Data Analytics In Supply Chain Management Research Articles

Digital transformation of supply chain management in retail and e-commerce

PurposeThe purpose of studying digitization transformation of the supply chain is to understand how digital technologies and processes are changing the way supply chains operate and to identify the opportunities and challenges associated with this transformation. Studying digitization transformation of the supply chain is important because it can help global businesses in identifying the best practices in supply chain management (SCM) systems and enhance supply chain performance. Hence, this research study is contributing in revealing the outcomes of digital inclusiveness in overall SCM for the growth of retail and e-commerce based platforms.Design/methodology/approachThis research is using both descriptive and explanatory research designs to provide a comprehensive understanding of the problems in SCM. Descriptive research provides a detailed description of the characteristics of the population under study, while explanatory research identifies the causal relationships between the variables. Descriptive research has helped us to develop hypotheses about the relationships between variables that can be tested using explanatory research. Explanatory research has been used to validate the findings of descriptive research. By using both descriptive and explanatory research designs, our research design has increased the generalizability of our findings.FindingsAccording to this study, businesses intend to change their supply chain strategies after the wake of competitive era to make them more robust, sustainable and collaborative with suppliers, customers and stakeholders by investing more in SCM technology like Blockchain, AI, analytics, robotic process automation and data control centers. This study evaluates the impact of digitization on supply chain systems. This includes assessing the benefits of digitization and identifying the factors that contribute to successful implementation. This research is studying the role of data analytics in SCM and how it can be leveraged to improve efficiency, reduce costs and increase transparency.Research limitations/implicationsThe study highlights the importance of adopting digitization in supply chain systems to improve supply chain robustness, sustainability and collaboration with stakeholders. This study's emphasis on data analytics in SCM presents an opportunity for businesses to gain insights into their supply chain systems and make data-driven decisions. This can enhance efficiency, reduce costs and improve overall supply chain performance. The study's focus on SCM technology and data analytics may overlook other factors that contribute to successful SCM, such as organizational culture, human resources and supply chain governance.Originality/valueThis study will complement to the existing body of information, management theory and practice and will benefit all. The research work is original and can be implemented worldwide to promote digitization in SCM for smooth transactions in the entire chain of wholesalers, retail distributors and customers.

Digital information's challenges and potential at the nexus of Big Data Analytics and supply chain management

Despite the breadth of supply chain management (SCM) research, little attention has been paid to the application of Big Data Analytics to maximize the utilization of information in a supply chain. The objective of this article is to contribute to the development of SCM theory by examining the potential effects of Big Data Analytics on information utilization in a business and supply chain setting. Because it is critical for supply chain firms to have access to current, accurate, and useful data, the exploratory research will shed light on the opportunities and problems associated with the implementation of Big Data Analytics in SCM. While management is increasingly focusing on Big Data Analytics, actual research on the subject is still sparse. Due of the scarcity of relevant content at the nexus of Big Data Analytics and Supply Chain Management, the authors employ the Delphi research technique. The given Delphi survey findings complement to existing knowledge by identifying 43 opportunities and problems associated with the emergence of Big Data Analytics from a corporate and supply chain perspective. These structures provide the research community with a starting point for tailoring future research at the nexus of Big Data Analytics and SCM. The research contributes to the current body of knowledge by examining possibilities and challenges at the corporate and supply chain level with a particular emphasis on the consequences imposed by Big Data Analytics.