Activity6
This week, you will add to the initial data management plan for the hypothetical use case that you created in from the “activity5” assignment. Specifically, the goal for this addition to the data management plan is to add provisions for data usage, transformation, and retention of data in the organization.
Add the following information to your initial data management plan (technical report):
1. Include provisions that describe how data retention and archival will be handled.
2. Describe transformation methods, usage requirements, policies, and procedures for adding datasets to the data repository to support the research team.
3. Describe how data governance and democratization will be managed to support the specified research team and related use of engaging in ongoing research in the specific domain area.
Length: 7 to 8-page technical report, not including title and references pages
References: Include a minimum of 3 scholarly references (be sure that at least one of the three is a peer-reviewed research study involving data usage and retention planning from the school li
ary to support your ideas).
NB:
Scholarly reference: check the attached PDF file.
7Big Data Usage and Big Data Analytics in Supply Chain: Leveraging Competitive Priorities
for Enhancing Competitive Advantages
Big Data Usage and Big Data Analytics
in Supply Chain: Leveraging Competitive
Priorities for Enhancing Competitive Advantages
Pankaj M Madhani*
© 2022 IUP. All Rights Reserved.
In the cu
ent competitive global scenario, developing a successful supply chain strategy which ensures a
distinct competitive advantage is critical to an organization’s long-term success. Creating a competitive
advantage requires numerous factors (i.e., competitive priorities of quality, delivery, flexibility, and cost) that
may put a firm’s supply chain in a better position in relation to its competitors. The supply chain effectiveness
and efficiency improvements require access to data from different functional areas of an organization and
different supply chain partners. Data is enabling new ways of organizing and analyzing supply chain processes
and leveraging this data drives supply chain performance. Big Data usage and Big Data Analytics (BDA) in
supply chains leverage various competitive priorities. The research develops various frameworks to emphasize
the digital transformation of the supply chain on Big Data usage and BDA and analyzes how competitive
priorities of Big Data-enabled supply chain drive customer value creation and ultimately help in building
competitive advantages. The research also illustrates how Walmart has achieved remarkable success in the
supply chain with the use of Big Data and BDA.
* Dean (Academics) and Professor, IBS Hyderabad (Under IFHE – A Deemed to be University u/s 3 of the UGC
Act, 1956), Hyderabad, Telangana, India. E-mail: XXXXXXXXXX
Introduction
Data is a driver of better decision-making processes and hence leads to improved business
performance for those firms able to leverage it. Firms from diverse sectors are leveraging
the use of data to their advantage (McAfee and Brynjolfsson, XXXXXXXXXXA supply chain
consists of all the activities that must be performed to create value, from procuring raw
materials, transforming them into finished products, and delivering those products to the
customers (Chen and Paulraj, XXXXXXXXXXSupply Chain Management (SCM) faces various
challenges such as delayed shipments, rising fuel costs, inconsistent suppliers, and ever-
increasing customer expectations. In the cu
ent era of the competitive global scenario,
developing a successful supply chain strategy is critical to an organization’s long-term
success. However, the management of supply chains has become increasingly important as
well as complex in the context of globalization, new product development, diffusion of
innovation, and changing customer preferences. The supply chain effectiveness and
efficiency improvements require access to data from different functional areas of an
organization and different supply chain partners (Sanders, 2014; and Yu, XXXXXXXXXXData is
The IUP Journal of Supply Chain Management, Vol. 19, No. 2, 20228
enabling new ways of organizing and analyzing supply chain processes and the leveraging
of this data drives supply chain performance (Hazen et al., 2014).
Information Technology (IT) has evolved as a strategic platform for supply chain
networks. The development of Big Data and Big Data Analytics (BDA) has introduced
fresh opportunities for firms as it helps in gaining competitive advantages. SCM is
adopting Big Data and BDA as a means to improve information flows and decision making
in supply chains, where high volumes of multidimensional data exceed the capacity of
traditional information technologies (George et al., 2014; and Ramanathan et al., 2017).
