Technology Decisions Research Articles

Optimizing In-Store Logistics: How AI Enhances Inventory Management and Space Utilization

This integrative literature review looks at the revolutionary impact of artificial intelligence (AI) in optimizing in-store logistics to assist retail managers and technology decision-makers in using AI to improve inventory management, spatial organization, and customer experience. Based on six core concepts—AI-driven demand forecasting, automated inventory replenishment, space utilization optimization, adaptive store layout design, operational efficiency, and customer satisfaction—the study's conceptual framework emphasizes AI's strategic value and the factors driving its adoption in retail logistics. The review uses rigorous criteria and systematic analysis of peer-reviewed articles, industry reports, and case studies to identify significant topics such as AI-enhanced demand forecasting, automated restocking, responsive shop layouts, data protection, and the changing responsibilities of retail staff. The paper advocates for balanced AI integration, integrating technology breakthroughs with human control and appropriate data management. Future research proposals include investigating AI's long-term implications, doing comparative assessments across retail forms, and developing frameworks for ethical data usage. These will all provide foundational insights for constructing sustainable, sophisticated retail environments that align with global development goals.

ESTIMATION OF THE CLEANING TIME OF THE BOTTOM-HOLE ZONE OF WELLS IN CONDITIONS OF UNCERTAINTY

The aim of the work is to analyze changes in the maximum optimal flow rates of wells operating various groups of productive formations of the Volga-Ural oil and gas province under conditions of uncertainty of geological and field data. The relevance of the work is due to the deterioration of the structure of residual oil reserves and the need for the most efficient extraction of liquid hydrocarbon resources, optimization of design methods for various treatments of the bottomhole formation zone. Using geological and statistical modeling systems, the timing of cleaning the bottomhole zones of wells from drilling products was determined at the time they reached their maximum optimal flow rate in conditions of deposits confined to various stratigraphic horizons, and appropriate models of changes in productivity coefficients were constructed. The factors influencing the recovery time of the real properties of the reservoir at the point of opening it by the well, which, among other things, determines the profitability of the process of selecting residual oil reserves, have been established. Among them, the most relevant and necessary for the implementation of an integrated approach to computer support procedures for the development of oil fields are the productivity of wells, fracturing of formations, their proximity to a particular tectonic-stratigraphic complex, geological, physical and physico-chemical properties of formations and their saturating fluids. For the deposits of the Famensk tier of deposits of the Volga-Ural oil and gas province, a multiple decrease in the time of removal of reaction products after hydrochloric acid exposure at objects with low fracturing has been established. At the same time, additional processing of the bottomhole formation zone within areas with high drilling density does not affect the total cleaning time of the bottomhole zone of wells. Stimulation of the removal of drilling mud filtrate and additional products of formation reactions by injection of hydrochloric acid compositions under certain conditions can lead to a decrease in the actual productivity of wells due to the introduction of less developed and dead-end cracks into the drainage process. The existence of the specifics of the procedure for cleaning the bottomhole formation zone depending on the geological and physical parameters of productive formations is substantiated and various patterns of changes in the productivity coefficient depending on the time of wells reaching the maximum optimal flow rate are revealed. The results obtained make it possible to make informed technological decisions to improve the efficiency of oil production, including in conditions of uncertainty, which may be caused by the lack of the necessary volume of hydrodynamic studies of wells.

Unveiling the strategic impact of big data analytics capabilities in the Saudi Arabian banking sector: an explorative approach

