Data-driven Decision Research Articles

Enhancing financial reporting and management efficiency through enterprise resource planning (ERP) systems: A theoretical review for large-scale energy operations

Enterprise Resource Planning (ERP) systems have emerged as essential tools in streamlining financial reporting and improving management efficiency across various industries, including large-scale energy operations. This theoretical review explores how ERP systems enhance financial reporting by integrating real-time data, automating financial processes, and fostering transparency. In energy operations, where financial activities are complex and data-intensive, ERP systems centralize financial data, reducing errors, improving compliance, and supporting decision-making through enhanced visibility and accuracy. By automating tasks such as budgeting, forecasting, and financial statement generation, ERP systems enable energy companies to efficiently manage their financial resources, thus optimizing cost control and profitability. Additionally, ERP systems provide significant benefits for management efficiency, particularly in large-scale operations. The integration of financial, operational, and managerial processes within a single platform allows for better resource allocation, improved communication across departments, and faster response times to market fluctuations. These capabilities are crucial in the energy sector, where companies face volatile market conditions, regulatory pressures, and high operational risks. ERP systems enhance managerial decision-making by providing real-time insights into key performance indicators (KPIs), enabling executives to make data-driven decisions that support long-term strategic goals. However, implementing ERP systems in large-scale energy operations presents challenges, including high costs, lengthy implementation periods, and the need for employee training. This review also examines these barriers and suggests strategies for successful ERP adoption, including phased implementation, stakeholder engagement, and customization to industry-specific needs. In conclusion, ERP systems hold the potential to revolutionize financial reporting and management efficiency in large-scale energy operations. By leveraging ERP's real-time data integration and process automation, energy companies can significantly improve their financial management, enhance decision-making, and adapt more quickly to market changes. Keywords: ERP Systems, Financial Reporting, Management Efficiency, Large-Scale Energy Operations, Process Automation, Real-Time Data Integration, Decision-Making, Cost Control, Market Fluctuations, Energy Sector.

FASECO: A Framework for Advanced Support of E-Commerce and digital transformation in SMEs with natural language processing-enhanced analysis

ObjectivesThe primary aim of this study is to develop a framework that identifies the factors, organizational processes, and mechanisms that facilitate the successful integration of e-commerce within the digital transformation journey of Small and medium-sized enterprises (SMEs). The FASECO framework seeks to enhance strategic alignment, IT infrastructure, customer experience, and data analytics in SMEs, and is compared with existing theoretical frameworks to provide a holistic solution. Methods/ApproachThe research employs a mixed-methods approach, starting with an extensive literature review on digital transformation and e-commerce integration in SMEs, followed by qualitative semi-structured interviews with 13 industry professionals. The data is analysed using thematic analysis, significantly enhanced by Natural Language Processing (NLP) tools, which allowed for the extraction of deeper insights into e-commerce adoption challenges and organizational dynamics that traditional methods might overlook. ResultsThe study reveals that effective e-commerce integration in SMEs requires robust IT infrastructure, strategic alignment, personalized customer experiences, and data-driven decision-making. The FASECO framework supports SMEs by providing a comprehensive structure that bridges the gap between strategy and technology, enhancing their digital maturity and competitiveness. The framework’s holistic approach and detailed comparison with existing models underscore its effectiveness. ConclusionsThe FASECO framework offers a novel approach tailored to SMEs for digital transformation and e-commerce integration, highlighting the critical role of strategic and technological alignment in achieving digital maturity and competitive advantage. The use of NLP techniques in this study enhanced the analysis of qualitative data, providing deeper insights into organizational processes. Future research should explore the adaptation of this model for micro-SMEs and the integration of emerging technologies.

