The big data phenomenon: The business and public impact

Chroneos-Krasavac, Biljana; Soldić-Aleksić, Jasna; Petković, Goran

doi:10.5937/industrija44-10036

Cited by 8 publications

(1 citation statement)

References 6 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wicher et al classified BDAEs into big data infrastructure construction, information technology and organisational management, high-level support, BDA management ability, and maintaining market competition [99]. Chroneos, Soldic, and Jasna classified BDAEs into establishing a good database collection system, improving the speed of data management and retrieval, improving the efficiency of big data analysis and processing, improving information technology and institutions, and developing intelligent technology and digitalisation processes [100]. Duman classified BDAEs into strengthening database security protection, establishing data acquisition systems, establishing big data centres, improving the data retrieval speed, enhancing big data infrastructure construction, and government support [101].…”

Section: Big Data Analysis (Bda)mentioning

confidence: 99%

Deploying Big Data Enablers to Strengthen Supply Chain Resilience to Mitigate Sustainable Risks Based on Integrated HOQ-MCDM Framework

Hsu

Zhang

et al. 2022

Mathematics

View full text Add to dashboard Cite

In the face of global competition, competitive enterprises should pursue sustainable development, and strengthen their supply chain resilience to cope with risks at any time. In addition, big data analysis has been successfully applied in a variety of fields. However, the method has not been applied to improve supply chain resilience in order to reduce sustainable supply chain risks. An approach for enhancing the capabilities of big data analytics must be developed to enhance supply chain resilience, and mitigate sustainable supply chain risks. In this study, a decision framework that integrates two-stage House of Quality and multicriteria decision-making was constructed. By applying this framework, enterprise decision-makers can identify big data analytics that improve supply chain resilience, and resilience indicators that reduce sustainable supply chain risks. A case study of one of China’s largest relay manufacturers is presented to demonstrate the practicability of the framework. The results showed that the key sustainable supply chain risks are risks regarding the IT infrastructure and information system efficiency, customer supply disruptions, transport disruptions, natural disasters, and government instability. To reduce risk in sustainable supply chains, enterprises must improve the key resilience indicators ‘financial capability’, ‘flexibility’, ‘corporate culture’, ‘information sharing’, and ‘robustness’. Moreover, to increase supply chain resilience, the following most important big data analysis enablers should be considered: ‘capital investment’, ‘building big data sharing mechanism and visualisation’, and ‘strengthening big data infrastructures to support platforms and systems’. This decision framework helps companies prioritise big data analysis enablers to mitigate sustainable supply chain risks in manufacturing organisations by strengthening supply chain resilience. The identified priorities will benefit companies that are using big data strategies and pursuing supply chain resilience initiatives. In addition, the results of this study show the direction of creating a fruitful combination of big data technologies and supply chain resilience to effectively mitigate sustainable risks. Despite the limited enterprise resources, management decision-makers can determine where big data analysis enablers can be most cost-effectively improved to promote risk resilience of sustainable supply chains; this ensures the efficient implementation of effective big data strategies.

show abstract

Section: Big Data Analysis (Bda)mentioning

confidence: 99%

Deploying Big Data Enablers to Strengthen Supply Chain Resilience to Mitigate Sustainable Risks Based on Integrated HOQ-MCDM Framework

Hsu

Zhang

et al. 2022

Mathematics

View full text Add to dashboard Cite

show abstract

Metamorphosis of Internal Audit in the Big Data Age

Onay

2020

Muhasebe Bilim Dünyası Dergisi

View full text Add to dashboard Cite

Büyük Veri kavramı ve uygulaması, son yıllarda işletme faaliyetlerini etkileyen en önemli gelişmelerden birisidir. 4. Endüstri Devrimiyle ortaya çıkan inovasyonlar ve teknolojiler, verinin özelliklerini değiştirmiştir. Büyük Veri; gelişmiş bilgi, karar verme ve süreç otomasyonunu mümkün kılan, maliyet etkin ve yenilikçi bilgi işleme biçimleri gerektiren yüksek hacimli, yüksek hızlı ve/veya çok çeşitli kaynaklardan derlenen veri kümeleri olarak tanımlanır. İç denetim, iç ve dış kaynaklardan elde ettiği veriyi, faydalı bilgiye dönüştürmek için ileri istatistiksel ve analitik teknikleri kullanmak zorundadır. Bu makalede Büyük Veri olarak tanımlanan verinin özellikleri, iç denetim faaliyetlerinde yaratacağı dönüşümle birlikte tartışılmıştır. Makalede büyük veri ve analitiğinin iç denetimin değer yaratma sürecine etkisi, etkinliği ve verimliliği artırma potansiyeli ve ortaya çıkardığı risk faktörleriyle birlikte değerlendirilmiştir.

show abstract

Implementation of a Virtual Instrument in the System for Measuring Forces and Temperatures in the Milling Process

Savković,

Rodić,

Sekulić

et al. 2024

Processes

View full text Add to dashboard Cite

This study presents the development of a measurement acquisition system for force measurement and temperature cutting using virtual instrumentation. The developed measurement chain enables very efficient, accurate, reliable and fast data collection during the measurement process. The presented acquisition system was developed for the face milling process, but its application is also intended for turning or grinding processes. Emphasis is placed on the creation and description of a software solution in order to manage hardware measuring sensors. Two versions of the virtual instrument are presented. The first one is based on the measurement of three components of cutting forces, obtained by means of a piezoelectric dynamometer. And the second one is upgraded with another channel that represents the temperature change record obtained by the thermocouple. At the end, the experimental results for face milling are shown. A graphical diagram obtained based on some of the data generated by the innovative measuring chain applied to the milling process is presented.

show abstract

The big data phenomenon: The business and public impact

Cited by 8 publications

References 6 publications

Deploying Big Data Enablers to Strengthen Supply Chain Resilience to Mitigate Sustainable Risks Based on Integrated HOQ-MCDM Framework

Deploying Big Data Enablers to Strengthen Supply Chain Resilience to Mitigate Sustainable Risks Based on Integrated HOQ-MCDM Framework

Metamorphosis of Internal Audit in the Big Data Age

Implementation of a Virtual Instrument in the System for Measuring Forces and Temperatures in the Milling Process

Contact Info

Product

Resources

About