Purpose The purpose of this study is to identify a critical pathway of the effect of big data analytics capabilities (BDACs) on strategic vigilance based on hierarchical process and a capability approach. Design/methodology/approach The researcher adopted a qualitative approach using interviews and a quantitative approach based on the interpretative structural modeling (ISM) fuzzy cross-impact matrix multiplication applied to classification (MICMAC) approach. A primary theoretical approach was also conducted to identify BDACs previously cited in the literature. Findings Four main subdivisions of BDACs were identified: management capabilities, infrastructure flexibility, talent capability and technology. Management capabilities followed by big data technical knowledge and associated with talent capabilities generate a flexible infrastructure to enhance SV. A dynamic capability perspective of knowledge and information is also required for SV. Research limitations/implications Despite the opportunity of this research and the originality of results, some limitations have to be mentioned and can constitute further directives for future researchers, such as the problem of result generalization. First, this research was based in Saudi Arabia, and a comparative approach to defining BDAC on an international level can be more beneficial in providing an exhaustive list of these capabilities. Second, reliability issues, in this research can be addressed due to the use of qualitative data collection which is considered by many researchers as unspecified and can lack scientific rigor. Future studies can improve the number of interviews during the data collection process and data process using an advanced methodological approach. Third, the effect of BDAC in SV according to the hierarchical final modal is not quantified, future work can use this research model to appreciate each effect using a quantitative approach such as correlation and structural equation modeling while considering respondents with different profiles to take into account different point of view in this concern. Practical implications This research enriches the BDAC and MICMAC literature and contributes to this aspect in three main levels. First, by providing an additional empirical asset in this field, this study offers by the way a new case to the big data literature on the banking sector. Based on the limited knowledge as well as results collected from different databases and rigorously analyzed, this subject was not treated previously and the author could not find similar studies with the same approach dealing with the key BDACs in Saudi Arabia. Social implications This research presents three main implications for policymakers and researchers interested in big data analytics (BDA) through a capability and strategic perspective. First, to attain SV, they should prioritize the development of interactive interfaces and open platforms as the primary step before collecting information and deconstructing it to guarantee the generation of knowledge and make decisions effectively. Second, policymakers must introduce organizational technologies in terms of technology management, technical knowledge and technology for decision-making. This requires simultaneous sharing and communication according to relational management. Third, the research conclusions have many critical managerial ramifications for banks in Saudi Arabia while considering the adoption of BDAC. The importance of BDACs (especially technical aspects) in shaping the decision-making to be strategically vigilant emphasizes policymakers’ orientation by paying close attention to these aspects and specific training programs to facilitate the use of such technologies and guarantee strong security measures. Moreover, findings support a balance between technical and functional BDAC. Originality/value The adoption of a knowledge-based dynamic capabilities (KBDCs) view to analyze the interaction between different BDACs in banks in Saudi Arabia to be strategically vigilant using a mixed approach.

Research on autonomous driving technology in intelligent transportation systems

Abstract. The convergence of scientific and technological advancement with the acceleration of urbanization has resulted in a notable intensification of the issue of traffic congestion. In this context, the development of autonomous driving technology has emerged as a pivotal area of focus for future transportation systems. Therefore, this paper presents a review of the current status of automatic driving technology in intelligent transportation systems, based on methods such as literature, data analysis and case studies. Furthermore, it delves into the evolution, applications, challenges and solutions of automated driving technologies in intelligent transport systems, and presents forward-thinking insights into the future development of intelligent transportation. As autonomous driving technology continues to evolve, the integration of deep reinforcement learning and other advanced technologies will facilitate the advancement of intelligent vehicle human-machine collaborative decision-making technology. It is imperative that the construction technology of safety-critical scenarios be reinforced in order to enhance the interpretability of algorithmic models and the real-time nature of scenario generation. All parties need to collaborate to promote the innovation and improvement of autonomous driving technology, achieve widespread application and sustainable development, and create a better travelling environment.

Inverting Cognitive Models With Neural Networks to Infer Preferences From Fixations.

Inferring an individual's preferences from their observable behavior is a key step in the development of assistive decision-making technology. Although machine learning models such as neural networks could in principle be deployed toward this inference, a large amount of data is required to train such models. Here, we present an approach in which a cognitive model generates simulated data to augment limited human data. Using these data, we train a neural network to invert the model, making it possible to infer preferences from behavior. We show how this approach can be used to infer the value that people assign to food items from their eye movements when choosing between those items. We demonstrate first that neural networks can infer the latent preferences used by the model to generate simulated fixations, and second that simulated data can be beneficial in pretraining a network for predicting human-reported preferences from real fixations. Compared to inferring preferences from choice alone, this approach confers a slight improvement in predicting preferences and also allows prediction to take place prior to the choice being made. Overall, our results suggest that using a combination of neural networks and model-simulated training data is a promising approach for developing technology that infers humanpreferences.