Effect of Management Information System on Growth of Agro-Firms in Anambra State

The study examined the effect of management information systems on the growth of agro-firms in Anambra State. The objectives were to: determine the effect of decision-making information systems on the Return on Investment (ROI) of agro-businesses in Anambra State, Nigeria, and ascertain the extent to which database management systems affect the ROI of agro-businesses in Anambra State, Nigeria. The study adopted a survey research design, with data generated from both primary and secondary sources. The data collection method was a questionnaire, which was randomly administered to staff of selected agro-firms. The population of the study was 6,727, while the sample size was 1,293, out of which 929 questionnaires were retrieved. The hypotheses were tested using the multiple regression analysis method at a 0.05% level of significance. The findings revealed that decision-making information systems had a significant effect on the ROI of agro-businesses in Anambra State, with a t-statistic of 7.743 and a probability value of 0.000. Additionally, database information systems had a significant effect on the ROI of agro-businesses in Anambra State, with a t-statistic of 2.476 and a probability value of 0.013. The study concluded that management information systems have a significant positive effect on agro-firms in Anambra State. It recommended that agricultural firms should invest in advanced decision support tools to aid data-driven decisions that maximize ROI. Furthermore, agro-business managers should focus on optimizing their database information systems to improve data organization and accessibility. Regular maintenance and updates of the database systems are crucial for ensuring accurate and timely information, which can positively impact ROI.

A Multi-Agent Financial Investment Decision Method Based on Evolutionary Game

Data-driven smart investment decisions are important for financial development, which has not received much attention from academia. As a result, this paper resorts to the evolutionary game theory, and proposes a novel multi-agent financial investment decision method. Specifically, an evolutionary game theory-based decision-making approach is formulated as the main model for the research purpose. By considering the strategic choices and adaptability among various entities, a comprehensive analysis of the behavior and decision-making process of entities in the financial market is achieved. This paper combines stock exchanges and financial data providers (Bloomberg and Thomson Reuters) to conduct case studies on this method, verifying its effectiveness and feasibility in practical applications. By comparing traditional financial investment decision-making methods, it can be seen that the proposal has significant advantages in improving investment efficiency, reducing risks, and responding to market volatility. This paper delves into the multi-agent financial investment decision-making method based on the evolutionary game, providing new ideas and methods for academic research and practical applications in the financial field.

Community-Based Health Information Systems in Africa: A Scoping Review of Data Generation, Utilization, and Community Empowerment

Introduction The community-based health information system (CBHIS) is a vital component of the community health system, as it assesses community-level healthcare service delivery and generates data for community health programme planning, monitoring, and evaluation. CBHIS promotes data-driven decision-making, by identifying priority interventions and programs, guiding resource allocation, and contributing to evidence-based policy development. Objective This scoping review aims to comprehensively examine the use of CBHIS in African countries, focusing on data generation, pathways, utilisation of CBHIS data, community accessibility to the data and use of the data to empower communities. Methods We utilised Arksey and O'Malley's scoping review methodology. We searched eight databases: PubMed, EMBASE, HINARI, Cochrane Library, Web of Science, Scopus, Google Scholar, and grey literature databases (Open Grey and OAIster). We synthesised findings using a thematic approach. Results Our review included 55 articles from 27 African countries, primarily in Eastern and Southern Africa, followed by West Africa. Most of the studies were either quantitative (42%) or qualitative (33%). Paper-based systems are primarily used for data collection in most countries, but some have adopted electronic/mobile-based systems or both. The data flow for CBHIS varies by country and the tools used for data collection. CBHIS data informs policies, resource allocation, staffing, community health dialogues, and commodity supplies for community health programmes. Community dialogue is the most common approach for community engagement, empowerment, and sharing of CBHIS data with communities. Community empowerment tends towards health promotion activities and health provider-led approaches. Conclusion CBHIS utilises both paper-based and electronic-based systems to collect and process data. Nevertheless, most countries rely on paper-based systems. Most of the CBHIS investments have focused on digitisation and enhancing data collection, process, and quality. However, there is a need to shift the emphasis towards enabling data utilisation at the community level and community empowerment.

Utility of syndromic surveillance for the surveillance of healthcare-associated infections in resource-limited settings: a narrative review.

