Version 1
: Received: 4 October 2021 / Approved: 6 October 2021 / Online: 6 October 2021 (10:38:42 CEST)
How to cite:
Sakib, S.M.N. Usage of Data Analytics in Improving Sourcing of Supply Chain Inputs. Preprints2021, 2021100103. https://doi.org/10.20944/preprints202110.0103.v1
Sakib, S.M.N. Usage of Data Analytics in Improving Sourcing of Supply Chain Inputs. Preprints 2021, 2021100103. https://doi.org/10.20944/preprints202110.0103.v1
Sakib, S.M.N. Usage of Data Analytics in Improving Sourcing of Supply Chain Inputs. Preprints2021, 2021100103. https://doi.org/10.20944/preprints202110.0103.v1
APA Style
Sakib, S.M.N. (2021). Usage of Data Analytics in Improving Sourcing of Supply Chain Inputs. Preprints. https://doi.org/10.20944/preprints202110.0103.v1
Chicago/Turabian Style
Sakib, S.M.N. 2021 "Usage of Data Analytics in Improving Sourcing of Supply Chain Inputs" Preprints. https://doi.org/10.20944/preprints202110.0103.v1
Abstract
One of the most remarkable features in the 20th century was the digitalization of technical progress, which changed the output of companies worldwide and became a defining feature of the century. The growth of information technology systems and the implementation of new technical advances, which enhance the integrity, agility and long-term organizational performance of the supply chain, can distinguish a digital supply chain from other supply chains. For example, the Internet of Things (IoT)-enabled information exchange and Big Data analysis might be used to regulate the mismatch between supply and demand. In order to assess contemporary ideas and concepts in the field of data analysis in the context of supply chain management, this literary investigation has been decided. The research was conducted in the form of a comprehensive literature review. In the SLR investigation, a total of 71 papers from leading journals were used. SLR has found that data analytics integrate into supply chain management can have long-term benefits on supply chain management from the input side, i.e., improved strategic development, management and other areas.
Keywords
Data Analytics; Analytics; Supply Chain Input; Supply Chain; Data Science; Data
Subject
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
