Application of big data analytics in process safety and risk management

2017 ◽  
Author(s):  
Pankaj Goel ◽  
Aniruddha Datta ◽  
M. Sam Mannan
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Process Safety

The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohamad Bahrami ◽  
Sajjad Shokouhyar
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Supply Chain ◽  
Firm Performance ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Management Culture

PurposeBig data analytics capability (BDAC) can affect firm performance in several ways. The purpose of this paper is to understand how BDA capabilities affect firm performance through supply chain resilience in the presence of the risk management culture.Design/methodology/approachThe study adopted a cross-sectional approach to collect survey-based responses to examine the hypotheses. 167 responses were collected and analyzed using partial least squares in SmartPLS3. The respondents were generally senior IT executives with education and experience in data and business analytics.FindingsThe results show that BDA capabilities increase supply chain resilience as a mediator by enhancing innovative capabilities and information quality, ultimately leading to improved firm performance. In addition, the relationship between supply chain resilience and firm performance is influenced by risk management culture as a moderator.Originality/valueThe present study contributes to the relevant literature by demonstrating the mediating role of supply chain resilience between the BDA capabilities relationship and firm performance. In this context, some theoretical and managerial implications are proposed and discussed.

scholarly journals RISK MANAGEMENT 4.0: THE ROLE OF BIG DATA ANALYTICS IN THE BANK SECTOR

2019 ◽  
Vol 9 (6) ◽  
pp. 40-47 ◽  
Author(s):  
Grazia Dicuonzo ◽  
Graziana Galeone ◽  
Erika Zappimbulso ◽  
Vittorio Dell'Atti
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics

Big data analytics and process safety

2021 ◽  
pp. 209-270
Author(s):  
Sagit Valeev ◽  
Natalya Kondratyeva
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Process Safety

How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Surajit Bag ◽  
Pavitra Dhamija ◽  
Sunil Luthra ◽  
Donald Huisingh
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Supply Chain ◽  
Supply Chains ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Hypothetical Model ◽  
Content Type ◽  
Supply Chain Resilience ◽  
Management Capabilities

PurposeIn this paper, the authors emphasize that COVID-19 pandemic is a serious pandemic as it continues to cause deaths and long-term health effects, followed by the most prolonged crisis in the 21st century and has disrupted supply chains globally. This study questions “can technological inputs such as big data analytics help to restore strength and resilience to supply chains post COVID-19 pandemic?”; toward which authors identified risks associated with purchasing and supply chain management by using a hypothetical model to achieve supply chain resilience through big data analytics.Design/methodology/approachThe hypothetical model is tested by using the partial least squares structural equation modeling (PLS-SEM) technique on the primary data collected from the manufacturing industries.FindingsIt is found that big data analytics tools can be used to help to restore and to increase resilience to supply chains. Internal risk management capabilities were developed during the COVID-19 pandemic that increased the company's external risk management capabilities.Practical implicationsThe findings provide valuable insights in ways to achieve improved competitive advantage and to build internal and external capabilities and competencies for developing more resilient and viable supply chains.Originality/valueTo the best of authors' knowledge, the model is unique and this work advances literature on supply chain resilience.

scholarly journals Big Data Analytics for Improved Risk Management and Customer Segregation in Banking Applications

2021 ◽  
Vol 3 (3) ◽  
pp. 235-249
Author(s):  
Subarna Shakya ◽  
S Smys
Keyword(s):  
Risk Management ◽  
Big Data ◽  
Data Analytics ◽  
Financial Institution ◽  
Big Data Analytics ◽  
Regulatory Compliance ◽  
Internet Banking ◽  
Digital Convergence ◽  
Data Volume ◽  
Asset Quality

While the phrase Big Data analytics is not only applicable for a certain realm of technology, diverse business segments like banking also benefit from the use of advanced mathematical and statistical models like predictive analysis, artificial intelligence, and data mining. If it is a query that is data volume generated in a bank or any financial institution is huge, it is absolutely a yes. As per the recent survey, it is observed that banks worldwide aren't just concentrating on improving the asset quality and fulfilling regulatory compliance but on the lookout for a digital convergence strategy to reach customers effectively in delivering services and products. As most of the data generated in internet banking and ATM transactions are unstructured accounting around for 2.5 quintillion bytes useful for fraud detection, risk management, and customer satisfaction, the use of trending Big Data Analytics methodology can be used to tackle the challenges and competition among banks. There are surplus advantages of Big Data strategy in the banking field and in this paper, we have made an analysis over Big Data Analytics on banking applications and their related concepts.

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