-Castro, Jorge Sofrony et al.

Adaptive synchronization protocols for heterogeneous multi-agent network are investigated. The interaction between each of the agents is carried out through a directed graph. We highlight the lack of communication between agents and the presence of uncertainties in each system among the conventional problems that can arise in cooperative networks. Two methodologies are presented to deal with the uncertainties: A strategy based on robust optimal control and a strategy based on neural networks. Likewise, an input estimation methodology is designed to face the disconnection that any agent may present on the network. These control laws can guarantee synchronization between agents even when there are disturbances or no communication from any agent. Stability and boundary analyzes are performed. Cooperative cruise control simulation results are shown to validate the performance of the proposed control methods.

Martiña Morantes, Pablo Herrera, Omar Colmenares et al.

RESUMEN Los sistemas de producción con rumiantes en Venezuela se caracterizan por el manejo de los animales a pastoreo, donde se han incorporado ovinos y bovinos de forma simultánea o alterna, lo cual se ha realizado sin información científica de base que respalde las decisiones de manejo a nivel de fincas. Con la finalidad de determinar la selección de herbáceas por ovinos y bovinos en pastoreo mixto continuo, se realizó un experimento en las sabanas bien drenadas de Venezuela, durante la época de transición lluvia–sequía. El área experimental estuvo constituida por un potrero de 20 hectáreas. Se determinó la frecuencia y densidad relativa de las especies vegetales. El recurso animal estuvo constituido por dos mautes mestizos Brahman (peso vivo) (PV) 140,05 ± 0,78 kilogramos (kg)) y 6 borregos West African (PV 23,60 ± 0,37 kg). La selección de herbáceas se determinó con la técnica de análisis microhistólogico, en muestreos semanales de heces durante cuatro semanas. Se determinaron las frecuencias relativas de las especies vegetales identificadas en las heces, y se analizaron a través de la prueba de Kruskal-Wallis, en un diseño completamente aleatorizado. La densidad relativa mostró una mayor participación numérica del Trachypogon spicatus (L.f.) Kuntze (17,62 %) y del Trachypogon vestitus Andersson (17,33 %), y una mayor frecuencia relativa del Trachypogon vestitus Andersson (9,85 %) y del Trachypogon spicatus (L.f. ) Kuntze (8,68 %). Se encontró diferencia (P<0,01) en la selección de herbáceas entre especies animales, con una mayor frecuencia de aparición de leguminosas en las heces de ovinos, predominando Desmodium spp. y Mimosa albida Willd, y de gramíneas para bovinos, como Trachypogon vestitus Nees y Axonopus spp. Estas diferencias indican que el pastoreo mixto, continuo y con una relación de carga ovino: bovino de 1:1, puede conducir a una utilización integral del recurso forrajero en sabanas bien drenadas. ABSTRACT The production systems with ruminants in Venezuela are characterized by the management of grazing animals, where sheep and cattle have been incorporated simultaneously or alternately, which has been done without basic scientific information to support management decisions at the level of farms. Animal resource was conformed by crossbreed Brahman weaned calves (live weight (LW) 140.05 ± 0.78 kilograms (kg)) and 6 West African lambs (LW 23.60 ± 0.37 kg). The selection of herbaceous was determined by microhistologycal analysis, in samples of feces weekly, during four weeks. The relative frequencies of the botanical species identified in feces were analyzed by Kruskal-Wallis test, as a completely randomized design. Relative density showed a higher numerical participation of Trachypogon spicatus (L.f.) Kuntze (17.62 %) and Trachypogon vestitus Andersson (17.33 %), and the highest relative frequencies for Trachypogon vestitus Andersson (9.85 %) and Trachypogon spicatus (L.f.) Kuntze (8.68 %). Selection of herbaceous was different (P<0.01) between animal species, with a high frequency of legumes in ovine feces, with Desmodium spp. and Mimosa albida Willd. as predominant species. Herbaceous species had the highest frequency in bovines, with Trachypogon vestitus Nees and Axonopus spp. as predominant. Differences indicate that mixed and continuous grazing, with a ratio of 1:1 (ovine: bovine) can contribute to an integral selection of forage native in well drained savannas.

Savchenko Maryna, Shaulska Larysa, Shkurenko Olga et al.

The article examines the methodological approaches to the assessment of the labour potential of the socio-economic system. On the basis of the research, it has been established that the proposed algorithm for assessing the labour potential of the socio-economic system at the micro level. The approbation of the methodology for assessing the labour potential of the socio-economic system was carried out on the basis of the performance of an industrial enterprise. To improve the methodology for assessing the labour potential of socio-economic systems, the use of an organizational and methodological model for assessing the labour potential of the system has been proposed. The use of the organizational and methodological model is carried out in several stages: the construction of the Ishikawa diagram, the rating score, the construction of the Pareto diagram and the conduct of the ABC analysis. The implementation of the organizational and methodological model for assessing the labour potential of the socio-economic will allow for the effective management of the labour potential of the system and, as a result, will increase its competitiveness in the information economy.