Big Data Needs and Challenges in Smart Manufacturing: An Industry-Academia Survey

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F21%3A00354568" target="_blank" >RIV/68407700:21730/21:00354568 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/ETFA45728.2021.9613600" target="_blank" >https://doi.org/10.1109/ETFA45728.2021.9613600</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ETFA45728.2021.9613600" target="_blank" >10.1109/ETFA45728.2021.9613600</a>

Result language

angličtina

Original language name

Big Data Needs and Challenges in Smart Manufacturing: An Industry-Academia Survey

Original language description

The increasing availability of data in Smart Manufacturing opens new challenges and required capabilities in the area of big data in industry and academia. Various organizations have started initiatives to collect and analyse data in their individual contexts with specific goals, e.g., for monitoring, optimization, or decision support in order to reduce risks and costs in their manufacturing systems. However, the variety of available application areas require to focus on most promising activities. Therefore, we see the need for investigating common challenges and priorities in academia and industry from expert and management perspective to identify the state of the practice and promising application areas for driving future research directions. The goal of this paper is to report on an industry-academia survey to capture the current state of the art, required capabilities and priorities in the area of big data applications. Therefore, we conducted a survey in winter 2020/21 in industry and academia. We received 22 responses from different application domains highlighting the need for supporting (a) fault detection and (b) fault classification based on (c) historical and (d) real-time data analysis concepts. Therefore, the survey results reveals current and upcoming challenges in big data applications, such as defect handling based on historical and real-time data.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF16_026%2F0008432" target="_blank" >EF16_026/0008432: Cluster 4.0 - Methodology of System Integration</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )

ISBN

978-1-7281-2989-1

ISSN

1946-0740

e-ISSN

1946-0759

Number of pages

8

Pages from-to

—

Publisher name

IEEE Industrial Electronic Society

Place of publication

Vienna

Event location

Västerås

Event date

Sep 7, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000766992600195