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13 pages/≈3575 words
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APA
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Management
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Essay
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English (U.S.)
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DATA-DRIVEN DECISION MAKING AT AMAZON (Essay Sample)

Instructions:
The assignment required the completion of an MBA-level paper, with a word count of up to 3,500, that would compare and contrast the data strategy of an organization of the author's choice, critically assess its advantages and limitations, and address ethical concerns. The paper presented the organization's choice, provided an overview of its data use case, and analyzed the roles of data in operation, customer perception, and strategic management (using the Amazon sample). Additionally, this task critically articulated contemporary ethical considerations around data use and protection, regulation, and bias decomposition. To this end, the sample paper began with an introduction to Amazon, detailing its origins, location, and the significant reliance it has on data. This article effortlessly and swiftly introduced every facet of Amazon's use of data, including customer customization, logistics, pricing, and its integration with AWS. These systems demonstrate how Amazon uses big data, artificial intelligence, and machine learning to improve its customer relations and operations and to remain relevant. The section on critical evaluation looked into obstacles tied to data in Amazon, including algorithm biases, ethical questions about data privacy, and data monopoly. We analyzed these broader implications using tools like the Business Environment and Michael Porter's Five Forces framework. Data ethics were a significant topic of discussion, with the firm taking into account the GDPR and CCPA regulations, as well as discussing issues related to data collection and devices like Alexa. The ethical issues concerning the application of AI and the third-party sellers were also discussed. In conclusion, the sample gathered information from Amazon's innovative yet intricate data strategy, including scholarly endnotes, business articles, and Harvard citations, as instructed by the paper. The choice of the sample also affords a rich analysis of how data strategies define contemporary enterprises, coupled with discussions of ethical questions.     source..
Content:
DATA-DRIVEN DECISION MAKING AT AMAZON: By (Name) The Name of the Class (Course) Professor (Tutor) Name of the School (University) The Date Background (800words) Amazon’s Data-Driven Operations and Strategy Jeff Bezos founded Amazon in 1994, and today it stands as one of the leading companies in e-business, cloud computing, music, movies, and AI services, with over 1.5 million employees globally. Amazon Web Services (AWS) revenues account for 13% of Amazon's revenue, with $91 billion in 2023, compared to the general company's $575 billion revenue (Amazon.com, 2023). Data-oriented processes are also one of its main strategic priorities, reflecting the company's mission to be "Earth's most customer-centric." Amazon’s Data-Driven Approach Amazon processes 1 million customer transactions per second, as well as 1 exabyte of data per year. These large amounts of data support the company's key business priorities and performance improvements, as well as the development of personalized customer offerings, delivery, and cloud solutions. Customer personalization and recommendation engine: Zweben & Sweeney (2021, 84) estimate that 35% of Amazon's sales are prescribed by its recommendation system, which uses the Collaborative Filtering Engine (CFE). One of them analyzes customer behavior, such as purchases and product browsing history, to recommend relevant products. This system, which has served 300 million active global customers, improves conversion rates based on user preferences, such as buying pet products after purchasing a dog leash. Supply Chain Optimization and Logistics This inventory management uses analytical methods to predict customer needs and runs at over 185 fulfillment centers. Its 'Anticipatory Shipping Model' places products closer to delivery points, offering same-day or next-day delivery to 7 billion pieces per year (Bogue, 2024, 382). Inventory control practices like Just-in-time (JIT) also improve operations flexibility by receiving and repackaging goods and sending them directly to the market, hence minimizing storage time. Robot use in the Amazon warehouse, which now houses about 200,000 robots, reduces operational expenses by $0—45 per unit in 2023, indicating that the price will continue to increase. Price optimization and dynamic pricing Amazon adjusts prices 2.5 million times daily, making it simple to adjust a strategy based on data such as consumers' demand and competitors' prices (Kopalle, 2023, 580). The pricing models are dynamic, allowing for the best profit margins at relatively attractive prices. It concluded that during its peak sales periods, such as Black Friday, the price changes helped customers save $24 billion in 2023 through deals and coupons. Amazon Web Services (AWS) AWS contributed $91 billion in 2023, serving millions of businesses and driving Amazon's online giant (Naseer, 2023, 125). AWS‘s tools, like SageMaker, for example, enhance customer categorization and predictive modeling. AWS executes one exabyte of data every year in near-real-time use cases for business and organizations' productivity. In 2023, AWS utilized Amazon Bedrock for AI-based workloads and launched the Amazon Trainium2 chips for machine learning. Operations Management and Automation The ten core competencies that define Amazon's operations are availability, order fulfillment, service, inventory management, supply chain, and quality. Automation is at the center, with robots working in tandem with human employees to pick, pack, or ship. By 2023, Amazon realized the fastest fulfillment rates, delivering over 4 billion items in the U.S. through same-day or next-day shipments (Thuermer, 2023, 50). Real-time data also helps automate warehouse scheduling and