Globally, Healthcare-associated infections (HCAIs) pose a significant threat to patient safety and healthcare systems. In low- and middle-income countries (LMICs), the lack of adequate resources to manage HCAIs, as well as the weak healthcare system, further exacerbate the burden of these infections. Traditional surveillance methods that rely on laboratory tests are cost-intensive and impractical in these settings, leading to ineffective monitoring and delayed management of HCAIs. The rates of HCAIs in resource-limited settings have not been well established for most LMICs, despite their negative consequences. This is partly due to costs associated with surveillance systems. Syndromic surveillance, a part of active surveillance, focuses on clinical observations and symptoms rather than laboratory confirmation for HCAI detection. Its cost-effectiveness and efficiency make it a beneficial approach for monitoring HCAIs in LMICs. It provides for early warning capabilities, enabling timely identification and response to potential HCAI outbreaks. Syndromic surveillance is highly sensitive and this helps balance the challenge of low sensitivity of laboratory-based surveillance systems. If syndromic surveillance is used hand-in-hand with laboratory-based surveillance systems, it will greatly contribute to establishing the true burden of HAIs in resource-limited settings. Additionally, its flexibility allows for adaptation to different healthcare settings and integration into existing health information systems, facilitating data-driven decision-making and resource allocation. Such a system would augment the event-based surveillance system that is based on alerts and rumours for early detection of events of outbreak potential. If well streamlined and targeted, to monitor priority HCAIs such as surgical site infections, hospital-acquired pneumonia, diarrheal illnesses, the cost and burden of the effects from these infections could be reduced. This approach would offer early detection capabilities and could be expanded into nationwide HCAI surveillance networks with standardised data collection, healthcare worker training, real-time reporting mechanisms, stakeholder collaboration, and continuous monitoring and evaluation. Syndromic surveillance offers a promising strategy for combating HCAIs in LMICs. It provides early warning capabilities, conserves resources, and enhances patient safety. Effective implementation depends on strategic interventions, stakeholder collaboration, and ongoing monitoring and evaluation to ensure sustained effectiveness in HCAI detection and response.

Use of School Data Management System (SDMS) For Performance of Students in Twelve Years Basic Education in Rwanda

This paper assessed the use of school data management system in performance of students in twelve years basic education in Musanze District. The Research project aimed to achieve the following objectives: to evaluate the DSMS types of financial services in use for the performance of students of 12YBE; to assess the role of SMS academic evaluation in the performance of students of 12YBE; to show how SDMS helps in school infrastructure management for the performance of students of 12YBE; and to ascertain the effects of SDMS personal identification in the performance of students of 12YBE. The research project employed a descriptive survey design, incorporating both quantitative and qualitative methodologies. The target population was 1,130 respondents projecting in education sector Musanze District. In this Research project 339 respondents were sampled from 1,130 teaching staff. This sample was scientifically determined using an appropriate research project formula. The sample size was determined using Slovin’s formula. Stratified random sampling was implemented, wherein strata were established according to the schools. The primary data were collected using questionnaires elaborated for 339 respondents. For validity, the questionnaire was shared with seasoned research project for their inputs which were taken into consideration before piloting the questionnaire and conducting comprehensive data collection. The data analysis encompassed both quantitative and qualitative methods. Quantitative research project involved precise measurement techniques to yield accurate mathematical outcomes. Data were presented in tables and graphs, and quantitative data were analyzed and interpreted using the Statistical Package for Social Sciences (SPSS) software. The findings were done for only 330 respondents as 9 questionnaires were not returned. So, the findings from 330 respondents showed that the use of School Data Management Systems (SDMS) in Rwanda’s 12-Year Basic Education system has demonstrated significant potential to enhance educational practices and improve student outcomes. The comprehensive analysis conducted in this Research project reveals strong correlations between SDMS implementation and various facets of student performance, academic evaluation, infrastructure management, and personal identification. Recommendations have been provided to both SDMS users in schools and MINEDUC to optimize SDMS utilization, ensure sustainable implementation, and foster data-driven decision-making.

The Role of Data Analytics and Sustainable Circular Economy in Developing Countries

In the twenty-first century, data analytics has become a vital tool in decision-making. Modern companies and organisations create enormous volumes of data, which need for sophisticated ways to analyse. Statistics help companies make data-driven decisions by organising, summarizing, and interpreting data. 21st-century enterprises, organisations, and people generate more data than ever. Due to rapid technological advancement and growing data, companies must make data-driven choices. As such, the place of data analytics is the era of digital economy cannot be overemphasize. This paper essentially review these roles in achieving sustainable circular economy in developing countries. The study was based solely on secondary data elicited from the review of previous studies on the subject being investigated. The examined the concepts and benefits of circular economy, the challenges in implementing circular economy in developing countries, as well as the concept, impact and role of data analytical impact on achieving sustainable circular economy. The overall findings from this study indicates that indeed, data analytics play important role in achieving sustainable circular economy in developing countries and thus recommends the system theory approach to circular economy where all parts are connected in somewhere to enhance effectiveness and reduce cost. Such connection can only be efficient through data analytics.

