Big Data Strategy

Valdez, Alicia; Cortes, Griselda; Castañeda, Sergio; Vazquez, Laura; Zarate, Angel; Salas, Yadira; Atondo, Gerardo Haces

doi:10.14569/ijacsa.2019.0100434

Cited by 11 publications

(8 citation statements)

References 7 publications

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“…Given these characteristics, it requires new technologies to capture, store and analyse them since, with current instruments, BD is difficult to be stored, processed and analysed (Jeble et al. , 2018; Valdez et al. , 2019).…”

Section: Discussionmentioning

confidence: 99%

Innovation in the supply chain and big data: a critical review of the literature

Centobelli

Cerchione

Cricelli

et al. 2022

EJIM

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PurposeThis paper aims to propose a framework investigating the diffusion and adoption process of big data (BD) in the supply chain (SC) as a tool to manage process innovation at technological, operational and strategical levels.Design/methodology/approachA comprehensive systematic literature methodology is used to develop the theoretical conceptual framework, which comprehensively describes and captures the innovative stages of BD technology adoption process in SC with a multilevel perspective.FindingsResults show that BD has modified the supply network concept, starting from the dyadic relationships, triads up to the creation of a streamlined and integrated network. These changes are reflected in a novel integrated vision including both benefits and barriers.Research limitations/implicationsThe proposed framework supports companies in redesigning the processes affected by the adoption of BD, helping them in identifying the critical elements, barriers, benefits and expected performance. One limitation is the focus of the study on the analysis of the processes of adoption of BD technology in the SC considering a particular structure of SC characterized by only two levels of supply and by a reduced number of members.Originality/valueAlthough the role of BD in supply chain operations management (SCOM) is well acknowledged in the literature, its adoption and diffusion process from an interorganizational perspective is still missing. Specifically, the adoption stages of BD in SC have been defined at a strategic level, and successively the SC operations and technological perspective have been integrated to depict the operationalization of BD implementation and diffusion.

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Section: Discussionmentioning

confidence: 99%

Innovation in the supply chain and big data: a critical review of the literature

Centobelli

Cerchione

Cricelli

et al. 2022

EJIM

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“…In order to implement and test the proposed framework, Google BigQuery 4 and R 5 are used as base technologies. A web-based application was developed using shiny package 6 available for R. The backend programming for computation of quality score and creation of visualizations like pie charts and line charts was performed using R programming language.…”

Section: Methodology and Implementationmentioning

confidence: 99%

“…The applicability of big data technologies [6], implemented using cloud-based infrastructures, to real-world, data-intensive scenarios has given rise to many sub-research areas. Big data has particularly found applications in healthcare [7], geospatial analytics [8] and business intelligence [9], in addition to many others.…”

Section: Related Workmentioning

confidence: 99%

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Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

Khan

Alam

Liu

et al. 2019

IJACSA

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Educational Intelligence is a broad area of big data analytical applications that make use of big data technologies for implementation of solutions for education and research. This paper demonstrates the designing, development and deployment of an educational intelligence application for real-world scenarios. Firstly, a quality assessment framework for higher education systems that evaluate institutions on the basis of performance of outgoing students was proposed. Secondly, big data enabled technological setup was used for its implementation. Literature was surveyed to evaluate existing quality frameworks. Most existing quality assessment systems take into account the dimensions related to inputs, processes and outputs, but they tend to ignore the perspective that assesses the institution on the basis of outcome of the educational process. This paper demonstrates the use of outcome perspective to compute quality metrics and create visual analytics. In order to implement and test the framework, R programming language and a cloud based big data technology that is Google, BigQuery were used.

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“…In Qarabsh et al [41], the author points out that in the current context of industry 4.0, industrial maintenance must evolve towards a model that integrates networks with sensors that allow the transfer of electrical signals in real time, supporting the internet of things (IoT) and artificial intelligence. In it is pointed out that ensemble methods try to improve the performance of machine learning models by improving their accuracy in order to solve a particular problem, within this method is the ensemble bagged trees classification algorithm, which is a powerful statistical method for estimating a quantity from a sample of data [42], [43].…”

Section: Literary Reviewmentioning

confidence: 99%

Automatic learning algorithm for troubleshooting in hydraulic machinery

Castro-Puma

Verónica

et al. 2022

IJEECS

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<span>In Peru, there are many companies linked to the category of heavy machinery maintenance, in which, on the one hand, although it is true they generate a record of events linked to equipment maintenance indicators, on the other hand they do not make efficient use of these data generating operational patterns, through machine learning, that contribute to the improvement of processes linked to the service. In this sense, the objective of this article is to generate a tool based on automatic learning algorithms that allows predicting the location of faults in hydraulic excavators, in order to improve the management of the maintenance service. When developing the research, it was obtained that the algorithm that assembles bagged trees presents an accuracy of 97.15%, showing a level of specificity of 99.04%, an accuracy of 98.56% and a sensitivity of 97.12%. Therefore, the predictive model using the ensemble bagged trees algorithm shows significant performance in locating the system where failures occur in hydraulic excavator fleets. It is concluded then that it was possible to improve aspects associated with the planning and availability of supplies or components of the maintenance service, also optimizing the continuity and response capacity in the maintenance process.</span>

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Big Data Strategy

Cited by 11 publications

References 7 publications

Innovation in the supply chain and big data: a critical review of the literature

Innovation in the supply chain and big data: a critical review of the literature

Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

Automatic learning algorithm for troubleshooting in hydraulic machinery

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