BDA provides a critical source of important information that may help supply chain
stakeholders to gain improved insights into understanding the changes in the business
and market environments and building a competitive advantage for the organization
(Wamba et al., XXXXXXXXXXBDA could lead to increased efficiency and profitability in the supply
chain by maximizing speed and visibility, improving supply chain stakeholders’
elationships, and enhancing supply chain agility. The common goal of SCM is to improve
performance in terms of various competitive priorities i.e., quality, delivery, flexibility, and
cost by building a portfolio of capabilities (Li et al., XXXXXXXXXXThis research focuses on how
Big Data usage and BDA can boost and enhance the performance of traditional SCM to
evolutionize supply chain performance.
Literature Review
The cu
ent economic environment is characterized by many challenges, including hyper-
competition, high uncertainty, increased tu
ulence, globalization of markets, and
increased product and service innovations (Alfalla-Luque et al., 2018; and Marin-Garcia
et al., XXXXXXXXXXAny organizational initiative, including SCM, should ultimately lead to
enhanced organizational performance (Li et al., XXXXXXXXXXSupply chains have been viewed by
firms as key levers for competitive advantage as the market competition has evolved from
“firm versus firm” toward “supply chain versus supply chain” (Ketchen and Hult, 2007).
A supply chain is defined as “the network of organizations that are involved, through
upstream and downstream linkages, in different processes and activities that produce value
in the form of products and services delivered to the ultimate consumer (Christopher,
2016). The short-term objectives of SCM are primarily to increase productivity and reduce
inventory and cycle time, while the long-term objectives are to increase market share and
profits for all members of the supply chain (Tan et al., 1998).
The traditional supply chain approach in which the customer is the final destination
of all supply chain processes is no more relevant today, as such efficiency-based, cost-
saving supply chains tend to be more vulnerable to unanticipated shifts in custome
demand (Lee, XXXXXXXXXXNowadays, market competition no longer happens between
individual companies but takes place between supply chains (Farahani et al., XXXXXXXXXXSupply
chain performance plays a vital role in gaining a competitive advantage and increasing
firm productivity. Supply chain performance refers to the effective use and monitoring
9Big Data Usage and Big Data Analytics in Supply Chain: Leveraging Competitive Priorities
for Enhancing Competitive Advantages
of supply chain practices (Chen et al., XXXXXXXXXXAny initiatives to improve supply chain
performance attempt to match supply and demand, thus simultaneously driving down
costs and improving customer satisfaction. To enhance supply chain performance,
there is a need to improve customer service quality, increase the value of goods and
services and reduce ca
ying costs (Wisner, XXXXXXXXXXProactive supply chain practices
help organizations stay on the right path to financial stability and operational
excellence (Chen et al., 2015).
In this highly dynamic business environment, managers prefer taking data-driven
decisions rather than trusting their intuitions (Arunachalam et al., XXXXXXXXXXFirms are
developing their organizational and technological capabilities for extracting value from
the data, which will provide them a competitive edge over the other firms. Past studies
have shown that data-driven decision-making, data science techniques, and technologies
can play an essential role in improving overall business performance (Raguseo, 2018).
Data-driven supply chains reduce product defects and rework within manufacturing plants
(Lee et al., 2013), respond quickly to changing customer and supplier needs (Sanders,
2014), reduce product development time (Manyika et al., 2011), and lead to overall
improvements in efficiency (Davenport et al., XXXXXXXXXXData-driven supply chains manage,
process, and analyze data across the supply chain to improve supply chain design and
competitive advantage (Waller and Fawcett, 2013).
There is significant interest in various information technologies for the
management of supply chains, which are generating enormous amounts of data
(Yesudas et al., 2014; and Arunachalam et al., XXXXXXXXXXSCM activities have become more
networked, resulting in the generation of a huge volume of real-time data, refe
ed to
as ‘Big Data’ (Chen et al., XXXXXXXXXXSuch data generation in supply chain networks is the
esult of advanced networking technologies, including embedded sensors, tags, tracks,
arcodes, Internet of Things (IoTs), Radio-Frequency Identification (RFID) tags, and
several smart devices that capture such data (Gunasekaran et al., 2017).
The adoption and use of innovative IT have been considered a critical resource fo
supply chain optimization. Prior studies identified numerous benefits related to IT-enabled
supply chain optimization, including end-to-end information sharing among supply chain
stakeholders (Sahin and Robinson, 2002; Saeed et al., 2005; and Wang and Wei, 2007);
improved decision-making within the supply chain (Vakharia, 2002); improved operational
efficiency (Johnston and Vitale, 1988; and Devaraj et al., 2007); and increased revenue (Rai
et al., XXXXXXXXXXThe findings of Wu et al XXXXXXXXXXshowed that IT is positively linked to supply
chain performance, which subsequently provides leverage for firms to achieve sustainable
productivity.
