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data-driven decision-making method for determining the handling department for online appeals

KYBERNETES 2024年

作者： Chen, Sheng-Qun You, Ting Zhang, Jing-Lin Fujian Business Univ Sch Informat Engn Fuzhou Peoples R China Fujian Business Univ Fujian Prov Univ Engn Res Ctr Big Data Analyt Busi Fuzhou Peoples R China Fujian Jiangxia Univ Sch Design Innovat Fuzhou Peoples R China

PurposeThis study aims to enhance the classification and processing of online appeals by employing a deep-learning-based method. This method is designed to meet the requirements for precise information categorization and decision support across various management ***/methodology/approachThis study leverages the ALBERT-TextCNN algorithm to determine the appropriate department for managing online appeals. ALBERT is selected for its advanced dynamic word representation capabilities, rooted in a multi-layer bidirectional transformer architecture and enriched text vector representation. TextCNN is integrated to facilitate the development of multi-label classification *** experiments demonstrate the effectiveness of the proposed approach and its significant superiority over traditional classification methods in terms of ***/valueThe original contribution of this study lies in its utilization of the ALBERT-TextCNN algorithm for the classification of online appeals, resulting in a substantial improvement in accuracy. This research offers valuable insights for management departments, enabling enhanced understanding of public appeals and fostering more scientifically grounded and effective decision-making processes.

关键词： Appeal information data-driven decision-making Classification

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data-driven decision-making model for determining the number of volunteers required in typhoon disasters

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Journal of Safety Science and Resilience 2023年第3期4卷 229-240页

作者： Sheng-Qun Chen Jie Bai School of Information Engineering Fujian Business UniversityFuzhou 350506China School of computer and information Fujian agriculture and Forestry UniversityFuzhou 350002China

Volunteer teams provide valuable support after large-scale ***,excessive volunteer participation poses challenges for formal ***,an appropriate decision-making method is required to quickly determine the number of volunteers required after a *** study proposes a data-driven decision-making(D^(3)M)method for typhoon disaster volunteerism that can effectively predict the number of volunteers *** data from actual cases were gathered,analyzed,and preprocessed to prepare the *** selection,D^(3)M model training and optimization,and model validation were performed to fine-tune the volunteer participant *** data from an actual typhoon in the Philippines,the rationality and efficacy of the method were verified through a comparative analysis of the experimental *** proposed method learns from disaster-event data to quickly predict the number of volunteers needed,such that it not only reasonably allocates volunteers to assist professional teams in rescue but also avoids secondary problems caused by an overwhelming response.

关键词： data-driven decision-making Optimization Rescue Typhoon

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data-driven decision-making in Shop Floor Quality Management - A Systematic Literature Review 1

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33rd International Conference on Flexible Automation and Intelligent Manufacturing

作者： Schamberger, Markus Breu, Michael Bodendorf, Freimut Friedrich Alexander Univ Erlangen Nurnberg Nurnberg Germany

ISBN: (数字)9783031744853

ISBN: (纸本)9783031744846;9783031744853

This paper presents a systematic literature review of applied methods for data-driven decision-making (DDD) in shop floor quality management (QM). The goal is to give an overview of publications in DDD QM and to highlight areas where future research can contribute to the advancement of the field. Relevant publications of the past decade are categorized across the following key dimensions: contribution area in QM DDD (e.g., 'know-what'), characteristic elements of applied methods within DDD (e.g., unsupervised machine learning), and type of manufacturing process (e.g., additive manufacturing). The review reveals a prevalent examination of initial DDD stages like detection ('know-what') and a predominance of supervised machine learning approaches. This indicates potential research opportunities in integrating advanced DDD stages (e.g., 'know-why') and exploring underutilized methodologies like semi-supervised learning. The findings also suggest a need for broader application across manufacturing processes and a deeper examination of data quality and explainable AI within DDD in QM. This review not only maps the current landscape but also identifies potential areas for future exploration, providing valuable insights for advancing DDD in QM.

