A big data analytics platform for smart factories in small and medium-sized manufacturing enterprises: An empirical case study of a die casting factory

2017 ◽  
Vol 18 (10) ◽  
pp. 1353-1361 ◽  
Author(s):  
Ju Yeon Lee ◽  
Joo Seong Yoon ◽  
Bo-Hyun Kim
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Die Casting ◽  
Big Data Analytics ◽  
Manufacturing Enterprises ◽  
Smart Factories

Examining the potential of textual big data analytics for public policy decision-making: A case study with driverless cars in Denmark

2020 ◽  
Vol 98 ◽  
pp. 68-78 ◽  
Author(s):  
Aseem Kinra ◽  
Samaneh Beheshti-Kashi ◽  
Rasmus Buch ◽  
Thomas Alexander Sick Nielsen ◽  
Francisco Pereira
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Policy Decision ◽  
Driverless Cars ◽  
Policy Decision Making

A Big Data Analytics-driven Lean Six Sigma framework for enhanced green performance: a case study of chemical company

2021 ◽  
pp. 1-24
Author(s):  
Amine Belhadi ◽  
Sachin S. Kamble ◽  
Angappa Gunasekaran ◽  
Karim Zkik ◽  
Dileep Kumar M. ◽  
...  
Keyword(s):  
Big Data ◽  
Six Sigma ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Lean Six Sigma ◽  
Chemical Company ◽  
Green Performance

What Can Fitness Apps Teach Us About Group Privacy?

2021 ◽  
pp. 1-30
Author(s):  
Miriam J. Metzger ◽  
Jennifer Jiyoung Suh ◽  
Scott Reid ◽  
Amr El Abbadi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Information Privacy ◽  
Individual Privacy ◽  
Recent Advances

This chapter begins with a case study of Strava, a fitness app that inadvertently exposed sensitive military information even while protecting individual users' information privacy. The case study is analyzed as an example of how recent advances in algorithmic group inference technologies threaten privacy, both for individuals and for groups. It then argues that while individual privacy from big data analytics is well understood, group privacy is not. Results of an experiment to better understand group privacy are presented. Findings show that group and individual privacy are psychologically distinct and uniquely affect people's evaluations, use, and tolerance for a fictitious fitness app. The chapter concludes with a discussion of group-inference technologies ethics and offers recommendations for fitness app designers.

scholarly journals Artificial Intelligence Imagery Analysis Fostering Big Data Analytics

2019 ◽  
Vol 11 (8) ◽  
pp. 178 ◽  
Author(s):  
Stefan Cremer ◽  
Claudia Loebbecke
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Quality Data ◽  
Background Information ◽  
Research And Practice ◽  
Management Approaches ◽  
Thumbnail Images

In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

Enabling integrated business planning through big data analytics: a case study on sales and operations planning

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alexander Schlegel ◽  
Hendrik Sebastian Birkel ◽  
Evi Hartmann
Keyword(s):  
Big Data ◽  
Information Processing ◽  
Data Analytics ◽  
Single Case ◽  
Big Data Analytics ◽  
Business Planning ◽  
Operations Planning ◽  
Content Type ◽  
The Impact

PurposeThe purpose of this study is to investigate how big data analytics capabilities (BDAC) enable the implementation of integrated business planning (IBP) – the advanced form of sales and operations planning (S&OP) – by counteracting the increasing information processing requirements.Design/methodology/approachThe research model is grounded in the organizational information processing theory (OIPT). An embedded single case study on a multinational agrochemical company with multiple geographically distinguished sub-units of analysis was conducted. Data were collected in workshops, semistructured interviews as well as direct observations and enriched by secondary data from internal company sources as well as publicly available sources.FindingsThe results show the relevancy of establishing BDAC within an organization to apply IBP by providing empirical evidence of BDA solutions in S&OP. The study highlights how BDAC increase an organization's information processing capacity and consequently enable efficient and effective S&OP. Practical guidance toward the development of tangible, human and intangible BDAC in a particular sequence is given.Originality/valueThis study is the first theoretically grounded, empirical investigation of S&OP implementation journeys under consideration of the impact of BDAC.

Sign in / Sign up

Export Citation Format

Share Document