Modeling Urban Expansion and Its Impacts on Carbon Storage Through Integrative Scenario Analysis for Sustainable Development in the Changchun-Jilin-Tumen Region

The Changchun-Jilin-Tumen (CJT) region in Northeast China is a key economic hub and a strategic gateway for China's integration into Northeast Asia, playing a crucial role in the Belt and Road Initiative. However, rapid urbanization in this region has precipitated substantial environmental challenges, particularly the degradation of carbon storage services (CSS) due to the expansion of impervious surface areas (ISA). This study employs multi-source remote sensing data, advanced geospatial analysis, and scenario-based decision-making technologies to evaluate the impacts of ISA expansion on CSS from 2000 to 2035 using the PLUS and InVEST models. The results reveal significant losses in CSS, with a net decrease of 8.10 × 10⁶ tons of carbon projected by 2022. Under the Natural Development scenario, these losses could escalate to 15.32 × 10⁶ tons by 2035. Conversely, the Ecological Protection and High-Quality Development scenarios suggest that strategic interventions could mitigate these losses, potentially leading to CSS gains of up to 3.42 × 10⁶ tons in the Ecological Protection scenario. This study highlights the pressing need to integrate ecological considerations into urban planning to balance development with environmental sustainability. The findings provide essential guidance for policymakers aiming to align urban growth with carbon neutrality targets, particularly in the context of global climate commitments and the increasing imperative for sustainable urban development.

Health Technology Assessment of Nailfold Capillaroscopy and Digital Retina Imaging in Diabetes Screening

AimThis study aims to identify the criteria for the evaluation of medical technologies in diabetes screening and then perform health technology assessment (HTA) for two technologies: digital retina imaging (DRI) and Nailfold Capillaroscopy (NFC). MethodsA multicriteria decision-making tool measuring attractiveness using a categorical-based evaluation technique (MACBETH) was used to calculate and compare alternatives. A focus group of ten decision-makers with more than ten years of experience in health technology was used for the decision-making. The Delphi Method was used to get a consensus about comparing criteria and alternatives to these criteria. The sampling method used in the study was the nonprobability purposive sampling method. ResultsThe study concluded that clinical efficacy (0.42) is the most important criterion for evaluating medical technologies, followed by cost-effectiveness (0.27). At the same time, ethical considerations were found to be less important (0.03). The sensitivity analysis concluded that the decision is sensitive to clinical efficacy and cost-effectiveness changes. The study found that none of the alternatives dominates others as an option for early detection of diabetes through vascular changes. LimitationsThis study primarily uses systemic literature review methods to identify criteria for evaluating alternatives. There may be additional criteria for evaluation. ConclusionThis study provides an approach for a multicriteria comparison of medical devices. The study findings are useful for public health professionals and health policymakers. Plain Language SummaryThis study assesses nailfold capillaroscopy (NFC) as a substitute for digital retina imaging (DRI) in diabetes screening using the MACBETH multicriteria decision-making tool. The research identifies five key evaluation criteria: clinical efficacy, cost-effectiveness, patient needs, user safety, and ethical considerations. A focus group of experienced health technology decision-makers was used to compare NFC and DRI. Results indicate that clinical efficacy and cost-effectiveness are the most critical factors, with NFC showing potential but not significantly outperforming DRI. Sensitivity analyses highlight the need for further research to validate NFC as an effective diabetes screening tool.

Devising a project risk management method under scrum conditions based on cognitive approach

Any project implemented using Scrum is characterized by the impact of risks, including negative changes in the environment and crisis circumstances. Therefore, the processes related to risk management, which is the object of this paper, become important. The problem solved in this study is to improve the efficiency of projects through the construction of a long-term strategy for reducing the level of risk and avoiding negative consequences for projects in the context of Scrum. The proposed method of risk management has been developed on the basis of the application of the synthesis of management of intelligent decision-making technologies and formalized methods. Difficult external circumstances are characterized by a high degree of uncertainty and do not always contribute to the successful implementation of the project. Therefore, this method of project risk management under Scrum conditions based on a cognitive approach is characterized by a combined combination of cognitive analysis, mathematical modeling, and expert methods. As part of the method, a model of project risk management under Scrum conditions has been built in the form of a fuzzy cognitive map, which could ensure determining the optimal strategic decision in dynamics, taking into account the effects of various factors. The result of applying this method is compliance with time limits, reduction of overspending of resources and losses in the project, as well as adaptation to rapidly changing circumstances and adequate response. The method of project risk management is characterized by solving the problem of formalizing management decision-making procedures and their information support, taking into account the availability of both quantitative and qualitative data. Within the framework of this method, a project risk management procedure under Scrum conditions has been proposed, which contributes to the systematization, monitoring, and control of risks under the conditions of complex, rapidly changing crisis circumstances