Opportunities for the use of artificial intelligence in supply chain management

Aim. To evaluate the potential of generative artificial intelligence technology in the context of supply chain management.Objectives. To analyze how artificial intelligence is changing traditional management practices and structures in supply chain management; to identify fundamental shifts brought about by AI, including automation, data-driven decision making, and human empowerment; to examine current statistics on the effectiveness of artificial intelligence in management processes and conclude the potential, promise of this technology in the logistics industry; to provide strategic insight and practicalMethods. The research combines qualitative and quantitative methods to provide a deeper understanding of the problems considered. Based on these methods, an array of relevant statistical data and expert opinions in the field of generative artificial intelligence has been studied.Results. In today’s business environment, supply chains face a number of challenges: globalization, market volatility, changing customer expectations, and force majeure (natural disasters, pandemics, or wars). To meet these challenges and strengthen their market position, companies are increasingly turning to new technologies and innovations. These technologies have the potential to revolutionize the approach to supply chain management, increasing transparency, efficiency and responsiveness. All emerging technologies, from artificial intelligence and blockchain to the Internet of Things (IoT) and robotics, potentially offer opportunities to streamline operations, improve decision-making, and enhance supply chain collaboration.Conclusions. In this article, the authors have shown how artificial intelligence is changing traditional governance structures in supply chain management. Current statistical data on the effectiveness of artificial intelligence in management processes are analyzed, and a conclusion is made about the potential and prospects of this technology in the logistics sphere. Strategic understanding is formed and practical recommendations for organizations seeking to implement new tools of generative artificial intelligence are given.

Translation Technology for Foreign Language Teachers and Improvement of Translation Teaching Competencies

The rapid development of translation technology has profoundly impacted traditional foreign language teaching methods, providing extensive support for both teachers and students. This paper explores the influence of translation technology on foreign language teaching, analyzing the current status, attitudes, usage habits, and needs of teachers in relation to technology application. The swift advancement of technology has enriched foreign language teaching support and transformed traditional teaching models. The paper focuses on analyzing the types and applications of translation technology, its functions and roles in teaching, including classroom instruction, instructional design, and assignment assessment. Additionally, it examines the core elements of translation teaching competence, pathways for development, and strategies for enhancement, with a particular emphasis on technology-driven innovation models and the application of intelligent tools. Finally, the paper proposes future research directions, such as in-depth exploration of technology integration and data-driven teaching decisions, aiming to provide references for the development of translation teaching.

Modern Educational Institution Management Strategies

Background and Aim: Modern educational institution management strategies are critical for adapting to students' and educators' rapidly changing needs, ensuring school effectiveness and sustainability. These strategies allow institutions to create responsive and flexible learning environments, which improves educational outcomes and operational efficiency. This paper aims to analyze Modern Educational Institution Management Strategies. Materials and Methods: To identify and evaluate effective management practices, a comprehensive review of existing literature and case studies was performed. The review combined findings from various sources to provide a comprehensive view of strategies for improving educational institution effectiveness and sustainability. Results: The study discovered that implementing modern educational institution management strategies is critical for providing a flexible, efficient, and responsive environment for today's students and educators. Institutions can improve their effectiveness and sustainability by focusing on data-driven decision-making, technology integration, collaborative leadership, and inclusive practices. Continuous stakeholder engagement, professional development, and a focus on student wellness are essential for fostering a dynamic and supportive learning environment. Conclusion: The study discovered that implementing modern management strategies is critical for fostering a flexible and responsive learning environment. Institutions can improve their effectiveness and sustainability by focusing on data-driven decision-making, technology, collaborative leadership, and inclusivity, as well as creating a supportive and dynamic learning environment through ongoing stakeholder engagement, professional development, and student wellness.

