Authors :
Wumi AJAYI; ArewaOladipupo Abiodun; AdekoyaDamola Felix; FajuyigbeOlugbenga Joseph
Volume/Issue :
Volume 8 - 2023, Issue 7 - July
Google Scholar :
http://tinyurl.com/2e2awmu9
Scribd :
http://tinyurl.com/5csvf8az
DOI :
https://doi.org/10.5281/zenodo.10638231
Abstract :
Consumers anticipate that businesses involved
in a supply chain will provide the required goods at the
proper moment, which requires adaptable
manufacturing and modification, high service standards
to address facility location challenges, fewer forecasting
errors, less expensive procurement, and quicker supply
chain interactions.However, with the availability of a
variety of information sources and the ability to manage
a massive and varied amount of real-time data on the life
cycles of goods, outbound/ inbound logistics, customer-
product interactions, and market needs, this issue may
now be reduced to a minimum. Hence, this paper
provides informative discussions on quality Assurance
Challenges for Supply Chain Management Big Data
Applications.The publications taken into consideration
for this study were published between 2011 and 2021. A
thorough literature study was used as the research
methodology. A minimum of 50 peer-reviewed academic
publications, conference proceedings, and business white
papers were examined. The articles were compiled using
the Thomson Reuters Web of Science, a descriptive
analysis was conducted, categories were created, and the
content of the articles was evaluated.
A discussion of quality assurance issues for supply
chain management applications using big data is
presented in this paper. Moreover, it introduces and
discusses supply chain analytics based on big data, its
importance, challenges, and applications. Furthermore,
quality assurance issues for big data applications were
presented.
Big Data Analytics requires expensive
infrastructure. Therefore, by focusing research efforts
on lowering the costs of storing Big Data, Big Data
Analytics could become more widely available.
Keywords :
Big Data Analytics, logistics systems, Quality assurance, Supply chain management, Manufacturing system.
Consumers anticipate that businesses involved
in a supply chain will provide the required goods at the
proper moment, which requires adaptable
manufacturing and modification, high service standards
to address facility location challenges, fewer forecasting
errors, less expensive procurement, and quicker supply
chain interactions.However, with the availability of a
variety of information sources and the ability to manage
a massive and varied amount of real-time data on the life
cycles of goods, outbound/ inbound logistics, customer-
product interactions, and market needs, this issue may
now be reduced to a minimum. Hence, this paper
provides informative discussions on quality Assurance
Challenges for Supply Chain Management Big Data
Applications.The publications taken into consideration
for this study were published between 2011 and 2021. A
thorough literature study was used as the research
methodology. A minimum of 50 peer-reviewed academic
publications, conference proceedings, and business white
papers were examined. The articles were compiled using
the Thomson Reuters Web of Science, a descriptive
analysis was conducted, categories were created, and the
content of the articles was evaluated.
A discussion of quality assurance issues for supply
chain management applications using big data is
presented in this paper. Moreover, it introduces and
discusses supply chain analytics based on big data, its
importance, challenges, and applications. Furthermore,
quality assurance issues for big data applications were
presented.
Big Data Analytics requires expensive
infrastructure. Therefore, by focusing research efforts
on lowering the costs of storing Big Data, Big Data
Analytics could become more widely available.
Keywords :
Big Data Analytics, logistics systems, Quality assurance, Supply chain management, Manufacturing system.
