Making sense of Big Data – can it transform operations management?

Matthias, Olga; Fouweather, Ian; Gregory, Ian C.; Vernon, A. Heygate

doi:10.1108/ijopm-02-2015-0084

Cited by 114 publications

(111 citation statements)

References 47 publications

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“…It underlines the role of big data in achieving agile manufacturing, and competitiveness and performance. In this vein, it adds to the existing literature that states the benefits of BDBA (Wamba et al, 2015;Akter et al, 2016;Dubey et al, 2016;Wang et al, 2016b;Amankwah-Amoah, 2016;Matthias et al, 2017), highlighting the capability of BDBA that leads to better performance (Akter et al, 2016;Wamba et al, 2017) through agile practices. Finally, our study contributes to the further conceptualization of BDBA (Akter et al, 2016;Wamba et al, 2017;Dubey et al, 2018) by looking explicitly at the impact of BDBA on agile manufacturing and its enablers.…”

Section: Discussionmentioning

confidence: 84%

“…Literature has outlined the benefits of BDBA (Wamba et al, 2015;Akter et al, 2016;Dubey et al, 2016;Wang et al, 2016b;Amankwah-Amoah, 2016;Matthias et al, 2017), stating that the predictive power of big data (through business analytics) is about using the power of statistics to forecast future events based on what has occurred in the past. Tools include, for instance, regression modeling, decision trees, Bayesian statistics, neural networks, Support Vector Machine (SVM), and nearest neighbor algorithms (Oztekin, 2017).…”

Section: Big Data and Business Analyticsmentioning

confidence: 99%

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Agile manufacturing practices: the role of big data and business analytics with multiple case studies

Gunasekaran

Yusuf

Adeleye

et al. 2017

International Journal of Production Research

155

107

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Section: Discussionmentioning

confidence: 84%

Section: Big Data and Business Analyticsmentioning

confidence: 99%

Agile manufacturing practices: the role of big data and business analytics with multiple case studies

Gunasekaran

Yusuf

Adeleye

et al. 2017

International Journal of Production Research

155

107

View full text Add to dashboard Cite

show abstract

“…Zhou et al investigated a trajectory detection method to improve taxi services using big data from GPS [150]. Applications of big data are also found in creating competitive advantages by troubleshooting, personalization, and detection of areas that require improvement [151]. For predictive modeling, high-cardinality features are not used very often because of their randomness.…”

Section: Applications Of Big Data and Pertinent Discussionmentioning

confidence: 99%

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Munawar

Qayyum

Ullah

et al. 2020

BDCC

115

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Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

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“…Although the value of general IT capabilities has been highlighted by practitioners and scholars alike, the knowledge obtained from and the central role played by data and information processing have not been explored. As it is an emerging and complex domain interconnected with many operational dimensions, any mediating roles played in the link between the use of the IoTs and agility therefore remain the subject of debate in the interdisciplinary IT-business literature (Bresciani et al, 2017;Marinagi et al, 2015;Matthias et al, 2017). This gap and discussion thus lead us to propose the following hypothesis.…”

Section: The Mediating Role Played By Ddipcsmentioning

confidence: 99%

The Internet of Things, dynamic data and information processing capabilities, and operational agility

Akhtar

Khan

Tarba

et al. 2018

Technological Forecasting and Social Change

127

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Whilst there are promising links between the Internet of Things (IoTs), dynamic data and information processing capabilities (DDIPCs), and operational agility, scholars have not conducted enough empirical studies that offer convincing evidence for the use of the IoTs and relevant linkages. This study therefore examines the links between such constructs and provides managerial implications for contemporary data and information driven managers who adopt evidence-based decision making for better operational outcomes. The results obtained from structural equation modelling indicate that the use of the IoTs is the key determinant for operational agility and also plays a vital role in establishing DDIPCs that further reinforce it. Additionally, DDIPCs mediate the relationship between the use of the IoTs and operational agility. By persuasively building these links based on theoretical arguments and testing them by using a unique dataset, this study contributes to the deeper understanding of the mechanisms by which the use of the IoTs and DDIPCs strengthen operational agility.

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Making sense of Big Data – can it transform operations management?

Cited by 114 publications

References 47 publications

Agile manufacturing practices: the role of big data and business analytics with multiple case studies

Agile manufacturing practices: the role of big data and business analytics with multiple case studies

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

The Internet of Things, dynamic data and information processing capabilities, and operational agility

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