Big Data Analytics in Supply Chain Strategy

This paper investigates the research regarding big data analytics research as well as applications in the field of supply chain management in 2010 and 2016. It also provides specific insights to the organization. The amount of data that are produced as well as communicated over internet is increasing in a way positively. Hence, creating challenges for the enterprises would be helpful for reaping the advantages from analyzing the big data.

BIG DATA
ANALYTICS
SUPPLY CHAIN
STRATEGY
Name of the Student
Name of the University
Author note:

INTRODUCTION
 The paper investigates the research
regarding big data analytics research as
well as applications in the field of supply
chain management in 2010 and 2016. It
also provides specific insights to the
organization. In addition, the current
years and the amount of data that are
important for the organizations. In
addition, it is important to develop
specific process that are arranged
properly.

SUMMARY OF THE REAL WORLD
CHALLENGE
 The amount of data that are produced
as well as communicated over internet
is increasing in a way positively. Hence,
creating challenges for the enterprises
would be helpful for reaping the
advantages from analyzing the big data.
 It is important for the organizations to
provide insight about big data as well as
the BDBA on the logistics as well as
supply chain management.

ISSUES IDENTIFIED
 It is important for the organizations to
improve the process, comprehend the
activities with the help of strategic
assets, and require managers
integrating the process through
analyzing the use of big data.
 In addition, it is important for the
organization to set the process and
enable the organization enabling the
business analytics (Tan et al. 2015). On
the other hand, big data analytics has
been a multi-step access.

OBJECTIVES OF THE INVESTIGATION
 Big data processing systems are
important to set the process and obtain
competitive benefits.
 On the other hand, the supply chain
management is important to the
technology as it has currently reached
to the layer insights (Chen et al. 2015).

STRATEGY
 SCM organizations that can assist the
process and reported.
 The organizations are inundated with
the data that the process helps and
markets or transformation. In addition,
an entity and apparently true in the
formation.

STRATEGY FOR MITIGATION
 A main strength lies powerful strong
foundation in the infrastructure in
physical as well as technological is
organized.
 As a small organization trying to
develop the market, it is significant that
the organization increases competitive
benefits to well-developed competitors.

OPERATIONAL PLAN
 In order to deploy the process and achieve
success in the processing, it is important to
involve in the process. On the other hand, it
is required to acquire the infrastructure for
deploying the retailing process and big data
analytics. The infrastructure is important the
process (Papadopoulos et al. 2016).
 On the other hand, it is required to enhance
the process with the help of effective
management. In addition, it helps the
organization by implementing the process
and big data and enhance the process.

DATA COLLECTION
 Intercultural evaluations are utilized as
the part of on boarding procedure and
contingency for employment. However,
it is required helping a new team
member get succeed in the cultural
diverse environments. The feedbacks
are based on an evaluation process that
can assist new hires emphasizing on the
areas required attention for personal
development.

DATA STORAGE
 A cloud based storage is considered as
flexible without a requirement of
physical systems where it is important
minimizing the costs in deploying big
data (Wamba et al. 2015). On the other
hand, it is important to assume the data
stored separately and integrate the
process and assist the data storage
professionals helping to transiting the
data.

DATA ANALYSIS
 Big data does not provide the
advantages. The analysis of data as well
as ability deriving the useful information
that provides value for the big data.
 In addition, the vendors are generally
renowned such as google as well as
oracle services that can assist in
analyzing the data as well as give a
proper insights as well as potential
course of actions for the customers (Wu
et al. 2017).

TERM 2 (3 MONTHS)
 It is also important to develop own
analysis team and working together
with the process. It is also important to
work in the procedure that can be sent
from the organization. In addition, it
assists the organization to develop the
process and the vendors are helpful to
be involved in the process (Akter et al.
2016).

TERM 3 (3 MONTHS)
 It is important to enhance the process. It
allows the seamless integration of the
procedures that are under single data
storage system. On the other hand, it is
important for the organization to
resemble the procedure that can help
the process and experience the
integrated method. The implementation
method can be helpful for the
procedure.

BENEFITS OF BIG DATA
ANALYTICS
 The major strength of the process lies
foundation in the infrastructures. It
provides strong effective management
and keeping up the process with
increasing the demand (Hazen et al.
2016). The core competency includes e-
commerce management, sales as well
as marketing.

RISKS INVOLVED
 The outsourcing of the fulfillment of the
strategy helps the process and several
issues in the logistics. In addition, it is
important to improve the process with
successful business requirement and
reach the million milestone that
enhances the process and efficient for
fulfilling the requirements.

CONCLUSION
 As the present business deals with
effective business management, it is
important to differentiate the process
and provide effective management that
the customers expect from the stores. It
is also important for the organization to
obtain the processing of customer
experience. In addition, it is important to
develop the procedure that can be
helpful to improve the process and
require quick process for competing the
competitors.

REFERENCES
 Akter, S., Wamba, S.F., Gunasekaran, A., Dubey, R. and Childe,
S.J., 2016. How to improve firm performance using big data
analytics capability and business strategy alignment?. International
Journal of Production Economics, 182, pp.113-131.
 Chen, D.Q., Preston, D.S. and Swink, M., 2015. How the use of big
data analytics affects value creation in supply chain
management. Journal of Management Information Systems, 32(4),
pp.4-39.
 Dubey, R., Gunasekaran, A., Childe, S.J., Wamba, S.F. and
Papadopoulos, T., 2016. The impact of big data on world-class
sustainable manufacturing. The International Journal of Advanced
Manufacturing Technology, 84(1-4), pp.631-645.
 Giannakis, M. and Louis, M., 2016. A multi-agent based system
with big data processing for enhanced supply chain agility. Journal
of Enterprise Information Management, 29(5), pp.706-727.