Hasil untuk "Management information systems"

Jiong Jin, J. Gubbi, S. Marusic et al.

David Xiaosong Peng, F. Lai

Martin J. Eppler, J. Mengis

C. S. Holling

R. Sanchez, Joseph T. Mahoney

Peter Gordon Roetzel

In the light of the information age, information overload research in new areas (e.g., social media, virtual collaboration) rises rapidly in many fields of research in business administration with a variety of methods and subjects. This review article analyzes the development of information overload literature in business administration and related interdisciplinary fields and provides a comprehensive and overarching overview using a bibliometric literature analysis combined with a snowball sampling approach. For the last decade, this article reveals research directions and bridges of literature in a wide range of fields of business administration (e.g., accounting, finance, health management, human resources, innovation management, international management, information systems, marketing, manufacturing, or organizational science). This review article identifies the major papers of various research streams to capture the pulse of the information overload-related research and suggest new questions that could be addressed in the future and identifies concrete open gaps for further research. Furthermore, this article presents a new framework for structuring information overload issues which extends our understanding of influence factors and effects of information overload in the decision-making process.

Ebru Erdem, Tolga Aydın, Burak Erkayman

Last-mile logistics operations in urban environments are becoming increasingly complex due to fragmented customer demands, multiple depots, vehicle capacity constraints, and the need for split deliveries across multiple trips. Classical optimization approaches often fail to address these challenges, as they typically rely on static heuristics or do not integrate real-time data and adaptive learning. Addressing the computational complexity of Multi-Depot Vehicle Routing Problems (MDVRPs) with last-mile split deliveries and multiple trips requires algorithmic innovation and system-level efficiency. To tackle this challenge, we propose a hybrid Artificial Intelligence (AI)-based framework that integrates list-based scheduling heuristics—As Soon As Possible (ASAP), As Late As Possible (ALAP), and List Scheduling—with Transformer networks, Deep Reinforcement Learning (DRL), NeuroEvolution of Augmented Topologies (NEAT), and Model-Agnostic Meta-Learning (MAML). Among the models evaluated, the List Scheduling + Transformer (LST-Former) configuration achieved the best performance regarding route accuracy, resource utilization, and robustness under varying demand conditions. While DRL-based models demonstrated strong adaptability to dynamic logistics, they incurred higher computational costs. This trade-off was mitigated by designing the proposed architecture with High-Level Synthesis (HLS) compatibility, enabling future deployment on low-latency, energy-efficient hardware platforms.The framework was validated using a real-world case involving a distribution company based in Istanbul, Türkiye. The scenario captures realistic daily last-mile operations with dynamic orders, multi-depot routing, and high-volume palletized deliveries. In addition to real-world data, five widely used Cordeau MDVRP benchmark instances (p01, p07, p11, p17, p22) were used to assess generalizability and solution competitiveness against best-known solutions (BKS). Experimental validation was conducted through K-Fold cross-validation and a suite of performance metrics, including MSE, MAE, RMSE, DTW, PAP10, POFP, and Coverage Score. Furthermore, comparative analyses with classical algorithms – List Scheduling (LS), Nearest Neighbor (NN), Genetic Algorithm (GA), and Ant Colony Optimization (ACO)—showed that while traditional heuristics offered simplicity or stability, the proposed LST-Former consistently achieved lower route costs and more balanced travel times across datasets. This explicit integration of split delivery, multi-depot coordination, and hardware-aware optimization distinguishes the proposed study from prior VRP research and underscores its practical relevance for urban last-mile logistics. The results confirm the effectiveness of combining learning-based optimization with hardware-aware design to support scalable, real-time routing in logistics. This integrated approach enhances solution quality under complex constraints and facilitates deployment feasibility in embedded systems for next-generation logistics platforms.

Mahdi Nakhaeinejad

This study proposes a novel multi-period, multi-product, and multi-echelon closed-loop supply chain (CLSC) model that simultaneously addresses economic and environmental objectives under dynamic demand and return conditions. Formulated as a mixed-integer linear programming (MILP) model, the framework incorporates key operational decisions including facility location, production planning, inventory control, recovery, and disposal while integrating sustainability factors such as the use of clean technologies and environmentally friendly materials. A weighted sum approach is applied to generate Pareto-optimal solutions, enabling decision-makers to explore trade-offs between cost minimization and environmental performance. The model is validated through a numerical example, and a detailed sensitivity analysis is conducted to assess the impact of critical parameters on supply chain behavior. The results reveal that while sustainability initiatives may increase operational costs, strategic planning and capacity optimization can achieve effective cost-environment trade-offs. The proposed model offers a comprehensive and practical decision-support tool for designing efficient and sustainable CLSC systems, contributing to both academic research and real-world supply chain practice.

