Big Data Analytics Supply Chain Strategy

This study explores the use of big data analytics in supply chain management and provides insights on developing effective strategies for competitive benefits. It covers issues, objectives, and operational plans for implementing big data analytics in the supply chain.

Running head: BIG DATA ANALYTICS SUPPLY CHAIN STRATEGY
Big data analytics supply chain strategy
Name of the Student
Name of the University
Author note:

1BIG DATA ANALYTICS SUPPLY CHAIN STRATEGY
Executive Summary
Big data analytics offers several prospects in business transformation. The business players are
considered as new paradigm for offering endless promises in business transformation as well as
operational enhancements. In supply chain management, the instances have captured attention of
monitoring the procedure and help to track the inventory. It is also important for the practitioners
as well as researchers in the financial service along with the marketing sectors in supply chain
management. It is also important for the investigation and has the basics of big data analytics.
The investigations have the ranges of solutions and implementing the process so that big data
analytics solutions have no issues in implementation process. On the contrary, most of the
organizations have expectations from big data analytics in the supply chain management system.
Hence, in pursuing the change it is important to make the process effective across transformation
as well as procurement. In this perspective, it is important to develop taxonomy regarding big
data and supply chain management system. Hence, conducting a research effective for making
proper procedure to deal with supply chain management-system, it has an important role for big
data analytics to implement it and gain competitive benefits. Present study deals with the issues
identified in the existing system and take effective steps for mitigating the issues.

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Table of Contents
Introduction..........................................................................................................................3
Summary of the real world challenge..................................................................................3
The use of big analytics data in the process of efficiency and responsiveness...................4
Issues identified...................................................................................................................4
Objectives of the Investigation............................................................................................5
Strategy................................................................................................................................5
Strategy for mitigation.........................................................................................................7
Operational plan...................................................................................................................7
Data collection.....................................................................................................................9
Data storage.......................................................................................................................10
Data analysis.....................................................................................................................10
Term 2 (3 months).............................................................................................................10
Term 3 (3 months).............................................................................................................11
Benefits of big data analytics.............................................................................................11
Risks involved...................................................................................................................11
Conclusion.........................................................................................................................12
Appendix............................................................................................................................16

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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. In addition, surveying the new techniques could be helpful to serve the
process and gain competitive benefits. On the other hand, it is important to develop the process
that could assist in making the big data analytics more reliable for this. It is important to develop
new techniques in order to investigate the procedure, capturing as well as organized in order to
provide the procedure that can be helpful in realizing the promising advantages and can assist the
industries.
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.
It is important to realize the importance for the organizations and enhance the process by
analyzing the customer buying patterns as well as make the process effective for the
organization. Realizing the process and significance of big data analysis assists in reviewing the
procedure and classify the significance of the patterns (Wang et al. 2016). The process is based
on the type of analytics as well as emphasis of the study. In addition, it is important to evaluate
the extent for SCA that can be applied with the maturity framework and the levels of capability.

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The use of big analytics data in the process of efficiency and responsiveness
Advancement in information and communication technology rise to explosion of data in
the field of operations. Working with enormous data volume extracts the usefulness information
in order to support the decision-making process effective. A big data centric for the supply chain
management is propose exploiting the present state of art regarding data management. However,
sharing the information acts an important role in integrating supply chain management system. In
this perspective, it is required to achieve the seamless coronation and harmony among specific
members of the supply chain. Hence, it is important to follow the process.
It is important to focus on several aspects that an organization focuses with the
production offshore. In current days, lean manufacturing as well as global production is
important for increasing efficiency as well as minimize the costs. However, the organizations
have moved the offshore production and the opportunity is diminishing the differences in the
process of global manufacturing. In addition, the organization focuses on the process that needs
to emphasize on the supply chain management system. On the other hand, the organizations can
bring production closer to the organization as well as home markets for reshoring the level of
production. It is also important for the organization to make the combination of the issues that
can be varied the streams regarding big data as well as advanced tools and techniques like
representing next fronting issues for the organization.
Big data as well as the applications are increasingly received. On the other hand, it is
missing evidence that big data is comprehended properly. It is also important to make the
application flow in supply chain management. Big data analytics assists to solve the issue. The
big data analytics and observations as well as insights from the study so that it could be helpful
to analyze the importance of big data analytics in supply chain management strategy. Visualizing