Patterns of HIV-1 Drug Resistance Observed Through Geospatial Analysis of Routine Diagnostic Testing in KwaZulu-Natal, South Africa.

HIV-1 drug resistance (HIVDR) impedes treatment and control of HIV-1, especially in high-prevalence settings such as KwaZulu-Natal (KZN) province, South Africa. This study merged routine HIV-1 genotypic resistance test (GRT) data with Geographic Information Systems coordinates to assess patterns and geographic distribution of HIVDR in KZN, among ART-experienced adults with virological failure. We curated 3133 GRT records generated between 1 January 2018 and 30 June 2022, which includes the early phase of dolutegravir (DTG) rollout, of which 2735 (87.30%) had HIVDR. Of the 2735, major protease, nucleoside, and non-nucleoside reverse transcriptase inhibitor mutations were detected in 41.24%, 84.97% and 88.08% of GRTs, respectively. Additional genotyping of HIV-1 integrase for 41/3133 (1.31%) GRTs showed that 17/41 (41.46%) had integrase strand transfer inhibitor resistance. Notably, of 26 patients on DTG with integrase genotyping, 9 (34.62%) had DTG-associated resistance mutations. Dual- or triple-class resistance was observed in four of every five GRTs. The odds of HIVDR increased significantly with age, with ≥60 years having 5 times higher odds of HIVDR compared to 18-29 years (p = 0.001). We identified geospatial differences in the burden of HIVDR, providing proof of concept that this could be used for data-driven public health decision making. Ongoing real-time HIVDR surveillance is essential for evaluating the outcomes of the updated South African HIV treatment programme.

A LiDAR-Based Digital Twinning Workflow for Traffic Monitoring and Simulation

Abstract. The process of ensuring efficient and safe urban transportation is closely linked to urban planning, particularly through the aspects of transportation planning. Transportation planning is a pivotal concern for urban regions worldwide, reflecting the growing need to increase mobility while ensuring safety and sustainability in densely populated areas. This research focuses on developing a novel digital-twin-based approach for micro-traffic simulation to support data-driven decision-making for increasing traffic safety through scenario planning. Leveraging the traffic data obtained through monitoring one of the busiest intersections in Sofia city, this research workflow shows the effective integration of LiDAR data and the urban digital twin concept in intelligent transportation systems (ITS). The research addresses problems related to moving object classification, trajectory analysis, and reclassification of unrecognised objects by processing the LiDAR data, pre-processed in a .osef format, thereby transforming it to make it suitable for simulation. The proposed solution for the monitoring of urban traffic is demonstrated by the usage of SUMO (Simulation of Urban MObility) for performing simulations and a Random Forest model for unrecognized object reclassification to pre-existing vehicles and pedestrian classes. The architecture of the proposed workflow can possibly be applied in other similar urban settings, providing a scalable solution for both traffic management and urban planning. The study’s results support the wider use of urban digital twin principles in ITS by highlighting the value of advanced modelling tools and high-quality data in addressing today's urban transportation challenges.

Advancing titanium dioxide coated photocatalytic depolluting surfaces: Leveraging ASINA's roadmap for safer and sustainable solutions

This report, the second of its kind from ASINA project, aims at providing a roadmap with quantitative metrics for Safe(r) and (more) Sustainable by Design (SSbD) solutions for titanium dioxide (TiO2) nanomaterials (NMs). We begin with a brief description of ASINA’s methodology across the product lifecycle, highlighting the quantitative elements, such as the Key Performance Indicators (KPIs). We then propose a decision support tool for implementing SSbD objectives across various dimensions—functionality, cost, environment, and human health safety. This is followed by the main innovative findings, a consolidation of the technical processes involved, design rationales, experimental procedures, tools and models, used and developed, to deliver photocatalytic depolluting surfaces by spray- finishing techniques based on TiO2 NMs formulations. The roadmap is thoroughly described to inform similar projects through the integration of KPIs into SSbD methodologies, fostering data-driven decision-making. While specific results are beyond this report's scope, its primary aim is to demonstrate the roadmap (SSbD know-how) and promote SSbD-oriented innovation in nanotechnology. Finally, we provide a comparison of the approaches followed in two case studies that target different industrial sectors. This case-specific SSbD assessments provide a concrete exemplification of the addressed methodology that contributes to the efforts towards attaining a common roadmap for implementing SSbD solutions aligned with the EU’s Green Deal objectives.