supplier shipment efficiency. Data privacy and ethical issues Amazon gathers vast consumer data, especially from the company's smart devices, such as Alexa. There are concerns about how the company uses data derived from IP addresses, browsing activity, and voice recordings for targeted advertising (Stommel & Rijk, 2021, 278) Due to these concerns; Amazon adheres to European GDPR and California CCPA laws. However, ethical issues regarding data use and sharing information with third parties remain a concern. Global expansion and future innovations Amazon is penetrating growing markets such as India and Brazil, earning $10 billion, 12% of its total revenues in North America, and $11 billion, 11% of its revenues internationally, in 2023(Reardon et al., 1250). They also aim to transform the delivery method by utilizing remotely operated vehicles, such as drones, as part of their Prime Air technology. Amazon also focuses on AI technologies, where AWS's newly unveiled AI chips will enhance its machine learning. All these innovations will go a long way in improving Amazon's operations, efficiency, and competitiveness in the global marketplace. Amazon's Data Strategy (1000 words) Amazon's extensive integration of data into business operations serves as its success engine in e-commerce and technology markets. All aspects of Amazon's operations, from customer selection and recommendation to warehouse management, involve analytical assistance. Amazon stores, processes, and analyzes big data, allowing it to customize its users' experiences, increase efficiency, and expand Amazon’s cloud offerings in successive steps. How data influences decision-making Customer Experience: AI-Driven Personalization and Pricing Strategies Amazon's sales directly benefit from artificial intelligence, which uses algorithms to understand customer data and recommend products the customer is most likely to buy. Even more astonishingly, the recommendation engine is accountable for 35% of Amazon's annual sales (Aravindhan, 2023, 5). This engine utilizes information compiled from customer purchase histories, usage patterns, or demographics to make exact product recommendations. For example, based on other consumers' purchasing patterns, the engine will recommend laptop bags to a customer searching for a laptop. In 2020, the company has handled more than twelve million products across its site. The company uses highly developed machine learning algorithms to coordinate with such a vast inventory and specific customer requirements. The acquired data shows that the company processes over 5 billion data points on a daily basis. This personalization helps increase sales and the quality of the user experience by offering them products they would not have known they needed. Dynamic pricing is another important aspect of Amazon's data usage. Amazon changes the price of its products every 10 minutes depending on factors such as competitors' prices, customers' buying habits, and stock availability. At high traffic times, including Black Friday, Amazon's AI models parse over billions of customer interaction data to tweak the price points of millions of its stock-keeping units to obtain maximum sales conversion rates (Aravindhan, 2023, 5). For instance, during Cyber Monday, 2023, AI assisted in handling 400 million orders for the products sold on Amazon while offering a price discount to increase the sales rate. Operations Management: Real-Time Data for Automated Warehouses and Inventory Systems Control Data-driven success demonstrates Amazon's extensive and expanding international supply chain. The company handles real-time data to run more than ten fulfillment centers across the globe, and AI has been useful in overseeing such a demanding supply line. Amazon, for instance, has applied robotics systems such as Sequoia, which optimize storage by decreasing the overall processing time by 25% but increasing the throughput by 75% effectively (Zweben &Sweeney, 2021, 80). Amazon's systems anticipate demands with what may be considered remarkable precision. For instance, employing historical sales data and external factors, including seasonal demands, Amazon predicts the demand for specific products. This system has forecast demand for products with 93% accuracy, enabling the company to manage its inventory levels. In products requiring frequent restocking, such as electronics, AI determines high demand months (for instance, during the launch of new gadgets) and ensures an adequate supply of the products in strategic warehousing facilities. Data plays a crucial role in the organization of transportation logistics. Real-time data from 400 thousand drivers and millions of orders employ Amazon's AI models to build the best delivery routes. This has reduced delivery time by as much as 20%, thereby enhancing the satisfaction of customers, especially during festive seasons (Alimahomed-Wilson, 2022, 20). From the placement of the warehouses and the designed routes, Amazon was able to cut the costs of transportation by one dollar. Further, the company anticipates reaching $6 billion in 2020 alone. AWS: On the Role of Cloud Computing Towards Supporting Business Data Strategies AWS, or Amazon Web Services, which the company uses and provides to its customers, is another significant aspect of Amazon's data management. AWS provides more than 175 comprehensive services to enable companies to gather, store, and process data effectively (Naseer, 2023, 125). For example, Amazon Redshift, the leading cloud-based data warehouse, processes petabytes of organization data and extracts insights from large volumes of data in real time. Airbnb and Lyft use Amazon Redshift as a data warehouse to improve their operations based on user data. Understanding Amazon's Data Strategy: Theories and Models Big Data Framework It is pertinent to discuss Amazon’s capability to process exabytes of data daily with the help of the Big Data Framework. Amazon receives data from its e-commerce site, Alexa, AWS, and many other sources Naseer, 2023, 125). What makes Amazon scale so effectively is that its AI systems constantly consume and process this data in real time. The Big Data Framewor...
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