关键词： data-driven decision-making Quality management Shop floor Applied AI

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data-driven decision-making challenges of local government in Indonesia

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TRANSFORMING GOVERNMENT- PEOPLE PROCESS AND POLICY 2024年第1期18卷 145-156页

作者： Sayogo, Djoko Sigit Yuli, Sri Budi Cantika Amalia, Firda Ayu Univ Muhammadiyah Malang Dept Econ Malang Indonesia

PurposeThis study aims to identify and outline the critical challenges affecting the inclination of executives to use data as the basis for making decisions at a local government ***/methodology/approachThe study conducted in-depth interviews with 21 public officials comprising middle- and top-level executives from 18 agencies and offices at the Bojonegoro Regency, one of Indonesia's most progressive regencies in pursuing open government and smart *** findings demonstrate that ensuring a good quality data architecture, nurturing data culture and developing analytics capability are essential in the case of a developing country such as Indonesia. However, insufficient policies and regulations, a nonexistent evaluative framework for data quality, disruptive local tradition and the ingrained autocratic administration represent significant and unique challenges to implementing data-driven decision-making in the local government in *** limitations/implicationsThe chosen research approach may result in a need for more generalizability beyond Indonesia, accentuating the necessity for the geographical objects to include other developing countries in future *** implicationsThe findings showcase that lack of awareness and acceptance from public officials and the general public of the importance of a data-driven approach;as such, a better understanding of the change in attitudes and mindsets of public officials is invariably one of the critical practical ***/valueThe findings signify the importance of creating robust accountability systems and evaluative frameworks that consider the many variables influencing decisions that capture the significance of organizational and local culture.

关键词： Indonesia Local government data data-driven decision-making

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data-driven decision-making method for Functional Upgrade Remanufacturing of used products based on Multi-Life Customization Scenarios

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JOURNAL OF CLEANER PRODUCTION 2022年第0期334卷 130238-130238页

作者： Wu, Bin Jiang, Zhigang Zhu, Shuo Zhang, Hua Wang, Yan Zhang, Yuping Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430081 Peoples R China Guangzhou Railway Polytech Sch Mech & Elect Engn Guangzhou 510430 Peoples R China Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol Acad Green Mfg Engn Wuhan 430081 Peoples R China Univ Brighton Sch Architecture Technol & Engn Brighton BN2 4GJ E Sussex England GEM Co Ltd Shenzhen 518101 Peoples R China

With rapid changes in technology and customer preferences, functional obsolescence of used products poses serious challenges to resumed remanufacturing. Upgrade remanufacturing is a potential solution for dealing with the problems of functional obsolescence. The multi-attribute remaining life and customized life of the functional unit are critical elements of decision-making for upgrade remanufacturing solution, yet there are many possible scenarios for the multi-attribute remaining life and the customized life. The optimal solution varies with the various scenarios, which makes the decision-making for choosing the optimal solution of Functional Upgrade Remanufacturing (FUR) very individual and complicated. To this end, the paper proposes a data-driven decision-making (DDDM) method for FUR of used products based on Multi-Life Customization Scenarios (MLCS). MLCS describes the relationship between the remaining physical, technical, economic life and customized life. Firstly, the used product is decomposed into several functional units that are taken as the objects for upgrade remanufacturing, and the mapping between MLCS and decision-making for FUR is established through data mining. Then the DDDM method of Bayesian network is employed to inference, which is constructed based on historical data, and the solution with the largest posteriori probability is taken as the optimal solution. Finally, a case study on decision-making for FUR of a used mechanical hydraulic power steering system is demonstrated to validate the proposed method.