Autonomous Vehicle Diagnostics and Support: A Framework for API-Driven Microservices

The rapid evolution of autonomous vehicles (AVs) demands robust and scalable diagnostic and support systems to ensure seamless operations, safety, and performance. This paper presents a framework for API-driven microservices tailored to address the unique diagnostic and support requirements of AVs. Traditional monolithic systems are insufficient for managing the complex, real-time data generated by AVs, necessitating a shift toward microservices architecture. By leveraging API-driven microservices, the proposed framework ensures modularity, scalability, and real-time communication between various vehicle components and external support systems. The framework is designed to support continuous vehicle monitoring, predictive maintenance, fault detection, and real-time decision-making, all while minimizing latency. APIs play a critical role in enabling interaction between different microservices, allowing for seamless data exchange across diverse platforms. This interoperability facilitates integration with third-party services, such as cloud-based diagnostics, real-time traffic information, and software updates. Additionally, the microservices architecture ensures fault isolation, meaning that failures in one service do not compromise the entire system. The framework also emphasizes the importance of cybersecurity and data privacy. Given the sensitive nature of AV data, API security protocols such as OAuth2 and TLS encryption are incorporated to safeguard communication between microservices and external systems. Furthermore, the design is scalable, allowing for the integration of future AV technologies and third-party service enhancements without disrupting existing functionalities. In conclusion, API-driven microservices provide an efficient, flexible, and secure solution for autonomous vehicle diagnostics and support. This framework has the potential to improve AV reliability, reduce downtime, and enhance safety by enabling real-time, data-driven decision-making and proactive maintenance. Future work may explore the integration of machine learning algorithms to further optimize diagnostics and predictive maintenance capabilities. Keywords: Autonomous Vehicles, Microservices, API-Driven Architecture, Diagnostics, Predictive Maintenance, Real-Time Data, Cybersecurity, Vehicle Support Systems, Modularity, Scalability.

Service for sustainability and overall value for money: sequential mediation of customer-generated communication on social media and perceived value

PurposeThis study investigated the predictors of overall value for money in the hospitality industry concerning routine practices reinforcing SDG goals. The sequential mediation underlines the mechanism of theoretical viability and sustainability relevance of restaurant environment factors and customer-generated communication on social media to influence the value impressions of hospitality customers.Design/methodology/approachCross-sectional design was utilized to obtain empirical data from customers of luxury restaurants in mega cities of Pakistan. Non-probability purposive sampling was deployed to collect data from 370 customers. The chained relationship of predictors was analyzed using Smart PLS.FindingsThe SDGs' integration with predictors provided theoretical insights into the role of physical spaces in achieving SDG 14 of sustainable consumption and production. The role of social media communication explains the mechanism of online discourse, which shapes perceptions of sustainable dining experiences.Practical implicationsThis study explained sustainable consumption's relation to consumers' psychological undertakings and reinforced the dinners' decision-making processes to evaluate expectations influencing dining choices. This study helps to understand how sustainability issues transformed consumers' value perception and helps them take measures to minimize the environmental impact of their dining choices.Originality/valueThe sequential mediation model investigated with the lens of expectation disconfirmation theory provided relational clarity, which is difficult to achieve when comparing the perception of customers with performance variables. Distinguishing overall value for money from perceived value is essential for making data-driven decisions to utilize monetary resources efficiently.

Metamorphosing forex: advancements in volatility forecasting using a modified fuzzy time series framework

The interplay of exchange rates among nations significantly influences both international and domestic trade, underscoring the pivotal role of the foreign exchange market (Forex) in a country's economic landscape. Forex fluctuations have a significant impact on the everyday lives of both government agencies and the public population, directly influencing a country's prosperity or misfortune. This work proposes an advanced fuzzy time series model that incorporates domain universe sub-partitioning, parameter adjustment optimization methodologies, and interval forecasting methods. We utilized this model to examine annual exchange rate patterns between the Pakistani rupee (PKR) and the US dollar (US$), comparing its forecast accuracy to that of other models. Our proposed methodology outperformed existing methodologies in terms of forecasting precision, providing stakeholders with valuable insights for making informed, data-driven business decisions that benefit both individual firms and the country overall.

