The Role of Big Data and Twitter Data Analytics in Healthcare Supply Chain Management

2019 ◽  
pp. 267-279 ◽  
Author(s):  
Shoayee Alotaibi ◽  
Rashid Mehmood ◽  
Iyad Katib
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Chain Management ◽  
Twitter Data

Adoption of Big Data Analytics in Supply Chain Management

2021 ◽  
Vol 14 (2) ◽  
pp. 88-107
Author(s):  
Amin Khalil Alsadi ◽  
Thamir Hamad Alaskar ◽  
Karim Mezghani
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Top Management ◽  
Management Support ◽  
Chain Management ◽  
Top Management Support ◽  
Intention To Adopt

Supported by the literature on big data, supply chain management (SCM), and resource-based theory (RBT), this study aims to evaluate the organizational variables that influence the intention of Saudi SCM professionals to adopt big data analytics (BDA) in SCM. A survey of 220 supply chain respondents revealed that both top management support and data-driven culture have a high significant influence on their intention to adopt BDA. However, the firm entrepreneurial orientation showed no significant effect. Also, the findings revealed that supply chain connectivity positively moderates the link between top management support and intention. This study contributes to the practical field, offering valuable insights for decision makers considering BDA adoption in SCM. It also contributes to the literature by helping minimize the research gap in BDA adoption in the Saudi context.

Big Data and Cloud Computing

2018 ◽  
pp. 1-28 ◽  
Author(s):  
Marcus Tanque ◽  
Harry J Foxwell
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
Chain Management ◽  
Computing Services ◽  
Cloud Computing Services

Big data and cloud computing are transforming information technology. These comparable technologies are the result of dramatic developments in computational power, virtualization, network bandwidth, availability, storage capability, and cyber-physical systems. The crossroads of these two areas, involves the use of cloud computing services and infrastructure, to support large-scale data analytics research, providing relevant solutions or future possibilities for supply chain management. This chapter broadens the current posture of cloud computing and big data, as associate with the supply chain solutions. This chapter focuses on areas of significant technology and scientific advancements, which are likely to enhance supply chain systems. This evaluation emphasizes the security challenges and mega-trends affecting cloud computing and big data analytics pertaining to supply chain management.

Big Data Analytics in Supply Chain Management

2021 ◽  
pp. 2443-2457
Author(s):  
Nenad Stefanovic
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Current Approach ◽  
Data Systems ◽  
Chain Management ◽  
Big Data Systems

The current approach to supply chain intelligence has some fundamental challenges when confronted with the scale and characteristics of big data. In this chapter, applications, challenges and new trends in supply chain big data analytics are discussed and background research of big data initiatives related to supply chain management is provided. The methodology and the unified model for supply chain big data analytics which comprises the whole business intelligence (data science) lifecycle is described. It enables creation of the next-generation cloud-based big data systems that can create strategic value and improve performance of supply chains. Finally, example of supply chain big data solution that illustrates applicability and effectiveness of the model is presented.

scholarly journals The impact of big data and business analytics on supply chain management

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Hans W. Ittmann
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Analytics ◽  
Chain Management ◽  
Business Competitiveness ◽  
The Impact ◽  
Proper Analysis

Background: Change is inevitable and as supply chain managers prepare for the future they face many challenges. Two major trends over the last few years are the growing importance of ‘big data’ and analysing these data though ‘analytics’. The data contain much value and companies need to capitalise on the variety of data sources by in-depth and proper analysis through the use of ‘big data’ analytics.Objective: This article endeavours to highlight the evolving nature of the supply chain management (SCM) environment, to identify how the two major trends (‘big data’ and analytics) will impact SCM in future, to show the benefits that can be derived if these trends are embraced and to make recommendations to supply chain managers.Method: The importance of extracting value from the huge amounts of data available in the SCM area is stated. ‘Big data’ and analytics are defined and the impact of these in various SCM applications clearly illustrated.Results: It is shown, through examples, how the SCM area can be impacted by these new trends and developments. In these examples ‘big data’ analytics have already been embraced, used and implemented successfully. Big data is a reality and using analytics to extract value from the data has the potential to make a huge impact.Conclusion: It is strongly recommended that supply chain managers take note of these two trends, since better use of ‘big data’ analytics can ensure that they keep abreast with developments and changes which can assist in enhancing business competitiveness.

scholarly journals Supply Chain Management and Logistics Big Data Challenges in Bulgaria

2021 ◽  
Vol 12 (1) ◽  
pp. 171-181
Author(s):  
Nikolay Dragomirov ◽  
Luben Boyanov
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Inventory Management ◽  
Chain Management ◽  
Order Processing ◽  
Communication Processes ◽  
Reporting Systems ◽  
Transformation Processes

Abstract The article focuses on the challenges of applying big data approaches to logistics and supply chain management in Bulgaria, while also presenting the challenges, opportunities and problems from a broader perspective. This includes a review of big data in logistics and its subsystems in relation to integration and transformation processes, as well as the role of big data and the relevant technologies in supply chain management. The research framework for the study of Bulgarian companies focuses on their experiences, both positive and negative, with big data. Data was collected using an electronic questionnaire. The respondents were from small, medium-sized and large companies alike. The results demonstrate and explain the companies’ experiences with big data and the level of application thereof. The findings showed that the main areas for the use of big data are order processing, communication processes, reporting systems, inventory management and vehicle database management.

scholarly journals Data analytics in supply chain management

2020 ◽  
Author(s):  
Hendro Wicaksono
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Industrial Revolution ◽  
Demand Forecasting ◽  
Chain Management ◽  
Strategic Sourcing ◽  
Management Processes ◽  
The Impact

The presentation discussed the impact of the technologies related to the 4th industrial revolution on big data. The 4th industrial revolution ecosystem is characterized by the presence of smart PPR (Product, Process, and Resource) who generates data. It transforms the product-based business model to product-data-driven service model. Big data also exist due to the digital transformation of supply chain management processes. Data analytics and machine learning can improve the supply chain management processes, such as demand forecasting, production, strategic sourcing, etc. Finally, the presentation gives some examples of the application of data analytics in real companies.

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