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the importance of the organization is important for delivering effectiveness of the process.
However, adding difficulty level of the alignment is helpful for delivering effective business
units or organizations. However, several ordinary systems are desired in generating important
slack in the schedules. In addition, adding the difficulty level for the deliveries and business units
are important for managing own delivering the system. It is also important for managing the
process and need to be effective in the process. In this perspective, it is important for the
organizations to manage the delivery system and need to work by managing the delivery system.
However, it is important to focus on the process that can be effective and bring competitive
benefits in the system. On the other hand, it is important for the organizations to focus on the
issues that can detect the scopes.
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. In addition, it is important for the organization to
understand the process and make the supply chain managers drowning the information in order
to take the part of it. On the other hand, big data supply chain managers wants using big data for
deriving the big insights. It helps understanding the infrastructure as well as technology that can
allow the specific concept in order to emerge first place.
Objectives of the Investigation
Big data revolution deals with the process that applies information. It may assist to
develop the procedure and enhances cost-effective and innovative forms of assets that are

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required to develop the process so that enhances the process and gain competitive advantages.
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). The business players have an important role in
business where it is required to enhance the process and have an endless promise the
transformation process. In addition, supply chain management in specific way. There are
important procedure for using the pattern and take attention of the practioners that solve the
issue. Furthermore, the current topic that assists the process and wall mart can handle compared
to the process.
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. Due to the success in the process of leveraging social
media and other channels to experience a steady development. It is important to a successful and
Omni-channel business to make the inventory management. A successful omni-insights into the
channel of business. The human resources as well as information technology to enhance the
technologies must be carefully considered successfully integrating the business. It is also
important to end up the process that has made the infrastructure in the human resources as well
as need to be careful with successful migration of the business.
Strength Weakness

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● Self-developed platform
● Low labor cost as well as short lead
time through outsourcing to Vietnam
for production
● Self-developed mobile application
● Built image of local trendy of the
urban fashion provider
● Inaccurate demand for forecasting
● Limited warehouse space
● Limited vehicles fulfilling demand
● Weak position in market due to being
new
● Return management are not properly
integrated with the operations
Opportunity Threats
● Enhanced customer service by the
returns management
● Higher efficiency
● Greater responsiveness
● enhanced accuracy in forecasts
through use of Big Data analytics
● Increase e-commerce revenue
● enhance customer shopping
experience through the processing of
Omni-channel retailing
● Unstable position in the market, for a
new competitor
● Decrease in the process of demand for
high-end
● Wage expand or disruptions in the
place
● Increase in competition
Table 1: SWOT analysis

8BIG DATA ANALYTICS SUPPLY CHAIN STRATEGY
(Source: Created by author)
The objective of providing a strategy operation blue print is important to be evaluated.
Through the business model in the present study is important to be evaluated that is helpful to
obtain for using the big data (Schoenher and Speier‐Pero 2015). On the other hand, the source
part of the business emphasizing the core-competencies as well as still the supply chain of the
system. Hence, a comprehensive plan is important to create value in the supply chain as well as
keeping with the competitors in the procedure of deployment.
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.
With the process of advancement in information technology, the development process in current
years. It is important to develop the competency and bring the procedure to manage the process.
The flaws lead to face several scopes and develop essential blocks for efficient as well as
responsive supply chain management.
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. On the other hand, 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. In house development as well as third party