Kecheng Liu

Олексій Чалий, Сергій Толюпа

This study presents a comparative analysis of three widely recognized audit-related international standards such as ISO 19011, ISO/IEC 27007, and ISA 200, aimed at identifying the most suitable methodological foundation for further research on the automation of audit management processes. The motivation for this work stems from the increasing complexity of audit activities in modern organizations and the growing need for structured, reproducible, and automatable audit procedures. A review of recent publications shows that comparative studies of audit standards remain limited, particularly in the context of information security and artificial intelligence, which underscores the relevance and originality of the present research. The evaluation methodology developed in this study incorporates a multi-criteria approach that considers academic visibility, general web presence, and the recency of each standard. Quantitative data from Google Scholar and Google Search were normalized using a dedicated formula to ensure comparability across different metrics. According to the calculated results, ISA 200 achieved the highest overall score due to its wide citation base and broad applicability across financial audit domains, while ISO/IEC 27007 received the lowest score because of its narrower scope and lower visibility. Despite ISA 200’s quantitative advantage, the qualitative assessment demonstrates that ISO 19011 provides the most structured, universal, and adaptable audit framework, built around a clearly defined PDCA lifecycle. This structure is particularly advantageous for audit automation, as it offers a systematic sequence of actions that can be formalized and later integrated into AI-driven decision-making systems. Therefore, ISO 19011 is identified as the most appropriate standard for guiding future research on automated audit methodologies and for developing intelligent tools capable of supporting audit planning, execution, and follow-up activities.

Mohammad Sepehri, Adel Farzaneh

Objective: main concern is lack of codification of indigenous intelligent command and control model, main goal is to develop indigenous intelligent command and control model using military Internet of Things, other goals: to count dimensions and components, determine relationships between dimensions and components of model design, and to count achievements, consequences, functions and requirements of model design. Method: type of applied-developmental research, descriptive-case research method, mixed research approach and method of data collection, field and library, with library study tools are books, articles, documents, interviews, questionnaires, and time domain of years 1402-1403 for five years and spatial domain of country's armed forces. Statistical population of 70 experts and experts, structural equation modeling is used to analyze and investigate relationship and correlation between factors. Results:By testing PLS model in SRMR test, since it is smaller than 0.08, overall model of PLS has a good fit and is therefore consistent with desired model in society.Conclusion:dimensions of model design are intelligence, information management, sustainability, interoperability, integration, and network-oriented. achievements and consequences of designing model are improvement (intelligence of c4isr, and decision-making, comprehensive defense readiness, deterrence), increasing military authority and capability, and increasing indigenous cyber power. Functions of model design are intelligence-making (action-oriented and strategic command systems, control, monitoring and evaluation systems, communication systems, computer and cyberspace systems, information collection systems, surveillance and identification systems) and online situational awareness on battlefield.requirements of designing model are battlefield intelligence, localization of IoT standards, IoT software security, and funding and credit, training and skill development.

Farhad Farokhi

Privacy-preserving state estimation for linear time-invariant dynamical systems with crowd sensors is considered. At any time step, the estimator has access to measurements from a randomly selected sensor from a pool of sensors with pre-specified models and noise profiles. A Luenberger-like observer is used to fuse the measurements with the underlying model of the system to recursively generate the state estimates. An additive privacy-preserving noise is used to constrain information leakage. Information leakage is measured via mutual information between the identity of the sensors and the state estimate conditioned on the actual state of the system. This captures an omnipotent adversary that not only can access state estimates but can also gather direct high-quality state measurements. Any prescribed level of information leakage is shown to be achievable by appropriately selecting the variance of the privacy-preserving noise. Therefore, privacy-utility trade-off can be fine-tuned.

Xudong Qi, Junfeng Yao, Ping Wang et al.

Abstract Accurate traffic flow forecasting is a critical component in intelligent transportation systems. However, most of the existing traffic flow prediction algorithms only consider the prediction under normal conditions, but not the influence of weather attributes on the prediction results. This study applies a hybrid deep learning model based on multi feature fusion to predict traffic flow considering weather conditions. A comparison with other representative models validates that the proposed spatial‐temporal fusion graph convolutional network (STFGCN) can achieve better performance.

John Bonar, John Hastings

Digital engineering practices offer significant yet underutilized potential for improving information assurance and system lifecycle management. This paper examines how capabilities like model-based engineering, digital threads, and integrated product lifecycles can address gaps in prevailing frameworks. A reference model demonstrates applying digital engineering techniques to a reference information system, exhibiting enhanced traceability, risk visibility, accuracy, and integration. The model links strategic needs to requirements and architecture while reusing authoritative elements across views. Analysis of the model shows digital engineering closes gaps in compliance, monitoring, change management, and risk assessment. Findings indicate purposeful digital engineering adoption could transform cybersecurity, operations, service delivery, and system governance through comprehensive digital system representations. This research provides a foundation for maturing application of digital engineering for information systems as organizations modernize infrastructure and pursue digital transformation.

Haoyu Ren, Xue Li, Darko Anicic et al.