Applications of Neuro Deep Learning Models in Predictive Data Analytics for the Movements and Trends of the Indian Stock Market: Financial Data Mining, Nonlinearity, and Quantum Finance

The financial world of today is high-frequency data-driven and characterized by the application and use of information technology for better business development and decision-making. The price movements of stock markets are mainly influenced by micro and macroeconomic variables, legal frameworks, and taxation policies of the respective economies. The crux of the issue lies in exactly forecasting the future stock price movements of individual firms, based on historical prices. Achieving accuracy for forecasting the market trend has become difficult due to the prevalence of stochastic behaviour in the stock market and volatility in the stock prices. This paper analyzes the stochastic movement pattern of the highly volatile thirty individual company stocks (in terms of market capitalization) of BSE-Sensex, using the Neuro Deep Learning method, a model of deep learning approach. The findings of the study would help the investors, to make rational and well-informed investment decisions, and to optimize the returns by investing in the most valuable stocks of listed corporate enterprises.

Defining the growth paths of strategic scientists based on case studies

In this paper, 18 scientists from China and other countries were selected for consideration as case studies of ‘strategic scientists’. They were chosen according to their involvement in science and technology policy decision-making, development of key and advantageous fields, creation of new disciplines, construction of academic traditions, and intersections between science and industry. On this basis, we found that the growth paths of strategic scientists can be divided into three phases: as talented young scientists, as gatekeepers in related disciplines or research fields, and as consultants for national science and technology decision-making. Based on this trichotomy, we explored the characteristics of strategic scientists and the conditions required for their growth in each phase. Finally, policy suggestions are proposed for the cultivation and use of strategic scientists.

IMPACT ASSESSMENT OF INFORMATION TECHNOLOGY ON INVESTMENT CHOICE DECISIONS OF ACADEMIC PROFESSIONALS

The substantial impact of information technology on investment decision-making is explored in this study, and strong empirical support is provided through a significant regression model. Understanding the extent to which technology influences investment choices is of utmost relevance in a time of fast technological advancement. The research makes use of a varied dataset that includes a variety of economic and market characteristics, employing robust statistical analysis to evaluate the relationship between technology and investment decisions. Our findings show that information technology and investing choices are statistically significantly correlated. The regression model illustrates how technology is increasingly being used into investment strategies and how it influences investors' decisions. This study emphasizes the importance of incorporating technical expertise into investment plans, with a focus on data analytics, artificial intelligence, and long-term tech investments. Furthermore, the ease with which mutual funds can be invested in, with alternatives for both novice and experienced investors, has made them a practical and accessible option as the study points mutual funds as the most preferred option for investment. Their liquidity and transparency also resonate with investors who appreciate the ability to monitor their investments easily and access their funds when needed. The results of the study highlight how crucial it is to comprehend investor preferences for financial goods. The popularity of mutual funds not only demonstrates their benefits but also emphasizes the necessity for financial institutions and advisors to customise their services and products in order to meet investor needs. KEYWORDS: Information Technology, Investment Choice Decisions, Mutual Funds

Emerging challenges of SMEs for the post-COVID-19 era: implications for gresilience and sustainability

PurposeThe main aim of this paper is to investigate key challenges that hinder sustainable and resilient green supply chain (GSC) building in small and medium-sized enterprises (SMEs) with respect to how they interact after the COVID era.Design/methodology/approachUsing a questionnaire, policymakers and industry managers from SMEs identify the main obstacles that will affect their resilience. These issues are then discussed with 50 experienced professionals from the top management field and further divided into sustainability and gresilience challenges. The Grey-DEMATEL (Grey sets and Decision-Making Trial and Evaluation Laboratory) has been employed to assess the significance of challenges to SMEs' development of green resilience. Overall, the paper aims to build up a novel decision-making technology for the interconnectedness of green and resilience in the post-pandemic supply chain world.FindingsThe 12 sustainability and gresilience challenges highlighted by experts in the sector were analyzed. Insufficient government assistance and the handling of resources are the most significant challenges to developing a sustainable and gresilient value chain system, according to the study. The study highlights supply chain restructuring, waste reduction management, shortages of skilled manpower and insufficient knowledge of digitalization as key challenges for SMEs to prioritize.Practical implicationsThe outcomes of this research are crucial for SMEs building resilient supply networks through the green concept. Policymakers may utilize the data to pinpoint shortcomings in the supply chain for tactical as well as strategic development and improvement.Originality/valueWhen it comes to building green resilience and sustainability in an unpredictable business climate, the outcomes of this research will be significant for SMEs.