关键词： Upgrade remanufacturing Multi-life customization scenarios data-driven decision-making Functional unit Bayesian network

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data-driven decision-making with weights and reliabilities for diagnosis of thyroid cancer

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2022年第8期13卷 2257-2271页

作者： Xue, Min Cao, Peipei Hou, Bingbing Liu, Weiyong Hefei Univ Technol Sch Management Box 270 Hefei 230009 Anhui Peoples R China Minist Educ Key Lab Proc Optimizat & Intelligent Decis Making Box 270 Hefei 230009 Anhui Peoples R China Univ Sci & Technol China Affiliated Hosp USTC 1 Dept Ultrasound Div Life Sci & Med Hefei 230001 Peoples R China

data science has revolutionized the paradigms of medical decision-making. In the past, medical data could not be recorded and stored indefinitely. In the present day, huge volumes of medical data have been collected electronically, such as medical records, medical images, and heterogeneous surgical data. Under this condition, how to help the radiologists diagnose the thyroid cancer by using the accumulated examination reports and pathologic findings has been a challenge needing to face. From the analysis of historical examination reports, the problem of diagnosing thyroid cancer is evidently considered as a multi-criteria decision-making problem. Thus, a data-driven fusion method of weights and reliabilities in decision-making is proposed in this paper to cope with the above challenge. Linguistic term sets are introduced to model and portray the assessments on each criterion in the problem of diagnosing thyroid cancer by using three types of linguistic scale functions. A data-driven way is then designed to determine the weights and reliabilities of the assessments on each criterion for each radiologist by considering the similarity between the assessments on each criterion and the overall assessments and the similarity between the assessments on criterion and the golden standard, which are derived from the historical data. Subsequently, assessments on each criterion will be combined with the weights and reliabilities to generate a data-driven solution to the problem. The applicability and effectiveness of the data-driven fusion method are verified by solving a real problem of diagnosing thyroid cancer using historical data collected from five radiologists in a tertiary hospital from January 2011 to February 2019.

关键词： data-driven decision-making data-driven fusion method Reliability of assessments Weight of assessments Diagnostic data of thyroid cancer

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data-driven decision-making in emergency remote teaching

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EDUCATION AND INFORMATION TECHNOLOGIES 2023年第1期28卷 489-506页

作者： Botvin, Maya Hershkovitz, Arnon Forkosh-Baruch, Alona Tel Aviv Univ Sch Educ POB 39040 IL-69978 Tel Aviv Israel Levinsky Coll Educ Tel Aviv Israel

decision-making is key for teaching, with informed decisions promoting students and teachers most effectively. In this study, we explored data-driven decision-making processes of K-12 teachers (N = 302) at times of emergency remote teaching, as experienced during the COVID-19 pandemic outbreak in Israel. Using both quantitative and qualitative methodologies, and a within-subject design, we studied how teachers' data use had changed during COVID-19 days, and which data they would like to receive for improving their decision-making. We based our analysis of the data on the Universal Design of Learning (UDL) model that characterizes the diverse ways of adapting teaching and learning to different learners as a means of understanding teachers' use of data. Overall, we found a decline in data use, regardless of age or teaching experience. Interestingly, we found an increase in data use for optimizing students' access to technology and for enabling them to manage their own learning, two aspects that are strongly connected to remote learning in times of emergency. Notably, teachers wished to receive a host of data about their students' academic progress, social-emotional state, and familial situations.

关键词： Teachers data-driven decision-making Emergency remote teaching Universal design for learning COVID-19

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Evaluating the performance of countries in COVID-19 management: A data-driven decision-making and clustering

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APPLIED SOFT COMPUTING 2025年 169卷

作者： Meraji, Hamed Rahimi, Danial Babaei, Ardavan Tirkolaee, Erfan Babaee Sharif Univ Technol Dept Ind Engn Tehran Iran KN Toosi Univ Technol Fac Ind Engn Tehran Iran Istinye Univ Dept Ind Engn Istanbul Turkiye Yuan Ze Univ Dept Ind Engn & Management Taoyuan Taiwan Western Caspian Univ Dept Mech & Math Baku Azerbaijan

