Activity6This 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...

It's a Data Science course at the PHD level. The assignment file is called "activity6.docx"
























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 library 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 current 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: [email protected] 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, 2008). A 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, 2004). Supply Chain Management (SCM) faces various challenges such as delayed shipments, rising fuel costs, inconsistent suppliers, and ever- increasing customer expectations. In the current 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, 2015). Data 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., 2017). BDA could lead to increased efficiency and profitability in the supply chain by maximizing speed and visibility, improving supply chain stakeholders’ relationships, 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., 2006). This research focuses on how Big Data usage and BDA can boost and enhance the performance of traditional SCM to revolutionize supply chain performance. Literature Review The current economic environment is characterized by many challenges, including hyper- competition, high uncertainty, increased turbulence, globalization of markets, and increased product and service innovations (Alfalla-Luque et al., 2018; and Marin-Garcia et al., 2018). Any organizational initiative, including SCM, should ultimately lead to enhanced organizational performance (Li et al., 2006). Supply 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 customer demand (Lee, 2004). Nowadays, market competition no longer happens between individual companies but takes place between supply chains (Farahani et al., 2014). Supply 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., 2015). Any 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 carrying costs (Wisner, 2003). Proactive 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., 2018). Firms 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., 2012). Data-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., 2018). SCM activities have become more networked, resulting in the generation of a huge volume of real-time data, referred to as ‘Big Data’ (Chen et al., 2015). Such data generation in supply chain networks is the result of advanced networking technologies, including embedded sensors, tags, tracks, barcodes, 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 for 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., 2006). The findings of Wu et al. (2006) showed 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, 2018). Hence, a vast amount of data is constantly being produced while fulfilling customers’ demands (Aydiner et al., 2019). Big 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, 2013). Business 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., 2018). Chen et al. (2015) highlighted 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, 2018). Big Data is becoming 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., 2019). Aydiner et al. (2019) explored 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 (2020) highlighted that Big Data adoption brings 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, 2009). At 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., 2017). Big 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., 2017). The term “supply chain analytics” can be used to define advanced BDA in SCM (Wang et al., 2016). BDA can improve services, mass customization, digital marketing, and the overall performance of the supply chain in highly competitive environments (Tien, 2015). BDA 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., 2019). BDA deployment in