The Impact of Big Data Applications on Supply Chain Management

2015 ◽  
pp. 127-135
Author(s):  
Dong-xiang Zhang ◽  
Bin Cheng
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Chain Management ◽  
Big Data Applications ◽  
The Impact

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 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.

scholarly journals INTENTION TO ADOPT BIG DATA IN SUPPLY CHAIN MANAGEMENT: A BRAZILIAN PERSPECTIVE

2019 ◽  
Vol 59 (6) ◽  
pp. 389-401 ◽  
Author(s):  
MACIEL M QUEIROZ ◽  
SUSANA CARLA FARIAS PEREIRA
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Business Models ◽  
Chain Management ◽  
Intention To Adopt ◽  
Use Of Technology ◽  
Big Data Applications ◽  
And Performance ◽  
Facilitating Conditions

ABSTRACT Big data applications have been remodeling several business models and provoking strong radical transformations in supply chain management (SCM). Supported by the literature on big data, supply chain management, and the unified theory of acceptance and use of technology (UTAUT), this study aims to evaluate the variables that influence the intention of Brazilian SCM professionals to adopt big data. To this end, we adapted and validated a previously developed UTAUT model. A survey of 152 supply chain respondents revealed that facilitating conditions (e.g., IT infrastructure) have a high influence on their intention to adopt big data. However, social influence and performance expectancy showed no significant effect. This study contributes to the practical field, offering valuable insights for decision-makers considering big data projects. It also contributes to the literature by helping minimize the research gap in big data in the Brazilian context.

scholarly journals The Impact of Big Data Analytics on Company Performance in Supply Chain Management

2019 ◽  
Vol 11 (18) ◽  
pp. 4864 ◽  
Author(s):  
Oncioiu ◽  
Bunget ◽  
Türkeș ◽  
Căpușneanu ◽  
Topor ◽  
...  
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Company Performance ◽  
Chain Management ◽  
Security Technologies ◽  
Main Component ◽  
The Impact

Big data analytics can add value and provide a new perspective by improving predictive analysis and modeling practices. This research is centered on supply-chain management and how big data analytics can help Romanian supply-chain companies assess their experience, strategies, and professional capabilities in successfully implementing big data analytics, as well as assessing the tools needed to achieve these goals, including the results of implementation and performance achievement based on them. The research method used in the quantitative study was a sampling survey, using a questionnaire as a data collection tool. It included closed questions, measured with nominal and ordinal scales. A total of 205 managers provided complete and useful answers for this research. The collected data were analyzed with the Statistical Package for the Social Sciences (SPSS) package using frequency tables, contingency tables, and main component analysis. The major contributions of this research highlight the fact that companies are concerned with identifying new statistical methods, tools, and approaches, such as cloud computing and security technologies, that need to be rigorously explored.

scholarly journals Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

2020 ◽  
Vol 13 (1) ◽  
pp. 56
Author(s):  
Tino Herden
Keyword(s):  
Supply Chain ◽  
Supply Chain Management ◽  
Success Factors ◽  
Research Collaboration ◽  
Future Research ◽  
Structured Interviews ◽  
Chain Management ◽  
Domain Specific ◽  
The Status ◽  
The Impact

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains.Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin.Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed.Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.

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