The COVID-19 outbreak, first reported in Wuhan, China, spread rapidly and endangered human lives and livelihoods globally. Researchers have utilized available tools and facilities to mitigate its impact across dimensions. In this study, we propose a comprehensive, data-driven framework to evaluate periodically 168 countries' performance, considering four distinct variable categories since the advent of COVID-19. We assess and leverage four clustering methods of K-means, Gaussian Mixture Model (GMM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Spectral, as well as three Multi-Criteria decision-making (MCDM) approaches, including Combined Compromise Solution (COCOSO), Grey Relational Analysis (GRA), and Evaluation Based on Distance from Average Solution (EDAS) for ranking the countries. The results are analyzed thoroughly-among the examined factors, "Total Recovered", "GDP Per capita", and "Hospital Beds / 1 K" most critically impacted evaluating outcomes, while" Male Smokers", "Diabetes Prevalence", and "Cardiovascular Death Rate" are least influential. The novel metric "Medical Waste" also demonstrates more vital than 86 % of existing indicators. Moreover, the findings reveal associations between countries' development levels and their corresponding cluster assignments. For more precise analysis, we investigate the intra-cluster and inter-cluster approaches, each of which revealed countries' promotion or degradation regarding rankings within a cluster or transitions between clusters. Finally, appropriate policy-making and management strategies are presented to enhance countries' preparedness for potential future outbreaks based on the results.

关键词： COVID-19 management Performance evaluation data-driven decision-making Clustering MCDM

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Enhancing Creativity and Sustainable Competitive Advantage Through data-driven decision-making and Digital Leadership

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT 2025年 72卷 1361-1375页

作者： Wang, Shaofeng Gao, Mengjia Zhang, Hao Fuzhou Univ Int Studies & Trade Int Business Sch Fuzhou 350202 Peoples R China Zhejiang Wanli Univ Sch Logist & Ecommerce Ningbo 315000 Peoples R China Zhejiang Shuren Univ Sch Econ & Management Hangzhou 310015 Peoples R China

Digital transformation is essential for businesses to maintain creativity and competitiveness. This study explores how digital transformation impacts corporate creativity and competitive advantage in SMEs by leveraging the TOE framework and RBT. Our model integrates both internal and external factors, focusing on data-driven decision-making and digital leadership. data from 537 SMEs were analyzed using PLS-SEM, IPMA, and fsQCA. Results indicate that technological competence, organizational support, and government support positively influence digital integration, enhancing data-driven decision-making. This decision-making process significantly boosts sustainable competitive advantage and organizational creativity. Digital leadership positively moderates these relationships. Our findings provide actionable insights for SMEs leveraging digital transformation for sustained competitive advantage and creativity. The unique long-term value of this research lies in its comprehensive examination of how digital transformation facilitates both creativity and sustainablecompetitive advantage in SMEs. This study offers a novel theoretical model and empirical evidence that can guide international practitioners and scholars in understanding and implementing effective digital transformation strategies.

关键词： Digital transformation Creativity decision making Leadership Technological innovation Government Collaboration Artificial intelligence Training Mathematical models data-driven decision-making digital integration organizational creativity SMEs digital transformation sustainable competitive advantage (SCA) technology-organization-environment (TOE)

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Next-generation data center energy management: a data-driven decision-making framework

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FRONTIERS IN ENERGY RESEARCH 2024年 12卷

作者： Milic, Vlatko Linkoping Univ Dept Management & Engn Div Energy Syst Linkoping Sweden Univ Gavle Dept Technol & Environm Div Bldg Energy & Environm Technol Gavle Sweden

In the era of society's ongoing digitization and the exponential growth in data volume, alongside a growing energy demand, energy management plays an integral role in data centers (DCs) and is a key factor in the quest for decarbonization. In light of the complex nature of DCs, traditional energy management strategies are inadequate. This research introduces a data-driven decision-making framework for DCs, grounded in the OODA (Observation, Orientation, decision, and Action) loop and based on insights from an Ericsson-operated DC in Link & ouml;ping, Sweden. The developed framework enables DCs to enhance energy efficiency effectively. Rooted in the OODA loop and leveraging extensive datasets from DCs' building management systems, this framework aids in decreasing cooling energy usage through strategic, data-driven decision-making. By adopting AI methods, specifically K-means clustering in this research, for continuous monitoring and fine-tuning (Proportional, Integral, Derivative) PID parameters, the framework aids in improving operational efficiency.

关键词： data center energy management AI K-means OODA loop data-driven decision-making framework

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