scholarly journals Big Data Analytics in Sustainable Supply Chain Management: A Focus on Manufacturing Supply Chains

2021 ◽  
Vol 13 (13) ◽  
pp. 7101
Author(s):  
Joash Mageto
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Supply Chains ◽  
Data Analytics ◽  
Social Issues ◽  
Big Data Analytics ◽  
Sustainable Supply Chain ◽  
Chain Management ◽  
Sustainable Supply Chain Management

Sustainable supply chain management has been an important research issue for the last two decades due to climate change. From a global perspective, the United Nations have introduced sustainable development goals, which point towards sustainability. Manufacturing supply chains are among those that produce harmful effluents into the environment in addition to social issues that impact societies and economies where they operate. New developments in information and communication technologies, especially big data analytics (BDA), can help create new insights that can detect parts and members of a supply chain whose activities are unsustainable and take corrective action. While many studies have addressed sustainable supply chain management (SSCM), studies on the effect of BDA on SSCM in the context of manufacturing supply chains are limited. This conceptual paper applies Toulmin’s argumentation model to review relevant literature and draw conclusions. The study identifies the elements of big data analytics as data processing, analytics, reporting, integration, security and economic. The aspects of sustainable SCM are transparency, sustainability culture, corporate goals and risk management. It is established that BDA enhances SSCM of manufacturing supply chains. Cyberattacks and information technology skills gap are some of the challenges impeding BDA implementation. The paper makes a conceptual and methodological contribution to supply chain management literature by linking big data analytics and SSCM in manufacturing supply chains by using the rarely used Toulmin’s argumentation model in management studies.

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.

Sustainable Supply Chain Management

2011 ◽  
pp. 136-152
Author(s):  
Iskra Dukovska-Popovska ◽  
Malcolm Bertoni ◽  
Hans-Henrik Hvolby ◽  
Paul Turner ◽  
Kenn Steger-Jensen
Keyword(s):  
Supply Chain ◽  
Information Systems ◽  
Supply Chain Management ◽  
Supply Chains ◽  
Environmental Drivers ◽  
Sustainable Supply Chain ◽  
Green Logistics ◽  
Chain Management ◽  
Sustainable Supply Chain Management ◽  
Efficiency Cost

Integrating environmental considerations into supply-chain management has become an increasingly important issue for industry, government and academic researchers. Supply chain managers are being required to respond to the challenges of new legislation, standards and regulations; changing customer demands; drivers for efficiency, cost effectiveness and return on investment; while simultaneously being ‘green’. The fundamental tension between business and environmental drivers is difficult, but critical to understanding how to effectively re-engineer and re-design existing supply chains in a manner that is sustainable both financially and environmentally. Information systems have a significant role to play in supporting corporate responses to environmental management and the development of holistic green logistic solutions. This chapter examines contemporary discussions on the current state of sustainable supply-chain management and green logistics. It presents a case study from the Fujitsu Corporation in Japan and explores models of information systems and RFID use in green logistics. Combining insights from the case and existing models the chapter explores an example of how a combined model can be used to explore the potential of a specific emerging technology (RFIDs) in ‘greening’ supply chains.

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 Drivers of Sustainable Supply Chain Management: Identification and Classification

2019 ◽  
Vol 11 (4) ◽  
pp. 1137 ◽  
Author(s):  
Muhammad Saeed ◽  
Wolfgang Kersten
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Social Issues ◽  
Supply Chain Network ◽  
Sustainable Supply Chain ◽  
Chain Management ◽  
Sustainable Supply Chain Management ◽  
Sustainable Solutions ◽  
Sustainability Practices ◽  
Sustainability Initiatives

With the increase in awareness of environmental and social issues associated with the development and the use of products, stakeholders—especially consumers—are showing more concern regarding these issues. To address new developments and changing trends, organizations are now compelled to identify and implement innovative and sustainable solutions, not only within their organizations’ boundaries, but also across the whole supply chain network. The primary goal of this paper was to identify and analyze drivers of sustainable supply chain management (SSCM) that influence or encourage organizations to undertake sustainability initiatives and implement sustainable solutions throughout their supply chains. For this purpose, a systematic literature review was conducted and 1559 drivers of SSCM were identified from 217 journal articles. Precise interpretation, clear definitions, restructuring, and classification into external and internal driver categories produced a list of 40 unique drivers of SSCM. The results revealed that regulatory and market pressures, with reference to the number of citations, are the most prevailing drivers of SSCM for the implementation of sustainability practices. Classification of the drivers of SSCM into primary and secondary driver categories may assist practitioners and decision makers in prioritizing sustainability-related initiatives and adopting sustainability practices across the whole supply chain network.

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.

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