Recent advances in Tiny Machine Learning (TinyML) empower low-footprint embedded devices for real-time on-device Machine Learning. While many acknowledge the potential benefits of TinyML, its practical implementation presents unique challenges. This study aims to bridge the gap between prototyping single TinyML models and developing reliable TinyML systems in production: (1) Embedded devices operate in dynamically changing conditions. Existing TinyML solutions primarily focus on inference, with models trained offline on powerful machines and deployed as static objects. However, static models may underperform in the real world due to evolving input data distributions. We propose online learning to enable training on constrained devices, adapting local models towards the latest field conditions. (2) Nevertheless, current on-device learning methods struggle with heterogeneous deployment conditions and the scarcity of labeled data when applied across numerous devices. We introduce federated meta-learning incorporating online learning to enhance model generalization, facilitating rapid learning. This approach ensures optimal performance among distributed devices by knowledge sharing. (3) Moreover, TinyML's pivotal advantage is widespread adoption. Embedded devices and TinyML models prioritize extreme efficiency, leading to diverse characteristics ranging from memory and sensors to model architectures. Given their diversity and non-standardized representations, managing these resources becomes challenging as TinyML systems scale up. We present semantic management for the joint management of models and devices at scale. We demonstrate our methods through a basic regression example and then assess them in three real-world TinyML applications: handwritten character image classification, keyword audio classification, and smart building presence detection, confirming our approaches' effectiveness.

André M. H. Teixeira, K. Sou, H. Sandberg et al.

Masaleva Maria, Nabiullina Viktoria

The priority of modern development of socio-economic processes is the trend of widespread implementation of information and digital technologies aimed at automating a variety of activities, including in the agricultural sector. The article deals with the information support in the management of the agroindustrial complex, which is a complex organizational system. The analysis of agroindustrial complexes management areas, their goals and objectives of functioning has been made. Based on the conducted research, the stages of information support and digital support are formed and the structure of the information system, allowing to achieve the strategic goals of management, is offered.

Nate B. Lust, Tim Jenness, James F. Bosch et al.

We present the Rubin Observatory system for data storage/retrieval and pipelined code execution. The layer for data storage and retrieval is named the Butler. It consists of a relational database, known as the registry, to keep track of metadata and relations, and a system to manage where the data is located, named the datastore. Together these systems create an abstraction layer that science algorithms can be written against. This abstraction layer manages the complexities of the large data volumes expected and allows algorithms to be written independently, yet be tied together automatically into a coherent processing pipeline. This system consists of tools which execute these pipelines by transforming them into execution graphs which contain concrete data stored in the Butler. The pipeline infrastructure is designed to be scalable in nature, allowing execution on environments ranging from a laptop all the way up to multi-facility data centers. This presentation will focus on the data management aspects as well as an overview on the creation of pipelines and the corresponding execution graphs.

Kalle Koivisto, Toni Taipalus

Knowledge is considered an essential resource for organizations. For organizations to benefit from their possessed knowledge, knowledge needs to be managed effectively. Despite knowledge sharing and management being viewed as important by practitioners, organizations fail to benefit from their knowledge, leading to issues in cooperation and the loss of valuable knowledge with departing employees. This study aims to identify hindering factors that prevent individuals from effectively sharing and managing knowledge and understand how to eliminate these factors. Empirical data were collected through semi-structured group interviews from 50 individuals working in an international large IT organization. This study confirms the existence of a gap between the perceived importance of knowledge management and how little this importance is reflected in practice. Several hindering factors were identified, grouped into personal social topics, organizational social topics, technical topics, environmental topics, and interrelated social and technical topics. The presented recommendations for mitigating these hindering factors are focused on improving employees' actions, such as offering training and guidelines to follow. The findings of this study have implications for organizations in knowledge-intensive fields, as they can use this knowledge to create knowledge sharing and management strategies to improve their overall performance.

Vítor V. Vasconcelos, Flávia M. D. Marquitti, Theresa Ong et al.

Complex adaptive systems (CASs), from ecosystems to economies, are open systems and inherently dependent on external conditions. While a system can transition from one state to another based on the magnitude of change in external conditions, the rate of change -- irrespective of magnitude -- may also lead to system state changes due to a phenomenon known as a rate-induced transition (RIT). This study presents a novel framework that captures RITs in CASs through a local model and a network extension where each node contributes to the structural adaptability of others. Our findings reveal how RITs occur at a critical environmental change rate, with lower-degree nodes tipping first due to fewer connections and reduced adaptive capacity. High-degree nodes tip later as their adaptability sources (lower-degree nodes) collapse. This pattern persists across various network structures. Our study calls for an extended perspective when managing CASs, emphasizing the need to focus not only on thresholds of external conditions but also the rate at which those conditions change, particularly in the context of the collapse of surrounding systems that contribute to the focal system's resilience. Our analytical method opens a path to designing management policies that mitigate RIT impacts and enhance resilience in ecological, social, and socioecological systems. These policies could include controlling environmental change rates, fostering system adaptability, implementing adaptive management strategies, and building capacity and knowledge exchange. Our study contributes to the understanding of RIT dynamics and informs effective management strategies for complex adaptive systems in the face of rapid environmental change.