Various supply chain stakeholders (e.g., retailers and manufacturers) capture data all
along their supply chains. It includes data collected from different sources such as RFID
tags, GPS locations, Member Card and Point of Sale (PoS), data emitted by social media
feeds, and equipment sensors (Gandomi and Haider, 2015; Choi et al., 2018; and
The IUP Journal of Supply Chain Management, Vol. 19, No. 2, 202210
Swaminathan, XXXXXXXXXXHence, a vast amount of data is constantly being produced while
fulfilling customers’ demands (Aydiner et al., XXXXXXXXXXBig Data refers to the storage and
analysis of such complex as well as voluminous data through the use of a series of
technologies (Ward and Barker, XXXXXXXXXXBusiness organizations can use these data (i.e., Big
Data) to acquire a competitive edge and improve their performance (Provost and Fawcett,
2013; and Akter et al., 2016).
Big Data refers to large and complex datasets that cannot be processed using traditional
software. Big Data has dramatically affected the traditional ways of managing a business
in the 21st century as Big Data will allow managers to be increasingly informed on the state
of internal operations, workforce performances, the consumers’ behavioral patterns, and
supply chain processes (Bresciani et al., XXXXXXXXXXChen et al XXXXXXXXXXhighlighted that many
companies are providing the best service facilities to their clients using
Big Data. Many business advantages can be achieved through harvesting Big Data,
including better customer services, higher operational efficiency, better informed strategic
direction, the identification of new markets and customers, and suggestions for new
services and products (Opresnik and Taisch, 2015; and Swaminathan, XXXXXXXXXXBig Data is
ecoming the basis for competition in today’s rapidly changing business environment as
it provides valuable knowledge to the firms (Tambe, 2014; Kache and Seuring, 2017; and
Kunc and O’Brien, 2019).
The use of Big Data can quickly convert potential challenges of business processes into
opportunities (Aydiner et al., XXXXXXXXXXAydiner et al XXXXXXXXXXexplored the association between
the use of Big Data and business process performance and concluded that prescriptive
Big Data is an important indicator that leads to higher firm performance. Akter et al.
(2016) studied Big Data Capabilities (BDC) (e.g., IT and human talent) and found that
BDC improves business processes, which in turn increases business values. Raguseo (2018)
investigated the relationship between the adoption of Big Data technologies, risk, benefits,
and firm performance and found that Big Data technologies have a positive effect on firm
performance. Ozemre and Kabadurmus XXXXXXXXXXhighlighted that Big Data adoption
ings
new growth opportunities for firms and assists them in strategic decision-making to
improve their productivity.
Firms are using Big Data to enable higher levels of supply chain coordination and the
creation of capabilities that allow fast and effective response to customer needs (Sanders,
2014). Information exchange in the supply chain can facilitate timely adjustments to
production, which in turn facilitate meeting customer requirements (Chang, XXXXXXXXXXAt a
supply chain level, companies are harnessing Big Data to gain new insights into elements
of product and process design, suppliers and customers, customer demand, and overall
market opportunities with data-driven supply chains (Chavez et al., XXXXXXXXXXBig Data
increases supply chain performances in terms of agility, flexibility, and ambidexterity and
hence enables the supply chain to scan the dynamic environment continually and obtain
a competitive edge with such capabilities.
11Big Data Usage and Big Data Analytics in Supply Chain: Leveraging Competitive Priorities
for Enhancing Competitive Advantages
Business analytics using information system support has a strong relationship to
supply chain performance (Sheng et al., XXXXXXXXXXThe term “supply chain analytics” can be
used to define advanced BDA in SCM (Wang et al., XXXXXXXXXXBDA can improve services,
mass customization, digital marketing, and the overall performance of the supply chain
in highly competitive environments (Tien, XXXXXXXXXXBDA would enhance the management
of supplier performance, improve demand forecasts, reduce safety stocks (Nguyen et al.
2018; and Tiwari et al., 2018), and also play a significant role in supply chain
sustainability assessment (Belaud et al., XXXXXXXXXXBDA deployment in