Data analytics for visualization of business insights for an online retail store using Python

In today’s world, data is becoming an organization's most important asset. Businesses have easy access to large amounts of data from a range of sources, and when those data are processed quickly and reliably, the chances of obtaining useful insights to inform data-driven decision-making are significantly increased. Modern corporate strategy must include data analytics because it offers critical insights that improve operational efficiency and decision-making. Using data analytics to visualize company insights can significantly improve a number of areas for an online retail store, including marketing strategy, customer experience, inventory management, and sales. This paper aims a thorough examination at the main areas, methods, and approaches that may be used to delve into business insights using data analytics, as well as the advantages it has for online retailers using a sample online retail dataset.. Keywords: Data Analytics, Data Visualization, Inventory, Sales Analysis, Descriptive Analytics.

MODELS FOR REAL-TIME TRAFFIC FLOW MANAGEABILITY AND DECISION-MAKING IN INTELLIGENT TRANSPORTATION SYSTEMS

This article explores models in Intelligent Transportation Systems for real-time traffic flow manageability, focusing on decision-making processes. It covers forecasting, planning, implementing, and controlling strategies to manage traffic flow and ease congestion. Traffic flow prediction models, like dynamic route guidance and traffic flow prediction, utilize historical data and real-time inputs for proactive decision-making. Traffic flow planning models, such as dynamic route guidance index and route efficiency factor, aid in route selection and signal timing optimization. In order to streamline the boundless complexity, the authors assume that it is effective to delineate the managerial capacity paradigm of intelligent transportation systems into the two separate scenarios of “stable and known situation” and “unstable and with large uncertainty situation”. The article proposes a hypothesis to improve the decision-making process in traffic flow. The distinction between these two situations is essential for the smooth running of the business and requires a thorough understanding of the traffic flow in real time, making decisions in intelligent transport systems in order to direct the traffic. The article focuses on data-driven decisions for smoother traffic flow.

Project Monitoring Based on View of All Project Partners At Digital Project Management Platforms

In the dynamic landscape of digital project management, effective project monitoring is critical for successful project outcomes. The emergence of digital project management platforms has revolutionized the way projects are monitored, enabling real-time collaboration and data-driven decision-making. Periodic site visits and gathering input from all project stakeholders are crucial for ensuring clear understanding of project progress by states institutional. With digital project management systems, on-site progress should be monitored in real-time and inclusive of all project stakeholders. The research investigates how collaborative monitoring fosters project progress of transparency and active engagement among project partners. By providing real-time visibility into project progress, tasks, and milestones, these platforms empower stakeholders with up-to-date information, enabling agile decision-making and prompt risk mitigation. This study explores how digital project management platforms integrate site monitoring evaluations and reporting tools, providing valuable data-driven insights into project performance. By considering periodic analytics, project partners can improve resource allocation, decision making process against possible bottlenecks at construction stages and boost productivity and inform states institutional to monitor project progress closely. Furthermore, the research examines the influence of these platforms on communication dynamics, promoting communication and efficient information exchange among project partners. Such enhanced communication capabilities result in improved team cohesion and collaboration, driving the project forward with synchronized efforts. In this study, the focus lies on tracking the progress of contractual agreements through digital platforms, enabling all stakeholders to monitor advancements. Specifically, the investigation centers on how the designated contractor responsible for project execution and the consulting firm appointed by the project owner can effectively track project progress on a periodic basis using digital platforms. In the context of transportation projects in Turkey, a digital project tracking system has been developed for the Ministry of Transport and Infrastructure, through which data obtained from on-site visits and consultant opinions regarding project advancements are shared with the Ministry. Subsequently, the responsible contracting authority, guided by the consultant's feedback, can respond and evaluate progress accordingly. This approach ensures that project advancements are accurately monitored by all stakeholders of the project. To do so, this study investigates the utilization of the prepared digital platform to facilitate rapid and precise contract monitoring by all project stakeholders, encompassing the opening information and assessment evaluations based on stakeholders' perspectives.