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Barriers to Big data analytics (BDA) implementation in manufacturing supply chains

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Journal of Management analytics 2023年第1期10卷 191-222页

作者： Amirhossein Dehkhodaei Bahar Amiri Hasan Farsijani Abbas Raad Department of Management and Accounting Shahid Beheshti UniversityTehranIran

big data analysis(BDA)can increase the capability of supply chain analysis of manufacturing companies.Therefore,many manufacturing companies want to use BDA,but it has been seen that BDA implementation is difficult,especially in developing countries due to the existence of various barriers related to finance,government regulations,etc.This paper aims to investigate the barriers to BDA implementation in Iranian companies.In literature,limited work has been done on identifying barriers to implementing BDA in developing countries.In this regard,34 barriers were identified to BDA adoption in Iran by employing a literature review and feedback received from experts.Then,the most important barriers(14)were analyzed using integrated Interpretive Structural Modeling and MICMAC approach.Results show that two barriers;namely,lack of sufficient knowledge of senior managers and weakness of governance policies,are the most significant.Finally,crucial policy measures and recommendations are proposed to assist managers and government bodies.

关键词： Barriers Big data analytics Interpretive Structural Modeling(ISM) MICMAC Analysis Modeling Supply Chain Management

Effects of Big data analytics on Sustainable Manufacturing:A Comparative Study Analysis

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Chinese Journal of Urban and Environmental Studies 2022年第4期10卷 6-30页

作者： Ching Horng ER Thikrait Al MOSAWI Arden University

Application of Big data analytics(BDA) is seen in various disciplines within an organization to predict trends, explore opportunities and monitor performance. Among all the industries, BDA presents immense value in sustainable manufacturing(SM) given that it is an industry that consumes a high amount of energy, emits high amounts of waste and carbon emissions and requires a large amount of manpower. This paper aims at illustrating the effects of BDA in supporting SM by studying the Indian manufacturing firms which have unfavorable labor laws compared to other developing countries. With an extensive literature review, this paper discusses the relationship between BDA and sustainability, the capabilities of BDA, the concept of SM, the BDA framework for SM, the relationship between Industry 4.0 and SM and the challenges of implementing BDA. Using qualitative meta-analysis research methodology, the paper examines the nine common critical success factors that enable SM through BDA implementation by comparing 15 primary studies. Finally, the paper concludes the research findings and outlines future research directions. The study provides theoretical and practical contributions to BDA implementation in achieving effective SM practices in emerging economies.

关键词： Big data analytics Indian manufacturing firms developing economies qualitative meta-analysis sustainability

Big data analytics for Improving Financial Performance and Sustainability

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Journal of Systems Science and Information 2021年第2期9卷 175-191页

作者： Xiangyu ZHU Yang YANG School of Economics and Management Beijing University of TechnologyBeijing 100124China

In this study,the key drivers of sustainability commitment,green supply chain management,big data integration and green human resource practice are explored,and the impact of these sustainable capabilities on the environmental and financial performance of banks is also elaborated.In addition,the influence of green management practices on integrating big data technology into operations is presented.As for the concept of dynamic ability,it has been used to recommend and empirically test conceptual models.data were collected through a self-administrated survey questionnaire on 317 people working in 37 banks in six Asian countries.Research suggests that Big data analytics strategies have an impact on internal processes and on the stability and financial performance of banks.Besides,it is indicated that banks are committed to proper data monitoring of their customers to complete operational efficiency and sustainability goals.Furthermore,our result proved that banks practicing Green Innovation strategies experience better environmental and economic performance because their employees are already trained in Green HR.Finally,from our study,it was found that internal and external green supply chain management practices have a positive effect on the environmental and financial performance of banks,thus ensuring that the bank of Association of Southeast Asian Nations(ASEAN)mitigates the environmental impact through its operations and ultimately experiences an increase in financial performance.

