scholarly journals Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

2017 ◽  
Vol 9 (4) ◽  
pp. 608 ◽  
Author(s):  
Venkatesh Mani ◽  
Catarina Delgado ◽  
Benjamin Hazen ◽  
Purvishkumar Patel
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Supply Chain Risk ◽  
Manufacturing Supply Chain

Managing supply chain resources with Big Data Analytics: a systematic review

2017 ◽  
Vol 21 (3) ◽  
pp. 177-200 ◽  
Author(s):  
Marcelo Werneck Barbosa ◽  
Alberto de la Calle Vicente ◽  
Marcelo Bronzo Ladeira ◽  
Marcos Paulo Valadares de Oliveira
Keyword(s):  
Systematic Review ◽  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics

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.

An examination of the importance of big data analytics in supply chain agility development

2018 ◽  
Vol 41 (10) ◽  
pp. 1201-1219 ◽  
Author(s):  
Santanu Mandal
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Technology Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Technical Knowledge ◽  
Content Type ◽  
Relational Knowledge ◽  
Management Knowledge ◽  
Business Knowledge

Purpose This paper aims to investigate the influence of big data analytics (BDA) personnel expertise capabilities in the development of supply chain (SC) agility. Based on extant literature, the study explores the role of BDA technical knowledge, BDA technology management knowledge, BDA business knowledge and BDA relational knowledge in SC agility development. Furthermore, the author also explores the inter-relationships among these four BDA personnel expertise capabilities. Design/methodology/approach An expert team consisting of IT practitioners (with a minimum experience of five years) were chosen to comment and modify the established scale items of the constructs used in the study. Subsequently, the measures were further pre-tested with 61 students specializing in computer science and information technology. The final survey was mailed to 651 IT professionals with a minimum experience of five years or more in an allied field. Repeated follow-ups and reminders resulted in 176 completed responses. The responses were analysed using partial least squares in SmartPLS 2.0.M3. Findings Findings suggested that BDA technology management knowledge, BDA business knowledge and BDA relational knowledge are prominent enablers of SC agility. Furthermore, BDA technology management knowledge is an essential precursor of BDA technical knowledge and BDA business knowledge. Originality/value The study is the foremost in addressing the importance of BDA personnel expertise capabilities in the development of SC agility. Furthermore, it is also the foremost in exploring the inter-relationships among the BDA personnel expertise capabilities.

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.

Improving Forecasting for Customer Service Supply Chain Using Big Data Analytics

2018 ◽  
pp. 25-41 ◽  
Author(s):  
Kedareshwaran Subramanian ◽  
Kedar Pandurang Joshi ◽  
Sourabh Deshmukh
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Customer Service ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Resource Planning ◽  
Road Map ◽  
Service Supply Chain ◽  
Data Ecosystem ◽  
Manufacturing Organization

In this book chapter, the authors highlight the potential of big data analytics for improving the forecasting capabilities to support the after-sales customer service supply chain for a global manufacturing organization. The forecasting function in customer service drives the downstream resource planning processes to provide the best customer experience at optimal costs. For a mature, global organization, its existing systems and processes have evolved over time and become complex. These complexities result in informational silos that result in sub-optimal use of data thereby creating inaccurate forecasts that adversely affect the planning process in supporting the customer service function. For addressing this problem, the authors argue for the use of frameworks that are best suited for a big data ecosystem. Drawing from existing literature, the concept of data lakes and data value chain have been used as theoretical approaches to devise a road map to implement a better data architecture to improve the forecasting capabilities in the given organizational scenario.

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