Право лица, ищущего работу, на объяснение при принятии решения с помощью автоматизированной обработки

Widespread use of algorithm-based decision-making technologies and their implementation by employers and HR agencies to optimize the search for personnel exponentially increase the probability of discrimination and unjustified denial of employment. That said, it seems necessary to include the rules for application of algorithm-based technologies in decision-making in the Labor Code of the Russian Federation to ensure enforcement and protection of rights of employment-seeking persons.

Profiling patent compounds in lipid nanoparticle formulations of siRNA

Lipid nanoparticles (LNPs) have emerged as a prominent delivery system for nucleic acid drugs, attracting significant attention especially through the successful development of several commercial products. As a key component in LNPs, cationic lipids along serve as key technical barrier to block competitors by building up complex patent thicket. However, there have been few studies as yet that have comprehensively analyzed the patented compounds in LNP formulations, despite a large number of technical reviews and original articles. In the context, this study focuses on analyzing the macroscopic landscapes and microscopic molecular characteristics of LNP patents, aiming to provide a valuable reference for researchers and developers in making informed technological and commercial decisions. By mining 2,994 patents, 265 formulas, 7,674 compounds and 28,789 fragments, this work sketched the empirical golden ratio of lipid materials in LNP formulation, disclosed the advanced technology in the formulation, characterized high-frequency fragments of heads, linkers and tails in both novel cationic lipids as well as targeting lipids, and established a virtual focus library of LNP materials.

Extreme, Outrageous, and (Un)reliable: Navigating Uncertainty in the Development of Sound-Based Fog Signals in Scotland, 1860-1900.

This case study demonstrates how an analysis grounded in sound studies, rather than visual studies, reveals a different technological outcome in the development of coastal navigation systems. In the late nineteenth century, engineers at Scotland's Northern Lighthouse Board developed and managed a growing network of sound-based fog signals, primarily using steam-powered sirens. Despite the prevailing perception that sound signaling was more unreliable and risky than lighthouses for coastal navigation, engineers focused on maximizing the loudness of these devices to address the uncertainty of sound transmission. By prioritizing the siren and narrowing the system's goals to warning sailors about imminent danger, the engineers created a system with fundamentally different aims, implementation methods, and design priorities than those of visually-based lighthouse technology. This sound-based analysis uncovers how sensory hierarchies shaped technological decisions, leading to a unique and distinct approach to coastal navigation.

Application of artificial intelligence in the new generation of underwater humanoid welding robots: a review

Underwater welding robots play a crucial role in addressing challenges such as low efficiency, suboptimal performance, and high risks associated with underwater welding operations. These robots face a dual challenge encompassing both hardware deployment and software algorithms. Recent years have seen significant interest in humanoid robots and artificial intelligence (AI) technologies, which hold promise as breakthrough solutions for advancing underwater welding capabilities. Firstly, this review delves into the hardware platforms envisioned for future underwater humanoid welding robots (UHWR), encompassing both underwater apparatus and terrestrial support equipment. Secondly, it provides an extensive overview of AI applications in underwater welding scenarios, particularly focusing on their implementation in UHWR. This includes detailed discussions on multi-sensor calibration, vision-based three-dimensional (3D) reconstruction, extraction of weld features, decision-making for weld repairs, robot trajectory planning, and motion planning for dual-arm robots. Through comparative analysis within the text, it becomes evident that AI significantly enhances capabilities such as underwater multi-sensor calibration, vision-based 3D reconstruction, and weld feature extraction. Moreover, AI shows substantial potential in tasks like underwater image enhancement, decision-making processes, robot trajectory planning, and dual-arm robot motion planning. Looking ahead, the development trajectory for AI in UHWR emphasizes multifunctional models, edge computing in compact models, and advanced decision-making technologies in expansive models.