关键词： financial performance sustainability Big data analytics banks green management

维普期刊数据库

Big data analytics in E-commerce for the U.S.and China Through Literature Reviewing

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Journal of Systems Science and Information 2021年第1期9卷 16-44页

作者： Weiqing ZHUANG Morgan C.WANG Ichiro NA KAMOTO Ming JIANG School of Internet Economics and Business Fujian Universily of TechnologyFuzhou 350108China Research Center for“One Belt and Road"Economic and Policy Frujian University of TechnologyFiuzhou 350108China College of Science University of Central rloridaFlorida 32525USA School of Internet Econormics and Business Furjian Universily of TechnologyPuzhou 350108China School of Internet Econormics and Business Irujian University of TechnologyPuzhou 350108China

Big data analytics(BDA)in e-commerce,which is an emerging field that started in 2006,deeply affects the development of global e-commerce,especially its layout and performance in the U.S.and China.This paper seeks to examine the relative influence of theoretical research of BDA in e-commerce to explain the differences between the U.S.and China by adopting a statistical analysis method on the basis of samples collected from two main literature databases,Web of Science and CNKI,aimed at the U.S.and China.The results of this study help clarify doubts regarding the development of China's e-commerce,which exceeds that of the U.S.today,in view of the theoretical comparison of BDA in e-commerce between them.

关键词： Big data analytics e-commerce U.S.and China literature review

维普期刊数据库

Big data analytics for Earth Sciences:the EarthServer approach

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International Journal of Digital Earth 2016年第1期9卷 3-29页

作者： Peter Baumann Paolo Mazzetti Joachim Ungar Roberto Barbera Damiano Barboni Alan Beccati Lorenzo bigagli Enrico Boldrini Riccardo Bruno Antonio Calanducci Piero Campalani Oliver Clements Alex Dumitrua,Mike Grant Pasquale Herzig George Kakaletris John Laxton Panagiota Koltsida Kinga Lipskoch Alireza Rezaei Mahdiraji Simone Mantovani Vlad Merticariu Antonio Messina Dimitar Misev Stefano Natali Stefano Nativi Jelmer Oosthoek Marco Pappalardo James Passmore Angelo Pio Rossi Francesco Rundo Marcus Sen Vittorio Sorbera Don Sullivan Mario Torrisi Leonardo Trovato Maria Grazia Veratelli Sebastian Wagner Large-Scale Scientific Information Systems Jacobs UniversityBremenGermany Rasdaman GmbH BremenGermany CNR-IIA National Research Council of ItalyInstitute of Atmospheric Pollution ResearchFlorenceItaly EOX IT Services GmbH ViennaAustria Consorzio COMETA CataniaItaly Division of Catania Italian National Institute for Nuclear PhysicsCataniaItaly Department of Physics and Astronomy University of CataniaCataniaItaly MEEO S.r.l. FerraraItaly Plymouth Marine Laboratory PlymouthUK Fraunhofer IGD DarmstadtGermany Athena Research and Innovation Center in Information Communication&Knowledge Technologies AthensGreece British Geological Survey EdinburghUK Software Engineering Italia S.r.l. CataniaItaly British Geological Survey KeyworthUK NASA Ames Research Center Moffett FieldCAUSA

Big data analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures.Earth and Environmental sciences are likely to benefit from Big data analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations.However,Earth Science data and applications present specificities in terms of relevance of the geospatial information,wide heterogeneity of data models and formats,and complexity of processing.Therefore,big Earth data analytics requires specifically tailored techniques and tools.The EarthServer big Earth data analytics engine offers a solution for coverage-type datasets,built around a high performance array database technology,and the adoption and enhancement of standards for service interaction(OGC WCS and WCPS).The EarthServer solution,led by the collection of requirements from scientific communities and international initiatives,provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization.The result is demonstrated and validated through the development of lighthouse applications in the Marine,Geology,Atmospheric,Planetary and Cryospheric science domains.

关键词： big data Big data analytics array databases Earth Sciences interoperability standards

Evaluation of a software positioning tool to support SMEs in adoption of Big data analytics

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Journal of Electronic Science and Technology 2024年第1期22卷 13-24页

作者： Matthew Willetts Anthony S.Atkins Faculty of Digital TechnologyInnovationand BusinessStaffordshire UniversityStoke-on-TrentST42DEUnited Kingdom

Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability,customer demand forecasting,cheaper development of products,and improved stock control.Small and medium sized enterprises(SMEs)are the backbone of the global economy,comprising of 90%of businesses worldwide.However,only 10%SMEs have adopted Big data analytics despite the competitive advantage they could achieve.Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt Big data analytics.The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK.This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners.The results of the evaluation are presented with a discussion on the results,and the paper concludes with recommendations to improve the scoring tool based on the proposed framework.The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and Big data analytics.