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service providers are important for accessing the organization to improve the process. On the
other hand, it is important to develop the procedure that can be helpful for monitoring as well as
measuring the aspects of the operations. The cost of implementation of GPS is approximately the
gained process.
Cross-cultural communication refers to communication between the people having
difference in the style of working, age, ethnicity, sexual orientation and nationality. In addition,
ethnicity, race and gender are also included in the category. On the other hand, the rapid growth
of globalization, the international activities as well as intercultural communications are
exchanging the process between several cultures (Giannakis and Louis 2016). In this perspective,
the most significant development is related to intercultural competence that is considered as
emergence of the investors that can evaluate intercultural competence. Several organizations
utilize evaluations for developing intercultural awareness as well as skills of the members. In
addition, intercultural evaluations provide the organizations with mechanism to comprehend the
areas that require most attention.
Culture is considered one of the ways of thinking as well as living where it is important
to select a set of activities, values as well as beliefs, which are taught as well as reinforced by
different members in the group. It sets the primary assumptions as well as solutions to the issues
of the world are known as a shared system, passed on from a generation to another ensuring
survival. On the other hand, crowd and evacuation system management is one of the active areas
in a research. There are several development continuing taken place in the procedure of efficient
evaluation system of people in the mass gatherings (Tiwari et al. 2018). Hence, it is important
providing a review of the intelligent evacuation system of management. It covers multiple
aspects of monitoring crowd, disaster prediction and evacuation modeling. Soft computing

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approach has an important role in designing as well as implementing applications that pertains to
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.
Cross-cultural communication is influenced through several academic disciplines. It is
essential to avoid the process of misunderstandings, which leads to generate conflicts between
the individuals. Cross-cultural communication can create trust feeling and enables cooperation.
The focus is on providing the appropriate response compared to providing the appropriate
message. When people from different background, the systems turns talking with different,
cross-cultural communication will be effective as well as easier whether the speakers include
knowledge of turning the process. In this aspect, it is important to improve the process through
which RFID as well as GPS systems can be helpful to enhance the process (Richey et al. 2016).
The costs of deploying the procedure can install and track the process. It allows the organization
keeping track of the process, where the deliverables are important for providing the product
tracking service. Here, the consumers utilize the omni-channel tracking. It utilizes the specific
process and need RFID tags are important for the organization. The level of tracking used in the
process and depends on the level of tracking. In addition, it is important for the organization
tracking the products as well as equipment to make the process stronger. On the other hand, It is
recommended that the procedure helps in making and gaining competitive benefits.

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Data storage
Infrastructure is required for storing the data collected through big data analytics. In this
perspective, it is important to enhance the process. It is also important to enhance the process and
provides massive data storage regarding cloud (Wamba and Akter 2015). It is also important to
improve the process and enhance the software framework. On the other hand, it is important to
develop effective procedure that can enhance the software framework. 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). It
is also important to analyze the process the team and enhance the procedure with the assistance
of big data analytics.
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). On the other hand, it is important for the organizations to make the
choice for selection and improve the process with the process. It makes the choice and analyzing

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the procedure so that the organization helps in making the procedure. It is also important taking
over the program. However, the practices are important for the consumers. It is important to have
proper outsourcing data analytics that can be helpful for coordinating with the professionals.
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. On the other hand, it is important for
the process and ensure that there are no issues for overhaul is achieved. It can prevent any type
of risks in the process and enhance the process (Zhong et al. 2016). The deployment procedure
can assist in making the process. On the other hand, it allows in making a better informed
decision on the procedure of having directing the organization. The procedure can assist in
making the process helpful for Omni channel effect.
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. The
flaws are helpful for developing essential blocks for efficient as well as responsive management
of supply chain. It requires the appropriate strategy for implementing the process.
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

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requirement and reach the million milestone that enhances the process and efficient for fulfilling
the requirements. It is also important to consider the amount of resources into the developing
logistics infrastructures as well as procedures. It is efficient for the providers to develop the
strategy and reach the milestone into the infrastructure.
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. Omni channel business is important to
select big data in order to assist in obtaining competitive benefits. It can provide retailing process
faster and shift for the business model (Dubey et al. 2016). Fulfillment of the deals with logistics
are important for providing the business aspect and costs pile up whether the organizations
deciding the most complicated algorithms as well as technology available. In addition, the
organizations have decided to use the process and make it available. It is important to analyze the
data by processing the data.

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Appendix

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