Challenges to Public Administration Related to the Implementation of the Scientific and Technological Development Strategy of the Russian Federation

The author considers the requirements of the Decree of the President of Russia “On the Scientific and Technological Development Strategy of the Russian Federation” to the characteristics and principles of functioning of the public administration system in Russia. It is shown that the list of these characteristics and requirements is incomplete and insufficient for the implementation of the Decree. The main gaps in the requirements formed by the Decree relate to limiting the reconfiguration of the management system to only one subsystem, management in the field of scientific and technological development. The article argues that the complex nature of grand scientific and technological challenges implies profound changes in the public administration system as a whole, the transition from a structural and functional management system (a combination of meritocratic and client-oriented models) to a project management system (indicative and public-state models). The changes should affect the nomenclature and the scheme of interaction between structural divisions of state bodies, increase the level of project intra- and interdepartmental coordination. In addition, significant changes must be made in the public service system. We should organize training of specialists capable of using data analytics tools and data-based decision-making technologies as well as of carrying out an indicative assessment of project implementation indicators. The structure of the civil service should be associated with project offices and temporary performance of duties in these offices, and not only with permanent functions, taking into account the specifics of project service activities and project teams within government agencies. Civil servants should actively interact with independent expert structures and with civil society, which is the ultimate beneficiary of science and technology projects.

Architecture decisions in quantum software systems: An empirical study on Stack Exchange and GitHub

Context:Quantum computing provides a new dimension in computation, utilizing the principles of quantum mechanics to potentially solve complex problems that are currently intractable for classical computers. However, little research has been conducted about the architecture decisions made in quantum software development, which have a significant influence on the functionality, performance, scalability, and reliability of these systems. Objective:The study aims to empirically investigate and analyze architecture decisions made during the development of quantum software systems, identifying prevalent challenges and limitations by using the posts and issues from Stack Exchange and GitHub. Methods:We used a qualitative approach to analyze the obtained data from Stack Exchange Sites and GitHub projects — two prominent platforms in the software development community. Specifically, we collected data from 385 issues (from 87 GitHub projects) and 70 posts (from 3 Stack Exchange sites) related to architecture decisions in quantum software development. Results:The results show that in quantum software development (1) architecture decisions are articulated in six linguistic patterns, the most common of which are Solution Proposal and Information Giving, (2) the two major categories of architectural decisions are Implementation Decision and Technology Decision, (3) Software Development Tools are the most common application domain among the twenty application domains identified, (4) Maintainability is the most frequently considered quality attribute, and (5) Design Issues and High Error Rates are the major limitations and challenges that practitioners face when making architecture decisions in quantum software development. Conclusions:Our results show that the limitations and challenges encountered in architecture decision-making during the development of quantum software systems are strongly linked to the particular features (e.g., quantum entanglement, superposition, and decoherence) of those systems. These issues mostly pertain to technical aspects and need appropriate measures to address them effectively.

An Automatic Vehicle Navigation System Based on Filters Integrating Inertial Navigation and Global Positioning Systems

The key technologies for advanced autonomous vehicles include those relating to perception, decision making, and execution. Path-tracking control in autonomous vehicles is heavily dependent on their positioning system. Therefore, the development of low-cost and reliable positioning systems is crucial to improving perception and decision-making technologies for autonomous vehicles. Although the accuracy of the global positioning system (GPS) is extremely high, it is vulnerable to interference. Further, despite the low positioning accuracy of inertial navigation systems (INSs), their robustness is notably high. Therefore, an integrated navigation information method based on the Adaptive Particle Filter and the Iterative Kalman Filter (APF-IKF) was developed in this study. Firstly, an integrated navigation system model was established. Then, the IKF was adopted to estimate the speed, latitude and longitude errors of the INS. Thirdly, the newest estimated error results were introduced into the APF to optimize the distribution function, and the particle quality was improved. In this process, the APF can filter non-Gaussian noise, preliminarily estimate the error, optimize the result with the IKF and correct the output information of the INS with the final estimated error. Finally, by using differential GPS positioning as the benchmark, we built a real-vehicle test platform with a low-cost and low-precision GPS and inertial units and carried out a series of real-vehicle tests. The experimental results show that compared with the traditional KF method, APF-IKF can significantly improve the positioning accuracy and robustness of the system.

Expert systems: an effective tool for risk management – the case of Naftal Skikda

This study aims to investigate the impact of expert systems on improving risk management at Naftal Skikda, a key player in the oil and gas sector in Algeria. It seeks to determine the effectiveness of these advanced technologies in risk detection, real-time decision-making, and overall risk management efficiency. The hypotheses tested aim to assess whether the integration of expert systems can accelerate risk detection and contribute to risk reduction, while enhancing the speed and accuracy of decision-making. This research aims to provide strategic recommendations to strengthen practices for managing risks within the petroleum and natural gas sectors, highlighting the potential of expert systems as a transformative tool.