关键词： Big data analytics Evaluation Small and medium sized enterprises (SMEs) Strategic framework

Big data analytics and big data science:a survey

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Journal of Management analytics 2016年第1期3卷 1-42页

作者： Yong Chen Hong Chen Anjee Gorkhali Yang Lu Yiqian Ma Ling Li Peking University Beijing 100871China Tianjin University Tianjin 300072China Institute of Engineering Tribhuvan UniversityKirtipur 44618Nepal University of Kentucky LexingtonKentucky 40506USA University of Manchester Manchester M139PLUK Shanghai University of Sport Shanghai 200438China Old Dominion University NorfolkVA 23529USA

big data has attracted much attention from academia and industry.But the discussion of big data is disparate,fragmented and distributed among different outlets.This paper conducts a systematic and extensive review on 186 journal publications about big data from 2011 to 2015 in the Science Citation Index(SCI)and the Social Science Citation Index(SSCI)database aiming to provide scholars and practitioners with a comprehensive overview and big picture about research on big data.The selected papers are grouped into 20 research categories.The contents of the paper(s)in each research category are summarized.Research directions for each category are outlined as well.The results in this study indicate that the selected papers were mainly published between 2013 and 2015 and focus on technological issues regarding big data.Diverse new approaches,methods,frameworks and systems are proposed for data collection,storage,transport,processing and analysis in the selected papers.Possible directions for future research on big data are discussed.

关键词： big data Big data analytics big data science survey literature review

Big data analytics with Optimal Deep Learning Model for Medical Image Classification

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Computer Systems Science & Engineering 2023年第2期44卷 1433-1449页

作者： Tariq Mohammed Alqahtani Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia

In recent years,huge volumes of healthcare data are getting generated in various forms.The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker.Due to such massive generation of big data,the utilization of new methods based on Big data analytics(BDA),Machine Learning(ML),and Artiﬁcial Intelligence(AI)have become essential.In this aspect,the current research work develops a new Big data analytics with Cat Swarm Optimization based deep Learning(BDA-CSODL)technique for medical image classiﬁcation on Apache Spark environment.The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately.BDA-CSODL technique involves different stages of operations such as preprocessing,segmentation,fea-ture extraction,and classiﬁcation.In addition,BDA-CSODL technique also fol-lows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image.Moreover,a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor.Stochastic Gradient Descent(SGD)model is used for parameter tuning process.Furthermore,CSO with Long Short-Term Memory(CSO-LSTM)model is employed as a classiﬁcation model to determine the appropriate class labels to it.Both SGD and CSO design approaches help in improving the overall image classiﬁcation performance of the proposed BDA-CSODL technique.A wide range of simulations was conducted on benchmark medical image datasets and the com-prehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.

关键词： Big data analytics healthcare deep learning image classiﬁcation biomedical imaging machine learning

Modified Buffalo Optimization with Big data analytics Assisted Intrusion Detection Model

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Computer Systems Science & Engineering 2023年第8期46卷 1415-1429页

作者： R.Sheeba R.Sharmila Ahmed Alkhayyat Rami Q.Malik Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Applications Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India College of Technical Engineering The Islamic UniversityNajafIraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University CollegeBabylonIraq

Lately,the Internet of Things(IoT)application requires millions of structured and unstructured data since it has numerous problems,such as data organization,production,and capturing.To address these shortcomings,Big data analytics is the most superior technology that has to be adapted.Even though big data and IoT could make human life more convenient,those benefits come at the expense of security.To manage these kinds of threats,the intrusion detection system has been extensively applied to identify malicious network traffic,particularly once the preventive technique fails at the level of endpoint IoT devices.As cyberattacks targeting IoT have gradually become stealthy and more sophisticated,intrusion detection systems(IDS)must continually emerge to manage evolving security threats.This study devises Big data analytics with the Internet of Things Assisted Intrusion Detection using Modified Buffalo Optimization Algorithm with Deep Learning(IDMBOA-DL)algorithm.In the presented IDMBOA-DL model,the Hadoop MapReduce tool is exploited for managing big data.The MBOA algorithm is applied to derive an optimal subset of features from picking an optimum set of feature subsets.Finally,the sine cosine algorithm(SCA)with convolutional autoencoder(CAE)mechanism is utilized to recognize and classify the intrusions in the IoT network.A wide range of simulations was conducted to demonstrate the enhanced results of the IDMBOA-DL algorithm.The comparison outcomes emphasized the better performance of the IDMBOA-DL model over other approaches.

关键词： Big data analytics internet of things security intrusion detection deep learning